Volume-6 Issue-1

Download Abstract Book

S. No

Volume-6 Issue-1, March 2016, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.



Vusat Afandiyev, Firangiz Gasimova

Paper Title:

Problems of Urbanization of Azerbaijan

Abstract: In the article the appearance of cities, urban development, socio-economic development, the concentration of production, population and settlement, based on scientific and technological progress is the result of industrial production, is talking about the problems arising in the process of urbanization. The regulation of agglomeration that is the result of urbanization development in spite of its being of current importance is a poor investigated and difficult-to-resolve problem. The thorough changes which have occurred in the economic, social and political life of the country in the course of last years from the geographical – territorial, industrial point of view are such an important, diversified and dynamic processes that the scientific perception and research on demand of time of the new realities connected with them has turned to a burning problem. The special role of urbanization stems out from its origin of occurrence of urban environment. Social-economic, cultural and demographic settling expands its positions on the historical stages of evolution processes and is developed qualitatively and quantitatively. Integration and planning, town studies from the urbanization viewpoint is a subject of inquiry by different sciences – economy, economic-social geography as well as urban planners. Within the circle of their attraction power and surrounded by unitary areas the towns cause the occurrence of tight reciprocal relationships, integration processes and promotes the vanish of insulation, isolation typical for the past.Nowadays, the increase of city agglomerations and transport-economic relationships, disappearance of major differences between towns and villages promotes economic-social housing in the territorial and regional system as well as migration relationships. Historically, Baku city was placed in area of oil-and-gas fields and has been surrounded with areas of their operation. Now because of an arrangement of Baku city and adjoining regions, oil fields, and also more than 50 densely populated settlements of city type and the working settlements located in an environment of crossed roads, are present very much limited opportunities for its horizontal extensive growth. Such situation in the certain district limits free ground territories for seaside recreational zones and developments of the agricultural industry in the area of city councils of Baku and Sumgait, especially in districts with dense built-on and compactness of communication lines. The mentioned process is conducted outside of intensively-urbanized Baku and Sumgait and has no opportunity for using recreational zones and the ground areas. The primary purpose in this work is conducting a research of activity and interrelation of development of cities, urbanization and the migratory processes connected to it in a certain territorial system from the position of the interconnected and functional system of social and economic, economically – geographical, ecological, housing aspects.The given work which for the first time is devoted to geographical problems of development of the urbanization of cities of Azerbaijan Republic, does not reflect completely multilateral and volumetric problems in the given area.

Settlement; Urbanization; Economic


1. G.M.Lappo.Urban geography (ЛаппоГ.М. Географиягородов). Moscow, Publishing House of ‘Vlados’.
2. A.A.Nadirov.Stages of economic development in Azerbaijan Republic (Azərbaycan Respublikasının iqtisadi inkişaf mərhələləri).Baku: Elm. 2002.

3. V.A.Afandiyev V.A. Sh.G.Damirgayayev. Geographical problems of development of cities in Azerbaijan Republic. (V.A,Əfəndiyev,Ş.Q.Dəmirqayayev. Azərbaycan Respublikası şəhərlərinin inkişafının coğrafi məsələləri). Baku: Nijat. 1995.

4. V.A.Afandiyev. Characteristic features of the urbanization of Azerbaijan in the modern stage.Journal of the Black Sea Universities Network, V.l. Constanta, Romania. 2002.

5. V.A.Afandiyev. Urbanization and urban settlements of Azerbaijan. (Əfəndiyev V.A. Urbanizasiya və Azərbaycanın şəhər yaşayış məskənləri). Baku. 2002.

6. V.A.Afandiyev, F.E.Gasimova. Urban settlement in Azerbaijan. (V.Ə.Əfəndiyev, F.E.Qasımova. Azərbaycanda qəsəbə). Baku. 2013.






M. Ramakrishna, G. Naresh

Paper Title:

Design of Multi-Machine Power System Stabilizers using Gravitational Search Algorithm

Abstract: Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system to damp electromechanical oscillations. This paper presents an approach based on the law of gravity and mass interactions called Gravitational Search Algorithm (GSA) fortuning the parameters of PSSs in a multi-machine power system.These stabilizers are tuned simultaneously to shift the lightly damped and undammed electromechanical modes of all plants to a prescribed zone in the s-plane. A multi objective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The performance of the proposed PSS under different disturbances, loading conditions, and system configurations is investigated on New England 10-machine, 39-bus power system. Non-linear time domain simulation results are presented under wide range of operating conditions and disturbances at different locations to show the effectiveness of the proposed GSA based PSS and their ability to provide efficient damping of low frequency oscillations.

Power System Stabilizer, Electromechanical Oscillations, Gravitational Search Algorithm, Multi-machine Power System.


1. De Mello, F.P. and Concordia, C. “Concepts of Synchronous Machine Stability as Effected by Excitation Control”, IEEE Transactions on Power Apparatus and Systems, Vol. PAS-88, No. 4, April 1969, pp. 316-329.
2. Larsen, E.V. and Swann, D.A. “Applying Power System Stabilizers, I, II and III”, IEEE Transactions on PAS, Vol. 100, No.6, June 1981, pp. 3017-3046.

3. Y.L.Abdel-Magid, M.A. Abido, S.AI-Baiyat, A.H. Mantawy: Simultaneous stabilization of multi-machine power systems via genetic algorithms. In: IEEE Transactions on Power Systems, Vol. 14, No. 4, November 1999, pp 1428-1439.

4. M.A.Abido, Y.L.Abdel-Magid: Eigenvalue Assignments in Multimachine Power System using Tabu search Algorithm. In: Computers and Electrical Engineering 28 (2002) 527-545.

5. M. A. Abido: Robust Design of Multimachine Power System Stabilizers Using Simulated Annealing. In: IEEE Transactions on Energy Conversion, Vol. 15, No. 3, September 2000, pp 297-304.

6. Y.L.Abdel-Magid and M.A. Abido “Optimal Multiobjective Design of Robust Power System Stabilizers Using Genetic Algorithms”, IEEE Transactions on Power Systems, Vol. 18, No. 3, Aug. 2003, pp. 1125-1132.

7. M.A.Abido ““Optimal design of Power System Stabilizers Using Particle Swarm Optimization”, IEEE Transactions on Energy Conversion, Vol. 17, No. 3, September 2002, pp. 406-413.

8. D.B. Fogel, Evolutionary Computation Towards a New Philosophy of Machine Intelligence, IEEE, New York, 1995.

9. Rini DP, Shamsuddin SM, Yuhaniz SS. Particle swarm optimization: technique, system and challenges. International Journal of Computer Applications 2011;14(1):19–27.

10. S.Mishra, M. Tripathy, J. Nanda , “Multi-machine power system stabilizer design by rule based bacteria foraging” , Electric Power Systems Research 77 (2007) pp. 1595–1607

11. G.Naresh, M.RamalingaRaju, S.V.L.Narasimham “Design of multi-machine power system stabilizer using bacterial foraging algorithm” Artificial Intelligence and Machine Learning (AIML) ICGST-AIML Journal, Volume 11, Issue 2, December 2011, pp.39-48.

12. G.Naresh,M.RamalingaRaju, M.Sai Krishna ”Design and Parameters Optimization of Multi-machine Power System Stabilizers Using Artificial Bee Colony Algorithm” IEEE 2012 International Conference on Advances in Power Conversion and Energy Conversion (APCET 2012), pp. 160-167

13. G.Naresh,M.RamalingaRaju, S.V.L.Narasimham “Application of Harmony Search Algorithm for the Robust Design of Power System Stabilizers in Multi-machine Power Systems” Journal of Electrical Engineering (JEE), Romania, Vol.13/2013, Edition 2, Article 13.2.2, pp.9-19.

14. P.W. Sauer, M.A. Pai, Power System Dynamics and Stability, Englewood Cliffs, Prentice Hall, NJ, 1998.

15. K.R.Padiyar.:Power System Dynamic Stability and Control, 2nd Edition, BS Publications, 1994.

16. Norlina MohdSabri, MazidahPuteh, and MohamadRusopMahmood, “AReview of Gravitational Search Algorithm”,Int. J. Advance. Soft Comput. Appl., Vol. 5, No. 3, November 2013, pp. 1-39.

17. M.A. Pai, Energy Function Analysis for Power System Stability, Kluwer,Norwell, MA, 1989.

18. A.Bazanella, A.Fischman, A. Silva, J. Dion, and L. Dugrad, “Coordinated robust controllers in power systems,” in Proc. IEEE Stockholm Power Tech Conference, 1995, pp. 256–261..






Fadi N. Al-Ayed, Mohammed F. Al-Haqbani

Paper Title:

Representational State Transform: A Synopsis

Abstract: This document describes and elaborates Representational State Transform (REST), utilization as well as a perspective towards future. REST is constantly on the achieve momentum since the most effective way intended for constructing web services, allowing numerous web architects to take into consideration regardless of whether and ways to include this strategy into their SOA as well as SOAP-dominated world. This paper discusses some of demystify the web as an application platform and to showcase how web architecture can be applied to common enterprise processing issues. Additionally, the overview of a data platform that is open and accessible to other application, which usually eschews integration in support of composition, nevertheless implements useful business behaviors such as a distributed, and hypermedia-driven system platform.

Hyper Text Transfer Protocol (HTTP), Service-Oriented Architecture (SOA), Simple Object Access Protocol (SOAP), Uniform Resource Identifier (URI), Uniform Resource Locator (URI)


1. R.T. Fielding, Architectural Styles and the Design of Network-based Software Architectures, Dissertation, Doctor of Philosophy, University of California, Irvine, 2000.
2. R. Kay, Quick Study: Representational State Transfer, 2007

3. J. Webber, S. Parastatidis, and I. Robinson, REST in Practice, O’Reilly Media, Inc, 2010

4. Dan and et al.: Business-to-Business Integration with TPAML and a Business-to-Business Protocol Framework, IBM System Journal, 2001

5. Berners-Lee, T., Masinter, L. and Mc Cahill, M. RFC 1738:Uniform Resource Locators (URL). IETF, December 1994.

6. R. Khare, and R. N. Taylor, Extending the Representational State Transfer (REST) Architectural Style for Decentralized Systems, University of California, Irvine, 2004

7. Allamaraju, S. (2008). Describing REST ful Applications. Retrieved May 4, 2009, from

8. http://www.infoq.com/articles/subbu-allamaraju-rest.

9. Fielding, R. (2008a, March 22). On software architecture »Untangled. Retrieved June 2, 2009, from

10. http://roy.gbiv.com/untangled/2008/on-software-architecture.






D. Sudha Devi, K. Thilagavathy

Paper Title:

Towards Efficient ECC Based Provable Data Possession Protocol with Data Dynamics for Secure Cloud Storage

Abstract: Cloud Computing is acquainted for its cost-effective on-demand services based on internet. The significant benefits of cloud services drive organizations and individuals to make hay by outsourcing data to cloud storages. Outsourcing data brings users’ an easy and economical way of data management and also relieves users’ from the burden of building and maintaining local data storage. However, data being residing in some third-party’s premises, users have no full control over their data which necessitates ensuring confidentiality and integrity of data stored in untrusted cloud storage servers. To verify the correctness of data in cloud storage, this paper proposes an efficient ECC based Provable Data Possession (EPDP) Protocol with data dynamics. The proposed protocol preserves confidentiality of data stored in cloud storage and allows data owner to verify the integrity of data without retrieving the whole original data. Also the protocol is designed to perform stateless auditing and supports data dynamics at block level retaining the same security assurance. Security and Performance analysis proves the proposed protocol to be secure and highly efficient for secure cloud storage.

confidentiality, cloud data storage, data integrity, elliptic curve cryptography, integrity verification, secure storage


1. P.Mell and T.Grance,“Draft NIST Working Definition of Cloud Computig”[Online]. Available: http://csrc.nist.gov/groups/SNS/cloudcomputing/index.html, 2012.
2. Amazon Web Services (AWS), [Online]. Available: http://aws.amazon.com/ec2.

3. Google AppEngine, [Online]. Available: http://googcloudlabs.appspot.com.

4. Rackspace Cloud sites, [Online]. Available: http://www.rackspace.com/cloud/sites.

5. Microsoft Azure, [Online]. Available: https://azure.microsoft.com/en-in.

6. Salesforce, [Online]. Available: http://www.salesforce.com/in.

7. VMware vCloud, [Online]. Available: http://vcloud.vmware.com.

8. Verizon, [Online]. Available: http://www.verizonwireless.com/support/verizon-cloud.

9. Citrix Cloud Services, [Online]. Available: https://www.citrix.com/solutions/cloud-services.

10. IBM Cloud, [Online]. Available: http://www.ibm.com/cloud-computing.

11. D. Sudhadevi and K. Thilagavathy, “A novel approach to enhance cloud data defense,” Asian Journal of Information Technology, Vol. 12, No. 9, 2013, pp. 305–311.

12. D. Sudha Devi, K. Thilagavathy, “An Elliptic Curve Cryptography based adaptive and secure protocol to access data outsourced to cloud server”, International Journal of Applied Engineering Research, Vol. 10, No. 18, 2015, pp 39443-39450.

13. H. Shacham and B.Waters, “Compact Proofs of Retrievability”, Proc.14th International Conference Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT), LNCS 5350,2008, pp.90-107.

14. C. Erway, A. Kupcu, C. Papamanthou, R. Tamassia, “Dynamic provable data possession”, In: Proceedings of the 16th ACM conference on computer and communications security, CCS 2009, pp. 213–22.

15. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson,and D. Song, “Remote Data Checking using Provable Data Possession”, ACM Transactions on Information and System Security,Vol. 14, No. 1, 2011, pp. 12.1–12.34.

16. Q. Wang, C. Wang, K. Ren W. Lou, and J. Li, “Enabling public verifiability and data dynamics for storage security in cloud computing,” IEEE Transaction on Parallel and Distributed Computing, Vol.22, No.5, 2011, pp.847-859.

17. Z. Hao, S. Zhong, N. Yu. “A privacy-preserving remote data integrity checking protocol with data dynamics and public verifiability”, IEEE Transaction on Knowledge Data Engineering, Vol.23, Issue. 9, 2011, pp.1432-1437.

18. M. van Dijk, A. Juels, A. Oprea, R. L. Rivest, E. Stefanov, and N. Triandopoulos, “Hourglass schemes: how to prove that cloud files are encrypted” In Proceedings of the 2012 ACM conference on Computer and communications security, 2012,pp. 265–280.

19. Q. Zheng, S. Xu,“Secure and efficient proof of storage with deduplication”, In: Proceedings of the second ACM conference on data and application security and privacy, CODASPY 2012,pp. 1–12.

20. P. Williams and R. Sion, “Single round access privacy on outsourced storage”, In Proceedings of the 2012 ACM conference on Computer and communications security, 2012, pp. 293–304.

21. C. Wang, Q. Wang, K. Ren, N. cao and W. Lou , “Towards Secure and Dependable Storage Services in Cloud Computing”, IEEE Transaction on Cloud Computing, Vol.5, Issue.2, 2012, 220-232.

22. Y. Zhu, H. Hu, G. Ahn and M. Yu, “Cooperative Provable Data Possession for Integrity Verification in Multicloud Storage”, In Proceedings of IEEE Transactions on Parallel Distributed Systems, 2012, pp. 2231-2244.

23. Y. Ren, J. Xu, J. Wang and J. Kim, “Designated-Verifier Provable Data Possession in Public Cloud Storage”,International Journal of Security and Its Applications, Vol.7, No.6, 2013, pp.11-20.

24. D. Koo, J. Hur, H. Yoon,“Secure and efficient data retrieval over encrypted data using attribute-based encryption in cloud storage”,Computers and Electrical Engineering, 39 (2013), pp. 34–46.

25. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, “Provable data possession at untrusted stores”, In: Proceedings of the 14th ACM conference on computer and communications security, 2007, pp. 598–609.

26. C. Wang, Q. Wang, K. Ren, W. Lou,” Privacy-preserving public auditing for data storage security in cloud computing”,IEEE proceedings, INFOCOM, 2010,pp. 1–9.

27. J. Yang, H. Wang, J. Wang, C. Tan and D. Yu, “Provable Data Possession of Resource-constrained Mobile Devices in Cloud Computing”, Journal of Networks,
28. Y. Zhu, H. Hu, G. Ahn, S. Yau, “Efficient audit service outsourcing for data integrity in clouds”, The Journal of Systems and Software, 85 (2012), pp. 1083– 1095.
29. P.Natu, S.Pachouly, “A Comparative Analysis of Provable Data Possession Schemes in Cloud”, International Journal of Computer Science and Information Technologies, Vol. 5, No.6 , 2014, pp.7927-7931.

30. H. Liu, P. Zhang, J. Liu, “Public Data Integrity Verification for SecureCloud Storage”, Journal of Networks, Vol.8, No.2, 2013, pp.373-380.

31. S.Worku, C. Xu, J. Zhao, X. He, “Secure and efficient privacy-preserving public auditing scheme for cloud storage”, Computers and Electrical Engineering, 40 (2014), pp. 1703–1713.

32. M. Vanitha, T. Jayapratha, K. Subramani, T. Pradeepa, “Elliptic Curve Cryptography Digital Signature Algorithm For Privacy-Preserving Public Auditing For Shared Data In The Cloud”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3 Issue. 3, pp.1497 – 1502.

33. D. SudhaDeviand K. Thilagavathy, “An Adaptive Multilevel Security Framework for the Data Stored in Cloud Environment,” The Scientific World Journal, (2015), pp. 1-11.

34. A.J. Menezes, V. Oorschot, and S.A. Vanstone, “Handbook of Applied Cryptography”, CRC Press Series on Discrete Mathematics and its Applications, CRC Press, Boca Raton, FL, 1997, pp. 320- 383.

35. N. Koblitz, A. Menezes, and S.A. Vanstone, “The state of elliptic curve cryptography”, Designs, Codes and Cryptography (19), 2000, pp. 173-193.

36. D. Hankerson, A.J. Menezes, and S. Vanstone, “Guide to Elliptic Curve Cryptography”, Springer-Verlag, New York, USA, 2004, pp.78-80.





Ahmed Saeed Obied, Hind Mowafaq Taha

Paper Title:

Noise Cancellation using NLMS Adaptive Filter

Abstract: This paper presents the simulation of noise canceller system which contains adaptive filter and using adaptive algorithm. The objective of noise cancellation is to produce the estimate of the noise signal and to subtract it from the noisy signal and hence to obtain noise free signal. This work basically focuses on using NLMS filter in noise cancellation. We used Matlab to simulate our adaptive filter.

Noise canceller; adaptive algorithm; NLMS


1. Emmanuel C.ifehor, Barrie W.jervis “Digital Signal Processing A Practical Approach” II edition, Pearson Education. ISBN81-7808 609-3.
2. Bernard widrow, Samuel D.Stearns.” Adaptive Signal Processing” third Impression, Pearson Education. 0532-2, (2009).

3. A.Bhavani Sankar; D.Kumar; K.Seethalakshmi “Performance Study of Various Adaptive filter algorithms for Noise Cancellation in Respiratory Signals” Signal Processing: An International Journal (SPIJ) Volume: 4 Issue: 5 Pages: 247-303 December 2010.

4. Md Zia Ur Rahman, ; Sk. Khaja Mohedden, ; Dr. B V Rama Mohana Rao ; Y. Jaipal Reddy ; G.V.S. Karthik “Filtering Non-Stationary Noise in Speech Signals using Computationally Efficient Unbiased and Normalized Algorithm” International Journal on Computer Science and Engineering Volume: 3 Issue: 3 Pages: 247-303 December 2011.

5. M. Z. U. Rahman, S. R. Ahamed and D. V. R. K. Reddy, “Noise Cancellation in ECG Signals using Computationally Simplified Adaptive Filtering Techniques: Application to Biotelemetry”, Signal Processing: An International Journal, CSC Journals, Volume 3, Issue 5, pp. 1-12, November 2009.

6. Z. Chen “Design and Implementation on a Sub-band based Acoustic Echo Cancellation Approach” IJACSA International Journal of Advanced Computer Science and Applications (2011) , Volume 2, Issue 6, pp. 43-48, 2009.

7. S. Haykin, Adaptive Filter Theory, 3rd ed., Upper Saddle River, New Jersy: Prentice Hall, 1996.





K. K. Myithili, R. Parvathi

Paper Title:

Chromatic Values of Intuitionistic FuzzyDirected Hypergraph Colorings

Abstract: A hypergraph is a set V of vertices and a set E of non-empty subsets of V, called hyperedges. Unlike graphs, hypergraphs can perform higher-order interactions in social and communication networks. Directed hypergraphs are much like directed graphs. Colors are used to distinguish the classes. Coloring a hypergraph H must assign atleast two different colors to the vertices of every hyperedge. That is, no edge is monochromatic. In this paper, upper and lower truncation, core aggregate of intuitionistic fuzzy directed hypergraph (IFDHG), conservative K-coloring of IFDHG, chromatic values of intuitionistic fuzzy colorings, elementary center of intuitionistic fuzzy coloring, f-chromatic value of intuitionistic fuzzy coloring, intersecting IFDHG, K-intersecting IFDHG, strongly intersecting IFDHG were studied. Also it has been proved that IFDHG H is strongly intersecting if and only if it is K-intersecting.

Core aggregate of IFDHG, intuitionistic fuzzy colorings (IFC), elementary center, f -chromatic value of IFC, intersecting IFDHG, K-intersecting, strongly intersecting IFDHG.


1. Atanassov. K. T, Intuitionistic fuzzy sets – Theory and Applications, New York, Physica-verlag, Berlin(1999).
2. Berge .C, Graphs and Hypergraphs, North-Holland, NewYork, 1976.

3. Mordeson N. John, Nair S. Premchand, Fuzzy graphs and fuzzy hypergraphs, New York, Physica-verlag, (2000).

4. Parvathi. R and Karunambigai. M. G, Intuitionistic fuzzy graphs, Proceedings of 9th Fuzzy Days International Conference on Computational Intelligence, Advances in soft computing: Computational Intelligence, Theory and Applications, Springer- Verlag, 20(2006), 139-150.

5. Parvathi. R, Thilagavathi. S and Karunambigai. M. G, Intuitionistic fuzzy hypergraph, Bulgarian Academy of Sciences, Cybernetics and Information Techonoliges, Vol.9 (2009) , No.2,46-53.

6. Parvathi. R, Thilagavathi. S , Intuitionistic fuzzy directed hypergraphs, Advances in Fuzzy Sets and Systems 14(1), 2013, 39-52.

7. Rosenfeld. A, Fuzzy graphs, Fuzzy sets and their applications, L.A.Zadeh, K.S.Fu and M.Shimura Eds., Academic Press, NewYork, 1975, 77-95.

8. Roy H. Goetschel Jr., Introduction to fuzzy hypergraphs and hebbian structures, Fuzzy Sets and Systems, 76 (1995), 113-130.

9. Roy H. Goetschel Jr., William L.Craine and William Voxman Fuzzy transversals of fuzzy hypergraphs, Fuzzy Sets and Systems, 84 (1996), 235-254.

10. Roy H. Goetschel Jr., Fuzzy colorings of fuzzy hypergraphs, Fuzzy Sets and Systems, 94 (1998), 185-204.

11. Myithili. K. K, Parvathi. R, Akram. M, Certain types of intuitionistic fuzzy directed hypergraphs, International Journal of Cybernatics and Machine Learning, http://link.springer.com/article/10.1007/s13042-014-0253-1 (2014), 1-9.

12. L.A.Zadeh, Fuzzy sets, Information and Control, 8, (1965), 338-353.






Cyrus BabuOng`ondo

Paper Title:

Investigating Pre-Construction Planning in the Construction Industry of Kenya: Practitioners Perspective

Abstract: The subject of pre-construction planning is central to the success of project controls and performance during the implementation of construction projects. However, projects have been failing pointing to the need to relook at the planning for projects implementation. In the construction industry of Kenya, performances challenges during projects implementation is a chronic problem. Projects do not achieve their planned cost, time and quality objectives among other performance measures. This study sought to investigate pre-construction planning in the construction industry of Kenya, with emphasis on its adequacy. This cross-sectional research adopted a mixed-method design consisting of analysis of a questionnaire survey administered to active 95No. (NCA1, NCA2 and NCA3) contractors selected by way of stratified random sampling. A similar approach was also used to select 92No.Consultants with a response rate of 54.73% and 46.73% respectively. In addition, 11No.practitioners wereinterviewed in the currentstudy. Data analysis techniques employed include descriptive statistics and thematic analysis.The study established five (5No.) issues that need to be given careful attention when planning for projects implementation. The issues in order of importance include; Clarity of Scope Statement (RII=0.896), Clarity of performance benchmarks (RII=0.865), Competency of the project team (RII=0.0.682), Clarity of roles definition (RII=0.764) and Contractors selection criteria (RII=0.726). The study concludes by compiling views of the practitioners on what they consider good practice in improving the pre-construction planning practice. The study recommends the use of the good-practice checklist developed for better projects performance.

Pre-construction planning, construction industry, good-Practice checklist, Kenya .


1. Abd El-Razek, M. (2008). Causes of Delay in Building Construction Projects In Egypt. Journal of Construction Engineering & Management, , 134(11),831-841.
2. Akintoye, A. (2007). Collaborative relationships in construction-The UK contractor`s perception. Engineering,Construction and Architectural Management.

3. Atkinson, R. (1999). Project management: Cost,time and quality,two best guesses and a phenomena.its time to accept other success criteria. International Journal of Project Management, Vol 17,Issue 6 December,1999,Pages 337-342..

4. Chandara, P. (2002). Projects Planning,Financing,Implementation and Review. Tata: McGraw-Hill Publishing Company.

5. Chitkara, K. (2002). Construction Project Management Planning,Scheduling and Control. Hill Publishing Company Ltd.

6. Christenson, D. (2008). Using vision as a critical Success element in Project Management. International Journal of Project Management.

7. Cooke, B. W. (2004). Construction Planning Programming and Control. Oxford: Blackwell Publishing.

8. Cooke-Davies, T. (2002). “The real success factors on projects. International Journal of Project Management.

9. Egbu, C. (1998). “Planning and Control processes and techinques for refurbishment management.”. Construction Management and Economics, 16(3),315-325.

10. Fena-Mora, F. (2001). Dynamic Planning and control methodologyfor design/build fast-track construction projects. Journal of Construction Engineering and Management, 127(1),1-17.

11. Floyd, L. (2004). ” Application of appropriate control tools for contract type”. Cost Engineering, 46(2),25-30.

12. Forsythe, P. (2008). Modelling customer perceived quality in housing. International journal of project management,Elsevier Science Ltd and IPMA.

13. Fortune, J. (2006). Framing of critical success factors by a systems model. International Journal of Project Management,Elsevier Science Ltd and IPMA.

14. Frimpong, Y. (2003). Delay and cost overruns in Construction of Ground water Projects in developing countries. International Journal of Project Management, 21,321-326.

15. George, R. (2008). Critical activities in front End planning process. Journal of Management of Engineering.

16. Gichunge, H. (2000). Risk management in the Building Industry in Kenya. Unpublished PHD.Thesis.University of Nairobi.

17. Goodman, L. (1988). Project Planning and Management-an integrated system for improving productivity. New York: Van Nostrand Reinhold Company Inc.

18. Greer, M. (1999). Handbook of Human performance Technology. San Francisco: Jossey-Bas.

19. Gwaya, A. (2014). Development of appropriate project management factors for the construction industry in Kenya. International Journal of Soft Computing and Engineering (IJSCE), ISSN:2231-2307,Vol 4,Issue 1.

20. Hendrickson. (1999). Causes of Delay in Construction. Journal of Construction Engineering and Management, Vol 134,issue 11,p831.

