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Volume-7 Issue-2, May 2017, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

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Priyanka B Karande, Rupali S Kumbhar, Priydarshanee A Pawale, Ajinkya. C. Bapat

Paper Title:

Identifying Efficient Frequency Standards of Wireless Network

Abstract:  For encouraging wireless network contineous improvement is important. this is done by comparing related protocol and resulting the efficient protocol. This paper shows the overview of IEEE 802.15.4 (x-bee), 802.11(wifi), 802.16 (wimax) and carefully observed the comparision between them on the basis of Throughput, PDR, Delay and energy through simulation on NS2s. On the basis of observed results, this paper proved the Efficient Standards among xbee wifi & wimax.

Throughput ,End to end delay,Power consumption,Packet delivery ratio, NS2


1.    Marina Petrova, Janne Riihij arvi,Petri Mahonen and Saverio Labella WTH Aachen University, Germany, “Performance Study of IEEE802.15.4 Using Measurements and Simulations”
2.    "ZigBee Wireless Networking", Drew Gislason (via EETimes)IEEE P802.15.4/D18,”Draft Standard: Low Rate Wireless Personal”Area Networks, Feb. 2003.

3.    Ms.Swati.V.Birje,Mr.Mahesh.S.Kumbhar,Mr.Raviraj.S.Patkar “Performance comparision of 802.11 and 802.15.4 based networks” International Journal of Advanced Research in Computer and Communication Engineering.

4.    Introduction to wifi technology, retrived on September 24, 2006 from
5.    IEEE 802.16 and wimax;broadband wireless access for every one, Intel Corporation 2003, http://www.intel.com/ebuisness/pdf/wireless/intel/802.16_wimax.pdf

6.    A.Wiling,”An architecture for wireless extension of profibus,”in proc.IEEE Int.conf.Ind.Electron.(IECON’03) ,Ronoku,VA, Nov2003,

7.    Chan,H.Anthony. “ssoverview of Wireless data network standards and there implementation issues.”talk presented at the 12th ICT Cape Town(2005)

8.    Morrow, R.”Wireless network coexistence”,McGraw-Hill:New York,NY(2004).

9.    Conexaxant,single-chip WLAN Radio CX53111.New port beach,CA,2006.

10. E.Ferro and F.Potorti, “Bluetooth and Wifi wireless protocols:A survey and a comparision,”IEEE wireless communication ,vol.12,no.1,pp.12-16,feb2005

11. J.S.Lee, “performance evolution of IEEE 802.15.4 for low rate wireless sspersonal area network,”IEEE trans.consumer electron,vol.52.no.3,pp.742-749,august2006






Shagufta Praveen, Umesh Chandra

Paper Title:

A Comparative Study On: Nosql, Newsql and Polygot Persistence

Abstract: After a long journey of decades, most of the leading web applications opted for non-relational database. Traditional database exist for so long but data mining application doesn’t find relational database as a right choice for it. NoSQL movement was a question mark for the future of SQL. The High Volume, rich heterogeneity and speedy velocity of data generation in entire world is responsible for the Big Data. NoSQL was introduced to us for resolving scalability issues but consistency issue after scalability moved us from NoSQL to NewSQL. This paper emphasizes about NoSQL and NewSQL and it also highlights the reason for recent arrival of Polygot Persistence. Both technologies are distinguished with the help of some parameters (Models, Properties and as per Current Scenario need).

 Big Data, Database, Polygot Persistence, NewSQL, NoSQL.


1.       S. J. Veloso, 2015, Data Analytics Topic: Big Data [Online]. Available:http://community.mis.temple.edu/sjveloso/data-analytics-topic-big-data/
2.       U.Banerjee,21 december 2012, Technology Trend Analysis[Online].Available:https://setandbma.wordpress.com/2012/12/21/definition-of-big-data/

3.       [Online].Available:https://en.wikipedia.org/wiki/NoSQL

4.       V. Sharma and M. Dave ,SQL and NoSQL Database ,Internation Journal of advance research in computer science and software engineering,2012

5.       Jose J, Subramoni H, Miao L, Minjia Z, Jian H, Wasiur M. Memcached design on high performance RDMA capable interconnects[C]. Parallel Processing(ICPP), 2011 IEEE International Conference on:743-752.

