TOP 10 COMPUTER AIDED TECHNOLOGY PAPERS: RECOMMENDED READING – COMPUTER AIDED TECHNOLOGY RESEARCH

Citation Count – 13

RESEARCH ISSUES IN WEB MINING

Dr.S. Vijiyarani¹ and Ms. E. Suganya²

¹Assistant professor, Department of Computer science, School of Computer Science and
Engineering, Bharathiar University, Coimbatore
² M.Phil Research Scholar, Department of Computer science, School of Computer
science and Engineering, Bharathiar University, Coimbatore

ABSTRACT

Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper, we have studied the basic concepts of web mining, classification, processes and issues. In addition to this, this paper also analyzed the web mining research challenges.

KEYWORDS: Web Mining, Classification, Application, Tools, Algorithms, Research Issues

For More Details: http://airccse.org/journal/ijcax/papers/2315ijcax05.pdf

Volume Link: http://airccse.org/journal/ijcax/vol2.html

References

[1] Joy Shalom Sona, Prof. Asha Ambhaikar” A Reconciling Website System to Enhance Efficiency with Web Mining Techniques” International Journal Of Scientific & Engineering Research Volume 3, Issue 2, February-2012 1 ISSN 2229-5518

[2] Aparna Ranade, Abhijit R. Joshi, Ph. D,” Techniques for Understanding User Usage Behavior on the Internet” International Journal of Computer Applications (0975 – 8887) Volume 92 – No.7, April 2014

[3] Karan Bhalla & Deepak Prasad,” Data Preparation and Pattern Discovery For Web Usage Mining”

[4] Amit Pratap Singh1, Dr. R. C. Jain 2,” A Survey on Different Phases of Web Usage Mining for Anomaly User Behavior Investigation” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Volume 3, Issue 3, May – June 2014 ISSN 2278-6856

[5] R. Lokeshkumar1, R. Sindhuja2, Dr. P. Sengottuvelan, “A Survey on Pre-processing of Web Log File in Web Usage Mining to Improve the Quality of Data” International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 8, August 2014

[6] Mitali Srivastava, Rakhi Garg, P. K. Mishra,” Preprocessing Techniques in Web Usage Mining: A Survey” International Journal of Computer Applications (0975 – 8887) Volume 97– No.18, July 2014

[7] http://www.slideshare.net/akhanna3/discovering-knowledge-using-web-structure-mining-27488978

[8] Ashish Kumar Garg, Mohammad Amir, Jarrar Ahmed, Man Singh, Sham Bansa,” Implementation of a Search Engine” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064

[9] C.Gomathi, M. Moorthi,” Web Access Pattern Algorithms in Education Domain” Computer and information science journal vol. 1, No.4, November 2008

[10] Md. Zahid Hasan, Khawja Jakaria Ahmad Chisty and Nur-E-Zaman Ayshik, “Research Challenges in Web Data Mining”, International Journal of Computer Science and Telecommunications Volume 3, Issue 7, July 2012

[11] Jaideep Srivastava, “Web Mining: Accomplishments & Future Directions”, University of Minnesota USA, srivasta@cs.umn.edu, http://www.cs.umn.edu/faculty/srivasta.html

[12] http://www.slideshare.net/AmirFahmideh/web-mining-structure-mining

[13] http://www.slideshare.net/Tommy96/web-mining-tutorial

[14] http://www.faadooengineers.com/threads/2177-PPT-Link-Mining

[15] Deepti Kapila, Prof. Charanjit Singh, “Survey on Page Ranking Algorithms for Digital Libraries”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 4, Issue 6, June 2014 ISSN: 2277 128X

[16] Alberto Sillitti, Marco Scotto, Giancarlo Succi, Tullio Vernazza,” News Miner: a Tool for Information Retrieval”

[17] Sandhya, Mala chaturvedi, “a survey on web mining algorithms”, The International Journal Of Engineering And Science (IIJES) Volume 2 Issue 3

[18] Ananthi.J,” A Survey Web Content Mining Methods and Applications for Information Extraction from Online Shopping Sites”, International Journal of Computer Science and Information Technologies, Vol. 5 (3), 2014