21. Hillebrandt, P. (2000). Economic theory and the construction Industry.3rd Edition. London: Macmillan.

22. Iyer, K. J. (2005). Factors affecting cost performance evidence from indian construction projects. International journal of project management,, 23 (4),283-295.

23. Jackson, B. (2004). Construction Management Jump Start. CA: Sybex Incorporated Alaneda.

24. Johnson, G. (2006). Exploring Corporate Strategy 7th Edition. London: Pearson Education.

25. Kagiri, N. (2005). Time and Cost overrun in Power projects in Kenya: A case study of Kenya Electricity Generating Company Ltd. Unpublished MBA Thesis.University of Nairobi.

26. Kaming, P. (1997). Factors Influencing Construction Time and Cost Overruns on High-Rise Projects in Indonesia. Journal of Construction Management and Economics, 7,83-94.

27. Kenny, C. (2007). Construction,Corruption and developing countries. World Bank policy Research working paper.

28. Kerzner, H. (2006). Project Management: A systems Approach to Planning,Scheduling and Controlling 9th Edition. John Wiley & Sons publications.

29. Kongere, N. S. (2010). Project Management,From Design to Implementation. Nairobi: Richmond Designers and Printers.

30. Lester, A. (2000). Project Planning and Control. Oxford: Butterworth Heinemann.

31. Lindahl, G. (2007). Client`s goals and the Construction Project Management Process. Journal of Construction Management and Economics.

32. Ling, F. (2009). How Project Managers can better control the performance of design build projects. International Journal of Project Management, 22(6),477-488.

33. Masu, S. (2006). An investigation into the causes and impact of resource mix practices in the performance of construction firms in Kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.

34. Morris, S. (1990). Cost and Time Overruns in Public Sector Projects.

35. Muchungu, P. (2012). The contribution of human factors in the performance of construction projects in kenya. Nairobi: Unpublished Phd.Thesis.University of Nairobi.

36. Munano, A. (2012). Pre-constrcution Planning: Exploring the factors that influence timelines of project completion for public sectors buildings in Kenya.
Unpublished Master of Construction Management Thesis.Jomo Kenyatta University.

37. Musa, G. (1999). Determination of Factors Influencing Projects Delays in Water Projects in Kenya: The case of Government Funded Projects. Nairobi: Unpublished MBA Thesis University of Nairobi.

38. Mwandali, D. (1996). Analysis of Major Factors that affect Projects Management: A Case of Kenya Railways Projects. Nairobi: Unpublished MBA Thesis,University of Nairobi.

39. Nguyen, A. (2004). A study on Project success factors in large construction projects in Vietnam.

40. Nicholas, J. (2001). Project Management for Business and Technology. New Jersey: Prentice Hall.

41. Olawale, Y. a. (2010). “Cost and time control of construction projects: Inhibitng factors and mitigating measures in practice”. Construction Management and Economics, 28 (5),509-526.

42. Pellicer, E. (2005). Cost control in Consulting engineering firms. Journal of Management in Engineering, 21 (4),189-192.

43. Project Management Institute. (2013). PMBOK: A guide to the Project Management Book of Knowledge. Project Management Institute.

44. Rozenes, S. (2006). “Project Control: Literature review”. Project Management Journal, 37(4) 4-14.

45. Samuelson, W. (2006). Managerial Economics.5th Edition. New Jersey: John Wiley & Sons.

46. Talukhaba, A. (1998). Time and Cost Performance of Construction Projects. Nairobi: Unpublished M.A.Thesis,University of Nairobi.

47. Tucker, L. A. (1987). Is Construction Project planning really doing its job?.A critical focus,role and progress in the construction management economic. Vol 5,243-266.

48. Wanyona, G. (2005). Risk Managment in the cost planning and control of building projects.The case of quantity Surveying profession in Kenya. Unpublished PhD Thesis.University of Cape Town.

49. White, D. F. (2002). Current practice in project management-An Emperical study. International Journal of Project Management, 20(2),1-11.

50. Yakubu, O. a. (2009). Cost and time control of construction projects: A survey of Contractors and Consultants. Construction Information Quarterly, , 11(2),53-59.

51. Zhen Yu, Z. (2010). Application of innovative Critical Chain Method for project planning and control. Journal of Construction Engineering and Management.






Pavan Kumar K, Daniel Raju Paga, V. Bagyaveereswaran

Paper Title:

I2C Master Protocol Implementation in VHDL for Data Acquisition From CDC

Abstract: Communication between devices plays an important role in data acquisition. Proper selection of communication method or protocols is an important task. For any process, hardware and software components are used to perform many tasks like controlling the process and actuators, data conversion, data acquisition etc. A large amount of hardware or software components cannot be used for data acquisition. A proper and efficient approach is intended to be made such that they are used in least possible numbers, also keeping in mind that the different existing devices are compatible without any major modifications. This paper presents an approach to acquire data through I2C protocol in an efficient way. The AD7746 which is capacitance to digital convertor uses I2C protocol for providing the digital data. The digitized information is acquired from the AD7746 by a FPGA which is designed using modular flow method.

AD7746, FPGA, I2C protocol, Master, FSM, Serialdata communication..

1. Designing Embedded Systems with PIC Microcontrollers Principles and applications by Tim Wilmshurst Amsterdam, Newnespublications 2nd edition, 2009
2. Digital Design Principles and Practices 4th Edition by John F Wakerly, Prentice Hall publication, 2005

3. AD7746 data sheet by Analog Devices

4. HDL Programming VHDLand Verilog byNazeih M Botros, Charles River Media, 2005

5. Circuit Design and Simulation with VHDL second edition Volnei A. Pedroni, The MIT press publication, 2010

6. AN10216-01 I2C Manual, Philips Semiconductor, Mar 2003

7. I2C Bus Specification, Philips Semiconductor, version 2.1, January 2000.
8. Model sim SE user manual by model technology.
9. Design and implementation of FPGA based interface model for scale-free network using I2C bus protocol on Quartus II 6.0 by VenkateswaranP.,Dept. of Electron. & Tele-Commun. Eng., JadavpurUniv.,Kolkata,India, Mukherjee M.; SanyalA.;Das S., Computers and Devices for communication ,2009 .CODEC 2009.

10. Design and implementation of a BIST embedded inter-integrated circuit bus protocol over FPGA by Saha, S. ; Dept. of Electron. &Commun. Eng., Khulna Univ. of Eng. & Technol., Khulna, Bangladesh ; Rahman, M.A. ; Thakur, A., Electrical Information and Communication Technology, (EICT) 2013.

11. A review on effectuation of serial communication Inter-IC Protocol by Ashwin Bhandekar, S.S.Thakare, D.S.Chaudhar, International Journal of Advanced Research in Computer Science and Software Engineering ,Volume 3, Issue 3, March 2013.

12. Design and Implementation of I2C Master Controller Interfaced with RAM Using VHDL by Sansar Chand Sankhyan, Research Article, International Journal of Engineering Research and Applications, Vol.4, Issue 7, July 2014, pp.67-70.

13. Designing of Inter Integrated Circuit using Verilog by Disha Malik, International Journal of Science, Engineering and Technology, Volume 02, Issue 6, July 2014.

14. Design and Simulation of I2C Protocol by Tripti Singh, International Journal for Research in Applied Science and Engineering Technology(IJRASET) , Volume 2, Issue XII, December 2014.

15. Design and Implementation of I2C Bus Controller using Verilog by Mr. J.J.Patel, Prof. B.H.Soni, Journal of Information ,Knowledge and Research in Electronics and Communication Engineering, Vol. 2, Issue 2, November 12 to October 13.






G. Thamizhendhi, R. Parvathi

Paper Title:

Intuitionistic Fuzzy Tree Center-Based Clustering Algorithm

Abstract: In this paper, the concepts of distance, eccentricity, radius, diameter and center of an intu-itionistic fuzzy tree are defined. Some of the domination parameters like independent domina-tion, connected domination and total domination on intuitionistic fuzzy trees are investigated. The procedure for intuitionistic fuzzification for numerical data set is proposed. Further, in-tuitionistic fuzzy tree center-based clustering algorithm is designed. The e ectiveness of the algorithm is checked with a numerical dataset and compared with two existing clustering meth-ods.

Intuitionistic fuzzy tree, distance, eccentricity, center, connected domination, connec-tivity, clustering.


1. K. Atanassov, Intuitionistic fuzzy sets: Theory and applications, Physica-Verlag, New York (1999).
2. K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87-96.

3. M.Akram and N.O. Alshehri, Intuitionistic fuzzy cycles and intuitionistic fuzzy trees, The Scientific Word Journal, 1(2014), 1-11.

4. M.Akram and B.Dawaz, Strong Intuitionistic fuzzy graphs, FILOMAT, 26(1)(2012), 177-196.

5. M.Akram and W.A.Dudek, Intuitionistic fuzzy hypergraphs with applications, Information Sci-ences, 2(18)(2013), 182-193.

6. R. Cai, Y.J. Lei and X.J. Zhao, Clustering method based on intuitionistic fuzzy equivalent dissimilarity matrix, Journal of Computer Applications 29, (2009), 123 – 126.

7. P. Chountas, A. Shannon, R.Parvathi and K.Atanassov, On intuitionistic fuzzy trees and their index matrix interpretation, Notes on Intuitionistic Fuzzy Sets, 15(4)(2009),52-56.

8. Y. H.Dong, Y. T.Zhuang, K.Chen and X. Y.Tai, (2006)A hierarchical clustering algorithm based on fuzzy graph connectedness, Fuzzy Sets and Systems, 157(11) (2006), 1760-1774.

9. M. G. Karunambigai and R. Parvathi, Intuitionistic fuzzy graphs, Proceedings of 9thFuzzy Days International Conference on Computational Intelligence, Advances in soft computing: Computational Intelligence, Theory and Applications, Springer-Verlag, 20 (2006), 139-150.

10. John N.Mordeson and Y.Y.Yao, Fuzzy cycles and fuzzy trees, The Journal of Fuzzy Mathe-matics, 10 (1) (2002), 189 – 201.

11. R. Parvathi and G. Thamizhendhi, Domination in intuitionistic fuzzy graphs, Notes on Intu-itionistic Fuzzy Sets, 12 (2010), 39-49.

12. R. Parvathi, and S. Thilagavathi, Intuitionistic fuzzy directed hypergraphs, Advances in fuzzy Sets and System, 14 (1) (2013), 39-52.

13. R. Parvathi, S. Thilagavathi, G. Thamizhendhi and M. G. Karunambigai, Index matrix rep-resentation of intuitionistic fuzzy graphs, Notes on Intuitionistic Fuzzy Sets, 20 (2) (2014), 100-108.

14. R. Parvathi and G.Thamizhendhi, Some results on domination number in products of intu-itionistic fuzzy graphs, Annals of Fuzzy Mathematics and Informatics 9 (3) ( 2015), 403-419.

15. A.Rosenfeld, Fuzzy graphs.In: fuzzy sets and their applications(L. A. Zadeh, K. S. Fu and M. Shimura, eds.), Academic Press, New York, 1975, 77-95.

16. Somasundram and S. Somasundaram, Domination in fuzzy Graphs-I, Pattern Recognition Letters, 19 (1998), 787-791.

17. G. Thamizhendhi and R. Parvathi, Some domination parameters of intuitionistic fuzzy graphs, Far East Journal of Mathematical Sciences, 55(1) (2011), 65-74.

18. Z.S. Xu and R.R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems, 35, (2006), 417 – 433.

19. L.A. Zadeh, Fuzzy sets, Information and control, 8(3)(1965), 338-353.

20. Zeshui Xu, Jian Chen, Junjie Wu, Clustering Algorthim for intuitionistic fuzzy sets, Informa-tion Sciences, 178 (19) (178)(2008), 3755-3790.

21. H.Zhao, Zeshui Xu, Shousheng Liu and Zhong Wang, intuitionistic fuzzy MST clustering al-gorithms, Computers and Industrial Engineering, (2012), 1130-1140.

22. C. T.Zahn, Graph-theoretical methods for detecting and describing gestalt clusters, IEEE Trans-actions on Computers, 20 (1)(1971), 68-86.

23. H. M. Zhang and Z.S.Q. Chen, On clustering approach to intuitionistic fuzzy sets, Control and Decision 22, (2007), 882 – 888.

24. Zhong Wang, Zeshui Xu, Shousheng Liu and Jian Tang, A netting clustering analysis method under intuitionistic fuzzy environment, Applied Soft Computing, (11),(2011), 5558 – 5564.

25. http://www.uni-koeln.de/themen/statistik/data/cluster/






Mukesh Kumar Dey

Paper Title:

Improved Design of Obstacle Avoidance Robot using three Ultrasonic Sensors and ATMEGA328P Microcontroller

Abstract: This paper proposes an alternative design for a cost effective and simplified version of the obstacle avoidance robot using three ultrasonic sensors. It also provides a dynamic algorithm which directs the robot to navigate smoothly in different environments, avoiding obstacles. The main objective of this paper is to improve the accuracy of the robot in detection and avoidance of obstacles at various angles. The paper talks about the design of the robot which is followed by the working principle of sensors and algorithm for the microcontroller. The paper finally makes a detailed explanation of the code for the obstacle avoidance strategy and draws a conclusion based on the designed robot.

ATMEGA328P, Binary Logic, HC-SR04, Dynamic algorithm..


1. Engelberger, J., Transitions Research Corporation, private communication, 1986.
2. Borenstein, J. and Koren, Y., “Obstacle Avoidance With Ultrasonic Sensors.” IEEE Journal of Robotics and Automation. Vol. RA-4, No. 2, 1988, pp. 213-218.

3. Borenstein, J., “The Nursing Robot System.” Ph. D. Thesis, Technion, Haifa, Israel, 1987.

4. Vivek Hanumante et al., “Low Cost Obstacle Avoidance Robot”, IJSCE, ISSN: 2231-2307, Volume-3, Issue-4, September 2013.

5. Paul Kinsky and Quan Zhou, “Obstacle Avoidance Robot.” Major Qualifying Project Report, WORCESTER POLYTECHNIC INSTITUTE, Project Number: YR-11E1.

6. Elec Freaks, Ultrasonic Ranging Module HC – SR04. Available: http://www.micropik.com/PDF/HCSRO4.pdf (Last Accessed on 16th February, 2016).
7. Adafruit Motor Shield – Arduino Playground. Available: http://www.playground.arduino.cc/Main/AdafruitMotorShield (Last Accessed on 31st January, 2016).
8. Jnaneshwar Das, Mrinal Kalakrishnan and Sharad Nagappa, “Obstacle Avoidance for a Mobile Exploration Robot with Onboard Embedded Ultrasonic Range
Sensor”, PES Institute of Technology, Bangalore.






Amit R. Khaparde, M. M. Raghuwanshi, L. G. Malik

Paper Title:

Industrial Application of Differential Evolution Algorithm

Abstract: Here, we presented, the use of differential evolution algorithm in industrial applications. The differential evolution algorithm is widely used to solve direct, continuous space optimizations problems. The application of DE is present in almost all fields of engineering problems. One real life problem, from the field of electronic engineering name frequency modulation for sound waves synthesis is used to show the efficiency of differential evolution algorithm. The results are further evaluated on the parameter name success rate and success performance , they shows that the DE have very fast convergence rate and having ability to solve the given problem

Evolutionary algorithm, sound waves, multi-model problem

1. R. Storn , K. Price “Differential Evolution – Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces” Journal of Global Optimization 11: 341–359, 1997.
2. R. Storn , K. Price “Differential Evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces” TR-95-012 March 1995
3. D. KARABOGA , S. OKDEM –“A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution algorithm” Turk J Elec Engin, VOL.12, NO.1 2004
4. QIAO Feng, LIN Ping –“A differential evolutionary based algorithm for multiuser OFDMA system adaptive resource allocation” Journal of Communication and Computer, ISSN1548-7709, 2008.

5. M.Maximiano, Miguel A. Vega-Rodríguez, Juan A. Gómez Pulido, Juan M. Sánchez-Pérez ,”A Hybrid Differential Evolution Algorithm to Solve a Real-World
Frequency Assignment Problem”IEEE,2008.

6. Draa, S. Meshoul, H.Talbi, M. Batouche “A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem “The International Arab Journal of Information Technology, Vol. 7, No. 1, January 2010
7. Chuan-wang Song, Xue-song Chu, Liang Li, Jing Wang “An improved differential evolution algorithm for the slope stability analysis “ ,IEEE , 2011.
8. A.K.M. Khaled Ahsan Talukder, Michael Kirley and Rajkumar Buyya “Multiobjective Differential Evolution for Workflow Execution on Grids”, MGC 2007.\
9. Samir SAYAH, Khaled ZEHAR ,Using Evolutionary Computation to Solve the Economic Load Dispatch Problem, Leonardo Journal of Sciences, 2008.
10. Radha Thangaraj Millie Pant Ajith Abraham “New mutation schemes for differential evolution algorithm and their application to the optimization of directional over-current relay settings” Applied Mathematics and Computation , 532–544.,2010.

11. S. K. Goudos, K.Siakavara, T. Samaras, E. Vafiadis, , J. N. Sahalos, “Self-Adaptive Differential Evolution Applied to Real-Valued Antenna and Microwave Design Problems “IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, VOL. 59, NO. 4, APRIL 2011

12. M. Vasile, , E Minisci, M. Locatelli “An Inflationary Differential Evolution Algorithm for Space Trajectory Optimization” IEEE TRANSACTIONS ON EVOLUTIONARYCOMPUTATION, VOL. 15, NO. 2, APRIL 2011

13. X. Tong-yu, SUN Yan- Substation Locating and Sizing in Rural Power System Based on GIS and Modified Differential Evolution Algorithm” , IEEE,2009.

14. M. Fatih Tasgetiren, P.N. Suganthan, Tay Jin Chua and Abdullah AlHajri” Differential Evolution Algorithms for the Generalized Assignment Problem”!EEE,2009.

15. Wen-Hong Wang, Feng-Rui Wang, Quan-Ke Pan, Feng-Chao Zuo “Improved Differential Evolution Algorithm for Location Management in Mobile Computing”,IEEE,2009.

17. Wang Zhiqiang1, Zhang Xin1, Liu Wenxia1 and LIU Boliang2” Substation Planning Based on Geographic Information and Differential Evolution Algorithm
18. Feng-rui WANG, Wen-hong WANG, Hua-qing YANG, Quan-ke PAN,”A Novel Discrete Differential Evolution Algorithm for Computer-aided Test-sheet Composition Problems”,IEEE,2009

19. Jun Wang, Xingguo Cai, Jun Wang, Dongdong Wang”Study of Dynamic Available Transfer Capability with the Improved Differential Evolution Algorithm”,IEEE,2009

20. SiBo Ding”Logistics Network Design Optimization Based on Differential Evolution Algorithm”,IEEE,2010.

21. Sibo Ding” Logistics Network Design and Optimization Based on Fuzzy Adaptive Differential Evolution Algorithm”,IEEE,2010.

22. Meng Zhang1 , Peiyong Sun 2 , Ruiting Cao1 , Jiangle Zhu1” LQG/LTR Flight Controller Optimal Design Based on Differential Evolution Algorithm”IEEE,2010.

23. Kun Chao1, 2, Zhenwei Zhao1, Zhensen Wu2 and Rui Lang1”Application of the Differential Evolution Algorithm to the Optimization of Two-Dimensional Synthetic Aperture Microwave Radiometer Circle Array “,IEEE,2010.

24. Adam Slowik”Application of Adaptive Differential Evolution Algorithm with Multiple Trial Vectors to Artificial Neural Networks Training” IEEE,2010






Parveen Sadotra, Chandrakant Sharma

Paper Title:

Cyber Education: A Need of the Time

Abstract: In the last two decades Internet has changed our lives in a great way. Almost everything is dependent on internet. Vast use of internet has created a new kind of threat to human civilization. Dependency of our day to day work in cyber world has become indispensable in present time. This revolution of internet has made our life much easy on one hand but at the other hand we need to be very much cautious. If we are not familiar of the pros and cons of the cyber world it may cost a huge loss sometimes to even life. So it is the need of time that every one of this earth should educated well about this beautiful cyber world along with all the security aspects and it needs a well-planned strategy to achieve 100 % cyber literacy.

Cyber Education, Cyber Security, Cyber Theft, Cyber World, Literacy.


1. <http://ijcse.academic-publication.org/>
2. <http://www.nativeintelligence.com/ni-programs/whyaware.asp>

3. Parveen Sadotra (CEH), Prof. Vinus Sharma, 2015, “Measuring And Combating Spam on Social Networks”, ‘Cyber Times International Journal of Technology & Management’, Vol 8. Issue 1. Pg.28-32

4. <http://deity.gov.in/content/information-security-education-and-awareness-project

5. <https://tdloffice.wordpress.com/2013/10/21/trust-in-digital-life-introduction/>

6. Parveen Sadotra (CEH), Dr. Anup Girdhar, 2015, “Role Of Cyber security In Private sector Domains”, ‘Cyber Times International Journal of Technology & Management’, Vol 8. Issue 1. Pg.7-11.

7. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=>

8. <http://www.witdom.eu/tdw2015>

9. <http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?reload=true&punumber=6883276>

10. <http://www.pewinternet.org/2014/03/11/digital-life-in-2025/>

11. Parveen Sadotra, 2015. Research Challenges and Issues in Web Security. International Journal of Computer Engineering & Technology (IJCET).Volume:6, Issue: 5, Pages: 1-7






Samuel A. Daramola, Abdulkareem Ademola

Paper Title:

Vehicle Classification Algorithm using Size and Shape

Abstract: Automatic classification of vehicles into different classes based on their sizes and shapes is very useful for traffic control and toll collection process. Effective intelligent transportation system that incorporates vehicle classification technique is needed in many cities to prevent road accident and traffic congestion caused by illegal movement of vehicles. This work presents method of getting structural information from detected vehicle images and then uses it to classify vehicles into different classes. The technique involves extraction of contour features from vehicle images side view using morphological operations. The extracted features were combined and used to generate feature vector that serve as input data to vehicle classification algorithm based on Euclidean distance measure. Impressive result was achieved from the proposed vehicle classification method.

Vehicle, Shape, Size, Features, Boundary, Classification.


1. S. Adebayo Daramola, E. Adetiba, A. U. Adoghe, J. A. Badejo, I. A Samuel and T. Fagorusi, Automatic Vehicle Identification System using License Plate, International Journal of Engineering Science and Technology, Vol. 3, No. 2, 2011, pp 1712-1719.
2. Kaewkamnerd, S., Chinrungrueng, J., Pongthornseri, R., Dumnin, S, Vehicle classification based on magnetic sensor signal, Information and Automation (ICIA), IEEE International Conference, 2010.pp.935-939.

3. Kaewkamnerd, S., Pongthornseri, R., Chinrungrueng, J., Silawan, T, Automatic vehicle classification using wireless magnetic sensor, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IEEE International Workshop, 2009, pp.420-424.

4. Zhang, W., Tan, G.Z., Nan, D., Yao, S., Lin, M.W, Vehicle Classification Algorithm based on Binary Proximity Magnetic Sensors and Neural Network, Intelligent Transportation Systems, International IEEE Conference pp.145-150, 2008.

5. Guohui Zhang, Ryan P. Avery, Yinhai Wang, A Video-based Vehicle Detection and Classification System for Real-time Data Collection Using Uncalibrated Video Cameras, National Research Council, Washington, DC , 2007.

6. Ma, X. and Grimson, W. E. L, Edge-based rich representation for vehicle classification, .Computer Vision, Tenth IEEE International Conference Vol.2, 2005, pp.1185-1192.

7. Mehran Kafai and Bir Bhanu, Dynamic Bayesian Networks for Vehicle Classification in Video, IEEE Transactions on Industrial Informatics, 2011, pp1-9.

8. Jun Yee Ng, Yong Haur, Image based Vehicle Classification System, The11th Asian-Pacific ITS Forum and exhibition, 2011, pp1-11.

9. Saeid Fazli , Shahram Mohammadi , Morteza Rahmani, Neural Network based Vehicle Classification for Intelligent Traffic Control, International Journal of Software Engineering & Applications, Vol.3, No.3, 2012, pp17-22.

10. Otsu, N, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.






S. Vadivel, B. Baskaran

Paper Title:

Comparison of Responses of the PI and the Fuzzy Logic Controlled Closed Loop DPFC Systems

Abstract: The proposed work deals with comparison of the PI and the fuzzy logic controlled closed loop DPFC systems. The voltage across the load decreases due to the addition of extra load and the load voltage are restored back to normal value by using closed loop system. The ability of closed loop system to bring the voltage and reactive power back to the set value is represented in this paper. The simulation studies for open loop and closed loop systems are performed on a standard ten bus radial test system.

DPFC – Distributed power flow control, DVR – Dynamic Voltage Regulator, ESS – Steady State Error, FACTS – Flexible AC Transmission System, Q – Reactive Power, STATCOM – Static Compensator, Ts – Settling Time, UPFC – Unified power flow control, V – Voltage, VAR – Voltage Ampere Reactive, VP – Peak Over Shoot Voltage.


1. S. Adebayo Daramola, E. Adetiba, A. U. Adoghe, J. A. Badejo, I. A Samuel and T. Fagorusi, Automatic Vehicle Identification System using License Plate, International Journal of Engineering Science and Technology, Vol. 3, No. 2, 2011, pp 1712-1719.
2. Kaewkamnerd, S., Chinrungrueng, J., Pongthornseri, R., Dumnin, S, Vehicle classification based on magnetic sensor signal, Information and Automation (ICIA), IEEE International Conference, 2010.pp.935-939.

3. Kaewkamnerd, S., Pongthornseri, R., Chinrungrueng, J., Silawan, T, Automatic vehicle classification using wireless magnetic sensor, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IEEE International Workshop, 2009, pp.420-424.

4. Zhang, W., Tan, G.Z., Nan, D., Yao, S., Lin, M.W, Vehicle Classification Algorithm based on Binary Proximity Magnetic Sensors and Neural Network, Intelligent Transportation Systems, International IEEE Conference pp.145-150, 2008.

5. Guohui Zhang, Ryan P. Avery, Yinhai Wang, A Video-based Vehicle Detection and Classification System for Real-time Data Collection Using Uncalibrated Video Cameras, National Research Council, Washington, DC , 2007.

6. Ma, X. and Grimson, W. E. L, Edge-based rich representation for vehicle classification, .Computer Vision, Tenth IEEE International Conference Vol.2, 2005, pp.1185-1192.

7. Mehran Kafai and Bir Bhanu, Dynamic Bayesian Networks for Vehicle Classification in Video, IEEE Transactions on Industrial Informatics, 2011, pp1-9.
8. Jun Yee Ng, Yong Haur, Image based Vehicle Classification System, The11th Asian-Pacific ITS Forum and exhibition, 2011, pp1-11.
9. Saeid Fazli , Shahram Mohammadi , Morteza Rahmani, Neural Network based Vehicle Classification for Intelligent Traffic Control, International Journal of Software Engineering & Applications, Vol.3, No.3, 2012, pp17-22.

10. Otsu, N, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.






Vaibhav V. Shah, Smitkumar J. Mirani, Yashvardhan V. Nanavati, Vishal Narayanan, Sheetal I. Pereira

Paper Title:

Stock Market Prediction using Neural Networks

Abstract: In this paper we present our efforts to predict the stock market using Artificial Neural Networks. We study different types of Neural Networks, their salient features along with the internal working of these networks and the various configurations that they can be run with. We go to comment on the advantages and disadvantages of these networks. Finally we select the one network with specific configurations and use it to predict the stock prices of a few selected companies from the National Stock Index. We achieve best case accuracy of 98% on the dataset.

Artificial Neural Networks, Neurons, Back propagation algorithm, Prediction methods, Stock markets.