6.       R. Hetch and S. jablonski ,NoSQL evaluation: A use case oriented survey, proceeding CSC’11 Proceedings of the 2011 International conference cloud and Service computing, 2011

7.       C. He,Survey on NoSQL Database technology ,JOURNAL OF APPLIED SCIENCE AND ENGINEERING INNOVATION,2015

8.       Grolinger et.al.,Database management in cloud environment: NoSQL and NewSQL DataStore, Journal of Cloud computing, Advances, system and application 2013.

9.       [Online].Available:http://natishalom.typepad.com/nati_shaloms_blog/2009/12/the-common-principles-behind-the-nosql-alternatives.html

10.    Pavlo and M. Asslett What’s really new with NewSQL by, SIGMOD Record,June 2016

11.    Google-Launches Cloud –spanner-A newSQL databse for enterpeise by Jankiran MSC

12.    [Online].Available:www.technopedia.com/29093/newsql

13.    [Online].Available:http://www.jamesserra.com/archive/2015/07/what-is-polyglot-persistence/

14.    [Online].Available:databasemanagement.wikia.com/wiki/Concurrency_Control

15.    ABM Moniruzzamam , New SQL:Towards Next Generation Scalable RDBMS for Online Transaction processing for Big Data Management,2016

16.    S. Praveen et.al., A literature review on evolving database, International journal of computer Application, 2017

17.    J.M. Monterio et.al ,What comes after NoSQL? NewSQL: A New Era of Challenges in DBMS Scalable Data Processing ,2016






Snehal S Awasare, Pratiksha K Chavan, Shital S Patil, Ajinkya C Bapat

Paper Title:

ATS: A New Way To Deal With Security of Public Places

Abstract: With the rising concern of the security at public places it is essential to find a solution to this issue.CCTV cameras only captures the movement and we need to monitor that continuously. Therefore it is necessary to design a system which can invigilate and traced out the suspicious object in real time without any human efforts. This paper is proposing an idea to develop a system which could find a threatening object and alert the security agencies about it. The proposed system will also have provision of IoT with an effective cryptographic technique to ensure the authenticity. A technologically improved system will surely boost up the security at public places over the traditional system.

public place security, Image processing, IoT, Cryptography..


1.       Quanfu Fan; Prasad Gabbur; SharathPankanti“ Relative attributes for large scale abandoned object detection”Computer vision(ICCV), 2013 IEEE International Conference
2.       Hemangi  R. Patil , Prof. K. S. Bhagat “ detection and Tracking of moving object: A Survey” .Department of Electronics and Telecommunications, J.T.Mahajan College of engg. North Maharashtra.

3.       Chih-Hsien, Ding-Wei Huang, Jen-ShiunChiang and zong-Jheng Wu, “ MovingObject Tracking using Symmetric Mask-Based Scheme”, 2009 IEEE FifthInternational conference on InformationAssurance and Security.

4.       Swati Thorat, ManojNagmode, “Detectionand Tracking of Moving Objects,”International Journal of Innovative Research

5.       in Advanced Engineering (IJIRAE), Volume1, Issue 1 (April 2014).

6.       Mrinali M. Bhajibhakare, Pradeep K.Deshmukh, “Detection and Tracking ofMoving Object for Surveillance System,”International Journal of Application orInnovation in Engineering & Management (IJAIEM) Volume 2, Issue 12, December2013.

7.       Ajinkya C. Bapat, S. U. Nimbhorkar,” RFID Based Object Tracking System Using Collaborative Security Protocol” IJESC Vol 4107.

8.       MamtaSood, Rajeev Sharma, ChavanDipakKumar D, “Motion Human Detection andTracking Based on Background

9.       Subtraction,” International Journal ofEngineering Inventions e-ISSN: 2278-7461,p- ISSN: 2319-6491 Volume 2, Issue 6(April 2013) PP: 34-37.

10.    Saeidbagheri-golzar, FaribaKaramisorkhechaghaei, Amir-MasudEftekhari- Moghadam,” A New Method for Video Object Tracking,” The Journal of Mathematics and Computer Science Vol 4 No. 2 (2012) 120-128.

