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Volume-6 Issue-4: Published on September 05, 2016
04
Volume-6 Issue-4: Published on September 05, 2016

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S. No

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

Page No.

1.

Authors:

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.

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


References:

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).

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2.

Authors:

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.

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


References:

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.

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3.

Authors:

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.

Keywords:
  Digital Library, Personal Digital Library, PDL.


References:

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.

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4.

Authors:

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.

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


References:

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.


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5.

Authors:

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.

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


References:

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2.       Zheng, W. T., & Sun, C. Q, "Electronic process of nitriding: Mechanism and applications. Progress in Solid State" Chemistry, 34, pp 1–20, (2006).

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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).

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

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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).

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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).

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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).

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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).


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6.

Authors:

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.

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


References:

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.


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7.

Authors:

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.

Keywords:
 FCM, EM, GMM, MRI image segmentation


References:

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>


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8.

Authors:

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.

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


References:

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.


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9.

Authors:

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.

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


References:

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.


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10.

Authors:

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.

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


References:

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).

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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).


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11.

Authors:

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:  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

Keywords:
 lead sulfide, chemical pyrolysis, thin film, semiconductor


References:

1.       X. 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.       N. 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.       S. 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.       N. 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.       J.L. Machol, F.W. Wise, R.C. Patel, D.B. Tanner, Phys. Rev. B 48(1993) 2819.

8.       Y. Wang, W. Suna, W. Mahler, and R. Kasowski, PbS in polymers. From molecules to bulk solids, J. Chem. Phys. 87, 1987, 7315-7322.

9.       Y. Wang, and N. Herron, Nanometer-Sized Semiconductor Clusters: Materials Synthesis, Quantum Size Effects, and Photophysical Properties, J. Phys. Chem. 95, 1991, 525-532.

10.    F.W. Wise, Lead salt quantum dots: the limit of strong quantum confinement, Acc. Chem. Res. 33, 2000, 773-780.

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17.    S. Kumar, T.P.Sharma, M. Zulfequar, M. Husain, Characterization of vacuum evaporated PbS thin films Physica B: Condensed Matter, 325, 8-16 (2003).

18.    L. 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.H. Afifi, S.A. Mahmoud, A. Ashour, Structural study of ZnS thin films prepared by spray pyrolysis, Thin Solid Films 263, 248-251(1995).

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12.

Authors:

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.


References:

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.


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13.

Authors:

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 8.825x10-10, Quality factor = 9.7 and total possible distance is found to be of 25 k.

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


References:

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.    Harry. 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.    Cisco.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..


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