[19] S.Balan, “A Study of Various Techniques of Web Content Mining Research Issues and Tools”, International journal of innovative research and studies ISSN 2319-9725  International Journal of Computer-Aided Technologies (IJCAx) Vol.2, No.3, July 2015 64

[20] D.Jayalatchumy, Dr. P.Thambidurai, “Web Mining Research Issues and Future Directions – A Survey”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278- 8727Volume 14, Issue 3

[21] Naga Lakshmi, Raja Sekhara Rao , Sai Satyanarayana Reddy, “An Overview of Preprocessing on Web Log Data for Web Usage Analysis”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-4

[22] Mamta M. Hegde, Prof. M.V.Phatak, “Developing an approach for hyperlink analysis with noise reduction using Web Structure Mining”, International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May2012

[23] Mr. Dushyant B.Rathod, Dr.Samrat Khanna, “A Review on Emerging Trends of Web Mining and its Applications” ISSN: 2321-9939

Citation Count – 08

IDENTIFY NAVIGATIONAL PATTERNS OF WEB USERS

Shiva Asadianfam and Masoud Mohammadi

Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran

ABSTRACT

RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.

KEYWORDS: Web Usage Mining, RapidMiner, Association Rule Mining, Web Log Analysis

For More Details: http://airccse.org/journal/ijcax/papers/1114ijcax01.pdf

Volume Link: http://airccse.org/journal/ijcax/vol1.html

REFERENCES

[1] M. Zdravco, D. T. Larose (2007) Data Minig the Web – Uncovering Patterns in Web Content, Structure and Usage. Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

[2] Xiu-yu Zhong, (2011)”The Research And Application of Web Log Mining Based On The platform Weka”, Procedia engineering 15,pp 4073 – 4078.

[3] Yao-Te Wang, Anthony J.T. Lee,( 2011)” Mining Web navigation patterns with a path traversal graph[J]”, Expert Systems with Applications, Vol. 38, No. 6, pp 7112-7122.

[4] Michal Munk, Jozef Kapusta,(2010)” Data preprocessing evaluation for web log mining: reconstruction of activities of a webvisitor[J]”, Procedia Computer Science, Vol. 1, No.1, pp 2273- 2280.  International Journal of Computer-Aided Technologies (IJCAx) Vol.1,No.1,April 2014 8

[5] Resul Das, Ibrahim Turkoglu,(2009)” Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method[J]”, Expert Systems with Applications, Vol. 36, No. 3, pp 6635-6644.

[6] Nicolas Poggi, Toni Moren,(2009)”Self-adaptive utility-based web session management[J]”, Computer Networks, Vol. 53, No. 10,pp 1712-1721.

[7] J.M.Kleinberg and A.Tomkins,( 1999)”Application of linear algebra in information retrieval and hypertext analysis”,In Proc.18 th,ACM Symp. Principles of Database Systems (PODS), Philadelphia, PA, (5), pp 185-193.

[8] Enrique Lazcorreta, Federico Botella, (2008)”Towards personalized recommendation by two-step modified Apriori data mining Algorithm Expert Systems with Applications”, Vol. 35, No. 3,pp 1422- 1429.

[9] Shiyong Zhang ,Jianping Zeng, (2008)” A framework for WWW user activity analysis based on user interes”t . Knowledge-Based Systems, Vol. 21, No. 8,pp 905-910.

[10] http://dictionary.sensagent.com/RapidMiner/en-en/

[11] http://www.gabormelli.com/RKB/RapidMiner_System

[12] C. E. Dinuca, D. Ciobanu,(2011)” improving the session identification using the mean time”, international journal of mathematical models and methods in applied sciences.

[13] Chu-Hui Lee, Yu-lung Lo, Yu-Hsiang Fu,(2011)”A novel prediction model based on hierarchical characteristic of web site”, Expert Systems with Applications, Vol. 38, No. 4, pp 3422-3430.

[14] Claudia Elena Dinucă, Dumitru Ciobanu,(2011)”On an Algorithm for Identifying Sessions from Web Logs”, Acta Universitatis Danubius, Vol. 7, No. 4.