11.    Kuihe Yang, ZhimingCai, Lingling Zhao, “Algorithm Research on Moving ObjectDetection of Surveillance Video Sequence,”

12.    Optics and Photonics Journal, 2013, 3, 308-312.

13.    Himani S. Parekh, Darshak G. Thakore,Udesang K. Jaliya, “ A Survey on ObjectDetection and Tracking Methods,”International Journal of Innovative Researchin Computer and Communication

14.    Engineering, Vol. 2, Issue 2, February 2014

15.    Priyanka S. Bhawale, Ruhi R. Kabra,“Object Detection and Motion BasedTracking of Moving Objects a Survey,”

16.    International Journal of Advance Researchin Computer Science and ManagementStudies, Volume 2, Issue 12, December2014

17.    Rajesh Kumar Tripathi, Anand Singh Jalal, CharulBhatnagar“ A framework for abandoned object detection from video surveillance” , computer vision pattern recognition,image processing and graphics,2013 fourth national confereance.

18.    Eric Jardim, Xiao Bian , Eduardo A.B. da Silva , Sergio L.Netto, Hamid Krim “ On the Detection of Abandoned Objects a moving camera using robust subspace recovery and sparse representation” 2015 IEEE International conference.

19.    Diego Ortego, Juan C. SanMiguel, and Jose M. Martinez “Long-Term Stationary Objet Detection Based On Spatio-Temporal Change Detection” IEEE Signal Processing Letters, VOL.22, NO.12,December 2015

20.    Xuli Li; Chao Zhang; Duo Zhang;”Abondoned object detection using dMMouble illumination invariant foreground masks”

21.    JinhuiLan, Yaoliang Jiang, Guoliang Fan, DongyangYu,QiZhang”Real time automatic obstacle detection method for traffic surveillance in urbon traffic”, Journal of Signal Processing System, March 2016

22.    Ajinkya C Bapat, Sonali U Nimbhorakar“ Designing RFID based object tracking system by multilevel security” IEEE WiSPNET, March 2016

23.    Ajinkya C Bapat, Sonali U Nimbhorakar “Multilevel Secure RFID based object tracking system” ICISP Procedia Computer science 78,336-341,2016






R.V. Patil, Aishwarya Bhosale, Ramdas Choramale, Shiwani Tummulwar, Vaibhav Rajguru

Paper Title:

Authentication and Encryption Based Cloud Data Access Privilege with Load Balancing Technique

Abstract: Cloud computing is a booming computing branch in which consists of a virtualized set of highly scalable computing resources and provided as an internet based computing where many users upload, download and modify data with cloud users. Problems in cloud computing are sharing data in a multi users, while data preservation and privacy of identity from a non-trustable cloud is still a challenge, due to the frequent change of the members of cloud. By allowing group signature and encryption techniques, any cloud user can anonymously share data with others. The main is to provide secure multi-owner data sharing in large groups. This poses a security challenge to the data stored on the cloud. As the result, the encryption cost is reduced; storage overhead and scheme are not dependent on the number of removed users with proof and experiments

 Cloud, Server, Encryption, Decryption, Anonymity, Shared authority.


1.    Taeho Jung, Xiang-Yang Li, Senior Member, IEEE, Zhiguo Wan, and Meng Wan, Member, IEEE, “Control Cloud Data Access Privilege and Anonymity with fully Anonymous attribute based encryption” IEEE transactions on information forensics and security, vol. 10, no. 1, January 2015
2.    Hong Liu, Student Member, IEEE, Huansheng Ning, Senior Member, IEEE, Qingxu Xiong, Member, IEEE, and Laurence T. Yang, Member, IEEE “Shared Authority Based Privacy preserving Authentication Protocol in Cloud Computing” IEEE transactions on parallel and distributed systems  VOL:PP NO:99 year 2014

3.    Suchita Khare, Abhishek Chauhan, “Balancing model based   on cloud Partitioning for public cloud” Department of Computer Science, NRIIST, Bhopal, India, 7 July 2014

4.    Mayanka Katyal, Atul Mishra, “A comparative study of Load Balancing Algorithms in Cloud Computing” volume 1 issue 2 December 2013.