1. http://wsc10.softcomputing.net/ann_chapter.pdf
2. https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/History/history1.html

3. https://en.wikipedia.org/wiki/AlphaGo

4. http://gizmodo.com/gmail-now-uses-artificial-neural-networks-to-sniff-out-1716975952

5. http://gizmodo.com/googles-neural-network-can-now-reply-to-gmail-messages-1740260404

6. https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/Neuron/index.html

7. https://www.google.co.in/imgresimgurl=http://www.extremetech.com/wpcontent/uploads/2015/07/NeuralNetwork.png&imgrefurl=
/215170-artificial-neural-networks-are-changing-the-world-what-arethey&h=590&w=1008&tbnid=ru1iSgMkF619M:&docid=uw9rq85fHW8ClM&ei=LtW1Vrb 7IoqeugTZ85fwDg&tbm=isch&ved=

8. 0ahUKEwi2ypfhgOPKAhUKj44KHdn5Be4QMwhCKBEwEQ

9. http://mnemstudio.org/neural-networks-backpropagation.htm

10. http://www.frank-dieterle.de/phd/images/image016.gif

11. http://in.mathworks.com/help/nnet/ref/trainlm.html?refresh=true

12. https://en.wikipedia.org/wiki/Gradient_descent
13. http://in.mathworks.com/help/nnet/ref/logsig.html
14. http://matlab.izmiran.ru/help/toolbox/nnet/logsig.gif

15. https://www.quandl.com/data/NSE?keyword=

16. http://www.measuringu.com/zcalc.htm





Rajesh B. Mapari, Govind Kharat

Paper Title:

Performance Analysis of Different Classifiers for American Sign Language Recognition

Abstract: American Sign Language alpha-numeric character recognition without using any embedded sensor, color glove or without the constraints of an environment is a really difficult task. This paper describes a novel method of static sign recognition using a leap Motion sensor by obtaining feature set based on hand position, distance and angle between different points of hand. A feature set is later trained and tested using different classifiers like MLP (Multilayer Perceptron), GFFNN (Generalized Feed forward Neural Network), SVM (Support Vector Machine). We have collected dataset from 146 people including students of age 20-22 years and few elders age between 28-38 who have performed 32 signs resulting in total dataset of 4672 signs. Out of this 90% dataset is used for training and 10% dataset is used for testing/Cross validation. we have got maximum classification accuracy as 90% on CV/testing dataset using MLP Neural Network.



1. Fu-Hua Chou, Yung-Chun Su., “An Encoding and Identification Approach for the Static Sign Language Recognition”, The 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics , pp. 885-889, July 11-14, 2012.
2. M.S. Sinith, Soorej G Kamal, Nisha B. ,Nayana S, Kiran Surendran, Jith P S, “Sign Language Recognition Using Support Vector Machine”, International Conference on Advances in Computing and Communications, pp. 122-125, Aug. 9-11, 2012.
3. Hee-Deok Yang, Seong-Whan Lee, “Combination of Manual and Non-Manual Features for Sign Language Recognition Based on Conditional Random Field and Active Appearance Model”, Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, pp. 1726-1731, July 10-13, 2011.
4. Fahad Ullah, “American Sign Language Recognition System for Hearing Impaired People Using Cartesian Genetic Programming”, 5th International Conference on Robotics and Application, p.p. 96-99, Dec. 6-8, 2011.

5. Asha Thalange, Dr. Shantanu Dixit, “Effect of Thinning Extent on ASL Number Recognition Using Open-finger Distance Feature Measurement Technique”, International Conference on Signal Processing And Communication Engineering Systems (SPACES), pp.39-43, Jan. 2-3, 2015.

6. Priyanka Mekala, Ying Gao, “Real-time Sign Language Recognition based on Neural Network Architecture”, in Proceedings IEEE, pp.195-199, March 14-16, 2011.

7. Taehwan Kim, Karen Livescu, Gregory Shakhnarovich, “American Sign Language Fingerspelling Recognition with Phonological Feature based Tandem Models”, Proceedings IEEE , pp.119-124, Dec. 2-5, 2012.

8. Dominique Uebersax, Juergen Gall et al., “Real-time Sign Language Letter and Word Recognition from Depth Data”, IEEE International Conference on Computer Vision Workshops, pp. 383-390, Nov. 6-13, 2011.

9. Jerome M. Allen, Pierre K. Asselin, Richard Foulds, “American Sign Language Finger Spelling Recognition System” Proceedings of IEEE 29th Annual Northeast Bioengineering Conference, pp.285-286, March 22-23, 2003.

10. Vasiliki E. Kosmidou, Leontios J. Hadjileontiadis , Stavros M. Panas “Evaluation of surface EMG features for the recognition of American Sign Language gestures” Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, pp. 6197-6200, Aug. 30-Sept. 3, 2006.

11. C. S. Weerasekera, M. H. Jaward, N. Kamrani, “Robust ASL Fingerspelling Recognition Using Local Binary Patterns And Geometric Features”, Proceedings of IEEE, pp. 1-8, Nov. 26-28,2013.

12. Lucas Rioux-Maldague, Philippe Gigu`ere, “Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network”, Canadian Conference on Computer and Robot Vision, pp. 92-97, May 6-9, 2014.

13. A.S. Elons, Menna Ahmed, Hwaidaa Shedid and M.F.Tolba, “Arabic Sign Language Recognition Using Leap Motion Sensor” 9th International Conference on Computer Engineering & Systems (ICCES), pp. 368-673, Dec. 22-23, 2014.

14. L. Nanni, A. Lumini, F. Dominio, M. Donadeo, and P. Zanuttigh. “Ensemble to improve gesture recognition”, International Journal of Automated Identification Technology, 2014.
15. Cao Dong, Ming C. Leu, Zhaozheng Yin, “American Sign Language Alphabet Recognition Using Microsoft Kinect”, The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (Boston, MA, , 2015)., pp. .44-52, June 7-12, 2015.
16. Giulio Marin, Fabio Dominio, Pietro Zanuttigh, “Hand gesture recognition with leap motion and kinect devices”, IEEE International Conference on Image Processing (ICIP), pp. 1565 – 1569, Oct. 27-30, 2014.

17. Makiko Funasaka, Yu Ishikawa, Masami Takata, and Kazuki Joe, ” Sign Language Recognition using Leap Motion Controller”, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA). pp. 263-269, 2015.

18. Wikipedia. “Leap Motion” Wikipedia.org. [Online]. Available https://en.wikipedia.org/wiki/Leap_Motion [Last Modified: 26 July 2015, 07:16].Wikipedia. “Leap Motion” Wikipedia.org. [Online]. Available https://en.wikipedia.org/wiki/Leap_Motion [Last Modified: 26 July 2015, 07:16].




Volume-6 Issue-3

Download Abstract Book

S. No

Volume-6 Issue-3, July 2016, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Abdisalam Issa-Salwe, Khurram Mustafa

Paper Title:

Security Assurance Through Strategic Information Systems Planning

Abstract:  Strategic Information Systems Planning (SISP) and pertinent Information Security Policy (ISP) in organisations are largely inevitable in the contemporary business systems. Embedding information security policy within the organisation’s strategic information system planning is essential for the effectiveness of using information systems in modern systems in a secure environment. A survey of relevant literature on SISP and ISP in organisations’ processes reveals a close relationship between them and draws attention to how contradictions within this relationship may threaten as well. We explore the importance of embedding the ISP process within the SISP, and how these two issues are vital to organisations. It is further established the inevitable complementary role of these to ensure the effectiveness of contemporary information systems. The strategic planning information system makes certain that new systems are deployed in a way that maintains the strategic objectives of an organisation while the security policy provides a framework for verifying that systems are shaped and managed in a secure manner. Embedding ISP in SISP appears to increase progressively the security capability of an organisation, and hence, the deliverables from the SISP process may be more effective, efficient and hencefsystems came with huge complexities  beneficial to the organisation. Although organisations may face security glitches throughout the application and operational phase, they must try hard such an inevitable embedding to avoid certain catastrophic risks, assure business continuity and enhance overall productivity. Finally, a cyber sensitive audit and control based ISP Components-based framework is proposed for embedding ISP into SISP, right from instantiation

Strategic Information Systems Strategy, Information Systems (IS), Information Technology (IT), Information Security Policy, Contemporary Business, Security Risk, Business Continuity Planning (BCP).


1.       Issa-Salwe, M. Ahmed, K Aloufi and M. Kabir, “Strategic Information Systems Alignment: Alignment of IS/IT with Business Strategy”, Journal of Information Processing Systems, Vol.6, No.1, pp….March 2010.
2.       Altameem , A. I. Aldrees and N. A. Alsaeed. 2014, “Strategic Information Systems Planning (SISP)”, Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA,  22-24 October, 2014,

3.       R.D. Austin and C.A. Darby, “. The Myth of Secure Computing”, Harvard Business Review, Vol….121–126, June 2003

4.       BERR (Enterprise and Regulatory Reform). 2008. Security Breaches Survey 2008, UK Department of Business.

5.       Beynon-Davies P. Business Information Systems. 2013. Palgrave/Macmillan, Houndmills, Basingstoke. 2nd edition.

6.       Bhatnagar, A. (2006). Strategic Information Systems Planning: Alignment of ‘IS/IT’ Planning and Business Planning. Unpublished thesis submitted in partial fulfillment of the degree of Master of Computing, Unitec Institute of Technology, New Zealand.

7.       Brian Fergerson. 2012.  Key Stages of Strategic Information System Planning (SISP) Methods and Alignment to Strategic Management Planning Concepts, ERP and Virtualization Services Columbia Forest Products, Applied Information Management Program, University of Oregon.

8.       Neil F. Doherty and Heather Fulford (November 2005): Aligning the information security policy with the strategic information systems plan. Computers & Security, Vol 25, Issue 1, 55–63. February 2006.

9.       Garg A., J. Curtis and H. Halper. 2003. Quantifying the Financial Impact of Information Security Breaches, Information Management and Computer Security 11 (2), 74–83.

10.    Höne Karin and J. H. P. Eloff. 2002.  Information Security Policy — What Do International Information Security Standards Say? Computers & Security, Volume 21, Issue 5, 1, 402-409.

11.    ISO/IEC 17799. 2005. ISO. Information technology — Security techniques — Code of practice for information security management. International Standards Organisation.

12.    John Lindström and Ann Hägerfors. 2009. A Model For Explaining Strategic IT and Information Security To Senior Management, International Journal of Public Information Systems. Vol 5:1. 17-29.
13.    Kajava J., Varonen R., Anttila J., Savola R., and Röning J. Senior Executives Commitment to Information Security – from Motivation to Responsibility, Proceedings of the International Conference on Computational Intelligence and Security, IEEE. 2006.
14.    King, W. and Teo, T. S. H. (1997) Integration between business planning and information systems planning: Validating a stage hypothesis. Decision Sciences, 28:2, pp. 279-308.

15.    Kolkowska E. Value Sensitive Approach to IS security – a socio-organisational perspective, Proceedings of the Eleventh Americas Conference on Information Systems. 2005.

16.    Lederer, A. L., & Sethi, V. Key prescriptions for strategic information systems planning. Management Information Systems, 35-60. 1996.

17.    Lindström John and Ann Hägerfors. A Model For Explaining Strategic IT and Information Security to Senior Management, Luleå University of Technology, Sweden, International Journal of Public Information Systems, Vol 1.2009.

18.    Luftman, J. N. Competing in the information Age Align in the Sand (2nd ed.). New York: Oxford University Press. 2003.

19.    Md Hafiz Selamat, Adam Suhaimi, Husnayati Hussin. January 2006. “Integrating Strategic Information Security with Strategic Information Systems Planning”. National ICT Conference 2006 (UiTM), Kangar.

20.    Newkirk, H., and Lederer, A. The Effectiveness of Strategic Information Systems Planning for Technical Resources, Personnel Resources, and Data Security in Environments of Heterogeneity and Hostility. The Journal of Computer Information Systems, 47 (3), 34-44. 2007.

21.    Jens Bartenschlager. 2011. Implementing IT Strategy: Laying a Foundation. Informatik. Management Research Centre Frankfurt School of Finance & Management.
22.    PWC, 2013 Information Security Breaches Survey: Technical Report. Department for Business, Innovation & Skills (BIS).
23.    Rogerson, S. & Fidler, C. 1994. Strategic Information Systems Planning: Its Adoption and Use, Information Management and Computer Security, (2),1-7.

24.    Sabherwal, R., & Chan, Y. E. (2001). Alignment between business and IS strategies: A study of prospectors, analysers, and defenders. Information Systems Research, 12(1), 11-33.

25.    Siponen, Mikko, Designing Secure Information Systems and Software: Critical Evaluation of the Existing Approaches and a New Paradigm. Academic Dissertation. University of Oulu. 2002

26.    Vincent LeVeque. 2006. Information Security – A Strategic Approach, John Wiley & Sons, pp. 3-20, 149-152.

27.    Ward,  J. & Griffith, P. Strategic Planning For Information Systems (2nd Edition). John Wiley & Son, London. 2000.

28.    Ward J. and J. Peppard, Strategic Planning for Information Systems, John Wiley & Sons, Chichester. 2002.

29.    Wylder J. 2004. Strategic Information Security, Auerbach/CRC Press LLC, pp1-16, 139-153.

30.    PWC, 2015 Information Security Breaches Survey: Technical Report, HM Government, UK.






Gekonge Dorcas Arama, Kamaara Mary

Paper Title:

Factors Influencing Implementation of Sustainable CSR Practices in the Energy Sector in Kenya: Case of Kenya Electricity Generating Company

Abstract:   In the last decade, the issue of sustainability has become very critical the world over for governments and business alike. This has seen an increased uptake of CSR activities by many organizations in the areas of environmental, social and economics as marked by increased expenditure in implementation of CSR programs in an effort to contribute to the sustainability agenda. However, most of the CSR programs adopted by organizations are not sustainable as they have weak integration to corporate strategies are faced by lack of project sustainability, financial sustainability, weak structures and systems and low visibility. Moreover, the companies also face threats from communities, complex regulatory framework and there being a mismatch between community needs and corporate objectives as most of the CSR projects are dictated by boardroom decisions. If a business is to have a deliberate positive and consistent impact on society, then its purpose and values should be shared by all those who may influence and be influenced, affect or get affected by its actions. This study therefore aimed at providing a predictive mechanism of the various influences of implementation of sustainable CSR practices in organizations through a case study approach of KenGen one of the key players in the energy sector of Kenya.  The findings of this study is that interventions to reduce the restraining effect of negative influences such as internal inhibitors that sap out the power of positive forces  on implementation of sustainable CSR practices. The paper therefore proposes management intervention measures such as development of measurement metrics that will help track performance and prioritization of CSR programs based on need and impact assessment to ensure improved livelihood of target beneficiaries.

 Corporate Social Responsibility, sustainable CSR practices, Sustainability


1.        Ambec, S., & Lanoie, P. (2008).  Does it pay to be green?  A systematic overview.  Academy of Management Perspectives, 22, 4, 45-62.
2.        Ayele, T.H.(2003). An Investigation Into the Practices of Social Responsibility Among Polythene Manufacturers In Kenya. Academy of Management Review
3.        Babbie, E. (2008). The Basic of Social Research  (4th ed.). California: Thomson Wadsworth Publishing Company: Belmont C.A.
4.        Babbie,E. (2004). The Practice of Social Research. Wadsworth Publishing Company: Belmont C.A.

5.        Bain, J. S. 2010. Social Responsible Investing. New York: Wiley.

6.        Baker, T.L. (1994), Doing Social Research (2nd Ed.), New York: McGraw-Hill Inc.being and why no one saw it coming. New York: Penguin

7.        Blackburn, W. R. (2007).  The sustainability handbook: The complete management guide to achieving social, economic, and environmental responsibility.  London: Earthscan

8.        Bull, N,L. (2006). Motivations for an organization within a developing country to report social responsibility information: evidence from Bangladesh. Accounting,Auditing 7 Accountability Journal, Vol 21(6)
9.        Burke,L. & Logsdon,M. (2006). A Strategic Posture Toward Corporate Social Responsibility. California Management Revie
10.     Burnes, B. 1996. No such thing as…a “one best way” to manage organizational change.Management Decision, 34 (10):11–18.

11.     Byrne, B. M. (2001). Structural equation modeling with AMOS:- Basic concepts, applications, and programming.  Mahwah, NJ: Lawrence Erlbaum.

12.     Campbell,V. (2010). Is it Quality Improves ethics or Ethics Improves Quality.Academy of Management Review

13.     Carroll & Budchholtz, (2012). The Pyramid of Corporate Social Responsibility: Toward the moral management of organizational stakeholders.Business Horizons Vol 34.

14.     Carroll A. B. & Shabana K. M. (2010). The Business Case for Corporate Social Responsibility: A Review of Concepts, Research and Practice. International Journal of Management Reviews .

15.     Carroll, A. B. (1991). The Pyramid of Corporate Social Responsibility: Toward the Moral Management of Organizational Stakeholders. Business Horizons, 34: 39–48.

16.     Cavico,F. & Mujtaba,B. (2012). Social Responsibility, Corporate Constituency Statutes, and the Social Benefit Corporation. International Journal of Management and Administrative Sciences,1(7).

17.     Clarkson, M. B. 1995. A Stakeholder Framework for Analyzing and Evaluating Corporate  Social Performance. Academy of Management Review. 20 (1): 92-117

18.     Cohen, A. J. (2006).  Capital markets at the crossroads: Sustainable investing, environmental focus.  Prepared for the Clinton Global Initiative Annual Meeting, September.

19.     Cornell,S. & Shapiro,A. (2007).Corporate Social Performance Revisited. Academy of Management Review 16(4).
20.     Covin,A. & Miles, J.(2007). Strategic Management Concepts and Case. (12th ed.). New York. McGraw-Hill.
21.     Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches(2nd ed.). Thousand Oaks, CA: Sage.

22.     Cronbach, L.J., (1951). Coefficient Alpha and the Internal Structure of Tests: Psychometrika, 6(3),297-334

23.     Deegan, C., Rankin,M. &  Tobin,J. (2002). An examination of the Corporate Social and Environmental Disclosures of BHP . A test of Legitimacy Theory. Accounting, Auditing and Accountability journal, Vol 15

24.     DiMaggio, J.& Powell,W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields, American Sociological Review.48.

25.     Esty, D. C., & Winston, A. S. (2006).  Green to gold: How smart companies use environmental strategy to innovate, create value, and build competitive advantage.  New York: Reed Elsevier.

26.     European Commisson (2002). Corporate Social Responsibility- A business Contribution to Sustainable Development, Office for Official Publications of the European Communities, Luxermbourg.

27.     Fox, A. (2007). Corporate Social Responsibility Pays Off. HR Magazine

28.     Galan, J. 2006). Corporate social responsibility and strategic management. Journal of     Management Studies 43 (7), 1629-1641.

29.     Galbreath ,P.(2010). The Question of organizational consciousness: Can Organizations have values, virtues and visions? Journal of Business Ethics. 29(3)

30.     Gall, M. D., Gall, J. P., & Borg, W. R., (2007) Educational Research: An introduction. Boston: Pearson Education.

31.     Giampalmi ,D. (2013). Attitudes about Corporate Social Responsibility: Business student predicitors. Journal of Business Ethics. Vol 91(2).

32.     Gichana D.B.O (2004). A Survey of Corporate Social Resonsibility Practices by Kenyan Companies: Acase of Companies listed in the Nairobi Securities Exchnage.
33.     Goldman Sachs Group (2007).  GS sustain.  New York: Goldman Sachs Global Research.
34.     Goldman Sachs Group. (2007).  GS sustain.  New York: Goldman Sachs Global Research.
35.     Gray, R., et al. (1996). Accounting and accountability-changes and challenges in corporate social and environmental reporting. Prentice Hall,London.
36.     Harish, N. (2012). Corporate Social Responsibility in indian Companies. International Journal of Management, IT and Engineering, Vol.2, Issue5.

37.     Harrington, E. et.al  (2004).Sensemaking in organizations. Sage.

38.     Hawken, P. (2007).  Blessed unrest: How the largest movement in the world came into

39.     Heemskerk, L. (2012). How responsible is responsible business? An analysis of the drivers and effects of the responsible business practices of Dutch enterprises operational in Kenyan agribusiness. Master’sThesis, University Utrecht.

40.     Herrmann, Kristina K. (2004). Corporate Social Responsibility and Sustainable Development: the European Union Initiative as a Case Study. Indiana Journal of Global Legal Studies Vol. 11: Iss. 2, Article 6. Retrieved from: http://www.repository.law.indiana.edu/ijgls/vol11/iss2/6  on 6December, 2015

41.     Ho, C. (2005).  Corporate governance and corporate competitiveness: An international analysis, corporate governance:  An International Review. Published PhD thesis HongKong Polytechnic University dissertations. 13(1), 211-253.

42.     Jamali ,D. & Mishak, R. (2007).The case for strategic corporate social repsonisbility in developing countries. Business and Society Review. Vol 112 (1).

43.     Jones, I., Pollitt, M., and D. Bek (2010). Multinationals in their communities: A social capitalapproach to corporate citizenship projects, University of Cambridge Working Paper 337,accessible from http://www.cbr.cam.ac.uk/pdf/WP337.pdf
44.     Jones, K.(2010).  Democratizing the Corporation, Humanism in Business. Cambridge: Cambridge University Press
45.     Kenya Electricity Generating Company Limited Annual Report & Financial Statements (2014) 

46.     Kenya Electricity Generating Company Limited, retrieved on 12th September 2015 from http://www.kengen.co.ke/index.php?page=press&subpage=releases

47.     Kiraison, H. (2015). Developing Stakeholder  Partnerships in Community Oriented Projects.

48.     Kothari, C. R. (2004). Research Methodology. New Delhi: New Age International.

49.     KPMG (2012). Kenya –Country Profile, KPMG Services Proprietary Limited, South Africa 2012 Retrieved  from: http://www.kpmg.com/Africa/en/KPMG-in-Africa/Documents/Kenya.pdf  on November 20th 2015

50.     Kumar, S.A. & Suresh, N. (2009). Operations management. New Delhi: New Age International. ISBN (13) : 978-81-224-2883-4

51.     Kumar,P.(2010). Corporate Social Responsibility-Public Sensitivity.Proficient.

52.     Kwalanda, R.(2007). Corporate Social Responisbility at Safaricom Ltd. Unpublished MBA Project, University of Nairobi.

53.     Lopez, M., Garcia, A., & Rodriguez, L. (2012). Sustainable development and corporate performance: A study based on the Dow Jones Sustainability Index. Journal of Business Ethics, 75, 285–300.

54.     MacWillaims, G. & Siegel,N. (2001). Researching Corporate Social Responsibility: An agenda for the 21st Century. Journal of Business Ethics (7).Management.

55.     McWilliams, A., & Siegel, D. (2001). Corporate social responsibility: A theory of the firm perspective. Academy of Management Review, 26(1), 117–127.

56.     Meyer,W. & Rowan, B.  (1977). Institutionalized organizations: Formal structure as myth and ceremony. American journal of sociology 83: 340–363

57.     Millon,D. (2011). Two Models of Corporate Social Responsibility. Wake Forest Law Review, Vol.46.

58.     Mugenda, M. & Mugenda, A. (2003).Research Methods: Quantitative and Qualitative Approaches (2ndEd.). Nairobi, Kenya: Acts Publishers.

59.     Mugenda, O.M  & Mugenda, A.G. (1999): Research Methods; Quantitative and Qualitative Approaches. Nairobi: Acts Press

60.     Muthuri, J. N. & Gilbert, V. (2011). An Institutional Analysis of Corporate Social Responsibility in Kenya. Journal of Business Ethics, vol. 98.

61.     Muthuri,J. (2005). Corporate Giving: How much is enough?  ; A theory of the Firm Perspective. Academy of Management Review,26.

62.     Ominde, J. (2006). The Link Between Corporate Social Respnsibility & Corporate Strategy among Companies listed in the NSE

63.     Omoro,N. & Okiro,K. (2014). Investing in Corporate Social Responsibility and Sustained Gorwth in Kenya Commercial Banks. Journal of Emerging Issues in Economics, Finance and Banking.

64.     Porter, M. & Kramer, M.R. (2002). Strategy and Society : The link between competitive advantage Corporate Social Responsibility. Harvard Business Review,84 (12)

65.     Pruzan ,B & Miller, L.(2006). A Survey of managers’ perceptions of corporate ethics and social responsibility and actions that may affect companies’ success.Journal of business Ethics. Vol 82 (3)

66.     Richard,S.  (2008). Institutions and Organizations: Ideas and Interests. Los Angeles, CA: Sage Publications.

67.     Roome,M.& Bergin, S. (2006). Corporate Social Responsibility Communication: Stakeholder information, response and involvement strategies. Business Ethics: A European Review, 5.

68.     Sagebien, J. & M. Whellams (2010). CSR and Development: Seeing the Forest for the Trees. Canadian Journal of Development Studies, vol. 31(3).

69.     Saleemi, N.A (2009). Statistics Simplified. Nairobi: N.A Saleemi

70.     Savitz, A., with Weber, K. (2006). The Triple Bottom Line. San Francisco: Jossey-Bass. 

71.     Savitz, A., with Weber, K. (2006). The Triple Bottom Line. San Francisco: Jossey-Bass.

72.     Schmidpeter René, Weidinger Christina & Fischler Franz (2014). The Evolution of CSR from Compliance to Sustainable Entrepreneurship: Business Success through Sustainability, Springer Berlin Heidelberg.

73.     Seidman, I. (2006). Interviewing as qualitative research: A guide for researchers in education and the social sciences, Third Edition.New York: Teachers College Press.

74.     Sekaran, U. (2003). Research method for business: A skill building approach

75.     Steurer, R., Langer, M.E., Konrad, A., Martinuzzi, A. (2005). „Corporations, stakeholders and sustainable development: a theoretical exploration of business-society relations”, Journal of Business Ethics, 61, pp. 263-281

76.     Suchman, M.C. (1995). Managing Legitimacy: Strategic and Institutional Approaches. Academy of Management Journal, Vol 20.

77.     Teijlingen van, E., Rennie, A.M., Hundley, V., & Graham, W. (2001). The importance of conducting and reporting pilot studies: The example of the Scottish Births Survey, Journal of Advanced Nursing 34: 289-295.

78.     UN (2012). The  Johansberg GSDR On Sustainability: Highlights and the way forward.

79.     UN (2015). The Global Sustainability Report: Transforming our World Voigt Christina (2009). Sustainable Development as a Principle of International Law: Resolving Conflicts Between Climate Measures and WTO Law, BRILL.

80.     WBCSD (2001). Corporate Scocial Responsibility: Making Good Business Snse. Retrieved from www.sbcsd.org on November, 10th, 2015.

81.     Willard, M. &Hitchcock, D., (2006). The Business Guide to Sustainability: Practical Strategies and Tools for Organizations. London: Earthscan.

82.     Wirtenberg, J., Harmon, J., Russell, W. G., & Fairfield, K. (2007).  HR’s role in building a sustainable enterprise. Human Resource Planning, 30 (1), 10–20.

83.     Wirtenberg, J., Harmon, J., Russell, W. G., & Fairfield, K. (2007). HR’s role in building a sustainable enterprise. Human Resource Planning, 30(1), 10–20.

84.     Zadek, S. (2002).  “Balancing Performance, Ethics, and Accountability”, Journal of Business Ethics, 17(13): 1421-41.






Dhiman Biswas, Nilesh Mukherjee, Partha Pratim Sarkar

Paper Title:

Design of Compact Frequency Selective Surface (FSS) by Loading Slits and Slots

Abstract:    This paper deals with the theoretical investigation on a reduced sized Frequency Selective Surface (FSS). The FSS is designed by loading slit and slot into square patch. It has been observed, how the variation of the dimension of the slot and slit results in shifting of resonant frequency . Compared to conventional square patch FSS the designed FSS can provide reduction in resonant frequency resulting in size reduction up to 87% corresponding to resonant frequency of 2.44 GHz..Theoretical investigations have been done by Ansoft Designer® software.