[15] Sonali Manoj Raut, Dhananjay Dakhane,(2012)”Comparative Study of Clustering and Association Method for Large Database in Time Domain”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 12, pp. 41 45.

[16] X. Fu, J. Budzik, K.J. Hammond , (2000) ” Mining navigation history for recommendation “, Intelligent User Interfaces, Vol. 10, No. 1.

[17]J. Li, O. R. Zaiane,(2004)”Combining usage, content and structure data to improve web site recommendation”, 5th International Conference on Electronic Commerce and Web.

[18] M. Eirinaki, C. Lampos, S. Paulakis, M. Vazirgiannis, (2004) “Web personalization integrating content semantics and navigational patterns”, Proceedings of the sixth ACM workshop on Web Information and Data Management WIDM.

[19] A.M. Wasfi, (1993) “Collecting user access patterns for building user profiles and collaborative filtering”, Proceedings of the 1999 International Conference on Intelligent User Interfaces.

[20] Jiawei Han, Micheline Kamber,(2001)Data Mining: Concepts and Techniques, Morgan Kaufmann.

[21] http://www.di.unito.it/~meo/Pubblicazioni/capitolo-libro-IGI-rare-patterns.pdf

[22] http://www.csis.pace.edu/~ctappert/dps/d861-13/session2-p1.pdf

Citation Count – 04

THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING
PROBLEM

Akbarzadeh¹, E. Shadkam²

¹Department of Industrial Engineering, Faculty of Eng.; Khayyam University, Mashhad, Iran
²Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran

ABSTRACT

Constrained Nonlinear programming problems are hard problems, and one of the most widely used and common problems for production planning problem to optimize. In this study, one of the mathematical models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is efficient method to solve continues non linear problem. Moreover, mentioned models of production planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo Algorithm is suitable choice for optimization in convergence of solution.

KEYWORDS: Meta-heuristic algorithms, Cuckoo Optimization Algorithm, Lot Sizing, Production Planning.