5.    C. Selvakumar, G. Jeeva Rathanam, M.R. Sumalatha, “Improving Cloud Data Storage Security Using Data Partitioning Technique” Department of Information Technology MIT Campus, Anna University Chennai, Tamil Nadu, Indian, 2013 3rd IEEE International Advance Computing Conference (IACC)

6.    Karen D. Devine, Erik G. Boman, Robert T. Heaphy, Bruce A. Hendrickson, “New Challenges in Dynamic Load Balancing”

7.    SuchitaKhare, Abhishek Chauhan, “A Review on Load Balancing Model Based on Cloud Partitioning for the Public Cloud” Department of Computer Science, NRIIST, Bhopal, India, July 2014






N. Dhanasekar, R. Kayalvizhi

Paper Title:

Hardware Implementation of Fuzzy Logic Controller for Triple-Lift Luo Converter

Abstract:  Positive output Luo converters are a series of new DC-DC step-up (boost) converters, which were developed from prototypes using voltage lift technique. These converters perform positive to positive DC-DC voltage increasing conversion with high power density, high efficiency and cheap topology in simple structure. They are different from other existing DC-DC step-up converters with a high output voltage and small ripples. Triple lift LUO circuit is derived from positive output elementary Luo converter by adding the lift circuit three times. Due to the time varying and switching nature of the Luo converters, their dynamic behavior becomes highly non-linear.The classical control methods employed to design the controllers for Luo converters depend on the operating point so that it is very difficult to select control parameters because of  the presence of parasitic elements, time varying loads and variable supply voltages. Conventional controllers require a good knowledge of the system and accurate tuning in order to obtain the desired performances. A Fuzzy Logic Controller (FLC) is a soft computing technique which neither requires a precise mathematical model of the system nor complex computations. Hence in this research work, design and hardware implementation of fuzzy logic controller have been carried out using TMS320C242 DSP for the Triple-lift Luo converter .The experimental results are presented and analyzed under line and load disturbances.

Fuzzy Logic Controller, Triple-lift Luo converter, Digital Signal Processor (DSP).


1.    F.L, Luo “Positive output Luo-converter lift technique”, IEE-EPA/proceedings, 146(4), pp.415-432, July 1999
2.    F.L, Luo. “Luo converters - Voltage lift technique” Proceedings of the IEEE Power Electronics Special Conference IEEE - PESC' 98. Fukuoka Japan, pp. 1783-1789, May 1998.

3.    R. Kayalvizhi, S.P. Natarajan, V. Kavitharajan and R.Vijayarajeswaran,“TMS320F2407 DSP Based Fuzzy Logic Controller for Negative Output Luo Re-Lift Converter: Design, Simulation and Experimental Evaluation” IEEE Proceedings of Power Electronics and Drive systems, pp. 1228-1233. Dec 2005.

4.    N.F.Nik Ismail, N. Hasim and R.Baharom, “A comparative study of proportional integral derivative controller and    fuzzy logic controller on DC/DC Buck Boost converter”, IEEE symposium on industrial Electronics and Applications(ISIEA), Langkwi, pp.149-154, Sep.2011.

5.    B.Achiammal and R.Kayalvizhi “ Hardware implementation of optimized PI controller for LUO converter”, International Journal of Applied Engineering Research(IJAER),Volume 10, no 14,pp.34899-34905,2015.

6.    Liping Guo,John Y.Hung and R.M. Nelms, “Evaluation of DSP-based PID-Fuzzy controller for DC-DC converter ”, IEEE Transaction on Industrial Electronics, Vol 56,no.6,June 2009.






Parminder Singh, Amarjit Kaur

Paper Title:

Enhance Decision Tree Techniques on Mobile Environment in Data Mining

Abstract: There are several techniques that are used in data mining, each one having advantages but also disadvantages. To find out which one is most appropriate for our case, when we want to use our databases in a decision-make process we need to have information about our data business and data mining techniques. Alternatively we can try them all and find out which one is the best in our case. This research is based on the findings maximum use of mobile service. The results in this report are based on data from mobile service related. As we look at Data Mining tools, we see that there are different algorithms used for creating a decision making (or predictive analysis) system. There are algorithms for creating decision trees such as ID3 and CART along with algorithms for determining known nearest neighbor or clustering when working on classification. The goal of this research is to look at one particular decision tree algorithm called enhanced algorithm and how it can be used with data mining for mobile service. The purpose is to manipulate vast amounts of data and transform it into information that can be used to make a decision.

 Techniques, Advantages, Appropriate (or predictive analysis), CART, ID3, Alternatively


1.    Margaret H.Dunham,”Data Mining Introductory and Advanced topic”, published by person education Delhi, India,[2004].
2.    K. Cios, W.Pedrycz, and R. Swiniarsski. Data Mining Methods for Knowledge Discovery. Boston: Kluwer Academic Publishers,[1998]

3.    Omer Adel Nassar,Dr.Nedhal A.Saiyd,”the integrating between web usage mining and data mining techniques,”5th internal conference on computer science and information technology,[2013].