   Frequency Selective Surface, Size Reduction, slot. slit


1.     N.D. Agrawal and W.A. imbraile,”Design of a Dichroic Cassegrain Sub Reflector” IEEE Trans, AP- 27(4), pp. 466-473(1979)
2.     Sung, G.H. –h, Sowerby, K.W. Neve, M.J.Williamson A.G, “A Frequency selective wall for Interface Reduction In Wireless Indoor Environments” Antennas and
Propagation Magazine, IEEE, Vol 48, Issue 5, pp 29-37 (Oct 2006).

3.     A Novel Dual-Band Frequency Selective Surface (FSS)Xiao-Dong Hu 1, Xi- Lang Zhou 1, Lin- Sheng Wu1, Liang Zhou1, and Wen-Yan Yin 2,1 Center for Microwave and RFTechnologies, Shanghai Jiao Tong University, Shanghai 200240, CHINA2 Center for Optics and EM Research, State Key Lab of MOI, Zhejiang University, Hangzhou 310058, CHINA

4.     R.Ray, A.Ray, S.Sarkar, D.Sarkar, P.P.sarkar, Reduction of Resonant Frequencies of Frequency Selective Surface by Introducing Different Types of Slots, IJCA Special Issue on “2nd National Conference Computing, Communication and Sensor Network” CCSN,2011

5.     PhD Thesis of P.P.Sarkar, Some Studies on FSS,Jadavpur University 2002.





Sakshi Sharma, Jang Bahadur Singh

Paper Title:

Optimal Number of Distributed Generators in Power System Network

Abstract:     The distribution generator (DG) may be defined as the small-scale power generation technology that provides electricity closer to customers. The positive impacts of DG are: voltage support, power loss reduction, support of ancillary services and improved reliability. The investigation is done to study the impact of multi-DGs on power losses using the line loss reduction index (LLRI). The study has been carried out on IEEE 14 bus system using Newton Raphson (NR) method for load flow analysis. The investigation is carried out with the insertion of DG at different locations and the minima point was found. The experiment was performed with different sizes of DGs in medium ranges. The analysis of the results show that simply by increasing the number of DGs in the system to reduce the losses is not beneficial all the time. Beside the optimal location and size of DG for the loss reduction in distribution system, number of DG is also a vital factor. In this work, it is concluded that the optimal number of DGs is very important parameter to reduce the losses. The study gives the optimal number of DGs that could be installed in the distribution system to have minimum line losses.

    Line loss reduction index, line loss reduction index, Multi-DGs, Newton Raphson.


1.     F. Gonzalez-Longatt and C. Fortoul. “Review of Distributed Generation Concept: Attempt of Unification,” International Conference on Renewable Energies and Power Quality, España, pp. 16-18. 2005.
2.     F. Sarabia, “Impact of distributed generation on distribution system,” PhD dissertation, Aalborg University, 2011.

3.     T. Ackermann, G. Andersson , and L. So¨der , “Distributed generation: a definition,” Electric Power Systems Research vol. 57, pp. 195, 2000.

4.     S. Gopiya Naik, D. K. Khatod, and M. P. Sharma, “Optimal Allocation of Distributed Generation in Distribution System for Loss Reduction,” IACSIT Coimbatore Conferences, vol. 28, pp. 42, 2012.

5.     S. Kansal, B.B.R. Sai, B. Tyagi, and V. Kumar, “Optimal placement of distributed generation in distribution networks,” International Journal of Engineering, Science and Technology, vol. 3, pp. 47-55, 2011.

6.     F.  G. Longatt, “Impacto de la Generación Distribuida en el Comportamiento de los Sistemas de Potencia,” Universidad Central de Venezuela 2008.

7.     Dharamjit, and D.K.Tanti, “Load Flow Analysis on IEEE 30 bus System,” International Journal of Scientific and Research Publications, vol. 2, 2012.

8.     H. B. Chowdhury, “Load-flow analysis in power systems,” Handbook of Electric Power Calculations 2004.

9.     Dr. Gleb V. Tcheslavski, “Power-Flow studies,” Fundamentals of Power Engineering, 2009.

10.  M. Ilyas, S. M. Tanweer, and A. Rahman, “Optimal Placement of Distributed Generation on Radial Distribution System for Loss Minimization & Improvement of Voltage Profile,” International Journal of Modern Engineering Research, vol. 3, pp-2296-2312, 2013.

11.  Available online: Electrical Power Lab, “Load Flow Analysis”.

12.  M. H. Yaa’kob, “Power Compensation by Distributed Generation,” PhD disertation, Universitiy Malaysia Pahang, 2010.
13.  N. Rugthaicharoencheep and S. Auchariyamet, “Technical and Economic Impacts of Distributed Generation on Distribution System,” World Academy of Science, Engineering and Technology, vol. 64, pp. 288, 2012.
14.  Paul M. Sotkiewicz, and Ing. Jesús Mario Vignolo,“Distributed Generation,” 2008.

15.  P. Paliwal and N.P. Patidar, “Distributed Generator Placement for Loss Reduction and Improvement in Reliability,” World Academy of Science, Engineering and Technology, vol. 45, pp. 803, 2010.

16.  Waseem, “Impacts of Distributed Generation on the Residential Distribution Network Operation,” PhD dissertation, Virginia Polytechnic Institute and State University, 2008.

17.  James O’Donnell, “Voltage Management of Networks with Distributed Generation,” The University of Edinburgh, 2008.






S.L. Badjate, Zoonubiya Khan Ali,  R.V Kshirsagar

Paper Title:

Energy Management of Hybrid Vehicle using Artificial Intelligence for Optimal Fuel Efficiency

Abstract:      In general, hybrid systems can be commanded by splitting the required power between the electric machine and ICE to meet the specific needs like fuel consumption, efficiency, performance, and emissions. This power splitting scenario, which is the key point of hybridization, is in fact the control strategy or energy management of the hybrid automobile. Performance of the system, therefore, depends on the control strategy which needs to be robust (independent from uncertainties and always be stable) and reliable. Moreover, in order to improve the system, the control strategy should be adaptive to track the demand changes from the driver or drive cycle for optimization purposes. In order to fulfill these conditions, there is a need to develop an efficient control strategy, which can split power based on demands of the driver and driving conditions. Hence, for optimal energy management of PHEV, interpretation of driver command and driving situation is most important. In view of this, a fuzzy logic based strategy for interpretation of driver command is proposed in this paper.

 Hybrid vehicles, fuzzy logic, driver command, parallel hybrid vehicles.


1.     Ehsani, M., Gao, Y., Gay, E.S.,  Emadi, A., (2005), Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, CRC PRESS, Boca Raton London, New York, ISBN 0-8493-3154-4
2.     M.F.Sabri,K.A.Danapalasingam , M.F.Rahmat, “A review on hybrid electric vehicles architecture and energy management strategies” RenewableandSustainableEnergyReviews53(2016)1433–1442,(2016)

3.     Huang X , Tan Y, HeX. An intelligent multi feature statistica lapproach for the discrimination of driving conditions of a hybrid electric vehicle.IEEE Trans Intell Transp Syst 2011;12:453–65.

4.     Murphey Y,Park J,Kiliaris L. Intelligent hybrid vehicle power control—part II: online intelligent energy management. Veh Technol IEEE Trans 2013; 62:69–79.

5.     Di Cairano S,Liang W,Kolmanovsky IV,KuangML, Phillips AM. Power smoothing energy management and its application to a series hybrid power train. IEEETransControlSystTechnol2013;21:2091–103.

6.     Cairano S,Di,BernardiniD,BemporadA,KolmanovskyIV.Stochastic MPC with learning for driver- predictive vehicle control and its application to the energy management. ControlSystTechnolIEEETrans2014;22:1018–31.

7.     Zhang Y, Liu H,Guo Q. Varying – domain optimal management strategy for parallel hybrid electric vehicles. IEEE Trans Veh Technol 2014;63:603–16.

8.     Zhang Y, Liu H-P. Fuzzy multi-objective control strategy for parallel hybrid electric vehicle. IET Electr Syst Transp 2012;2:39.

9.     Borhan H,VahidiA,Phillips AM, Kuang ML,Kolmanovsky IV, Cairano SDi. MPC-based energy management of a power-split hybrid electric vehicle. Control SystTechnolIEEETrans2012;20:593–603.

10.  Samanta CK  Panigrahi SP, Panigrahi BK, ,Padhy SK. Hybrids warm intelligence methods for energy management in hybrid electric vehicles.IETE lectr Syst Transp 2013;3:22–9.

11.  Van KeulenT,Gillot J,de Jager B,Steinbuch M. “Solution for state constrained optimal control problems applied to power split control for hybrid vehicles”. Automatica2013;50: 187–92

12.  Powell, B. K.,  Bailey, K. E., and Cikanek, S. R.(1998), Dynamic modeling and control of hybrid electric vehicle powertrain systems, IEEE Conference. Syst. Mag., pp. 17–33, Oct. 1998.

13.  Rahman, Z., Butler, K. L. and Ehsani, M., (1999),Designing parallel hybrid electric vehicles using V-ELPS 2.01,    Proc. American Control  Conference, San Diego, CA, June 1999, pp. 2693–2697.





I. A. Kamani C Samarasinghe, S. R. Kodituwakku, Y. P. R. D. Yapa

Paper Title:

Impact of Data Mining on Telecommunication Company Revenues

Abstract: Rapid advancement of the technology has made the telecommunication sector very competitive. In order to keep up with the competition, telecommunication operators have to identify the exact needs of the customers and offer services in-line with customer needs. The aim of this research is to investigate the applicability of data mining in identifying customer needs and how it can be adapted to increase the revenue of telecommunication companies. The objectives of this study include an  investigation  into the relationship between data mining practices and customer behavior patterns, relationship between customer needs and products or services, relationship between new product design initiatives and revenue increases of the companies, the impact of data mining on the revenue of telecommunications companies, and the development of a data mining framework to improve the overall Average Revenue Per User (ARPU) levels in the industry in addition to the designing of a Business Intelligence (BI) tool to enhance decision making processes for improving the overall ARPU levels of the industry. Firstly, the conceptual model is developed based on the feedback of a sample of employees who hold positions in the telecommunications sector. This model has four main variables; data mining, customer behavior, product and increased revenue.  Secondly, a preliminary study was carried out to test the variables and to find out how data mining can be applied to identify customer needs and how companies can benefit from using data mining techniques in their businesses. Next, a Data Mining framework was developed to make sure that the expected results could be received from the data mining exercise in place.  Finally, a Business Intelligence tool was developed to validate the data mining framework. The preliminary study revealed a clear relationship between the variables of the conceptual framework. Furthermore, it was evident that data mining could lead to better business decisions, apart from the other key benefits of using it, such as timely delivery of services and an increase in customer satisfaction which may affect the revenue of the company. The post survey validation from the target users (managers of telecom companies) indicated that the proposed BI tool is capable of retrieving much needed information for business decisions, which would lead to increased revenues of the companies. The long term results are likely to be positive in this context and it is also evident that the role of data mining can be expanded by the companies and that this practice could eventually lead to companies providing markets with the exact requirements.

 Business Intelligence, data mining, revenues, telecommunication


1.     Segura, A., Castro, C., Domínguez, V., Campos, P.G., and Prieto, M., (2011) “Using data mining techniques for exploring learning object repositories”, Electronic Library, the, Vol. 29 Iss: 2, pp.162 – 180
2.     Pendharkar, P.C., and Rodger, J.A., (2000) “Data mining using client/server systems”, Journal of Systems and Information Technology, Vol. 4 Iss: 2, pp.72 – 82

3.     Data mining using SAS/EM: A Case Study Approach,SAS,Institute Inc.2003.4

4.     Jiawei Han and Micheline Kamber Data Mining Concepts and Techniques BeiJing Higher Education Press, 2001:279-299.

5.     Elder J F & Abbott D W, A Comparison of Leading Data Mining Tools, KDD-98.

6.     Jiawei Han, Michelin Kamber;Fan Ming, Meng Xiaofeng(interpret),Data Mining: Concepts and Technologies [M] ,Beijing Machinery Industry Press,2001,8

7.     Ren Mingshu, Study on Web Mining and Electronic Commerce, Shandong University of Science and Technology.2002.

8.     Larson, B (2006) Delivering Business Intelligence with Microsoft SQL Server 2005, McGraw-Hill/Osborne

9.     Inmon W.H., (1993) Building the Data Warehouse, A Wiley QED publication, John Wiley and Sons, Inc. New York 123-133

10.  Liu, S.S., and Chen, J., (2009) “Using data mining to segment healthcare markets from patients’ preference perspectives”, International Journal of Health Care Quality Assurance, Vol. 22 Iss: 2, pp.117 – 134

11.  McAdams, A., Camp, J., and Divakaruni, S., (2000) “The evolution of US telecommunications infrastructure”, info, Vol. 2 Iss: 2, pp.107 – 110

12.  Lee, Y., Yen, S., and Hsieh, M., (2005) “A lattice-based framework for interactively and incrementally mining web traversal patterns”, International Journal of Web Information Systems, Vol. 1 Iss: 4, pp.197 – 208

13.  Simpson, S., (2010) “Governing information infrastructures and services in telecommunications”, Aslib Proceedings, Vol. 62 Iss: 1,                                            pp.46 – 56

14.  Sacripanti, A.M., (1999) “Liberalizing telecommunications in Italy: the role of the regulator”, info, Vol. 1 Iss: 5, pp.449 – 453

15.  Premalatha, S., and Baskar, N., (2012) “Implementation of supervised statistical data mining algorithm for single machine scheduling”, Journal of Advances in Management Research, Vol. 9 Iss: 2, pp.170 – 177






Mahmudun Nabi Chowdhury, Ju-Ri Kim

Paper Title:

Mechanical and Metallurgical Effect on Tubular Shape by Laser Forming

Abstract: Mechanical properties of indirect hot stamping tubes are tailored by laser assisted partial rapid heating. A spiral heated region is generated on a rotating hot stamping steel tube by applying linearly moving laser. A microstructural analysis confirms that martensite phase transformation is occurred in the spiral heated region, thus inducing inhomogeneous microstructures along the length. Mechanical tests show that the mechanical performance of the indirect hot stamping tube can be tailored by properly selecting process parameters of the laser assisted heating. A microstructural analysis confirms that the laser locally induces a martensitic phase transformation in the heated region and results in inhomogeneous microstructures along the length of the tube

 indirect hot stamping, laser assisted heating, mechanical property.


1.        Karbasian, H., Tekkaya, A., 2010. A review on hot stamping. Journal of Materials Processing Technology 210, 2103
2.        Zhu, B., Zhang, Y., Li J., Wang, H., Ye, Z., 2011. Simulatio of hot stamping and phase transition of automotive high strength steel. Materials Research Innovations 15, 426

3.        Lu G, Yu T. Energy absorption of structures and materials. Cambridge: Woodhead Publishing; 2003.

4.        Nia AA, Hamedani JH. Comparative and deformations of thin walled tubes with various section geometries. Thin Wall Struct 2010

5.        Vesenjak M, Krstulovic ´-Opara L, Ren Z, Öchsner A, Domazet Z ˇ. Experimental study of open-cell cellular structures wit material. Exp Mech 2009; 49:501

6.        Belova IV, Veyhl C, Fiedler T, Murch GE. Analysis of anisotropic behaviour of thermal conductivity in cellular metals. Scripta Mater 2011; 65:436–9.

7.        DAUSINGER F. Beam-matter interaction in laser surface modification [C]// Proceedings of LAMP’92. 1992; 697

8.        WOO H G, CHO H S. Estimation of hardened flayer dimensions in laser hardening process with variations of coatings thickness [J]. Surface and Coating Technology, 1998, 102; 205

9.        BENDOGNI V, CANTELLO M, CERRU W, CRUCIANI D, FESTA R, MOR G, NENCI F. Laser and electron beam in surface hardening of turbine blades [C]// Proceedings of LAMP’87. 1987: 567

10.     READY J F. LIA handbook of laser material processing [M]. Laser Institute of America, 2001: 223-262.

11.     CHEN T L, GUAN Y H, WANG H G, ZHANG J T. A study on austenite transformation during laser heating [J]. Journal of Material Process Technology, 1997, 63: 546

12.     Standard Test Methods for Flexural properties of Unreinforced and Reinforced Plastics and Electrical Insulating materials; Designation: D 790-02.

13.     Marion Merklien, Wolfgang Böhm material properties of aluminum by local laser heat treatment. Physics Procedia 39 (2012) 232-239.

14.     S. K. Hajra Choudhury, A. K. HajraChoudhury, N. Roy, “Elements of Workshop Technology,” Volume – I, Media Promoters and Publishers Pvt. Ltd. 2001.

15.     G. E. Dieter, “Mechanical Metallurgy,” SI Metric Edition, McGraw Hill, Singapore, 1988.

16.     American Society for Metals. Committee on Induction Hardening,“Induction Hardening and Tempering,” Metals Park, Ohio, American Society for Metals, 1964.

17.     American Society for Metals. Committee on Induction Hardening, “Metals Handbook: Forging and Casting,” Metals Park, Ohio,American Society for Metals, 8th Edition, Volume 5,1977.

18.     S. Zinn, S. L. Semiatin, “Elements of Induction Heating: Design,Control, and application,” ASM International, 1988.

19.     K. Z. Shepelyakovskii, N.M. Fonshtein, V.P. Devyatkin, A. N. Mirza, B. K. Ushakov and B. O. Bernshtein, “Strength Characteristics of High-Carbon Steel with Controlled Hardenability After Bulk-Surface Hardening,” Volume 18, Number 5, New York , Metal Science and Heat Treatment, May, 1976, pp. 439-442.

20.     K. Z. Shepelyakovskii, “Heat Treatment of Steel with Induction Heating,” Volume 19, Number 10, New York, Metal Science and Heat Treatment, October, 1977, pp. 909-916.





Geetanjali, Shashank Sahu

Paper Title:

Similarity of Articles using Hierarchical Clustering

Abstract:  RSS technology is to find similarity in the articles to provide better services to user. The research is going on to find out semantic similarity in articles to reduce same type of articles read by user. Objective of RSS is to deliver content which is latest and consist of most relevant information to the user. Here, the research focus is to find out the suitable distance method that can be use to check similarity in the articles. Hierarchical clustering is one of the best methods to cluster the articles which are similar on some parameter various methods are used in HC (hierarchical clustering). Which method is best suitable to find the semantic similarity, similarity is the focus area of this paper. We have collected various articles from many news channel websites for a category (terrorist) by observing the articles. Thirty keywords are selected for the implementation for the proposed technique and comparison. We perform similarity checking on various numbers of articles like 18, 16, 12, 10, 9, and 6.  After calculating the distance the cityblock distance method gives the best result. For this research work article from last one decade (2003-2013) has been selected.

 Really Simple Syndication (RSS), Hierarchical Clustering.


1.        Den Ma, “Use of RSS feeds to push online content to users”, Published in Elsevier Journal of Decision Support Systems, Volume 54, Issue 1, December 2012, pp 740–749, doi: 10.1016/j.dss.2012.09.002.
2.        Just van den Broecke, “Pushlets” Published in IEEE Transactions of Web Technology, Vol. 22, no. 5, may 2011.

3.        Isabel delaTorre-Dıez, Saul Alvaro-Munoz, MiguelLo pez-Coronado, Joel Rodrigues, “Development and performance evaluation of a new RSS tool for a Web-based system: RSS_PROYECT”, Published in Elsevier Journal of Network and Computer Applications, Volume 36, Issue 1, January 2013, pp 255–261, doi:

4.        Lijing Zhang, “Research on Web-based Real-time Monitoring System on    SVG and Comet”, Published in International Journal Vol.10, No.5, September 2012, pp 1142~1146, doi: 10.11591/telkomnika.v10i5.1347.

5.        Manfred Hauswirth and Mehdi Jazayeri, “A Component and Communication Model for Push Systems”, Published in International Journal of Web Engineering and Technology, September 6-10, 1999.

6.        Fekade Getahun Taddesse, Joe Tekli, Richard  Chbeir, Marco Viviani & Kokou Yetongnon, “Semantic-based Merging of RSS Items”,  Published   in Springer Journal of  World Wide Web March 2010, Volume 13, Issue 1-2, pp 169-207,  doi: 10.1007/s11280-009-0074-4.

7.        Fekade Getahun, Richard Chbeir, “RSS query algebra: Towards a better  news        management”,  Published  in   Elsevier  Journal  of   Information Sciences Volume 237, 10 July 2013, pp 313–342, doi: 10.1016/j.ins.2013.02.025.

8.        Wang Lei, Wang Tongsen & Yang Ronghua, “ Data Compression Algorithm based on Hierarchical Cluster Model for Sensor Networks”,  Published in International Conference of Future Generation Communication and Networking, 2008,  Volume:2, pp 319-323, doi: 10.1109/FGCN.2008.96.

9.        Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander, “Fast Hierarchical Clustering Based on Compressed Data and OPTICS”, Published in Proc. 4th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD 2000), Lyon, France, Volume 1910, 2000, pp 232-242 doi: 10.1007/3-540-45372-5_23.

10.     I-Ching Hsu, “Personalized web feeds based on ontology technologies”, Published in Springer Journal of Information Systems Frontiers, July 2013, Volume 15, Issue 3, pp 465-479, doi:  10.1007/s10796-011-9337-6.

11.     Petros Belimpasakis  & Anne Saaranen, “Sharing with people: a system for user-centric content sharing”, Published in Springer Journal of Multimedia Systems, November 2010, Volume 16, Issue 6, pp 399-421, doi: 10.1007/s00530-010-0200-2.

12.     Wen Hu & Qing he Pan, “Data clustering and analyzing techniques using    hierarchical clustering method”, Published in Springer Multimed Tools Applications, July 2013, doi: 10.1007/s11042-013-1611-9.

13.     John Garofalakis, Vassilios Stefanis, “Using RSS feeds for effective mobile web browsing”, published in Springer Journal of  Universal Access in the Information Society, November 2007, Volume 6, Issue 3, pp 249-257, doi: 10.1007/s10209-007-0086-8.

14.     Chen Wu, Elizabeth Chang, “Aligning with the Web: an atom-based architecture for Web services discovery”, published in Springer Journal 24 May 2007, doi: 10.1007/s11761-007-0008-x.

15.     Saha, S, Sajjanhar, A., Shang Gao, Dew, R., Ying Zhao, “Delivering Categorized News Items Using RSS Feeds and Web Services”, Published in International Conference of Computer and Information Technology (CIT), July 2010, pp 698-702, doi: 10.1109/CIT.2010.136.

16.     Preechaveerakul, L, Kaewnopparat, W, “A Novel Approach: Secure Information Notifying System Using RSS Technology “, Published in International Conference of Future Networks, March 2009, pp 95-99, doi: 10.1109/ICFN.2009.35.

17.     Segaran T, O’Brien MT (2007), “Programming Collective Intelligence, O’Reilly Media, Sebastopol”






Vrushaly K. Shinglot, Monika R. Tiwari, Shardav U. Bhatt, Narendra J. Shrimali

Paper Title:

Application of Soft Computing Techniques to Predict Reservoir Water Level

Abstract:   In this work, the reservoir water level has been predicted using one of the soft computing techniques named Artificial Neural Network. The reservoir water level is influenced by many parameters. Among which the most influencing parameters have been considered here: amount of rainfall, temperature and evaporation. For this analysis, the reservoir made on Shetrunji River  Dam in Dhari, Amreli district, Gujarat, India has been chosen as it was overflown seven times in last ten years. This shows the importance of water level prediction at this particular reservoir. The Neural Network is trained using the past data collected and further used to predict water level for the unknown data. The approach of the multiple regression is also shown for its comparison with the Soft computing approach. Computations and experimental works were done by programming in software MATLAB. Such modeling is useful for planning and decision making of opening gates for reservoir operation particularly during monsoon and water scarcity.

   Soft Computing, Artificial Neural Network, Regression, Back Propagation Algorithm, Reservoir water level Prediction


1.     . N. Gujarati, “Forecasting reservoir water level & release decision by Artificial Neural Network – A caseStudy of  Shetrunji reservoir”, Gujarat Technological University, M. E. Thesis, , 2012.
2.     S. Haykin, “Neural Networks and Learning Machines”, Third Edition, PHI Leaning Pvt. Ltd., 2012.
3.     J. M. Zurada, “Introduction to Artificial Neural Systems”, First Edition, Jaico Publishing House, 2004
4.     D. E. Rumelhart, J. L. McClelland, The PDP Research Group, “Parallel Distributed Processing: explorations in the microstructure of cognition”, vol. 1, MIT Press,1986.

5.     G. Cybenko, “Approximation by superpositions of a sigmoidal function”, Mathematics of Control, Signals and Systems 2, 1989.

6.     M. H. Beale, M. T. Hagan, H. B. Demuth,“Neural Network Toolbox: Users Guide”,TheMathworks Inc., 2015.






Bhagyashri Sarda, Bhagyashri Kapre

Paper Title:

Contemporary Approach for Graphical Password using CAPTCHA

Abstract:    Cyber security is an important issue to implement. Different types of user authentication methods are used to achieve this goal. It helps to avoid misuse or illegal use of highly sensitive and confidential data. Text and graphical passwords are mainly used for authentication functioning. But due to various pitfalls, they are erroneous for data security. Text passwords are un assured for reasons and graphical are tight secured in comparison but are sensitive to shoulder surfing attacks. Hence by using graphical password system and CAPTCHA technology a new security primitive is proposed. We call it as CAPTCHA as gRaphical Password (CaRP). CaRP is a combination of both a CAPTCHA and a graphical password scheme. In this paper we conduct a encyclopedic analysis of existing CaRP techniques namely ClickText, ClickAnimal and AnimalGrid. We discuss the advantages and disadvantages of each method and point out research direction in this area. We also try to answer “Are CaRP as secured as graphical passwords and text based passwords?” and “Is CaRP protective to relay attack?

    Captcha, CaRP, dictionary attack, password, graphical password. 


1.        Bin B. Zhu, Jeff Yan, Guanbo Bao, Maowei Yang, and Ning Xu, “CAPTCHA as Graphical Passwords—A New Security Primitive Based on Hard AI Problems”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, NO. 6, JUNE 2014.
2.        R. Biddle, S. Chiasson, and P. C. van Oorschot, “Graphical passwords: Learning from the first twelve years,” ACM Comput. Surveys, vol. 44, no. 4, 2012.HP Tipping Point

3.        HP TippingPoint DVLabs, Vienna, Austria. (2010). Top Cyber Security Risks Report, SANS Institute and Qualys Research Labs [Online].

4.        B. Pinkas and T. Sander, “Securing passwords against dictionary attacks,” in Proc. ACM CCS, 2002, pp. 161–170.

5.        P. C. van Oorschot and S. Stubblebine, “On countering online dictionary attacks with login histories and humans-in-the-loop,” ACM Trans. Inf. Syst. Security, vol. 9, no. 3, pp. 235–258, 2006.

6.        M. Alsaleh, M. Mannan, and P. C. van Oorschot, “Revisiting defenses against large-scale online password guessing attacks,” IEEE Trans. Dependable Secure Comput., vol. 9, no. 1, pp. 128–141, Jan./Feb. 2012.  

7.        L. von Ahn, M. Blum, N. J. Hopper, and J. Langford, “CAPTCHA: Using hard AI problems for security,” in Proc. Eurocrypt, 2003, pp.
8.        K. Chellapilla, K. Larson, P. Simard, and M. Czerwinski, “Building segmentation based human-friendly human interaction proofs,” in Proc. 2nd Int. Workshop Human Interaction Proofs, 2005, pp. 1–10.