For More Details: http://airccse.org/journal/ijcax/papers/2315ijcax01.pdf

Volume Link: http://airccse.org/journal/ijcax/vol2.html

REFERENCES

[1] Shirneshan, H., Bijari, M., Moslehi Gh., Determination the lot sizing with probable demand and the level of the service, Production and Operations Management, 1391.
[2] Sereshti, N., Bijari, M., the Maximization of profit in general lot sizing and sequencing problem, the 7th International Conference of Industrial Engineering, 1389.
[3] Abdullahi, M. Abdullahi, D., Karimpur, J., Cuckoo optimization algorithm for non linear programming , 9th International Conference on Industrial Engineering, 1391.
[4] Mahmoudi, Sh., the segmentation of color image by Cuckoo optimization algorithm, the first PC-IT and Electronics Engineering Conference, Islamic Azad University.
International Journal of Computer-Aided Technologies (IJCAx) Vol.2, No.3, July 2015 9
[5] Hakimi Gilani, S., Vafrakhth, H., Determine the capacity of wind power plants by Cuckoo optimization algorithm, the second Conference on Renewable Energy and Distributed Generation Iran, Tehran University, 2012.
[6] Merzifonluo lu, Y., and Geunes, J., Uncapacitated production and Location planing models with demand fulfillment flexibility, International Journal of production Economics, Vol.102, No. 2, pp. 199-216, 2006.
[7] Hwa Huang, R., Yang, CL., Overlapping Production Scheduling Planning with Multiple
Objectives—An ant Colony Approach. International Journal of Production Economics 115, 2008, pp. 163-170.
[8] Loukil, T., Teghem, J., Fortemps, Ph., A Multi-Objective Production Scheduling Case Study Solved by Simulated Annealing. European Journal of Operational Research 179, 2007, pp. 709-722.
[9] Zegordi, S, Kamal Abadi, I., Beheshti Nia, M., A Novel Genetic Algorithm for Solving
Production and Transportation Scheduling in a Two-Stage Supply Chain. Computers & Industrial Engineering 58, 2010, pp. 373-381.
[10] Ying Wu, L., Dong Hu, Y., Mei Xu, D., Hua, B., Solving Batch Production Scheduling using Genetic Algorithm. Computer Aided Chemical Engineering 15, 2003, pp. 648-653.
[11] Tat Chan, W., Hu, H., An Application of Genetic Algorithms to Precast Production
Scheduling. Computers & Structures 79, 2001, pp. 1605-1616.
[12] Sankar, A.S., Ponnanbalam, S.G., Rajendran, C., A Multiobjective Genetic Algorithm for Scheduling a Flexible Manufacturing System. International Journal of Advanced Manufacturing Technology, 22(3), 2003, pp. 229–236.
[13] Liu, J., Tang, L., A Modified Genetic Algorithm for Single Machine Scheduling. Computers and Industrial Engineering, 37(1), 1999, pp. 43–46.
[14] Li, Y., Ip, W., Wang, D., Genetic Algorithm Approach to Earliness and Tardiness Production Scheduling and Planning Problem. International Journal of Production Economics 54, 1998, pp. 65- 76.
[15] Knosala, R., Wal, T., A Production Scheduling Problem using Genetic Algorithm. Journal of Materials Processing Technology 109, 2001, pp. 90-95.
[16] Jou, C., A Genetic Algorithm with Sub-Indexed Partitioning Genes and its Application to Production Scheduling of Parallel Machines. Computers & Industrial Engineering 48, 2005, pp. 39- 54.
[17] Ho Ko, C., Fan Wang, Sh., Precast Production Scheduling using Multi-Objective Genetic Algorithms. Expert Systems with Applications 38, 2011, pp. 8293-8302.
[18] Chung, SH., Chan, F., Chan, H., A Modified Genetic Algorithm Approach for Scheduling of Perfect Maintenance in Distributed Production Scheduling. Engineering Applications of Artificial Intelligence 22,2009, pp. 1005-1014.
[19] Averbakh, I., On-Line Integrated Production Distribution Scheduling Problems with Capacitated Deliveries. European Journal of Operational Research 200, 2010, pp. 377-384.
[20] X. S. Yang and S. Deb, Cuckoo search via Lévy Flights, In: World Congress on Nature & Biologically Inspired Computing (NaBIC2009). IEEE Publications, pp. 210–214, 2009.
[21] R. Rajabioun, Cuckoo Optimization Algorithm, In: Applied Soft Computing journal, vol. 11, pp.5508 – 5518, 2011.
[22] Brank, J., Sceckenbach, B., Staein, M., Deb, K., Scmeck, H., Portfolio Optimization with an Envelope-Based Multi-Objective Evolutionary Algorithm, Eouropean Journal of Operational Reaserch, Vol. 199, 2009, pp. 684-693.

Citation Count – 03

Survey on Content Based Image Retrieval

Anuradha Shitole¹ and Uma Godase²

¹Department of Information Technology, Pune University, Pune
²Department of Information Technology, Pune University, Pune

Abstract

Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques using different feature extraction techniques are discussed in this paper.

Keywords: CBIR; colour feature; texture feature shape feature

For More Details: http://airccse.org/journal/ijcax/papers/1114ijcax03.pdf

Volume Link: http://airccse.org/journal/ijcax/vol1.html

REFERENCE

[1] Ela Yildizer, Ali Metin Balci, Mohammad Hassan, Reda Alhajj, “Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble”, Expert Systems with Applications 39, 2385–2396, 2012
[2] Nishant Singh, Shiv Ram Dubey, Pushkar Dixit, Jay Prakash Gupta, “Semantic Image Retrieval by Combining Colour, Texture and Shape Features”, International Conference on Computing Sciences,2012.
[3] Jipsa Kurian, V.Karunakaran, “A Survey on Image Classification Methods”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 1, Issue 4, October 2012.
[4] Lijun Zhao, Jiakui Tang, “Content-Based Image Retrieval Using Optimal Feature Combination and Relevance Feedback”, International Conference on Computer Application and System Modeling,volume-4, v4-436 – v4-442, 2010.
[5] Francesca Bovolo, Lorenzo Bruzzone and Lorenzo Carlin, “A Novel Technique for Subpixel Image Classification Based on Support Vector Machine”, IEEE transactions on image processing, vol. 19, no. 11, november 2010.
[6] Bingxin Xu, Qian Yin, Guangjun Lv, “Using SVM to Organize the Image Database”, International Conference on Computational Intelligence and Security, 2009.
[7] P. S. Hiremath , Jagadeesh Pujari, “Content Based Image Retrieval using Colour, Texture and Shape features”, 15th International Conference on Advanced Computing and Communications,2007.
[8] Lenina Birgale, Manesh Kokare and Dharmpal Doye, “Colour and Texture Features for Content Based Image Retrieval”, Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation, 2006.
[9] Howarth, P., & Rüger, S, “Evaluation of texture features for content-based image retrieval”, In International conference on image and video retrieval (pp. 326–334), 2004.
[10]James Z. Wang, Jia Li and Gio Wiederhold, “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries”, IEEE transactions on pattern analysis and machine intelligence, vol. 23, NO. 9, september 2001.