4.    Shahida Sulaiman, “Data Mining Technique for Expertise Search in a Special Interest Group Knowledge Portal”, 2011 3rd Conference on Data Mining and Optimization (DM O) 28-29 June [2011].

5.    Ren Yanna, “ The Design of Algorithm for Data Mining System Used for Web Service” ,IEEE [2011] .

6.    B.N.Lakshmi,G.H Raghunandhan “A conceptual overview of data mining”,IEEE ,Proceeding of the national conference on innovation in emerging technology,pp.27-32,17&18 feb,[2011].

7.    G. Sathyadevi “application of CART algorithm hepatitis disease diagnosis”, IEEE-International Conference on recent trends in information technology, ICRTIT 2011, June 3-5,[2011].

8.    Quinlan J R,” Induction of decision tree,” Machine Learning, vol.4,no.2,pp.81-106,[1986].

9.    Shiow-yang wu, Hsiu-Hao Fan” Activity-based proactive data management in mobile environments IEEE transaction on mobile computing ,vol 9,no.3 March[2010].






Abhishek Bhatt, Romil Gopani, Lukash Chaddwa, Gajanan Bherde

Paper Title:

Systematic Investment Plan Date Prediction

Abstract:  Neural networks have been used on variety of prediction problems in field of finance. Mutual funds in particular SIP (Systematic Investment Plan) have been very lucrative form of high gain investment in recent years [A3]. In the paper, we have proposed a way to maximize investors return by providing an insight on possible values of NAV thought the month in the beginning of the month so they can buy units at Low rates. We have used artificial neural network (ANN) along with resilient propagation algorithm for prediction. We want to create a system which will help an investor to gain more profit compared to another investor investing in the same SIP. The proposed system will notify the user the date on which investment to be made to maximize profit. Results of our experiment have been attached which shows good performance on HDFC TOP 200 fund (G).

 Systematic Investment Plan, Mutual Fund, Artificial Neural Net.


1.    X. Wu, M. Fund and A. Flitman, "Forecasting Stock Performance using Intelligent Hybrid Systems", Springerlink, 2001, pp. 4 4 7-4 56.
2.    Yunus YETISl, Halid KAPLAN2, and Mo JAMSHIDI3, Fellow IEEE Department of Electrical and Computer Engineering, University of Texas at San Antonio San Antonio, Texas, USA

3.    D. E. Rumelhart, G. E. Hinton, R. 1. Wiliams, "Learning Internal Representation by Error Propagation Parallel Distributed Processing Explorations in the Microstructures of Cognition ", McClelland J. L. (eds.), 1:318-362, MIT Press, Cambridge, 1986.

4.    Riedmiller, Martin, and Heinrich Braun. "A direct adaptive method for faster backpropagation learning: The RPROP algorithm." Neural Networks, 1993, IEEE International Conference on. IEEE, 1993. APA

5.    http://www.livemint.com/Money/VBRowqYA6XWPQo55asrVDJ/Mutual-fund-folios-rise-a-record-14-in-fiscal-year-201516.html

6.    Data source http://www.hdfcfund.com/products/equity-growth-fund






Harmandeep Kaur, Vijay Kumar Joshi

Paper Title:

To Improve Performance Response of Economic Load Dispatch by using Optimization Technique

Abstract: The power systems are grown in complexity of power demands. The focus is shifted to enhance the performance of power system, customer focus, increasing the reliability, clear power and reducing cost. Optimal system includes the economy of operation, fuel costs, system security with the aim of improving the efficiency of electric power system. The Economic load dispatch is the scheduling of power generators with respect to the load to minimize the total cost of transmission and operational costs of generating units while meeting the constraints. The objective of the ELD is to allocate the total transmission loss and total load demand among power plants while satisfying the operational constraints simultaneously. This paper presents solution for improvement of performance response of ELD by using genetic algorithm and fuzzy logic optimization approaches.