9.        R. Lin, S.-Y. Huang, G. B. Bell, and Y.-K. Lee, “A new CAPTCHA interface design for mobile devices,” in Proc. 12th Austral. User Inter. Conf., 2011, pp. 3–8.

10.     M. Motoyama, K. Levchenko, C. Kanich, D. McCoy, G. M. Voelker, and S. Savage, “Re: CAPTCHAs—Understanding CAPTCHA-Solving Services in an Economic Context,” in Proc. USENIX Security, 2010, pp. 435–452.

11.     S. Kim, X. Cao, H. Zhang, and D. Tan, “Enabling concurrent dual views on common LCD screens,” in Proc. ACM Annu. Conf. Human Factors Comput. Syst., 2012, pp. 2175–2184.





Hamdy Mohamed Soliman, S.M.EL. Hakim

Paper Title:

Modified PI Controller to Improve the Performance Characteristics of the PMSM

Abstract:    Classical vector control for the permanent magnet synchronous motor (PMSM) is depending upon the mathematical model and hence any problem in the machine parameters or AC drives will deteriorate the performance of the drive system over all.  So this paper suggested using four PI current controllers to improve the performance characteristics of the drive system. Three of them is used in the bang-bang control of inverter by rate of one for each phase and the other PI current controller is used to improving the q-  axis current component at sudden applies or removes the load. this reflects the performance over all and improve it. The MATLAB Simulink is used to simulating the drive system. The proposed model of the vector control is compared to classical vector control to show the improvement occurs in the performance characteristics of the system with proposal method. The proposed cases are simulated through the MATLAB program and are operated in the laboratory. The laboratory results agreed with the simulating results that have been obtained

 Bang-bang inverter control, PI control, PMSM, vector control.


1.     Goed, I. da Silva and P. Jose, A. Serni, “A hybrid controller for the speed control of a permanent magnet synchronous motor drive,” Control Engineering Practice, Vol. 16, Issue 3, pp. 260-270, March, 2008.
2.     C. Mademlis and N. Margaris, “Loss minimization in vector-controlled interior permanent-magnet synchronous motor drives”, Industrial Electronics, IEEE Transactions on, vol. 49, pp. 1344-1347, 2002.

3.     Jian-Xin, S. K. Panda, P. Ya-Jun, L. Tong Heng, and b. H. Lam, “a modular control scheme for pmsm speed control with pulsating torque minimization”, Industrial Electronics, IEEE Transactions on, vol. 51, pp. 526-536, 2004.

4.     Jinggang zhang, Zhiyuan Liu and Run Pei, “Two-Degree-of-Freedom internal model control for AC servo system (Periodical style),” Transactions of China Electrotechnical Society, vol. 17, no. 4, pp. 45-48, 2002.

5.     Shengxian Zhuang, Xuening Li and Zhaoji Li, “ The application in the speed regulating of asynchronous machine vector frequency changing based on adaptive internal model control (Periodical style),” Journal of University of Electronic Science and Technology of China, vol. 28,no.5, pp.502-504, 1999.

6.     P. L. Jansen and R. D. Lorentz, “Transducerless position and velocity estimation in induction and salient AC machines”, IEEE Trans. Ind. Applicat., vol. 31, pp. 240–247, Mar./Apr. 1995.

7.     P. L. Jansen, R. D. Lorenz, and D. W. Novotny, “Observer-based direct field orientation: Analysis and comparison of alternative methods”,” IEEE Trans. Ind. Applicat., vol. 30, pp. 945–953, July/Aug. 1994.

8.     M. P. Kazmierkowski, and L. Malesani, “Current control techniques for three-phase voltage-source PWM converters: a survey”, IEEE Trans. Ind. Electron., vol. 45, no. 5, October, 1998, pp. 691-703.

9.     B. k. Bose, “An adaptive hysteresis-band current control technique of a voltage – fed PWM inverter for machine drive system”, IEEE Trans., on Ind. Appl., Vol.IA-37,
pp.402-408, 1990

10.  Hamdy Mohamed soliman and S. M. EL. Hakim,” Improved Hysteresis Current Controller to Drive Permanent Magnet Synchronous Motors Through the Field Oriented Control”, International Journal of Soft Computing and Engineering , Vol. 2, No. 4, September 2012, pp. 40-46.



Volume-6 Issue-4

 Download Abstract Book

S. No

Volume-6 Issue-4, September 2016, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Vahid Nourani

Paper Title:

Comparative Investigation of Trial load and Finite Element Methods in Analysis of Arch Dams

Abstract: Because of important role of dams and dam construction in human life, in the present paper the method of analysis of an important kind of dam (arch dam) has been presented in two different scientific ways and their results have been compared. In the method presented herein, the dam has been divided into horizontal elements of arcs and vertical elements of cantilevers, and using compatibility of displacements and trial and error (trial load method) the share of cantilevers and arcs from applied loads on dam have been determined. Then another analysis has been performed using Finite Element Method (FEM) by indicating stiffness matrix using iso-parametric hexahedral elements with eight nodes. Using the available equations, the displacements of nodes have been calculated. Because of high volume of calculations, computer has been used and software has been prepared. The results of these two methods have been compared to each-other. The results show that the trial load method is a reliable method in spite of the fact that simplifying assumptions have been used in its theory. As a result, an arch dam can be easily analyzed by trial load method. Also, to get more accurate results, more complete methods are necessary to solve FEM equations.

 Arch dam, Trial load method, Finite Element Method, Arc analysis, Cantilever analysis.


1.    Design of arch dams, USBR (1977).
2.    Zienkiewicz, O.C., the finite element method, McGraw-Hill (1977).

3.    Greager W.P. & Justin J.D. & Hinds J., Engineering for Dams, Wiley (1964).

4.    Nourani, V. A comparative investigation of trial load and finite element method in the analysis of arch dams. M.Sc. thesis, Tabriz University, (in Persian) (2000).

5.    Bathe, K. J., Finite element procedures, Prentice-Hall (1996).

6.    Jennings A. & McKeown J.J. , Matrix computation, John Wiley and Sons (1992).

7.    Belegundu T.D. & Chandrupatla D.A., Introduction to finite elements in engineering, Wiley (1992).





Deepali Bhadane, K. N. Pawar

Paper Title:

Feature Based Mosaicing of Images

Abstract:  Image Mosaicing is a process of assembling the multiple overlapping images of the identical scene into a larger image. The output of the image mosaic will be the union of two input images. Image – mosaicing algorithms are used for gaining a mosaiced image. In this paper we have described the feature based mosaicing of two images. Feature based image mosaicing is the combination of corner detection, corner matching, motion parameter estimation and image stitching .For corner detection there are various algorithms – HARRIS, SUSAN,CSS. This corner detection algorithm produces an efficient and informative output mosaiced image. After corner detection RANSAC algorithm is used for Homography. After that image warping and image blending is done. Importance of Image Mosaicing can be seen in the field of medical imaging, computer vision, data from satellite, military automatic target recognition. In this paper we compare result CSS, SUSAN, HARRIS.

 Image mosaicing, Feature Extraction, Image registration, corner detection using HARRIS,SUSAN,CSS algorithm, Homography using RANSAC, Image warping, Image Blending.


1.       D. Ghosh, S. Park, N. Kaabouch, W. Semke,” Quantum Evaluation of Image Mosaicing In Multiple Scene Categories”, IEEE Conference on Electro/Information Technology, pp. 1-6, 2012.
2.       S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: A technical review,” IEEE Signal Processing Mag., vol. 20, pp. 21–36, May 2003.

3.       C.D. Kuglin, D.C. hines, “The phase correlation image alignment method”, Proc IEEE 1975,pp.163-165.

4.       Hemlata Joshi, “ASurvey on Image Mosaicing Techniques”, IJARCET, volume 2,Issue 2,February 2013

5.       Deepak Kumar Jain,Gaurav Saxena, “Image Mosaicing Using Corner Techniques”, International Conference on Communication System and Network Technologies,2012

6.       Richard Szeliski, Image Alignment and Stitching: A Tutorial, Technical Report,MSR-TR-2004-92,Microsoft Research 2004.

7.       Brown, M. and Lowe, D. G. 2007. Automatic Panoramic Image Stitching using Invariant Features. Int. J. Comput. Vision 74, 1 (Aug.2007), 59-73.

8.       Vittorio Ferrari, TinneTuytelaars and  Luc   VanGool, Wide Baseline Multiple view Correspondences,In Proceedings of IEEE Computer SocietyConference on Computer Vision PatternRecognition, Madison, USA, 2003, pp. 718-725

9.       JirMatas, Ondrej Chum, Martin Urban And etc, Robust Wide Baseline Stereo from  Maximally Stable Extremal Regions, In Proceedings of British Machine Vision Conference, Cardiff, UK, 2002, pp. 384.

10.    Tinne Tuytelaars and Luc Van Gool, Wide    Baseline Stereo Matching Based on Local, AffinelyInvariant Regions, In Proceedings of British MachineVision Conference, Bristol,  UK, 2000 ,pp. 412-425.

11.    Vittorio Ferrari, TinneTuytelaars and Luc VanGool, Integrating Multiple Model Views for Object Recognition In Proceedings of IEEE ComputerSociety Conference on Computer Visionand Pattern Recognition, USA,2004.

12.    StepanObdrzalek and Jin Matas, Object RecognitionUsing Local Affine Frames on Distinguished Regions, In Proc. Of British MachineVision Conference, UK, 2002,pp. 113-22.

13.    Josef Sivic and Andrew Zisserman,VideoGoogle: a Text Retrieval Approach to ObjectMatching in Videos, In Proceedings of International Conference on Computer Vision, Nice, France, 2003, pp. 1470-1477.

14.    F. Mokhtarian and A. K. Mackworth, “A theory  of multi-scale curvature-based shape representation for planar curves,” IEEE Trans.Pattern Anal. Mach. Intell. 14_8_, 789–805_1992.

15.    F. Mokhtarian and R. Suomela, “Robust image corner detection through curvature scale space,” IEEE Trans. Pattern Anal. Mach. Intell. 20_12_, 1376–1381 _1998_.

16.    F. Mokhtarian and F. Mohanna, “Enhancing the curvature scale space corner detector,”Proc. Scandinavian Conf. on Image Analysis, pp 145–152 _2001.

17.    Lin Zhang” A Multi-Scale Bilateral Structure Tensor Based Corner Detector” Biometrics Research Center, Department of Computing The Hong Kong Polytechnic University Hong Kong, China.

18.    Qi Zhi and Jeremy R. Cooperstock, “Toward Dynamic Image Mosaic Generation With Robustness to Parallax” ,IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 1, JANUARY 2012

19.    Kevin E. Loewke, David B. Camarillo, Wibool  Piyawattanametha, Michael J. Mandella, Christopher H. Contag, Sebastian Thrun, and J. Kenneth Salisbury, “In Vivo Micro-Image Mosaicing”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 58, NO. 1, JANUARY 2011

20.    Hemlata Joshi1andMr.KhomLal Sinha2, “A Survey on Image Mosaicing Techniques”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 2, February 2013.

21.    P.R. Wolf. Elements of Photogrammetry. McGraw-Hill, 2 edition, 1983.

22.    S. C. Chen, “Quicktime VR: An image-based approach to virtual environment navigation,” in Proc. 22nd Annu. Conf. omput. Graph. Interactive Techn., SIGGRAPH, 1995, pp. 29–38.

23.    H. Y. Shum and R. Szeliski, “Construction and refinement of panoramic mosaics with global and local alignment,” in Proc. Int. Conf. Comput.Vis.,1998,pp.953–9

24.    Yu Wang, Yong-tian Wang, “The image matching algorithm based on SIFI and Wavelet transform”, Journal of Beijing Institute of Technology. Vol.5, 2009.

25.    S.B. Kang. A survey of image-based rendering techniques. Technical Report CRL 97/4, Digital Equipment Corp. Cambridge Research Lab, Aug 1997.

26.    J. Lengyel. The convergence of graphics and vision. Computer, IEEE Computer Society Magazine, pages 46-53, July 1998.

27.    Soo-Hyun CHO, Yun-Koo CHUNG and Jae Yeon LEE, Automatic Image Mosaic System Using Image Feature Detection and Taylor Series, In Proceedings of the 7th International Conference on Digital Image Computing: Techniques and Applications, Sydney, Australia, 2003, pp. 549-556.

28.    C. Harris. “Determination of ego-motion from matched points”. In Proc. Alvey Vision Conf., Cambridge UK, 1987.

29.    L. Kitchen and A. Rosenfeld. “Gray level corner detection” Pattern Recognition Ltters, pp. 95-102, 1982.

30.    S. Smith and J. Brady. “SUSAN—A new approach to low-level image processing”. International Journal of Computer Vision on, 23(1) :45-48,1997.

31.    W.C. Chen and P. Rockett, “Bayesian Labelling of Corners Using a Grey-Level Corner Image Model,” IEEE Int’l Conf. Image Processing, vol. 1, pp. 687-690, 1997.

32.    F. Mokhtarian and R. Suomel. “Robust image corner detection through curvature scale space”. IEEE Trans. On Pattern Analysis and Machine Intelligence, 20(12): 1376- 1381, 1998.

33.    F. Mokhtarian and F. Mohanna, “Enhancing the curvature scale space corner detector”, Proc. Scandinavian Conf. on Image Analysis, pp. 145-152, Bergen, Norway 2001.





Ali Abdulraheem Alwan

Paper Title:

A Framework and Prototype for Personal Digital Library System

Abstract:   Nowadays, Digital Libraries have an inescapable role on delivering information resources to their users and new trends in Digital Libraries are to change from the passive state of only providing information resources to their users to a more active state by enabling the users of Digital Library to collaborate and share knowledge with each other. This project addresses the situation of managing a personal library and converting it from manually supported system to digitally supported system, because the current system makes it hard to serve the user effectively. The main objective was to develop a digital library management system, with a search functionality to facilitate the search and management of library resources. In general, this project offers the following services to the user: (a) Identify the transaction movement for the books and other resources of the library; (b) Know the detailed information about the books and other resources of   the library; (c) The ability to have reports from different kind of view for books and other resources of the library; (d) the ability to add, delete, update, find, etc. for the most data in the system in an easy way; (e) Offer a kind of security for the system by allowing to use it by having a specific password. The designed model is employed to develop the concept of personal library and make its resources available to use, integrate and share among many users efficiently and effectively.

  Digital Library, Personal Digital Library, PDL.


1.       Avison, D. and Fitzgerald, G. (2003) Information Systems Development: Methodologies, Techniques and Tool,  McGraw-Hill, London.
2.       Alireza I.M., Mohsenzadeh F., “Application of Information Technologies in Academic Libraries. Electronic  Library, 2009, 27 (6), pp.:986 – 998.

3.       Bafoutsou G., Mentzas, G., “A Comparative Analysis of Web-based Collaborative Systems”, 2001, Published by  the IEEE Computer Society.

4.       B. Jeyapragasg, K.S. Sivakumaren, S. Swaminathan, “Open Source Software in LIS Education and Library  Operations: An Overview”, In proceeding of the National Conference on 100 Years of LIS Education in India.  2012, pp. 254-259.

5.       IBM. (2004). User management module. Retrieved February 16, 2010,from http://lms1.srce.hr/LMShelp/en/courseadmin/ch_users_overview_b.html

6.       J. Bamgbade, B. A. Akintola, D. O. Agbenu, et al, “Comparative Analysis and Benefits of Digital Library Over  Traditional Library”, World Scientific News, 2015, vol. 24, pp.: 1-7.

7.       Jagjit Singh, “Libraries: Traditional to Modernization”, Laxmi Book Publication, Solapur, India, 2015.

8.       Kenneth Nkuma-Udah, “Advanced Information Storage and Retrieval”, published by National Open University of Nigeria, 2009.

9.       MOKHTAR, S. & HARUDIN, H. 2007. Interoperability in E-Government: Adopting the  Service Oriented Architecture (SOA) Framework for A Transparent Malaysian
Public Delivery System. Austrian Computer Society, iiWAS, 24, 463–469.

10.    Olson G. M., “The Challenges of Remote Scientific Collaboration”, National e-Science Centre, 2008.

11.    Rhys. (December7, 2007). Media Manager- Drupal. Retrieved (March27, 2008), from  http://drupal.org/project/mmedia

12.    Sheng-Uei Guan, Xiaobiao Zhang, “Design and Implementation of a Web-Based Personal Digital Library”, Journal of the Institution of Engineers, 2004, 44(3), pp.: 59-77.

13.    Tariq Ashraf, Jaideep Sharma, Puja Anand Gulati, “Developing Sustainable Digital Libraries: Socio-Technical Perspectives”, Information Science Reference, 2010, New York, USA.

14.    TechTerms.com. (2005). “The Tech Terms computer Dictionary”. Retrieved February 15, 2010, from  http://www.techterms.com/definition/php

15.    Valdez-Ramirez, A., M. (2010). Content Management System for Phprojekt. Retrieved  2010 From http://www.mariovaldez.net/software/cm_4p/

16.    VIDGEN, R., AVISON, D. & WOOD, B. 2002. Developing web information systems: from  strategy to implementation, Butterworth-Heinemann.





Peter. U.  Eze, Dawn. C. Walker, Ifeyinwa E. Achumba

Paper Title:

Constructive Initialization of a Genetic Algorithm for the Solution of a Highly Constrained Departmental Timetabling Problem

Abstract: The University or Departmental Timetabling Problem (UTP or DTP) is a scheduling problem ridden with numerous constraints. Each of the constraints has a complex effect on the ideal solution and their combined effect makes the problem harder to solve. As a solution to this problem, a genetic algorithm (GA) approach was augmented by a process of constructive initialisation and applied to an exemplar scheduling problem in the Department of Computer Science at the University of Sheffield. The problem entailed scheduling of timetabled slots for 33 modules across a range of taught programmes at various levels, delivered by 29 lecturers in 10 lecture theatres and 6 laboratories.  A total of eight hard constraints and four soft constraints were considered, for problems of five levels of increasing complexity. It was found that the synergistic solution satisfied all the hard constraints, achieved up to 75% optimisation of the soft constraints, and converged within 500 iterations or an average of 2.74 minutes. These results indicate that the GA, when combined with constructive initialization, will give efficient solution to the DTP problem with constrained variables.

Departmental Timetabling Problem, Constructive Initialization, Genetic Algorithm, Scheduling, Constraints


1.       Sadaf N.S and Shengxiang Y., (2009). “A guided search Genetic Algorithm for the University Course timetabling problem” In Multidisciplinary International Conference on Scheduling: Theory and applications 10 -12 August 2009, Dublin Ireland.
2.       Even S., Itai A., and  Shamir A., (1976) .“On the complexity of timetable and multi commodity flow problems” In SIAM Journal on Computing, 5(4) pp 691 – 703.

3.       Jeffrey H. Kingston (2006). “Hierarchical Timetable  Construction”  In  Edmund  K. Burke & Hana Rudova (Eds.). Practiceand Theory of Automated Timetabling. Proceedings of  the 6th International Conference on the practice and Theory of Automated Timetabling, 30th August – 1st September 2006. pp 196 -208.

4.       Rushil Raghavjee and  NetishaPilley (2008) “An Application of Genetic Algorithms to the School Timetabling Problem” In SAICSIT Conference Proceeding, 6-8 October, 2008. pp.193 – 199.  url: www.titan.cs.unp.ac.za/~nelishiap/uploads/45.pdf.

5.       Dario Floreano and  Claudio Mattiussi (2008).Bio-Inspired Artificial Intelligence: theories, methods and technologies. MIT Press Cambridge, USA.

6.       Bashir S.A (2014). Developing a Java-based Genetic Algorithm to solve the Travelling Salesmans Problem.  MSc Dissertation, Department of Computer Science, University of Sheffield.

7.       Mehdi et al (2012). “Solving University Course Timetabling Problem using Genetic A l g o r i t h m ” I n 2 n d W o r l d C o n f e r e n c e o n I n f o r m a t i o n T e c h n o l o g y . A W E R P r o c e d ia Information Technology and Computer Science. Vol 1 (2012).pp 1033 – 1040.

8.       Abubakar M.S et al (2006). “Maintaining diversity for Genetic Algorithm: A case study of timetabling problem”In JurnalTeknologi 44 (D) June 2006, pp.123 – 130.

9.       Tormos P. et al.(2008). “AGenetic Algorithm for Railway Scheduling Problems”In Studies inComputational Intelligence (SCI) 128, pp. 255–276.

10.    Fraser G. and Acuri A. “ALarge Scale Evaluation of Automated Unit Test Generation UsingEvoSuite”. [online]: http://www.evosuite.org/wp-content/papercite-data/pdf/tosem_evaluation.pdf , retrieved on 19th March, 2015.

11.    Els R. and Pillay N. (2010).“An Evolutionary Algorithm Hyper-Heuristic for Producing Feasible Timetables for the Curriculum Based University Course Timetabling Problem” In 2010 Second World on Nature and Biologically Inspired Computing Dec. 15-17, 2010 in Japan.Pp 460 – 466.

12.    E. Yu and K. Sung (2002).“A genetic Algorithm for weekly courses timetabling problem” In International transactions in Operational Research, 9 (2001), pp 703 – 717.

13.    W. Rupert, B. Edmund and E. Dave (1995). A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems. Computer Science Technical Report No. NOTTCS-TR-1995-8.

14.    S.A. Oyebanjo (2013). Development of a University Timetabling Automation System.B.Sc Project, Department of Computer and Information Science, Covenant University, Nigeria.

15.    D.W. Dayer (2010). Evolutionary Computation in Java: A practical guide to the watchmaker Framework. [Online]: http://watchmaker.uncommons.org/manual/index.html retrieved 3rd March 2015.

16.    W. Chinnasri, S. Krootjohn, and N. Sureerattanan (2012) “Performance comparison of Genetic Algorithm’s crossover operators on University Course Timetabling Problem” In Proceedings of 8th International Conference on Computing and Information Management (ICCM), 24th -26h April, 2012 in South Korea.






Nguyen Trong Dung, Nguyen Chinh Cuong

Paper Title:

Influence of the Particle Size on the Microstructure and the Curie Temperature (TC) of Nano-Iron Particles Model

Abstract:  This paper studies the influence of the particle size (with the chosen sizes 2.122 nm; 2.49 nm; 2.884 nm; 3.128 nm; 3.254 nm; 4.07 nm; 4.68 nm; 4.978 nm; 5.3 nm; 6.602 nm; 7.774 nm; 8.392 nm) on the microstructure and the Curie temperature (Tc) of nano-iron particles model. The nano-iron particles were created by Molecular Dynamics Simulation method with the Pak-Doyama pair interaction potential and aperiodic boundary conditions which is called soft boundary or free boundary. The microstructure characteristics were analyzed through the radial distribution function (RDF), the energy and the coordination number. The Curie temperature (Tc) is the point at which materials switch from the ferromagnetic phase to the paramagnetic phase and it is determined through the Ising model. The study purpose of this paper is to determine the relationship between the particle size and the Curie temperature (Tc) of the model. The obtained results showed that there was specific influence of the particle size on the microstructure and the Curie temperature (Tc) of the nano-iron particles model: when the size of the nano-iron particles was increased from 2.122 nm to 2.49 nm; 2.884 nm; 3.128 nm; 3.254 nm; 4.07 nm; 4.68 nm; 4.978 nm; 5.3 nm; 6.602 nm; 7.774 nm; 8.392 nm, the phase transition temperature of the model increased from 8.9 K to 9.3 K; 9.5 K; 9.6 K; 9.7 K; 10 K; 10.1 K; 10.2 K; 10.3 K; 10.4 K; 10.5 K; 10.6 K respectively. The results have also been compared with the results from the theoretical – experimental model showing the significant influence of the particle size on the Curie temperature of the nano-iron particles model. In addition, the nano-iron particles model at different sizes had the different microstructure characteristics and different Curie temperatures.

Particle size, microstructure, Curie temperature (Tc), nano-iron particles model, Molecular Dynamics.


1.       Z. K. Wang, M. H. Kuok, S. C. Ng, D. J. Lockwood, M. G. Cottam, K. Nielsch, R. B. Wehrspohn, and U. Gösele  “Spin-wave quantization in ferromagnetic nickel nanowires”, Physical Review Letters, 89, 027201, (2002).
2.       Zheng, W. T., & Sun, C. Q, “Electronic process of nitriding: Mechanism and applications. Progress in Solid State” Chemistry, 34, pp 1–20, (2006).

3.       Lalehzari, J. Desper, C. J. Levy, Inorg. Chem. 47(2008) 1120.

4.       S. Shit, S. Sen, S. Mitra, D. L. Hughes, Transition Met. Chem3. (2009) 269.

5.       G. B.. Bagihalli, P. G. Avaji, S. A. Patil, P. S. Badami, Eur. J. Med. Chem. 43, pp 2639-2649, (2008).

6.       M. M. Miller, G. A. Prinz, S.-F. Cheng and S. Bounnak, “Detection of a micron-sized magnetic sphere using a ring-shaped anisotropic magnetoresistance-based sensor: A model for a magnetoresistance-based biosensor”, Appl. Phys. Lett. 81, 2211, (2002). 

7.       Tapan K. Jain , Marco A. Morales , Sanjeeb K. Sahoo , Diandra L. Leslie-Pelecky , and Vinod Labhasetwar, ” Iron Oxide Nanoparticles for Sustained Delivery of Anticancer Agents”, Mol. Pharm,  2 (3), pp 194–205, (2005).

8.       Chourpa I, Douziech-Eyrolles L, Ngaboni-Okassa L, Fouquenet Jf, Cohen-Jonathan S, Souce M, Marchais H, Dubois P, “Molecular Composition of iron oxide nanoparticles, precursors for magnetic drug targeting, as characterized by confocal raman microspectroscopy” Pubmed – indexed for medline, analyst, 130 (10): pp 1395-403, (2005).

9.       Lee, K. B., Park, S., & Mirkin, C. A, “Multicomponent magnetic nanorods for biomolecular separations”, Angewandte Chemie International Edition, 43, pp 3048–3050, (2004).

10.    L.L.Vatta, R.D. Sanderson, and K.R. Koch, “Magnetic Nanoparticles: Properties and Potential Applications”, Pure Appl. Chem, 78, 1793, (2006).

11.    M. M. H. Khalil, E. H. Ismail, G. G. Mohamed, E. M. Zayed and A. Badr, Open Journal of Inorganic chemistry, 2, pp 13-21, (2012).

12.    Katarzyna BRODOWSKA, Elżbieta ŁODYGA-CHRUŚCIŃSKA, CHEMIK, 68, pp 129-134, (2014).

13.    Iqbal, H. L. Siddiqui, C. M. Ashraf, M. H Bukhari C. M. Akram, Pharm. Bull. 55 (2007) 1070.

14.    Andreas Jordan “Hyperthermia classic commentary: Inductive heating of ferrimagnetic particles and magnetic fluids: Physical evaluation of their potential for hyperthermia”, Int. J. Hyperthermia, 25 (7), pp 512–516, (2009).

15.    Poornima Budime Santhosh, Nataša Poklar Ulrih “Multifunctional superparamagnetic iron oxide nanoparticles: Promising tools in cancer theranostics”, Cancer Letters, 336 (1), pp 8–17, (2013).  

16.    Marinin, “Synthesis and Characterization of Superparamagnetic Iron Oxide Nanoparticles Coated with Silica”, Master Thesis, Stockholm, (2012).

17.    Parak WJ, Boudreau R, Gros ML, Gerion D, Zanchet D, Micheel CM, Williams SC, Alivisatos AP, Larabell CA: “Cell motility and metastatic potential studies based on quantum dot imaging of phagokinetic tracks”, Adv Mater, 14: pp 882-885, (2002).