Citation Count – 01

Decision Tree Algorithm Implementation Using Educational Data

Priyanka Saini¹ ,Sweta Rai² and Ajit Kumar Jain³

^1,2 M.Tech Student, Banasthali University, Tonk, Rajasthan
³ Assistant Professor , Department of Computer Science, Banasthali University

ABSTRACT

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

Keywords: Decision tree, ID3 algorithm, Entropy and Information Gain

For More Details: http://airccse.org/journal/ijcax/papers/1114ijcax04.pdf

Volume Link: http://airccse.org/journal/ijcax/vol1.html

 References

[1] Quinlan, J.R. 1986, Induction of Decision trees, Machine Learning.
[2] Anand Bahety,’ Extension and Evaluation of ID3 – Decision Tree Algorithm’. University of Maryland, College Park.
[3] Mary Slocum,‟,Decision making using ID3,RIVIER ACADEMIC JOURNAL, VOLUME 8,
NUMBER 2, FALL 2012.
[4] Kumar Ashok, Taneja H C, Chitkara Ashok K and Kumar Vikas,’ Classification of Census Using Information Theoretic Measure Based ID3 Algorithm’ . Int. Journal of Math. Analysis, Vol. 6, 2012, no. 51, 2511 – 2518.
[5] Sonika Tiwari and Prof. Roopali Soni,’ Horizontal partitioning ID3 algorithm A new approach of detecting network anomalies using decision tree’, International Journal of Engineering Research & Technology (IJERT)Vol. 1 Issue 7, eptember – 2012

A Multi Criteria Decision Making Based Approach for Semantic Image Annotation

Hengame Deljooi and Somayye Jafarali Jassbi

Department of Computer Engineering, Science and Research Branch, Islamic Azad
University, Tehran, Iran

ABSTRACT

Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. This paper presents a model, which integrates visual topics and regional contexts to automatic image annotation. Regional contexts model the relationship between the regions, while visual topics provide the global distribution of topics over an image. Previous image annotation methods neglected the relationship between the regions in an image, while these regions are exactly explanation of the image semantics, therefore considering the relationship between them are helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed model.

KEYWORDS: Automatic Image Annotation, Regional Contexts, Visual Topics, PLSA, Multi Criteria Decision Making