Economic load dispatch, optimization, fuzzy logic, genetic algorithm


1.       Kumari, Rajani, Sandeep Kumar, and VivekKumar Sharma. "Fuzzified Expert System for Employability Assessment", Procedia Computer Science, 2015.
2.       Coelho L.d.S, Mariani V.C,2009“Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units,” International Journal of Chaos, Solitons and Fractals( Elsevier) , pp 2376–2383.

3.       Liang, Zi-xiong; Duncan Glover, J., “A zoom feature for a dynamic programming solution to economic dispatch including transmission losses,” IEEE transaction on power systems, volume 7(2), pp. 544-550 , 1902.

4.       Dhillon J.S., Parti S.C. and Kothari D.P., “Multiobjective Stochastic Optimal thermal power dispatch”, ibid, pp. 136-140, 1987.

5.       Palanichamy C. and Shrikrishna K. (1991), “Simple algorithm for economic power dispatch” electrical power syst. Res, Vol.21 pp 174-153.

6.       Lee. K.Y, Bai. X and Park. Y.M, “Optimization method for Reactive power Planning by Modified Simple Genetic Algorithm” IEEE Transactions on Power Systems Vol.10, No.4, November1995                   

7.       King T.D., El-Hawary M.E. and El-Hawary F., “Optimal environmental dispatching of electric power systems via an improved hopfield neural network model.” IEEE Transactions on Power Systems, vol. 10, no. 3, pp.1559-1565, Aug. 1995. 

8.       Hess S.W., Parker D., Alms John E., Le K.D., Day J.T. and Malone M.J., “Planning System Operations to meet NOx constraints”, IEEE Computer Applications in Power, vol. 5, no.3, pp. 10-14, July 1992.

9.       Zhuang F. and Galiana F.D., “Unit Commitment by Simulated Annealing,” IEEE Transactions. on Power Systems, vol. 5, no.1, pp. 311-318,1990.                                           

10.    Hamid Bouzeboudja, Abdelkader Chaker, Ahmed Alali, Bakhta Naama., “Economic Dispatch Solution Using A Real-Coded Genetic Algorithm,” Acta Electrotechnica et Enformatica No. 4, Vol. 5, 2005              

11.    Vijayalakshmi. G.A., Rajsekaran. S, “Neural Networks, Fuzzy Logic, and Genetic Algorithms” synthesis and application                                  

12.    George. J Klir/Boyuan, “Fuzzy Sets and Fuzzy Logic”, prentice Hall of India Private Limited, New Delhi - 2000.

13.    Happ H.H., "Optimal power dispatch- a comprehensive survey", IEEE Transactions on Power Apparatus and Systems, Vol.PAS-96,no.3, May/June1977.  

14.    Hopfield J.J. and Tank D.W., “Simple neural optimization networks: an A/D converter, signal decision network, and a linear programming circuit,” IEEE Transactions on Circuit and Systems, vol. CAS-33, pp. 533-541, May 1986.         

15.    Wood Allen.J. & Woolenberg Bruce.F. (1996), “power generation, operation and Control”, johnwilley & sons.

16.    D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.

17.    Sarat Kumar Mishra, Sudhansu Kumar Mishrab, “A Comparative Study of Solution of Economic Load Dispatch Problem in Power Systems in the Environmental Perspective”, International Conference on Intelligent Computing, Communication & Convergence (ICCC-2014), Procedia Computer Science 48 ( 2015 ) 96 – 100,

18.    ELSEVIER G. A. Bakare, Removal of overloads and Voltage problems in Electric Power Systems using Genetic Algorithm/Expert System Approaches, Shaker Verlag, Aachen Germany, 2001.   

19.    C.-L. Chiang, "Genetic-based algorithm for power economic load dispatch", IET Gen., Transm., Distrib., vol. 1, no. 2, pp.261 -269, 2007 

20.    T. Adhinarayanan and M. Sydulu ,"A directional search genetic algorithm to the economic dispatch problem with prohibited operating zones", Proc. IEEE/PES Transmission and Distribution Conf. Expo., pp.1 -5,2008. 

21.    GiridharKumaran, V. S. R. K. Mouly, “Using evolutionary computation to solve the economic load dispatch problem”, IEEE Transactions on power systems, Vol. 3, pp. 296-301, 2001.        

22.    J. H. Park, S. O. Yang, K. J. Mun, H. S. Lee and J. W. Jung, “An application of evolutionary computations to economic load dispatch with piecewise quadratic cost functions”, IEEE Transactions on power systems, Vol.69,pp.289-294,1998.       