18.    Zhang Y, Kohler N, Zhang M, “Surface modification of superparamagnetic magnetite nanoparticles and their intracellular uptake”, Biomaterials, 23 (7), pp 1553-61, (2002). 

19.    Kaneyoshi, T “Phase diagrams of a transverse Ising nanowire” Journal of Magnetism and Magnetic Materials, . J. Magn. Magn. Mater. 322,  pp 3014–3018, (2010).

20.    M. Sprik “Molecular dynamics simulation in statistical mechanics and materials science”, Workshop on Computational Methods in Materials Science and Engineering 12-23 June, Triest, Italy, (1995).

21.    Vo Van Hoang and Suhk Kun Oh “Annealing effects on structure in amorphous A12O3 models”, Physica B 364, pp 225-232, (2005).

22.    Vo Van Hoang “Molecular dynamics study on structure and properties of liquid and amorphous Al2O3”, Physical Review B 70, pp 134204-134213, (2004).

23.    Hirata et al. “Change of nanostructure in (Fe0.5Co0.5)72B20Si4Nb4 metallic glass on annealing”, Materials Science Forum 2077, pp 539-543, (2007).

24.    G. Kresse “Ab initio molecular dynamics for liquid metals”, Journal of Non-Crystalline Solids, vol 192-193, pp 222-229, (1995).

25.    L. Supmann “Monte Carlo calculation in materials science”, Workshop on Computational Methods in Materials Science and Engineering 12-23 June, Triest, Italy, (1995).

26.    L. Puzztai, O. Gereben “Reverse Monte-Carlo approach to the structure of amorphous semiconductors”, Journal of Non-Crystalline Solids vol 192-193, pp 634-640, (1995).

27.    P. Ballone “Overview of computational methods in materials science”, Workshop on Computational Methods in Materials Science and Engineering 12-23 June, Triest, Italy, (1995).

28.    Sergei P Gubin, Yurii A Koksharov, G B Khomutov and Gleb Yu Yurkov, “Magnetic nanoparticles: preparation, structure and properties”, Russian Chemical Reviews, Volume 74, Number 6, pp 489-520, (2005).

29.    Laurent Levy, Yudhisthira Sahoo, Kyoung-Soo Kim, Earl J. Bergey and Paras N. Prasad, “Nanochemistry:  Synthesis and Characterization of Multifunctional Nanoclinics for Biological Applications”, Chem. Mater, 14 (9), pp 3715–3721, (2002); (b) Zhang Y, Kohler N,  Zhang M., ” Surface modification of superparamagnetic magnetite
nanoparticles and their intracellular uptake”, Biomaterials, 23(7), pp1553-61, (2003).

30.    Lu AH, Salabas EL, Schüth F, “Magnetic Nanoparticles: Synthesis, Protection, Functionalization, and Application” Angew Chem Int Ed Engl, 46 (8): pp 1222-44, (2007).

31.    Z. H. Chohan, M. Arif, A. J. Rashid, J. Enzyme Inhib. Med. Chem. 23 (2008) 785.

32.    Youwei Du, Mingxiang Xu, Jian Wu, Yingbing Shi, Huaixian Lu and Ronghua Xue “Magnetic properties of ultrafine nickel particles”, J. Appl. Phys. 70, 5903. (1991).

33.    R. Z. Valiev, Ya. D. Vishnyakov, R. R. Mulyukov and G. S. Fainshtein, “On the Decrease of Curie Temperature in Submicron-Grained Nickel”, physica status solidi (a), Volume 117, Issue 2, pp 549–553, (1990).

34.    Sun L, Searson PC, Chien CL, “Finite-size effects in nickel nanowire arrays”, Phys. Rev. B 61, R6463 (R), (2000).

35.    Chang Q. Sun, W. H. Zhong, S. Li and B. K. Tay, H. L. Bai and E. Y. Jiang, “Coordination Imperfection Suppressed Phase Stability of Ferromagnetic, Ferroelectric, and Superconductive Nanosolids”, J. Phys. Chem. B, 108 (3), pp 1080–1084, (2004).

36.    Sadeh B., Doi M., Shimizu T., Matsui M.J, “Dependence of the Curie temperature on the diameter of Fe3O4 ultra fine particles”, J. Magn. Soc. Jpn., vol 24, pp 511–514, (2000).

37.    Kenji Ishikawa, Kazutoshi Yoshikawa, and Nagaya Okada, “Size effect on the ferroelectric phase transition in PbTiO3 ultrafine particles”, Phys. Rev. B 37, pp 5852-5855., (1988).

38.    Zhe Zhao, Vincenzo Buscaglia, Massimo Viviani, Maria Teresa Buscaglia, Liliana Mitoseriu, Andrea Testino, Mats Nygren, Mats Johnsson, and Paolo Nanni, “Grain-size effects on the ferroelectric behavior of dense nanocrystalline BaTiO3 ceramics”, Phys. Rev. B 70, 024107,  (2004).

39.    Soma Chattopadhyay, Pushan Ayyub, V R Palkar, A V Gurjar, R M Wankar and Manu Multani, “Finite-size effects in antiferroelectric PbZrO3 nanoparticles”, Journal of Physics: Condensed Matter, Volume 9, Number 38, pp 8135–8145, (1997).

40.    P. Tsai, N. Chandrasekhar and K. Chattopadhyay, “Size effect on the superconducting transition of embedded lead particles in an Al–Cu–V amorphous matrix”, Appl. Phys. Lett. 75, 1527, (1999).

41.    W.-H. Li, C. C. Yang, F. C. Tsao, and K. C. Lee, “Quantum size effects on the superconducting parameters of zero-dimensional Pb nanoparticles”, Phys. Rev. B 68, 184507, (2003).

42.    S. Li, T. White, C.Q. Sun, Y.Q. Fu, J. Plevert and K. Lauren, “Discriminating Lattice Structural Effects from Electronic Contributions to the Superconductivity of Doped MgB2 with Nanotechnology”, J. Phys. Chem. B 108, 16415, (2004).

43.    H. Lutz, P. Scoboria, J. E. Crow, and T. Mihalisin, ” Effects of finite size on critical phenomena: The resistivity anomaly in Ni films”, Phys. Rev. B 18, 3600, (1978).

44.    B A Strukov, S T Davitadze, S N Kravchun, S A Taraskin, M Goltzman, V V Lemanov andS G Shulman, ” Specific heat and heat conductivity of BaTiO3polycrystalline films in the thickness range 20–1100 nm”, Journal of Physics: Condensed Matter, Volume 15, Number 25, (2003).

45.    Myron Strongin, R. S. Thompson, O. F. Kammerer, and J. E. Crow, “Destruction of Superconductivity in Disordered Near – Monolayer Films”, Phys. Rev. B 1, 1078, (1970).

46.    K.L. Ekinci and J.M. Valles, “Morphology of Quench Condensed Lead Films near the Insulator to Metal Transition” Phys. Rev. Lett 82, 1518, (1999). 

47.    Yang Guo , Yan-Feng Zhang , Xin-Yu Bao , Tie-Zhu Han, Zhe Tang, Li-Xin Zhang, Wen-Guang Zhu,E. G. Wang, Qian Niu, Z. Q. Qiu, Jin-Feng Jia, Zhong-Xian Zhao, Qi-Kun Xue, ” Superconductivity Modulated by Quantum Size Effects” Science, Vol. 306, Issue 5703, pp. 1915-1917, (2004).

48.    V. Pogrebnyakov, J. M. Redwing, J. E. Jones, X. X. Xi, S. Y. Xu, Qi Li, V. Vaithyanathan, and D. G. Schlom, “Thickness dependence of the properties of epitaxial MgB 2 thin films grown by hybrid physical-chemical vapor deposition”, Appl. Phys. Lett, vol 82, number 24, pp 4319-4321, (2003).

49.    K. Yoshii, H. Yamamoto, K. Saiki, and A. Koma, “Superconductivity and electrical properties in single-crystalline ultrathin Nb films grown by molecular-beam epitaxy” Phys. Rev. B 52, 13570, (1995).

50.    Jiang Q, Li JC, Chi BQ, “Size-dependent cohesive energy of nanocrystals”, Chem Phys Lett, 366, pp 551–554, (2002).

51.    Qing Jiang* and Xing You Lang, “Glass Transition of Low-Dimensional Polystyrene”, Macromolecular Rapid Communications, Volume 25, Issue 7, pp 825–828, (2004).

52.    Q. Jiang, J.C. Li, B.Q. Chi, “Size-dependent cohesive energy of nanocrystals”, Chemical Physics Letters 366, pp 551–554, (2002).

53.    Chun Cheng Yang and Sean Li, “Investigation of cohesive energy effects on size-dependent physical and chemical properties of nanocrystals”, Phys. Rev. B 75, 165413, (2007)

54.    W. Dürr, M. Taborelli, O. Paul, R. Germar, W. Gudat, D. Pescia, and M. Landolt, “Magnetic Phase Transition in Two-Dimensional Ultrathin Fe Films on Au(100)”, Phys. Rev. Lett. 62, 206 (1989)

55.    Liu C, Bader SD, “Twodimensional magnetic phase transition of ultrathin iron films on Pd(100)”, J Appl Phys,  67: 5758, (1990).

56.    Z. Q. Qiu, J. Pearson, and S. D. Bader, “Magnetic phase transition of ultrathin Fe films on Ag(111)”, Phys. Rev. Lett. 67, 1646, (1991).

57.    Z. Q. Qiu, J. Pearson, and S. D. Bader, “Asymmetry of the spin reorientation transition in ultrathin Fe films and wedges grown on Ag(100)”, Phys. Rev. Lett. 70, 1006, (1993).

58.    P.K.Hung and P.H.Kien, “New model for tracer-diffusion in amorphous solid”, Eur.Phys.J.B 78, pp 119-125, (2010).

59.    R. Yamamoto, T. Mihara, K. Taira, M. Doyama “Amorphous structures of iron obtained by quenching of the liquid state” Physics Letters A, Volume 70, Issue 1, pp 41–43, (1979).

60.    Xuemin He, Huigang Shi, “Size and shape effects on magnetic properties of Ni nanoparticles”, Elsevier B.V., Particuology 10, pp 497–502, (2012).

61.    Lu, H. M., Zheng, W. T., & Jiang, Q. “Saturation magnetization of ferromagnetic and ferrimagnetic nanocrystals at room temperature”, Journal of Physics D: Applied Physics, 40, pp 320–325, (2007).

62.    Jiang, Q., Zhao, D. S., & Zhao, M, “Size-dependent interface energy and related interface stress”. Acta Materialia, 49, pp 3143–3147, (2001).

63.    C.C. Yang, Q. Jiang, “Size and interface effects on critical temperatures of ferromagnetic, ferroelectric and superconductive nanocrystals”, Acta Materialia 53, pp 3305–3311, (2005).






B.  Sowmiya, S. Amal Raj

Paper Title:

Review of the Self-Organizing Map (SOM) Approach in the Field of Environmental Engineering

Abstract: In environmental engineering field, the use of artificial neural networks (ANNs) has received steadily increasing interest over the last decade or so. In ANN, self-organizing map (SOM) is an unsupervised learning method to analyze, cluster, and model various types of large databases. There is, however, still a notable lack of comprehensive literature review for SOM along with training and data handling procedures, and potential applicability. As a result the present paper, first explains the basic structure and algorithm of self-organizing map (SOM) and secondly, to review published applications with special importance on environmental engineering related problems in order to assess how well SOM can be used to solve a particular problem. Finally, concluded that self-organizing map (SOM) is a hopeful technique suitable to investigate, model, and rule environmental related problems. However, in recent years, self-organizing map (SOM) has displayed a steady increase in the number of applications in environmental engineering related problems due to the robustness of the method.

Linear and non- linear process, Artificial Neural Network, Self Organizing Map, Environmental Engineering, Review.


1.       Aguado D., Montoya, T., Borras, L., Seco, A., Ferrer, J.Using Self Organizing Maps and PCA analysis and interpreting data from a P- removal. Engineering apllication of Artificial intelligence. 2008.21.919-930.
2.       Alhoniemi, E., Hollmen, J., Simula, O., Vesanto, J.,. Process monitoring and modeling using the self-organizing map. Integrated Computer-Aided Engineering. 1999. 6 (1), 3-14.

3.       ASCE Task Committee on application of Artificial Neural Networks in Hydrology,. Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering. 2000a . 5 (2), 115-123.

4.       ASCE Task Committee on application of Artificial Neural Networks in Hydrology,. Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering. 2000b. 5 (2), 124-137.

5.       Berthold T., Peter Milbradt.Artificial Neuronal Networks in Environmental Engineering: Theory and Applications. 2009.18th International Conference on the Application of Computer Science and Mathematics in Architecture and Civil Engineering.

6.       Bowden, G.J., Dandy, G.C., Maier, H.R.,. Input determination for neural network models in water resources applications. Part 1- background and methodology. Journal of Hydrology. 2005a .301, 75-92.

7.       Bowden, G.J., Maier, H.R., Dandy, G.C.,. Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. Journal of Hydrology. 2005b. 301, 93-107.

8.       Dawson, C.W., Wilby, R.L.,. Hydrological modelling using artificial neural networks. Progress in Physical Geography. 2001. 25 (1), 80-108.

9.       Heikkinen, M., Hiltunen, T., Liukkonen, M., Kettunen, A., Kuivalainen, R., Hiltunen, Y.,. A modelling and optimization system for fluidized bed power plants. Expert Systems with Applications. 2009. 36, 10274-10279.

10.    Heikkinen, M., Kettunen, A., Niemitalo, E., Kuivalainen, R., Hiltunen, Y.,. SOM-based method for process state monitoring and optimization in fluidized bed energy plant. In: Duch, W., Kacprzyk, J., Oja, E., Zadro_zny, S. (Eds.), Lecture Notes in Computer Science. 3696. Springer-Verlag Berlin, Heidelber. 2005.  409 -414.

11.    Heikkinen, M., Poutiainen, H., Liukkonen, M., Heikkinen, T. & Hiltunen, Y.Subtraction analysis based on self-organizing maps for an industrial wastewater treatment process. Mathametica. Computation Simulation. 2011. 82. 450–459.

12.    Hilario Lopez Garcia, Ivan Machon Gonzalez. Self-organizing map and clustering for wastewater treatment monitoring. Engineering Applications of Artificial Intelligence. 2004. 17. 215–225.

13.    Kohonen, T., 2001. Self-Organizing Maps. Springer-Verlag, Berlin.

14.    Liukkonen, M., Heikkinen, M., Hälikkä, E., Kuivalainen, R., Hiltunen, Y.,. Emission analysis of a fluidized bed boiler by using self-organizing maps. In:Kolehmainen, M., Toivanen, P., Beliczynski, B. (Eds.), Lecture Notes in Computer Science 5495. Springer-Verlag, Berlin Heidelberg, 2009c. 119-129.

15.    Liukkonen, M., Heikkinen, M., Hiltunen, T., Hälikkä, E., Kuivalainen, R., Hiltunen, Y., 2009b. Modeling of process states by using artificial neural networks in a fluidized bed energy plant. In: Troch, I., Breitenecker, F. (Eds.), Proceedings of MATHMOD 09 VIENNA. Argesim Publishing House, Vienna,pp. 2008.397-402.

16.    M. Pavan, R. Todeschini. 2008. Scientific Data Ranking Methods: Theory and Applications, Elsevier, Amsterdam.

17.    M.Liukkonen, T. Hiltunen, E.Halikka, Y. Hiltunen. Modeling of the fluidized bed combustion process of NOx emission using SOM. An application to the diagnosis of process state. Environmenatl modeling and Software. 2011. 26, 605-614.

18.    Maier, H.R., Dandy, G.C.,. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research. 1996. 32 (4), 1013-1022.

19.    Maier, H.R., Dandy, G.C.,. Determining inputs for neural network models of multivariate time series. Microcomputers in Civil Engineering. 1997. 12 (5), 353-368.

20.    Maier, H.R., Dandy, G.C.,. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modelling and Software. 2000. 15, 101-124.

21.    Richard Olawoyin,Antonia Nieto, Robert Larry Grason, Frank Hardisty, Samuel Oyewole,. Application of Artificial Neural Networks and Self organizing Maps for the categorization of water, soil and sediment quality in Petrochemical regions.Expert system with applications. 2013. 40,3634-3648.

22.    Tsakovski, S., Simeonov, V., .Hasse diagram technique as exploratory tool in sediment pollution assessment, Journal of Chemometrics . 2011.25 (5) 254–261.

23.    Tsvetomil Voyslavov, Stefan Tsakovski, Vasil Simeonov ,.Surface water quality analysis using Self organising maps and Hasse diagram technique. Chemometrics and Intelligent laboratory systems. 2012. 118, 280-286.

24.    Vesanto, J., Alhoniemi, E., 2000. Clustering of the self-organizing map. IEEE Transactions on Neural Networks. 2000. 11 (3), 586-600.

25.    Vesanto, J., Himberg, J., Alhoniemi, E., & Parhankangas, J.,. Self-organizing map in Matlab: the SOM Toolbox. 1999.      In Proceedings of the Matlab DSP conference, Espoo, Finland, Comsoloy.

26.    Yan An, Zhihong Zou, Ranran Li, Descriptive.Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map. International Journal of Environmental Research and Public Health. 2016.13(1).115.

27.    Young-Seuk Park , Yong-Su Kwon, Soon-Jin Hwang , Sangkyu Park.Characterizing effects of landscape and morphometric factors on water quality of reservoirs using a self-organizing map. Environmental modelling and software. 2014. 55. 214-221.






Hayfa Masghouni

Paper Title:

Comparison Between Algorithms of MRI Image Segmentation

Abstract: In this article, we present different algorithms of MRI image segmentation based on classification of pixels. First, we present FCM (Fuzzy C_Means) and its different extensions with a comparison between them, after we present GMM (Gaussian Mixture Model) and EM (Expectation Maximization) and its extensions with a comparison between them.

 FCM, EM, GMM, MRI image segmentation


1.       Balafar, “Spatial based Expectation Maximizing (EM),” Diagnostic Pathology 2011 6:103.
2.       Tanga H, Dillensegerb J, Baoa XD, Luoa LM, “A Vectorial Image Soft Segmentation Method Based on Neighborhood Weighted Gaussian Mixture Model,” Computerized Medical Imaging Graphics 2009, 33:644-650

3.       Weiling Cai, Songcan Chen, Daoqiang Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,”Nanjing 210016, PR China27, July 2006

4.       M.A. Balafar, A. R. Ramli and S. Mashohor,”Edge-preserving Clustering Algorithms and Their Application for MRI Image Segmentation,” Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 Vol I, IMECS 2010,March 17-19.2010.Hong Kong

5.       Krinidis S, Chatzis V, “A Robust Fuzzy Local Information C-Means Clustering Algorithm,”IEEE Transactions on Image Processing 2010,19:1328-1337.

6.       Wang J, Kong J, Lub Y, Qi M, Zhang B,”A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints,”Computerized Medical Imaging and Graphics 2008, 32:685-98.

7.       Vincent Roulier,”Fuzzy classification and MRI modeling: Application to the quantification of fat for optimal evaluation of health hazards associated with obesity,”  Ph.D. Thesis, Doctoral school  of ANGERS, 2008

8.       Stephen O. Rice,” Mathematical Analysis of Random Noise,”Bell System Technical Journal, vol. 24, 1945, p. 46–156

9.       Silva ARFD, “Bayesian mixture models of variable dimension for image segmentation,”Computer methods and programs in biomedicine 2009,94:1-14.

10.    Leemput FMKV, Vandermeulen D, Suetens P,”Automated model-based tissue classification of MR images of the brain,” IEEE Transactions on Medical Imaging 1999, 18:897-908.

11.    Marroquin BCVJL, Botello S, Calderon F, Fernandez-Bouzas A,”An accurate and efficient Bayesian method for automatic segmentation of brain MRI,” IEEE Transactions on Medical Imaging 2002, 21:934-945.

12.    Zouaoui Hakima, Moussaoui Abdelouahab, “Clustering fuzzy data fusion applied to the segmentation of brain MRI images,” CIIA, 2009

13.    Weiling Cai, Songcan Chen, Daoqiang Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognition 40(2007) 825 – 838

14.    Michel Menard, Vincent Courboulay, Pierre-Andrée Dardignac, “Possibilistic and probabilistic fuzzy clustering unification within the framework of the non-extensive thermostatistics,”Pattern Recognition 36 (2003) 1325 – 1342

15.    Ahmed S. Ghiduk,E.A.Zanaty, “Modified Fuzzy C-Means for Segmenting Magnetic Resonance Images (MRIs),” International Journal of informatics and medical data processing (JIMDP) vol.1, no.2, pp. 48-58, 2016. 

16.    Adelino R. Ferreira da Silva, “Bayesian mixture models of variable dimension for image

17.    segmentation,” Rua Dr. Bastos Goncalves, n. 5, 10A, 1600-898 Lisboa, Portugal,2008

18.    Bjoern H Menze, Koen Van Leemput, Danial Lashkari, Tammy Riklin-Raviv, Ezequiel Geremia, al, “A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke,”IEEE Transactions on Medical Imaging, 2015. <hal-01230846>






Majid Khan, Abdus Salam, Javed Iqbal, Syed Irfan Ullah

Paper Title:

Comparative Analysis of Automated Software Testing Tools

Abstract:  The most significant segments of software development is software testing. Automated software testing is an effective testing process that reduces the effort and cost of manual testing. However, it is difficult to select a suitable software testing tool from the huge list of freely available tools. The main contribution of this work is to conduct a comparative study of three readily available automated software testing tools such as QTP, Silk4j and Load Runner. The selected tools are evaluated and compared on the basis of their usability, maintenance and effectiveness. For this purpose we have taken an existing JAVA based applications to perform automated testing on these three tools. The results will assist testers to effectively select the best automated software testing tool for related applications.

significant segments, However, Automated software, QTP, JAVA based,


1.       IEEE, “IEEE Standard 610.12-1990, IEEE Standard Glossary of Software Engineering Terminology,” 1990.
2.       S. Rajeevan, and B. Sathiyan. “Comparative Study of Automated Testing Tools: Selenium and Quick Test Professional.” International Journal Of Engineering And Computer Science, Vol. 3, No. 7, pp. 7354-7357, 2014.

3.       Adamoli, D. Zaparanuks, M. Jovic, and M. Hauswirth, “Automated GUI performance testing,” Software Quality Journal, Vol. 19, No. 4, pp. 801-839, 2011.

4.       R. N. Khan, and S. Gupta. “Comparative Study of Automated Testing Tools: Rational Functional Tester, Quick Test Professional, Silk Test and Loadrunner”, International Journal of Advanced Technology in Engineering and Science, Vol. 3, No. 1, 2015.

5.       http://www.differencebetween.net/technology/software-technology/differences-between-qtp-and-rft/ (Dec-2015).

6.       H. Kaur, and G. Gupta, “Comparative Study of Automated Testing Tools: Selenium, Quick Test Professional and Testcomplete.” International Journal of Engineering Research and Applications, pp. 2248-9622, 2013.

7.       Kaur and H. S. Sohal. “Automatic Test Case Generation with SilK Testing.” International Journal of Computer Applications, Vol. 79, No. 15, 2013.

8.       L. White and B. Robinson. “Industrial real-time regression testing and analysis using firewalls” Proceedings of 20th IEEE International Conference on Software Maintenance, pp. 18-27, 2004.

9.       S. K. Shaveta, and N. Snehlata “Comparative Study of Automated Testing Tools: Quick Test Pro and Load Runner“ International Journal of Computer Science and Information Technologies, Vol. 3, No. 4 , pp. 4562-4567, 2012

10.    Kuhn, D. Richard, and Michael J. Reilly. “An investigation of the applicability of design of experiments to software testing.” Software Engineering Workshop, 2002. Proceedings. 27th Annual NASA Goddard/IEEE. IEEE, 2002.

11.    Poston, Robert M., and Michael P. Sexton. “Evaluating and selecting testing tools.” Software, IEEE 9.3 (1992): 33-42.

12.    Börjesson, Emil, and Robert Feldt. “Automated system testing using visual GUI testing tools: A comparative study in industry.” Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on. IEEE, 2012.

13.    Tuszynski, Tobias, et al. “Evaluation of software tools for automated identification of neuroanatomical structures in quantitative β-amyloid PET imaging to diagnose Alzheimer’s disease.” European journal of nuclear medicine and molecular imaging (2016): 1-11.

14.    Kos, Tomaž, Marjan Mernik, and Tomaž Kosar. “Test automation of a measurement system using a domain-specific modelling language.” Journal of Systems and Software 111 (2016): 74-88.

15.    Alégroth, Emil, Robert Feldt, and Pirjo Kolström. “Maintenance of automated test suites in industry: An empirical study on Visual GUI Testing.” Information and Software Technology 73 (2016): 66-80.

16.    Charest, Thomas, Nick Rodgers, and Yan Wu. “Comparison of Static Analysis Tools for Java Using the Juliet Test Suite.” 11th International Conference on Cyber Warfare and Security: ICCWS2016. Academic Conferences and publishing limited, 2016.

17.    Fraser, Gordon, et al. “Does automated unit test generation really help software testers? a controlled empirical study.” ACM Transactions on Software
Engineering and Methodology (TOSEM) 24.4 (2015): 23.

18.    Shafique, Muhammad, and Yvan Labiche. “A systematic review of state-based test tools.” International Journal on Software Tools for Technology Transfer 17.1 (2015): 59-76.

19.    Candea, George, Stefan Bucur, and Cristian Zamfir. “Automated software testing as a service.” Proceedings of the 1st ACM symposium on Cloud computing. ACM, 2010.

20.    Parnin, Chris, and Alessandro Orso. “Are automated debugging techniques actually helping programmers?.” Proceedings of the 2011 International Symposium on Software Testing and Analysis. ACM, 2011.

21.    Cadar, Cristian, et al. “Symbolic execution for software testing in practice: preliminary assessment.” Proceedings of the 33rd International Conference on Software Engineering. ACM, 2011.






Aditi Kacheria, Nidhi Shivakumar, Shreya Sawkar, Archana Gupta

Paper Title:

Loan Sanctioning Prediction System

Abstract: People operating in banks face lots of issues which involve approval of a loan. In the 21st century, people often rely on technology to tackle such issues. This paper proposes a loan sanctioning system which determines whether or not a loan should be given to a person, based on certain attributes. In spite of banks following stringent rules and regulations and conducting meticulous background checks while sanctioning a loan and keeping in mind the probability of the person’s ability to return the loan, often such situations are faced where in, the person is unable to repay the loan that has been given to him. In this paper, the system that we propose for the bankers will help them predict the credible customers who have applied for loan, thereby improving the chances of their loans being repaid in time. This classification is done using Naïve Bayesian algorithm. In order to improve the classification accuracy, the quality of the data is improved before classifying it by using K-NN and Binning algorithms. This system uses these algorithms in order to yield a better efficiency so as to reduce the possibility of such a problem. The proposed system additionally facilitates self-confirmation regarding the same for the commoner.

 Binning, Data mining, K-NN, Naïve Bayesian.


1.       Nirmala Devi M.; Appavu S.; Swathi U.V., “An amalgam KNN to predict diabetes mellitus”, Emerging Trends in Computing, Communication and Nanotechnology
(ICECCN), 2013 International Conference on, pages 691 – 695, 25-26 March 2013.

2.       S.X. Wu, S.F. Liu, M.Q. Li, “The Method of Data Preprocessing in Grey Information Systems”,  Control Automation, Robotics and Vision, 2006. ICARCV ‘06. 9th International Conference on, pages 1-4, 5-8 Dec. 2006.

3.       Ranganatha S.; Pooja Raj H.R.; Anusha C.;Vinay S.K., “Medical data mining and analysis for heart disease dataset using classification techniques”, Research & Technology in the Coming Decades (CRT 2013), National Conference on Challenges in, pages 1 – 5, 27-28 Sept. 2013.