For More Details: http://airccse.org/journal/ijcax/papers/2115ijcax02.pdf

Volume Link: http://airccse.org/journal/ijcax/vol2.html

REFERENCES

[1] A. Tousch, S. Herbin, J. Audlibert, (2012) “Semantic Hierarchies for Image Annotation: A Survey,” Elsevier Ltd., Pattern Recognition, vol. 40, pp. 333-345.
[2] D. Zhang, Md.M. Islam, G. Lu, (2011) “A review on automatic image annotation techniques,” Elsevier Ltd., Pattern Recognition, vol. 45.
[3] R. Datta, D. Joshi, J. Li, J.Z. Wang, (2008) “Image retrieval: ideas, influences, and trends of the new age,” ACM Comput. Surv, vol. 40, no. 2, Article 5.
[4] Y. Liu, D. Zhang, G. Lu, W. Ma, (2007) “A survey of content-based image retrieval with high-level semantics,” Elsevier Ltd., Pattern Recognition, vol. 40, pp. 262-282.
[5] .Y. Pan, H.J. Yang, P. Duygulu, Ch. Faloutsos, (2004) “Automatic image captioning,” In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), pp. 1987-1990.
[6] .S. Hare, P.H. Lewis, (2005) “Saliency-based models of image content and their application to autoannotation by semantic propagation,” In Proceedings of Multimedia and the Semantic Web / European  Semantic Web Conference.
[7] C. Cusano, G. Ciocca, R. Schettini, (2003) “Image Annotation Using Svm,” In Proceedings of Internet Imaging IV, SPIE 5304, vol. 5304, pp. 330-338.
[8] E. Chang, K. Goh, G. Sychay, G. Wu, (2003) “CBSA: content based soft annotation for multimodal image retrieval using Bayes point machines,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26-38.
[9] J. Li, J. Wang, (2003) “Automatic linguistic indexing of pictures by a statistical modeling approach,” IEEE Trans. on PAMI, 25(19), pp. 1075-1088.
[10] A. Fakhari, A. M. Eftekhari Moghadam, (2013) “Combination of classification and regression in decision tree for multi-labeling image annotation and retrieval,” Elsevier Ltd., Applied Soft Computing, Vol. 13, pp. 1292-1302.
[11] J. Tang, H. Li, G. Qi, T. Chua, (2010) “Image annotation by graph-based inference with integrated multiple/single instance representations,” IEEE Trans. on Multimedia, vol. 12, no. 2.
[12] J. Liu, M. Li, Q. Liu, H. Lu, S. Ma, (2009) “Image annotation via graph learning,” Elsevier Ltd., Pattern Recognition, vol. 42, pp. 218-228.
[13] J. Liu, B. Wang, H. Lu, S. Ma, (2008) “A graph-based image annotation framework,” Elsevier Ltd., Pattern Recognition Letters, vol. 29, pp. 407-415.
[14] J. Liu, M. Li, W. Ma, Q. Liu, H. Lu, (2006) “An Adaptive Graph Model For Automatic Image Annotation,” In Proceedings of the 8th ACM international workshop on Multimedia information retrieval.
[15] G. Carneiro, A.B. Chan, P.J. Moreno, N. Vasconcelos, (2007) “Supervised learning of semantic classes for image annotation and retrieval,” IEEE Trans. on PAMI, vol. 29, no. 3.
[16] Y. Mori, H. Takahashi, R. Oka, (1999) “Image-to-word transformation based on dividing and vector quantizing images with words,” In Proceedings of First International Workshop Multimedia Intelligent Storage and Retrieval Management.  International Journal of Computer-Aided Technologies (IJCAx) Vol.2,No.1,January 2015 30
[17] P. Duygulu, K. Barnard, J. Freitas, D. Forsyth, (2002) “Object recognition as machine translation: learning a lexicon for a fixed image vocabulary,” In Proceedings of the 7th European Conference on Computer Vision, vol. 2353, pp. 97-112.
[18] J. Jeon, V. Lavrenko, R. Manmatha, (2003) “Automatic image annotation and retrieval using CrossMedia Relevance Model,” In Proceedings of the 26th annual international ACM SIGIR, pp. 119-126.
[19] V. Lavrenko, R. Manmatha, J. Jeon, (2003) “A model for learning the semantics of pictures,” In Proceedings of Advance in Neutral Information Processing.
[20] S. Feng, R. Manmatha, V. Laverenko, (2004) “Multiple Bernoulli Relevance Models for image and video annotation,” In IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1002-1009.
[21] Y. Wang, T. Mei, Sh. Gong, X.Sh. Hua, (2009) “Combining global, regional and contextual features for automatic image annotation,” Elsevier Ltd., Pattern Recognition, vol. 42, pp. 259-266.
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[23] Y. Deng, B.S. Manjunath, (2001) “Unsupervised Segmentation of Color-Texture Regions in Images and Video,” IEEE Trans. on PAMI, Vol. 23, No. 8, pp. 800-810.
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TRACE LENGTH CALCULATION ON PCBS

Shlaghya S Vasista M.Tech CSE, BMS College of Engineering, India

ABSTRACT

The design of silicon chips in every semiconductor industry involves the testing of these chips with other components on the board. The platform developed acts as power on vehicle for the silicon chips. This Printed Circuit Board design that serves as a validation platform is foundational to the semiconductor industry. The manual/repetitive design activities that accompany the development of this board must be minimized to achieve high quality, improve design efficiency, and eliminate human-errors. One of the time consuming tasks in the board design is the Trace Length matching. The paper aims to reduce the length matching time by automating it using SKILL scripts.