23.    Ah King R. T. F. and Rughooputh H. C. S., “Elitist Multiobjective Evolutionary Algorithm for Environmental/Economic Dispatch”, IEEE Congress on Evolutionary Computation, Canberra, Australia, vol. 2, pp. 1108-1114, 2003.






Vijayalakshmi.B, Amreen Atiq, Jyoti Bhadoriya, Nithya

Paper Title:

Some Studies on Energy of Triple Connected Graphs

Abstract:  The field of mathematics plays a vital role in various fields, one of the important areas in mathematics is graph theory. The concept of connectedness plays an important role in any networks.Let G be a simple graph with n vertices and m edges. The ordinary energy of a graph is defined as sum of absolute values of eigen values of its adjacency matrix. In recent times analogous of energies are being considered based on eigen values of variety of other graph matrices. In this paper we analyzed various energies of triple connected graphs and obtained bounds.

 energy, eigen values, triple connected graphs, incidence energy, AMS Mathematics Subject Classification (2010): 05C78


1.       Paulraj Joseph J,M.K. AngelJebitha, P.chitradevi and G.Sudhana,Triple connected  Graphs,  Indian Journal of Mathematical Sciences ,Vol 8,No 1(2012) ,61-75.
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3.       Chandrashekar Adiga, E. Sampathkumar, M.A. Sriraj, Shrikanth A. S. ,Color Energy of a Graph Proceedings of the Jangjeon Mathematical Society • January 2013.

4.       R Balakrishnan, Energy of a Graph, Proceedings of the KMA National Seminar on Graph Theory and Fuzzy Mathematics, August (2003), 28-39.

5.       Mohammadreza Jooyandeh, Dariush Kiani, Maryam Mirzakhah, Incidence energy of a graph, MATCH Commun. Math. Comput. Chem. 62 (2009) 561-572

6.       Laura buggy, Amalia culiuc, Katelyn mccall,Duy nguyen, The energy of graphs and matrices,

7.       M. Lazi_c, On the Laplacian Energy of a Graph, Czech. Math. Journal, 56 (131) (2006), 1207-1213.

8.       Gutman, et. al., On Incidence Energy of a Graph, Linear Algebra Appl. (2009) -in press.

9.       S.Meenakshi  and S. Lavanya A Survey on Energy of Graphs, Annals of Pure and Applied Mathematics, Vol. 8, No. 2, 2014, 183-191






Emmanuel Thyaka Mbusi, Moses Mitau Mulwa

Paper Title:

Behavior Description of Monetary and Fiscal Policy Factors That Impact Construction Output in Kenya for the Period 2000 - 2013

Abstract: The main function of construction industry in the world is provision of physical and constructed facilities to give other activities space for taking place as seen in Hillebrandt, (2000). She further observes that, these physical and constructed facilities are referred to as construction output and are usually quantified in monetary terms. This quantification is done by Kenya National Bureau of Statistics in this country. Construction industry in Kenya mostly maintains a steady and an upward trend in its growth. Recently; 2013 and 2014, an economic survey report released by Kenya National Bureau of Statistics (KNBS) indicated that Kenya’s building and construction as having contributed 4.8% to the Gross Domestic Product (GDP). The GDP had risen from Kshs.4.73 trillion to Kshs.5.36 trillion in 2014 as Macharia, (2015) indicates. This gives a clear picture that the sector is growing, though at a little bit slow pace. Description of the behavior of monetary and fiscal policy factors in Kenya was thought of as a means of enlightening the construction sector stakeholders and players about their existence. The factors play a major role in decision making regarding construction projects anywhere in the world, but they are usually not accounted for keenly at this crucial stage of decision making. Time series data was collected from KNBS and CBK on quarterly basis for the period starting from 2000 up to 2013, for the five factors. These data showed varied behavior; some displayed upward trends while others showed a zigzag behavior. Conclusion was drawn that in Kenya, there are five monetary and fiscal policy factors that have influence on construction output and therefore policy makers, stakeholders and players in the construction sector should ensure a keen consideration of the factors during decision making stage.  This will avert the problem of many construction projects stalling and ensure steady growth of the sector. This shall therefore contribute towards achieving the much taunted two digit growth of the country’s GDP.

Keywords: construction output, fiscal policy, monetary policy, time series.


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