4.       Sudhakar, K.; Manimekalai, Dr. M., “Study of Heart Disease Prediction using Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1, ISSN: 2277 128X, pages 1157-1160, January 2014.

5.       D.Lavanya; Dr.K.Usha Rani. “Performance Evaluation of Decision Tree Classifiers on Medical Datasets”International Journal of Computer Applications (0975 – 8887), Volume 26– No.4,pages 1-4, July 2011.

6.       Dr. K. Usha Rani, “Analysis of Heart Diseases Dataset Using Neural Network Approach”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.5, September 2011.

7.       Karthika Jayprakash, Nidhi Kargathra, Pranay Jagtap, Suraj Shridhar and Archana Gupta, “Comparison of Classification Techniques for Heart Health Analysis System”, International Journal of Computer Sciences and Engineering(IJCSE), Volume-04, Issue-02, E-ISSN: 2347-2693, pages 92-95, Feb. 2016.

8.       Ms. Neethu Baby, Mrs. Priyanka L.T., “Customer Classification And Prediction Based On Data Mining Technique”, International Journal of Emerging Technology and Advanced Engineering (IJETAE), Volume 2, Issue 12, ISSN 2250-2459, ISO 9001:2008 Certified Journal, pages 314-318, December 2012.

9.       Rucha Shinde, Sandhya Arjun, Priyanka Patil, Prof. Jaishree Waghmare, “An Intelligent Heart Disease Prediction System Using K-Means Clustering and Naïve Bayes Algorithm”,  International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 6 (1), ISSN: 0975-9646, pages 637-639, 2015.

10.    L.Pandeeswari, K.Rajeswari, “K-Means Clustering and Naive Bayes Classifier For Categorization of Diabetes Patients”,   International Journal of Innovative Science, Engineering & Technology (IJISET), Vol. 2 Issue 1, ISSN 2348 – 7968, pages 179-185, January 2015.

11.    Sivasree M S, Rekha Sunny T, “Loan Credibility Prediction System Based on Decision Tree Algorithm”, International Journal of Engineering Research & Technology (IJERT), Vol. 4 Issue 09, ISSN: 2278-0181, pages 825-830, September-2015.






S. A. Asklany, Khaled Elhelow, M. Abd El-Wahab

Paper Title:

On using Adaptive Hybrid Intelligent Systems in PM10 Prediction

Abstract: A comparative study based on producing two intelligence systems applied to PM10 prediction was presented in this work. Adaptive Network –based Fuzzy Inference System (ANFIS) used in build a system has three weather elements as input variables (Wind Speed, Wind Direction and Temperature) and the PM10 as output variable for PM10 nowcast model. Another technique used ANFIS in prediction of chaotic time series to get 6 hours forecast for PM10 from the present data. For developing the models, thirteen years hourly data for Mansoria station coordinates 29° 300′ 0″ N, 45° 45′ 0″ E from 1995to 2007 has been used.  Different models employing a different training and testing data sets had been studied. The criteria of performance evaluation are calculated for estimating and comparing the performances of the two techniques.  The results show that the two presented models success tools in PM10 prediction with acceptable root mean square error (RMSE); the model built on using ANFIS for chaotic time series prediction has smaller error compared with the adaptive network fuzzy inference system.

  Air quality, artificial intelligence, pollution, ANFIS, soft computing


1.       Alvim-Ferraz, M.C., M.C. Pereira, J.M. Ferraz, A.M.C. Almeida e Mello and Martins, F.G.  European Directives for Air Quality: Analysis of the New Limits in Comparison with Asthmatic Symptoms in Children Living in the Oporto Metropolitan Area. Portugal. Hum. Ecol. Risk Assess.11(3 . )pp. 607-616. (2005).
2.       Kim, K.-H. , Kabir, E. , Kabir, S. A review on the human health impact of airborne particulate matter. Environ. Int. 74: 136–143. ( 2015)

3.       Zhiguo Zhang and Ye San .Adaptive wavelet neural network for hourly NOX and NO2 Concentrations. Winter Simulation Conference (WSC’04) – Volume 2(2004).

4.       Jose´ Luis Aznarte M., Jose´ Manuel Benı´tez Sa´nchez , Diego Nieto Lugilde , Concepcio´n de Linares Ferna´ndez, Consuelo Dı´az de la Guardia , Francisca Alba Sa´nchez  Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models, Expert Systems with Applications. 32: 1218–1225. (2007)

5.       Yuehui Chen, Bo Yang and Ajith Abrahan.Time-series forecasting using flexible neural tree model. Information Sciences: an International Journal. 174, Issue 3-4 :219 – 235  (2005)

6.       Patricio Perez and Jorge Reyes. An integrated neural network model for PM10 forecasting. Atmospheric Environment. 40: 2845–2851. ( 2006).

7.       Bogdana VUJIĆ, Srđan VUKMIROVIĆ, Goran VUJIĆ, Nebojša JOVIČIĆ,   Gordana JOVIČIĆ and   Milun, BABIĆ. Experimental and Artificial Neural Network Approaches for forecasting of traffic air pollution in urban areas: the case of Subotica. Thermal Science. 14:1-7( 2010).

8.       Oprea, M. and Alexandra, M. Applying Artificial Neural Networks in Environmental Prediction Systems, Recent Advances in Electrical Engineering Proceedings of the 11th WSEAS international conference on Automation & information:110-115. ( 2010).  

9.       Somia A. Asklany a,  Khaled Elhelow  , Youssef I.K.  and Abd El-wahab, M. Rainfall events prediction using rule-based fuzzy inference system. Atmospheric Research. 101 : 228–236 ( 2011)

10.    Manish Kakar, Hakan Nystrom, Lasse Rye Aarup, Trine Jakobi Nøttrup and Dag Rune Olse. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). Phys. Med. Biol. 50 :4721–4728(2005).

11.    Ciji Pearl Kurian , George, V.I., Jayadev Bhat and Radhakrishna S Aithal. ANFIS Model  for the time series Prediction of interior daylight illuminanc.AIML Journal. 6 (3): 35-40( 2006).

12.    Mehmet Tektaş Weather Forecasting Using ANFIS and ARIMA MODELS  A Case Study for Istanbul, Environmental Research, Engineering and Management.1(51):5 – 10. (2010).

13.    Song, Q., & Kasabov, N. Weighted data normalizations and feature selection for evolving connectionist systems proceedings. In Proceedings of the eighth Australian and New Zealand intelligence information systems conference : 285–290. (2003). 

14.    Qun Song, Nikola Kasabov . TWNFI—a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling, Neural Networks, Vol. 19 ,  Issue 10, pp. 1591-1596         (2010).

15.    Weiping Liu. Forcasting exchange rate change between USD and JPY by using dynamic adaptive Neuro-Fuzzy logic system. Asia Pacific Journal of Finance and Banking Research. 2(4) pp. 1-12. (2008).

16.    Jang, J.S.R. ANFIS: Adaptive-Network-Based Fuzzy Inference Systems, IEEE Transactions on Systems. 23(3): 665-685. (1993).

17.    Casdagli, M.  A Dynamical Systems Approach to Modeling Input-output Systems, in Nonlinear Modeling and Forecasting. SFI Studies in the Sciences of Complexity Process, Addison-Wesley, New York, 12: 265-281. (1992).

18.    Maysam Behmanesh, Majid Mohammadi, Vahid Sattari Naeini. Chaotic Time Series Prediction using Improved ANFIS with Imperialist Competitive Learning Algorithm, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-4 Issue-4:25-33.( 2014  )

19.    Glass, L. and Mackey, M.C. From Clocks to Chaos, the Rhythms of Life, Princeton University Press. (1988).

20.    Yadav, R.N.  , Kalra, P.K. and  John, J. Time series prediction with single multiplicative neuron model. Applied Soft Computing. 7 :1157–1163. (2007).

21.    Sugeno, M. Industrial Applications of Fuzzy Control.  Elsevier Science Publication Company. ( 1985)

22.    Kodogiannis, V. and Lolis, A. Forecasting Financial Time Series Using Neural Network and Fuzzy System-based Techniques. Neural Computing & Applications. 11: 90-102. (2002).

23.    Pejman Tahmasebi, Ardeshir Hezarkhani. Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran. Australian Journal of Basic and Applied Sciences .4 (3):408-420. ( 2010).






Rana Kadhim Abdulnabi

Paper Title:

Using Spray Pyrolysis Technique to Prepare PBS Lead Sulfide Thin Films and Study Their Structural and Electrical Properties as Function of Thickness

Abstract:  Using Spray Pyrolysis Technique to Prepare PBS Lead Sulfide Thin Films and Study Their Structural and Electrical Properties as Function of ThicknessAbstract: Lead sulfide (PbS) thin films are prepared by “solution “from, lead acetate” pb (CH3COO)2 and Thiourea CS(NH2) using Spray Pyrolysis Technique (SPT) on glassy substrates at temperature of 200Cº with a variable thicknesses of 100, 200, 300 nm. The “structural properties are approved by X-Ray Refraction (XRR), which helped in displaying the films having a cubic structure such as NaCl The size of the crystal grains of PbS films are shown to be increased when increasing the thickness of PbS films. Roughness of the films was measured according to the Root Mean Square (RMS). This was achieved using Scanning Microscope (SM). With increasing film thickness to the mentioned values, results showed an increase with RMS, electrical conductivity of the PbS film to 0.0186*10­³, 0.4166*10-3 and 0.9090*10­³ (Ω.cm)­¹ and with charge-carrier concentration as well to 0.298*10¹¹, 1.1*1011, 12*10¹¹ cm− 3.الخلاصة:
في هذا البحث تم تحضير اغشية كبريتيد الرصاص( PbS ) بأستخدام محلول من خلات الرصاص 2(CH3COO ) PbS والثايوريا CS(NH2) على قاعدة زجاجية بدرجة حرارة 200 Cͦ بطريقة الرش الكيميائي الحراري .
تم دراسة الخصائص التركيبية عند اسماك متغيرة (100,200,300) بأستخدام اشعة X- Ray التي اوضحت الشكل البلوري للاغشية وقد لاحظنا زيادة الحجم الحبيبي للاغشية بزيادة سمك الغشاء , كما تم قياس خشونة السطح للاغشية بأستخدام فحص STM.
كما تم دراسة الخصائص الكهربائية ومنها التوصيلية الكهربائية والتي قيمها 0.0186,0.4166,0.9090)*10ˉ³( Ω.cm)ˉ¹) وحاملات الشحن للاغشية والتي قيمها (0.298,1.1,12)*10¹¹ cmˉ³

lead sulfide, chemical pyrolysis, thin film, semiconductor.


  1. Lui, M. Zhang, Studies on PbS and PbSe Detectors for IR System, International Journal of Infrared and Millimeter Waves 21, 1697–1701 (2000).
  2. Kumara, G. Agarwal, B. Tripathi, D. Vyas, V. Kulshrestha, Characterization of PbS nanoparticles synthesized by chemical bath deposition, Journal of Alloys and Compounds 484, 463–466 (2009).
  3. I. Fainer, M. L. Kosinova, Yu. M. Rumyantsev, E. G. Salman, F. A. Kuznetsov, Growth of PbS and CdS thin films by low-pressure chemical vapour deposition using dithiocarbamates, Thin Solid Films 280, 16-19 (1996).
  4. Seghaier, N. Kamoun, R. Brini, A. B. Amara, Structural and optical properties of PbS thin films deposited by chemical bath deposition, Materials Chemistry and Physics 97, 71-80 (2006).
  5. Pop , C. Nascu, V. Ionescu, E. Indrea , I. Bratu, Structural and optical properties of PbS thin films obtained by chemical deposition, Thin Solid Films 307, 240-244 (1997).
  6. Choudhury, B.Sarma, Structural characterization of lead sulfide thin films by means of X-ray line profile analysis, Bulletin of Materials Science 32, 43-47 (2009).
  7. L. Machol, F.W. Wise, R.C. Patel, D.B. Tanner, Phys. Rev. B 48(1993) 2819.
  8. Wang, W. Suna, W. Mahler, and R. Kasowski, PbS in polymers. From molecules to bulk solids, J. Chem. Phys. 87, 1987, 7315-7322.
  9. Wang, and N. Herron, Nanometer-Sized Semiconductor Clusters: Materials Synthesis, Quantum Size Effects, and Photophysical Properties, J. Phys. Chem. 95, 1991, 525-532.
  10. W. Wise, Lead salt quantum dots: the limit of strong quantum confinement, Acc. Chem. Res. 33, 2000, 773-780.
  11. Gadenne, Y. Yagil, G. Deutscher, J. Appl. Phys. 66 (1989) 3019.
  12. K. Nair, O. Gomezdaza, M.T.S. Nair, Adv. Mater. Opt. Electron.1 (1992) 139.
  13. K. Nair, V.M. Garcia, A.B. Hernandez, M.T.S. Nair, J. Phys. D:Appl. Phys. 24 (1991) 1466–1472.
  14. Ileana Pop, Cristina Nascu, VioletaIonescu, E. Indrea, I. Bratu, ThinSolid Films 307 (1997) 240–244.
  15. Hirata, K. Higashiyama, Bull. Chem. Soc. Jpn. 44 (1971) 2420.
  16. K. Chaudhuri, S. Chatterjes, Proceedings of the International ConferenceonThermoelectronics, vol. 11, 1992, p. 40
  17. Kumar, T.P.Sharma, M. Zulfequar, M. Husain, Characterization of vacuum evaporated PbS thin films Physica B: Condensed Matter, 325, 8-16 (2003).
  18. Raniero , C.L. Ferreira, L.R. Cruz, A.L. Pinto, R.M.P. Alves , Photoconductivity activation in PbS thin films grown at room temperature by chemical bath deposition , Physica B: Condensed Matter 405, 1283-1286 (2010).
  19. H. Afifi, S.A. Mahmoud, A. Ashour, Structural study of ZnS thin films prepared by spray pyrolysis, Thin Solid Films 263, 248-251(1995).
  20. Yonghong Ni, Fei Wang, Hongjiang Liu, Gui Yin, Jianming Hong,Xiang Ma, ZhengXu, Cryst. Growth 262 (2004) 399–402.
  21. Sharon, K.S. Ramaiah, Mukul Kumar , M. Neumann-Spallart, C. Levy- Clement, Electrodeposition of lead sulphide in acidic medium, Journal of Electroanalytical Chemistry 436, 49-52 (1997).
  22. Thangaraju, P. Kaliannan, Spray pyrolytically deposited PbS thin films Semiconductor Science and Technology 15, 849–853 (2000
  23. Yu Zhao, Xue-Hong Liao, Jian-Min Hong, Jun-Jie Zhu, Mater.Chem. Phys. 87 (2004) 149–153.
  24. L.S. Birks, H. Friedman, Particle Size Determination from X‐Ray Line Broadening, Journal of Applied Physics 17, 687–692 (1946). 





Surinder Kumar

Paper Title:

Module Allocation for Maximizing Reliability of Distributed Computing Systems using Genetic Algorithms

Abstract: The problem of the module allocation in distributed computing system is to need to allocate a number of modules to different processors for execution. The paper deals with the problem of module allocation in heterogeneous distributed computing systems with the goal of maximizing the system reliability. We present a genetic algorithm to obtain the optimal solution for this problem. In the performance of the algorithm we consider more one parameter such as the number of modules, the number of processors, and module interaction density of applications. The experimental results illustrate the effectiveness of this algorithm over conventional algorithms.

Keywords:  Distributed computing systems, Genetic algorithms, Module allocations and Maximizing reliability.


  1. Kartik, S., Murthy, S.R., “Task allocation algorithms for maximizing reliability of distributed computing systems,” IEEE Transactions on Computers 46, 719–724, 1997.
  2. Srinivasan, S., Jha, N.K., “Safety and reliability driven task allocation in distributed systems,” IEEE Transactions on Parallel and Distributed Systems 10, 238–251, 1999.
  3. Kartik, S., Murthy, S.R., “Improved task-allocation algorithms to maximize reliability of redundant distributed computing systems,” IEEE Transactions on Reliability 44, 575–586, 1995.
  4. Hsieh, C.C., Hsieh, Y.C., “Reliability and cost optimization in distributed computing systems,” Computers and Operations Research 30, 1103–1109, 2003.
  5. Kumar, V.K.P., Raghavendra, C.S., Hariri, S., “Distributed program reliability analysis,” IEEE Transactions on Software Engineering 12, 42– 50, 1986.
  6. Shatz, S.M., Wang, J.P., Goto, M., “Task allocation for maximizing reliability of distributed computer systems,” IEEE Transactions on Computers 41, 1156–116, 1992.
  7. Lin, M.S., Chen, D.J., “The computational complexity of the reliability problem on distributed systems,” Information Processing Letters 64, 143- 147, 1997.
  8. Verma, A.K., Tamhankar, M.T., “Reliability-based optimal task allocation in distributed-database management systems,” IEEE Transactions on Reliability 46, 452–459, 1997.
  9. Glover, F., “Tabu search – Part I,” ORSA Journal of Computing 1, 190–206, 1989.
  10. Dorigo, M., Gambardella, L., “Ant colony system: a cooperative learning approach to the travelling salesman problem,” IEEE Transaction on Evolutionary Computation 1, 53–66, 1997.
  11. Kennedy, J., Eberhart, R.C., “Particle swarm optimization,” Proceedings of the IEEE International Conference on Neural Networks IV, 1942– 1948, 1995.
  12. Shigenori, N., Takamu, G., Toshiku, Y., Yoshikazu, F., “A hybrid particle swarm optimization for distribution state estimation,” IEEE Transaction on Power Systems 18, 60–68, 2003.
  13. Vidyarthi, D.P., Tripathi, A.K., “Maximizing reliability of distributed computing system with task allocation using simple genetic algorithm,”Journal of Systems Architecture 47, 549–554, 2001.
  14. Goldberg, D.E., “Genetic Algorithms in Search, Optimization and Machine Learning.,”Addison-Wesley, Reading, MA.1989.,
  15. Peng-Yeng Yin., Shiuh-Sheng Yu, Pei-Pei Wang, Yi-Te Wang, “Task allocation for maximizing reliability of a distributed system using hybrid particle swarm optimization,” The Journal of Systems and Software 80 ,724–735, 2007.
  16. Peng-Yeng Yin., Shiuh-Sheng Yu, Pei-Pei Wang, Yi-Te Wang, “Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization,” Applied Mathematics and Computation 184, 407– 420, 2007.
  17. Qin- Ma Kang, Hong He., “Task allocation for maximizing reliability of distributed computing systems using honeybee mating optimization, “The journal of Systems and Software 83, 2165-2174, 2010.
  18. Hsieh, C.C., “Optimal task allocation and hardware redundancy policies in distributed computing systems,” European Journal of Operational Research 147, 430–447, 2003.
  19. Gamal Attiya, Yskandar Haam, “Task allocation for maximizing reliability of distributed systems: A simulated annealing approach,” Journal Parallel Distribution Computer- 66,1259-1266, 2006.





Mohammed AbdALLA Adam Elmaleeh, Fadalalla Suleiman Mahmoud Gamer

Paper Title:

Implementation of FBG Mechanism for the Removal of Optical Signal Spreading

Abstract: Single mode fiber has been used in long haul communications systems to increase the transmission capacity and to meet the increasing of demand for the communication services. Therefore, any type of the signal degradation effects should be strappingly minimized. The performance of a digital communication system is measured by probability of error per bit, which is referred to as the bit error rate (BER). Error occurs as a result of noise in the received signal, or due to pulse spreading into neighboring bits which result in symbol interference. In this paper the optical signal degradation effects were studied. Initially the optical signal Eye diagram for an optical fiber of different lengths before applying compensation mechanism were obtained using OPTsys simulation tool. The data rate of 20 – 40 Gbps is introduced to the system and examined using specified fiber lengths parameters. It is observed that the received signals were significantly degrade due to the signal degradation effects. Fiber Bragg Grating is implemented as spreading lessens mechanism and the optical amplifier (EDFA) is used to compensate the reduction of signal power when propagates through the fiber strand. The results obtained showed that the width of the pulse spreading significantly reduced from 0.43ps to 0.18ps, with BER of 8.825×10-10, Quality factor = 9.7 and total possible distance is found to be of 25 k.

 Digital communication, FBG, BER, Optical Signal, Optical amplifier Bearings, Fast Fourier transform.


  1. Subir Kumar Sarkar.” Optical Fiber and Fiber Optic Communications System”, Second edition, S. Chand, 2009.
  2. Suleiman Al kurtas,”Introduction to the Communication System”, first edition, Al Obeican, 2010
  3. R. Duffon, “Understanding Optical Communications”, first edition, International business machines Corporation, 1998.
  4. International Journal of Mobile Network Communications & Telematics. (IJMNCT) Vol.2, No.3, June 2012
  5. Vijay K. GARG,” Wireless Communications and Networking “, second edition, Morgan Kanfman, 2010.
  6. David R. Goff,” Single Mode Fiber Types”. Olson Technology, 2010
  7. In-Public Information “Chromatic Dispersion in a Single-Mode Fiber”com/en/us/prod/…White paper Pdf, 2008.
  8. Mario F.S.Fereir, “Nonlinear effect in Optical Fiber“, First Edition, John Wiley. 2011.



Volume-6 Issue-5

 Download Abstract Book

S. No

Volume-6 Issue-5, November 2016, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Hassan Ali Salman

Paper Title:

A Digital Meter for Measuring the Rate of Heat Transferred Through Walls

Abstract:  Heat transfers through a wall are one of the most important parameters that must be considered in designing systems for heating or cooling building. In this paper a digital meter for measuring the rate of heat transferred through Walls have been designed to measure Time rate of heat transfer through the wall which gives an indication of the amount of energy lost through this path.

transfers, Heat, measure, Walls, systems for heating,


1.    Jan F. Kreider and Frank Kreith, Solar energy Hand book, McGraw-Hill book company, Inc, 1981.
2.    S.Song, M.M. Yovanovich, and F.O.Goodman. “Thermal Gab Conductance of Conforming Surfaces in Contact”, Jornal of Heat transfer 115(1993).

3.    L.S. Fletcher. “Recent Development in Contact Conductance Heat Transfer”, Journal of Heat Transfer , 1988.

4.    Y. A. Çengel.” Heat and Mass Transfer: A PracticalApproach”. 3rd ed. New York: McGraw-Hill, 2007.

5.    Robert J. Ribando. “Heat Transfer Tools”. New York:McGraw-Hill, 2002.

6.    Theodore L., “Heat transfer applications for the practicing engineer”, Wiley, New Jersey, 2011.






Chander Shekhar Devra

Paper Title:

Interoperability Between Product Life Cycle Management (PLM) & Electronic Lab Note Book (ELN) for Chemical Companies to Accelerate Product Development

Abstract: Many companies struggle to integrate various system & sub-system used for their Product development. Even though product lifecycle Management System are claimed to be eco-system to integrate all systems & sub-system into one place but that is still not reached to a certain level of maturity. There are challenges being faced by companies. Information are still available on island of silos. These Island of Silos impacts business productivity, time to market, cost, rework etc. This paper presents need & challenges of the interoperability between Electronic Lab note book (ELN) used by R & Din design & develop phase of product life cycle management (PLM).Earlier there wireless focus on ELN software integration with PLM in context to process industries like Chemicals. ELN plays a significant role to complete the product evolution chain. In context to discrete industry like automobile & aerospace, PDM (Product Data Management) is handled through design tools like CAD / CAM. These tools are sufficiently integrated with PLM and resulting into higher productivity, better collaboration, less product cost & launch time. Whereas in case of chemical industry, CAD/CAM role is played by ELN. ELN is not sufficiently integrated with PLM. The purpose of this paper is to study, how to realize interaction & integration between ELN & PLM. Find out “what data” need to be exchanged between ELN & PLM. There is possibility to increase productivity, better collaboration between different stages of PLM which may result into reduction in product launch time & cost.

(ELN), (PLM). PLM, ELN & PLM, CAD/CAM, (Product Data Management).


1.       http://www.kinematik.com/blog/bid/239721/9-Benefits-of-Integrating-SAP-with-your-Electronic-Lab-Notebook
2.       Peiyu Ren, Yancang Li, Huiping Song , CHEMICAL ENGINEERING TRANSACTIONS, VOL. 46, 2015 A publication of The Italian Association of Chemical Engineering website: www.aidic.it/cet

3.       https://www.cimdata.com

4.       Stark, J. (2004) Product Lifecycle Management: 21st century Paradigm for Product Realisation, Springer-Verlag, New York.

5.       Subrahmanian, E., Rachuri, S., Fenves,S.J., Foufou, S., Sriram, R.D. (2005) ‘Product lifecyclemanagement support: a challenge in supporting product design and manufacturing in anetworked economy’, International Journal of Product Lifecycle Management 2005 – Vol. 1,No.1 pp. 4 – 25

6.       Julien Le Duigou, Alain Bernard, Nicolas Perry, Jean-Charles Delplace ‘Application of PLM processes to respond to mechanical SMEs needs’  https://arxiv.org/abs/1011.5713

7.       Dr Cheryl Lund, Dassault Systèmes Biovia, ‘Race to Innovate’ http://www.specchemonline.com/featuredarticles/the-race-to-innovate

8.       White paper ‘PDM VS. PLM: IT ALL STARTS WITH PDM’htps://www.solidworks.com/sw/docs/PDMvsPLM_2010_ENG_FINAL.pdf

9.       Pikosz, P., Malmström, J. and Malmqvist, J. (1997) „Strategies for introducing PDM systems in engineering companies‟, Advances in Concurrent Engineering – CE97, 20–22 August, Rochester Hills, MI, USA, pp.425–434

10.    Rangan, R., Rohde, S., Peak, R., Chadha, B. and Bliznakov, P. (2005) „Streamlining product lifecycle processes: a survey of product lifecycle management implementations, directions, and challenges‟, Journal of Computing and Information Science in Engineering, Vol. 5, No. 3, pp.227–237






Abdulhassan Nasayif Aldujaili

Paper Title:

Low Error Down Sampling Filter for Mobile System

Abstract:  This paper present, the design and simulation of decimation filter to work with wireless mobile system under software defined radio (SDR) technology. The decimation filter is designed with three stages of decimation filter that consists of Cascaded Integrated Comb (CIC) decimation filter, Compensating Finite Impulse Response (CFIR) filter and Programmable Finite Impulse Response (PFIR) filter. The three cascaded filters could operate to reduce the intermediate frequency from 100MHz to 100 KHz baseband signal in order to more processing. System Generator offers the multiplier less   Multiply-Accumulate (MAC) Finite Impulse Response block which reduce the implementation area. The three filters has been verified in investigated by mean of fixed point and floating point values using matlab 7.4 and Xilinxe Sys. Gen. The simulation and verification results achieved minimum error of approximately 2×10-4.  Therefore, the introduced techniques support the current and future wireless generation in intermediate frequency up to 100 MHz.  

 Decimation Filter, SDR, MATLAB, System Generator


1.    Rajesh Mehra, “Reconfigurable Optimized WCDMA DDC for Software Defined Radios”, Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), December Edition, 2010.
2.    RAVI KISHORE KODALI, “DDC and DUC Filters in SDR platforms”, Conference on Advances in Communication and Control Systems, Atlantis Press PP.203-208, 2013

3.    Maruthi G.B, et al, “Implementation of High Performance DUC and DDC for Software Defined Radio Applications”, International Journal of Computer Applications (0975 – 8887) Volume 110 – No. 6, pp. 23-25, January 2015

4.    Harjinder Singh, Sursumel Singh, “DESIGN & IMPLEMENTATION OF DIGITAL DOWN CONVERTOR FOR WCDMA SYSTEMS”, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 5(6), pp. 553- 556, 2013

5.    Rajesh Mehra, “Reconfigurable Design of GSM Digital down Converter for Enhanced Resource Utilization”, International Journal of Computer Applications (0975 – 8887) Volume 57 – No.11, November 2013

6.    Jasmine. S and 2 R.Latha, “Design of Digital Down Converter (DDC) for GSM”, International Journal of Innovative Science, Engineering & Technology, Vol. 2 Issue 10, October 2015.

7.    Sursumel Singh, “Design & Implementation of Digital down Converter for Wcdma System”, International Journal of Engineering Research and Development, Volume 8, Issue 11, PP. 47-50, 2013






Anjali S. Jadhav, Smita. S. Ponde

Paper Title:

Saliency Based Ulcer Detection using Wireless Capsule Endoscopy Diagnosis

Abstract: Ulcer is one of the most common indications of many serious diseases in the human digestive tract. Especially for ulcers in the small intestine where other methods may not display properly, capsule endoscopy (WCE) is increasingly used in the diagnosis and clinical management. Since WCE generates lots of images of the entire inspection process, computer-aided detection ulcer is considered an essential relief for clinicians. In this work, a CAD system is proposed for fully automated computer in two stages to detect images WCE ulcer. In the first step, a detection method based on the effective prominence superpixel multilevel outline representation candidates proposed ulcer. To find the perceptual and semantically meaningful salient regions, the first image segment in multi-level superpixel segmentations. Each level corresponds to different initial sizes of super pixels region. Then the corresponding prominence according to the characteristics of color and texture of each level superpixel region is evaluated. At the end, we merge the salience maps of all levels together to obtain the final saliency map. The experiment results achieved promising accuracy 94.72% 94.63% sensitivity and, validating the effectiveness of the proposed method. Moreover, the results of the comparison show that our detection system outperforms the methods of prior art in the detection task of the ulcer.

 linear-town with limited coding (LLC), multilevel superpixel representation, prominence and the max-sharing method based on prominence.


1.       L. Zhang, Z. GU, and H. Li, “SDSP: A novel saliency detection method by combining simple priors,” in Proc. 20th IEEE Int. Conf. Image Process., 2013, pp. 171–175.
2.       M. Manno, R. Manta, and R. Conigliaro, “Single-balloon enteroscopy,” in Ileoscopy, A. Trecca, Ed. New York: Springer, 2012, pp. 79–85.

3.       R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,” IEEE Trans. Patt. Anal.Mach. Intell., vol. 34, no. 11, pp. 2274–2282, Nov. 2012.

4.       S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 10, pp. 1915–1926, Oct. 2012.

5.       Charisis, L. Hadjileontiadis, and G. Sergiadis, “Enhanced ulcer recognition from capsule endoscopic images using texture analysis,” New Adv. Basic Clin. Gastroenterol, pp. 185–210, 2012.

6.       S. Charisis, L. J. Hadjileontiadis, J. Barroso, and G. D. Sergiadis, “Intrinsic higher-order correlation and lacunarity analysis for WCE based ulcer classification,” in Proc. 25th Int. Symp. IEEE Comput. – Based Med. Syst., 2012, pp. 1–6.

7.       M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu, “Global contrast based salient region detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2011, pp. 409–416.

8.       Al-Rahayfeh and A. A. Abuzneid, “Detection of bleeding in wireless capsule endoscopy images using range ratio color,” Int. J. Multimedia Appl., vol. 2, no. 2, pp. 1–10, 2010.

9.       N. M. Lee and G. M. Eisen, “10 years of capsule endoscopy: An update,” Expert Rev. Gastroenterol. Hepatol, vol. 4, no. 4, pp. 503–512, Aug. 2010

10.    R. Achanta and S. Susstrunk, “Saliency detection using maximum symmetric surround,” in Proc. 17th IEEE Int. Conf. Image Process., 2010, pp. 2653–2656.

11.    R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency tuned salient region detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 1597–1604.

12.    Upchurch and J. Vargo, “Small bowel enteroscopy,” Rev. Gastroenterol. Disorders, vol. 8, no. 3, pp. 169–177, 2008.

13.    L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell, “Sun: A Bayesian framework for saliency using natural statistics,” J. Vision, vol. 8, no. 7, p. 32, 2008.

14.    J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” in Adv. Neural Inform. Process Syst., 2006, pp. 545–552.

15.    Karargyris and N. Bourbakis, “Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos,” IEEE Trans. Biomed Eng., vol. 58, no. 10, pp. 2777–2786, Oct. 2011.






Song-Woo Sok, Young-Woo Jung, Cheol-Hun Lee

Paper Title:

ViMo-S: A Lightweight Hypervisor based on ARM Virtualization Extensions

Abstract:  The ViMo-S, a type 1 hypervisor for ARMv7 and ARMv8-based ARM server systems, supports full virtualization to run existing operating systems and applications unmodified. It uses ARM hardware virtualization extensions to optimize the performance of virtual machines. Therefore, its virtual machines’ system call latency is near physical machine’s, while other hypervisors like Xen and KVM show relatively slower and unstable performances in benchmark tests.

 ARM, Hypervisor, Virtualization.


1.    Dall, C., Li, S. W., Lim, J. T., Nieh, J., and Koloventzos, G. (2016, June) “ARM Virtualization: Performance and Architectural Implications,” In Proceedings of International Symposium on Computer Architecture (ISCA 2016).
2.    Varanasi, P., and Heiser, G. (2011, July). Hardware-supported virtualization on ARM. In Proceedings of the Second Asia-Pacific Workshop on Systems (p. 11). ACM.

3.    Stabellini, S., and Campbell, I. (2012). Xen on arm cortex a15. Xen Summit North America, 2012.

4.    Dall, C., and Nieh, J. (2014, February). KVM/ARM: the design and implementation of the Linux ARM hypervisor. In ACM SIGPLAN Notices (Vol. 49, No. 4, pp. 333-348). ACM.

5.    Oh, S. C., Kim, K., Koh, K., and Ahn, C. W. (2010). ViMo (virtualization for mobile): a virtual machine monitor supporting full virtualization for ARM mobile systems. In Proceedings of Advanced Cognitive Technologies and Applications, COGNITIVE.

6.    McVoy, L. W., & Staelin, C. (1996, January). lmbench: Portable Tools for Performance Analysis. In USENIX annual technical conference (pp. 279-294).




Volume-6 Issue-6

Download Abstract Book

S. No

Volume-6 Issue-6, January 2017, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.



Prajakta G. Kulkarni, Rubeena A. Khan

Paper Title:

Parallel Mining of Frequent Item sets using Map Reduce Technique: A Survey

Abstract: In the task of data mining, the most important job is to find out frequent itemsets. Frequent itemsets are useful in various applications like Association rules and correlations. These systems are using some algorithms to find out frequent itemsets. But these parallel mining algorithms lack some features like automatic parallelization, well balancing the load, distribution of data on large number of clusters. So there is a need to study the parallel algorithms which will overcome the disadvantages of the existing system. In this paper a technique called fidoop is implemented, In this technique the mappers work independently as well as concurrently. This is done by decomposing the data across the mappers. Reducers work is to combine these jobs by developing small ultra metric trees. To show this fidoop technique on the various clusters is very delicate in distribution of data because different datasets are with different partition of data. This fidoop technique is also useful in heterogeneous clusters[16].

Frequent item sets, mappers, reducers, Ultrametric trees, FiDoop.


1. R. Agrawal, T. Imieli´nski, and A. Swami, “Mining association rules between sets of items in large databases,” ACM SIGMOD Rec., vol. 22,no. 2, pp. 207–216, 1993.
2. M.-Y. Lin, P.-Y. Lee, and S.-C. Hsueh, “Apriori-based frequent itemset mining algorithms on MapReduce,” in Proc. 6th Int. Conf. Ubiquit. Inf. Manage. Commun. (ICUIMC), Danang, Vietnam, 2012, pp. 76:1–76:8. [Online]. Available: http://doi.acm.org/10.1145/2184751.2184842L. Zhou et al., “Balanced parallel FP-growth with MapReduce,” in Proc. IEEE Youth Conf. Inf. Comput. Telecommun. (YC-ICT), Beijing, China, 2010, pp. 243–246

3. L. Zhou et al., “Balanced parallel FP-growth with MapReduce,” in Proc. IEEE Youth Conf. Inf. Comput. Telecommun. (YC-ICT), Beijing, China, 2010, pp.243–246.

4. Y.-J. Tsay, T.-J. Hsu, and J.-R. Yu, “FIUT: A new method for mining frequent itemsets,” Inf. Sci., vol. 179, no. 11, pp. 1724–1737, 2009.

5. Kiran Chavan, Priyanka Kulkarni, Pooja Ghodekar, S. N. Patil,” Frequent itemset mining for Big data ”, IEEE,Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on Year: 2015 ,Pages: 1365 – 1368, DOI: 10.1109/ICGCIoT.2015.7380679

6. M. Riondato, J. A. DeBrabant, R. Fonseca, and E. Upfal, “PARMA:A parallel randomized algorithm for approximate association rules mining in MapReduce,” in Proc. 21st ACM Int. Conf. Inf. Knowl. Manage.,Maui, HI, USA, 2012, pp. 85–94.

7. Wei Lu,Yanyan Shen,Su Chen,Beng Chin Ooi,“Efficient Processing of k Nearest Neighbor Joins using MapReduce”2012 VLDB Endowment 2150-8097/12/06

8. Shekhar Gupta, Christian Fritz, Johan de Kleer, and Cees Witteveen, “Diagnosing Heterogeneous Hadoop Clusters”

9. Yi Yao, Jiayin Wang, Bo Sheng, Chiu C. Tan, Ningfang Mi, “Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters ”

10. L. Cristofor. (2001). Artool Project[J]. [Online]. Available: http://www.cs.umb.edu/laur/ARtool/, accessed Oct. 19, 2012.

11. J. Dean and S. Ghemawat, “MapReduce: A flexible data processing tool,” Commun. ACM, vol. 53, no. 1, pp. 72–77, Jan. 2010.

12. https://www.tutorialspoint.com/map_reduce/map_reduce_tutorial.pdf

13. https://www.tutorialspoint.com/hadoop/hadoop_introduction.htm

14. https://en.wikipedia.org/wiki/Association_rule_learning

15. https://www.google.co.in/search?q=ultrametric+tree&client=ubuntu&channel=fs&biw=1315&bih=673&tbm=isch&imgil=CttqmLzrkUFM6M%


16. Yaling Xun, Jifu Zhang, and Xiao Qin,” FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce ” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, VOL. 46, NO. 3, MARCH 2016

17. Ramakrishnudu, T, and R B V Subramanyam.”Mining Interesting Infrequent Itemsets from Very Large Data based on MapReduce Framework”, International Journal of Intelligent Systems and Applications, 2015.

18. Bechini, Alessio, Francesco Marcelloni, and Armando Segatori. “A MapReduce solution for associative classification of big data”,Information Sciences, 2016.

19. Yun Lu, , Mingjin Zhang, Shonda Witherspoon,Yelena Yesha, Yaacov Yesha, and Naphtali Rishe. “SksOpen: Efficient Indexing, Querying, and Visualization of Geo-spatial Big Data”, 2013 12th International Conference on Machine Learning and Applications, 2013.

20. He Lijun. “Comparison and Analysis of Algorithms for Association Rules”, 2009 First International Workshop on Database Technology and Applications, 04/2009

21. vldb.org

22. http://slideplayer.com/slide/5769249/






Abulameer Khalaf Hussain, Muthnna Abdulwahid Khudhair

Paper Title:

A New Security Level Oriented Multisignature Scheme

Abstract: This paper presents a new multisignature scheme. The idea behind this scheme is that all authenticated users in the system are classified according to their security levels . Each level has its own trusted group manager. To generate the signature, the proposed system selects one of these levels .Each level consists of a group of users . Each user has its own private and public keys. In addition, this scheme implements the cascade encryption for the generated signature, and thus it is necessary to perform the cascade encryption to use a global private key for each level. The system also assigns trustworthiness score for each user to select the proper one to sign on the behalf of his/her group in that level. Finally, the generated multisignure is proved to be more secure and thus it can be used in sensitive applications.

Digital Signature, multisignature system, Security Levels, Multi-level proxy signature.


1. K. Itakura and K. Nakamura, “A public-key cryptosystem suitable for digital multisignature”, NEC Research and Development, Vol. 71, October 1983, pp. 1-8.
2. S. Micali, K. Ohta and L. Reyzin, Accountable-subgroup multisignatures: extended abstract”, Proceedings of the ACM Conference on Computer and Communications Security 2001 (CCS 2001), ACM press, 2001, pp. 245-254.

3. K.R.P.H. Leung and L.C.K. Hui, “Signature management in workflow systems”, Proceedings of the 23rd Annual International Computer Software and Applications Conference (COMPSAC’99), IEEE, 1999, pp. 424-429.

4. K.R.P.H. Leung and L.C.K. Hui, “Handling signature purposes in workflow systems”, The Journal of Systems and Software, Vol. 55, 2001, pp. 245-259.

5. S. Mitomi and A. Miyaji, “A Multisignature Scheme with Message Flexibility, Order Flexibility and Order Verifiability”, Proceedings of the 5th Australasian Conference on Information Security and Privacy (ACISP 2000), Spring-Verlag, 2000, pp. 298-312.

6. P. Kotzanikolaou, M. Burmester and V. Chrissikopoulos, “Dynamic multi-signatures for secure autonomous agents”, Proceedings 12th International Workshop on Database and Expert Systems Applications (DEXA 2001), IEEE Computer Society, 2001, pp. 587–591.

7. C.J. Mitchell and N. Hur, “On the security of a structural proven signer ordering multisignature scheme”, in: B. Jerman-Blazic and T. Klobucar (eds.), Proceedings of the IFIP TC6/TC11 Sixth Joint Working Conference on Communications and Multimedia Security (CMS 2002), Kluwer Academic Publishers (IFIP Conference Proceedings
228), Boston, 2002, pp.1-8.

8. D. Boneh, H. Shacham and B Lynn, “Short signatures from the Weil pairing”, Advances in Cryptology – AISACRYPT 2001, Springer-Verlag, 2001, pp. 514-532.

9. K. Itankura and K. Nakamura, “A public-key crptosysterm suitable for digital multisignatures”, NEC J. Res. & Dev., vol. 71, (1983).

10. M. Bellare and G. Neven, “Identity-Based Multisignatures from RSA”, In CT-RSA, 2007, LNCS p. 4377, (2007).

11. C. Gentry and Z. Ramzan, “Identity-Based Aggregate Signatures”, In PKC 2006, LNCS, 3958, (2006).

12. D. Boneh, C. Gentry, B. Lynn and H. Shacham, “Aggregate and verifiably encrypted signatures from bilinear maps”, In Proceedings of Euro-crypt 2003, LNCS, 2656, (2003).

13. Haitner, J. J. Hoch, O. Reingold and G. Segev, “Finding Collisions in Interactive Proto-cols – A Tight Lower Bound on the Round Complexity of Statistically-Hiding Commitments”, (2008).

14. K. Ohta and T. Okamoto, “Multisignature schemes secure against active insiderattacks”, IEICE Trans. Fundamentals, E82-A, vol. 1, 1999.

15. W,Lihua , O.Eiji, M.Ying, O.Takeshi,and D.Hiroshi,” ID-Based series-parallel multisignature schemes for multi-messages from bilinear maps” Proceeding of WCC’05 Proceedings of the 2005 international conference on Coding and Cryptography , Pages 291-303 ,Springer-Verlag Berlin, Heidelberg ,2006 .

16. C. WEI , and J. ZHANG, “ Multi-level proxy signature scheme based on strong Diffie-Hellman assumption “, Computer Engineering and Applications, 2008.

17. S. Rahul, R. C. Hansdah , “ Multisignature Scheme for Implementing Safe Delivery Rule in Group Communication Systems “ , Chapter Distributed Computing – IWDC 2004, Volume 3326 of the series Lecture Notes in Computer Science pp 231-239, 2004.

18. T. Jia-lun , W. Tzong ,and T . Kuo-yu ,”A novel multisignature scheme for a special verifier group against clerk and rogue-key attacks” , Journal of Zhejiang University SCIENCE C April 2010, Volume 11, Issue 4, pp 290–295, 2010.

19. Y. Han-u , W. Tzong L. Ming-Lun, and Y. Chi-Kuang ,” New Efficient Identity-Based Key-Insulated Multisignature Scheme”,International Journal of Machine Learning and Computing, Vol. 3, No. 1, February 2013.

20. L. Harn, C.-Y. Lin and T.C. Wu , “ Structured multisignature algorithms”, IEE Proceedings online no. 20040247 , IEE Proc.-Comput. Digit. Tech., Vol. 151, No. 3, May 2004.

21. K. Kei , M. KawauchiHiroshi , and T. MiyajiMitsuru,” A Multi-signature Scheme with Signers’ Intentions Secure against Active Attacks”, Proceeding ICISC ’01 Proceedings of the 4th International Conference Seoul on Information Security and Cryptology , Pages 328-340 , December 06 – 07, 2001.

22. C. Shenjun , and W. Fengton , “ A New ID-based Designated Verifier Proxy Multi-Signature Scheme” , International Journal of Computer Theory and Engineering, Vol. 3, No. 2, ISSN: 1793-8201, 2011.






Neelesh Chourasiya, Nirmiti Pawar, Kiran Patil, Swapnali Tiwari, Snehal Mangale

Paper Title:

Security in Cloud Storage using Data Shuffling and Data Self Destruction

Abstract: Cloud computing is the most popular technology today. It is used by most of the social media sites to store the data. In cloud storage, the data uploaded by the user is prone to various strong attacks and can be easily hacked. Data stored in a cloud by the user is private so it must not be tampered by any other entities. We propose a system to enhance the security. The data uploaded by the user is shuffled between a numbers of directories within cloud after a particular interval of time to avoid tracking of data. The backup of the data will be taken timely into the backup directory. The proposed system enhances the system security as well as the ease to use the cloud using

Cloud storage, data shuffling, data de-duplication, self-destruction, encryption algorithms.

1. X. Fu, Z. Wang, H. Wu, J. qi Yang, and Z. zhao Wang, “How to send a self-destructing email: A method of self-destructing email system,” in Prof. of the IEEE International Congress on Big Data, 2014, pp.304–309.
2. R. Lu, H. Zhu, X. Liu, J. Liu, and J. Shao, “Toward efficient and privacy preserving computing in big data era,” IEEE Network, vol. 28, no. 4,pp. 46–50.

3. M. Arafati, G. G. Dagher, B. C. M. Fung, and P. C. K. Hung, “Dmash: A framework for privacy-preserving data-as-a-service mashups,” in Proc. of the 8th IEEE
International Conference on Cloud Computing (CLOUD), 2014.

4. R. Geambasu, T. Kohno, A. Levy, and H. M. Levy, “Vanish: Increasing data privacy with self-destructing data,” in Proc. of the USENIX Security Symposium, Montreal, Canada, August 2009, pp. 299–315.

5. Lingfang Zeng , Yang Wang , and Dan Feng , “CloudSky: A Controllable Data Self-Destruction System for Untrusted Cloud Storage Networks” ,School of Computer, Huazhong University of Science and Technology ,IBM Center for Advanced Studies (CAS Atlantic) University of New Brunswick






Sabah Shehd Abdulabas

Paper Title:

Apparent Thermal Conductivity Digital Meter In Trains as an Energy – Conserving Measure

Abstract: Energy conserving measure is one of important factors that must be measured in constructions of building, an energy conserving measure leads to the practical thickness of the proper used insolent, the function of both the temperatures of the bounding surfaces and the thickness of the air layer are considered the apparent thermal conductivity of the air layer due to the combined modes of conduction and radiation which increases with the thickness. This paper introduces the design and implementation of an electronic digital meter suitable for the measurement of the apparent thermal conductivity and has been tested in eight departments of institute of technology/ Baghdad in four labs.

Energy, constructions, implementation, temperatures.


1. W.j. Batty, S.D. Prober and j.W. Lane, “Convection and Radiation in Layers of low Density Fibrous Insulants”, Applied Energy, 18(1984)
2. EMSD.”Code of Practice for Energy Efficiency of Building Services Installation”, Electrical and Mechanical Services Department, 2015.

3. BSR,”Energy Management Handbook”,2012.

4. High Temperatures ^ High Pressures”Measurements of comparative apparent thermal conductivity of large monolithic silica aerogels for transparent superinsulation applications”, 2002, volume 34, pages 549 ^ 559.

5. Ladevie B, Batsale J C, Fudym O, 1999 Int. Commun. Heat Mass Transf. 27 473 ^ 484.

6. Manohar, Krishpersad and David W. Yarbrough, “A Comparison of Biodegradable Fiber Insulation with Conventional Insulation”, Proc. of the International Conference on Thermal Insulation 18 133-140 (2003)

7. Manohar, K., D.W. Yarbrough, and G.S. Kochhar, “Building Thermal Insulation-Biodegradable Sugarcane and Coconut Fiber,” J. of Thermal Envelope and Building Science 23 (3) 263-276 (2000).

8. Olivier, Paul A., “The Rice Hull House”, Engineering, Separation, & Recycling, Washington, LA (2003).






Kirandeep, Harish Kundra

Paper Title:

Ancient Degraded Document/Image Restoration using Hybrid Intelligent Water Droplets Algorithm and Sauvola Thresholding Technique

Abstract: A historical document that have been affected by degradation and that are of poor image quality is difficult and continues to be a focus of research in the field of image processing. So, there is the need of image restoration techniques that can improve the visibility for the human eye to directly read these documents. Document image restoration aims to improve the document image quality by reducing the noise level, which not only enhance human perception, but also facilitate the subsequent automated image processing. In this research work, we are using hybrid approach of swarm intelligence based Intelligent Water Drops Algorithm (IWD) and Sauvola Binarisation method. IWD is a nature inspired optimization algorithm that work as per the moving water droplets with soil particle obstacles in their path. Sauvola’s algorithm is an improvement of Niblack’s method which is based on the local mean and standard deviation of the image. Sauvola’s approach computes the threshold value by using the dynamic range of gray-value standard deviation. The obtained results are compared with the Sauvola, Niblack, Wolf, M1-S, M2-N, M3-W algorithms. The results are also evaluated in parametric form with PSNR and F-Measure values.

Intelligent Water Drops Algorithm, Niblack Method, Sauvola Method, Image Enhancement, Ancient Documents.


1. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Education India.
2. Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285.

3. de Albuquerque, M. P., Esquef, I. A., & Mello, A. G. (2004). Image thresholding using Tsallis entropy. Pattern Recognition Letters, 25(9), 1059-1065.

4. Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern recognition, 19(1), 41-47.

5. Shah-Hosseini, H. (2007, September). Problem solving by intelligent water drops. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (pp. 3226-3231). IEEE.

6. Shah-Hosseini, H. (2009). The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation, 1(1), 71-79.

7. Salmanpour, S., Omranpour, H., & Motameni, H. (2013, November). An intelligent water drops algorithm for solving robot path planning problem. In Computational Intelligence and Informatics (CINTI), 2013 IEEE 14th International Symposium on (pp. 333-338). IEEE.

8. Kamkar, I., Akbarzadeh-T, M. R., & Yaghoobi, M. (2010, October). Intelligent water drops a new optimization algorithm for solving the vehicle routing problem. In Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on (pp. 4142-4146). IEEE.

9. Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184.

10. Shah-Hosseini, H. (2008). Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem. International Journal of Intelligent Computing and Cybernetics, 1(2), 193-212.

11. Sehad, A., Chibani, Y., & Cheriet, M. (2014, September). Gabor Filters for Degraded Document Image Binarization. In Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on (pp. 702-707). IEEE.






Divya C. Patil, Pravin S. Patil

Paper Title:

Abandoned Object Detection with Region of Interest

Abstract: Abandoned object detection is an essential requirement in many video surveillance contexts. In this paper, we propose a method to detect abandoned object from surveillance video. Different from conventional approaches that mostly rely on pixel-level processing, we perform region-level analysis in both background maintenance and static foreground object detection. In background maintenance, region-level information is fed back to adaptively control the learning rate. In static foreground object detection, region-level analysis double-checks the validity of candidate abandoned blobs. Different from conventional approaches that mostly rely on pixel-level processing, we perform region-level analysis. In this paper, we present an abandoned object detection system based on blob detection methods are aimed at detecting regions. In a digital image that differs in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant. All the points in a blob can be considered in some sense to be similar to each other. In this paper we are performing a real time application using the Raspberry Pi processor and a Raspberry Pi camera.

Abandoned object, Video surveillance, Framing, Image, Pixels.


1. J. Martínez-del-Rincón, J. E. Herrero-Jaraba, J. R. Gómez, and C. Orrite-Urunuela, “Automatic left luggage detection and tracking using multi-camera UKF,” in Proc. IEEE 9th IEEE Int. Workshop PETS, Jun. 2006, pp. 59–66.
2. F. Porikli, Y. Ivanov, and T. Haga, “Robust abandoned object detection using dual foregrounds,” EURASIP J. Adv. Signal Process., vol. 2008, Jan. 2008, Art. ID 30.

3. R. H. Evangelio, T. Senst, and T. Sikora, “Detection of static objects for the task of video surveillance,” in Proc. IEEE WACV, Jan. 2011, pp. 534–540.

4. Y. Tian, R. S. Feris, H. Liu, A. Hampapur, and M.-T. Sun, “Robust detection of abandoned and removed objects in complex surveillance videos,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 5, pp. 565–576, Sep. 2011.

5. Q. Fan and S. Pankanti, “Modeling of temporarily static objects for robust abandoned object detection in urban surveillance,” in Proc. 8th IEEE Int. Conf. AVSS, Aug./Sep. 2011, pp. 36–41.

6. Q. Fan, P. Gabbur, and S. Pankanti, “Relative attributes for largescale abandoned object detection,” in Proc. IEEE ICCV, Dec. 2013, pp. 2736–2743.

7. H.-H. Liao, J.-Y. Chang, and L.-G. Chen, “A localized approach to abandoned luggage detection with foreground-mask sampling,” in Proc. IEEE 5th Int. Conf. AVSS, Sep. 2008, pp. 132–139.

8. J. Pan, Q. Fan, and S. Pankanti, “Robust abandoned object detection using region-level analysis,” in Proc. 18th IEEE ICIP, Sep. 2011, pp. 3597–3600.

9. F. Lv, X. Song, B. Wu, V. K. Singh, and R. Nevatia, “Left-luggage detection using Bayesian inference,” in Proc. IEEE Int. Workshop PETS, 2006, pp. 83–90.

10. L. Li, R. Luo, R. Ma, W. Huang, and K. Leman, “Evaluation of an IVS system for abandoned object detection on PETS 2006 datasets,” in Proc. IEEE Workshop PETS, 2006, pp. 91–98.

11. E. Auvinet, E. Grossmann, C. Rougier, M. Dahmane, and J. Meunier, “Left-luggage detection using homographies and simple heuristics,” in Proc. 9th IEEE Int. Workshop PETS, 2006, pp. 51–58.

12. C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Comput. Soc. Conf. CVPR, vol. 2. Jun. 1999, pp. 246–252.

13. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Realtime foreground-background segmentation using codebook model,” Real-Time Imag., vol. 11, no. 3, pp. 172–185, 2005.

14. Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction,” in Proc. 17th ICPR, 2004, pp. 28–31.

15. Y.-T. Chen, C.-S. Chen, C.-R. Huang, and Y.-P. Hung, “Efficient hierarchical method for background subtraction,” Pattern Recognit., vol. 40, no. 10, pp. 2706–2715, 2007.

16. PETS 2006 Dataset. [Online]. Available: http://www.cvg.reading.ac.uk/ PETS2006/data.html, accessed Mar. 17, 2015.