KEYWORDS: Trace length calculation, Board design, SKILL script, python script

For More Details: http://aircconline.com/ijcax/V4N3/4317ijcax01.pdf

Volume Link: http://airccse.org/journal/ijcax/current.html

REFERENCES

[1] Engineering Drawing and Design by David.A. Madsen, David.P.Madsen, 2012, P 811
[2] Computer Aided Design and Manufacturing by K.Lalit Narayan, K.Mallikarjun Rao, M.M.M.Sarcar
[3] http://www.intel.com
[4] http://library.intel.com  International Journal of Computer- Aided Technologies (IJCAx) Vol.4, No.3, July 2017 8
[5] https://intelpedia.intel.com
[6] http://www.extremetech.com
[7] https://en.wikipedia.org
[8] https://www.tutorialspoint.com

Citation Count – 13

ADVANCED E-VOTING APPLICATION USING ANDROID PLATFORM

Ganaraj K PG Scholar, Computer Science Department,

Bearys Institute of technology and Engineering Mangalore, Karnataka, India

ABSTRACT

The advancement in the mobile devices, wireless and web technologies given rise to the new application that will make the voting process very easy and efficient. The E-voting promises the possibility of convenient, easy and safe way to capture and count the votes in an election[1]. This research project provides the specification and requirements for E-Voting using an Android platform. The e-voting means the voting process in election by using electronic device. The android platform is used to develop an evoting application. At first, an introduction about the system is presented. Sections II and III describe all the concepts (survey, design and implementation) that would be used in this work. Finally, the proposed evoting system will be presented. This technology helps the user to cast the vote without visiting the polling booth. The application follows proper authentication measures in order to avoid fraud voters using the system. Once the voting session is completed the results can be available within a fraction of seconds. All the candidates vote count is encrypted and stored in the database in order to avoid any attacks and disclosure of results by third person other than the administrator. Once the session is completed the admin can decrypt the vote count and publish results and can complete the voting process.

KEYWORDS: Electronic voting, e-mail Network

For More Details: http://aircconline.com/ijcax/V4N2/4217ijcax01.pdf

Volume Link: http://airccse.org/journal/ijcax/current.html

REFERENCES

[1] Dr.Aree Ali Mohammed and Ramyar Adbolrahman Timour,Efficient E-voting Android Based System, IJARCSSE,vol.3,Issue 11,2013
[2] A.S. Belenky and R.C. Larson, “To Queue or not to Queue?,” OR/MS 27, October 2013, pp. 30-34.
[3] “An Electronic Polling Service to Support Public Awareness Using Web Technologies”, Christos Bouras, Nikolaos Katris, Vassilis Triantafillou.  International Journal of Computer- Aided Technologies (IJCAx) Vol.4, No.1/2, April 2017
9
[4] “E-voting on Android System” paper (International Journal of Emerging Technology and Advanced Engineering) prepared by : Kirti Autade, Pallavi Ghadge, Sarika Kale ,Co-authors- Prof. N. J. Kulkarni, Prof. S. S. Mujgond, February 2012.
[5] “Electronic Voting,” Encyclopedia of Computers and Computer History, prepared by Lorrie Faith Cranor and edited by Raul Rojas, published by Fitzroy Dearborn, 2001.
[6] “Voting – What is, What Could be,” Caltech/MIT Voting Technology Project (VTP) Report, July 2001.
[7] Java Cryptography an e-book by Jonathan B. Knudsen, First edition May 1998, ISBN:1-56592-402-9

NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGES

Hossein Sahlani

Department of Information, Amin University, Tehran, Iran

ABSTRACT

Semantic annotation of images is an important research topic on both image understanding and database or web image search. Image annotation is a technique to choosing appropriate labels for images with extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy compared to the another methods.

KEYWORDS: Automatic Image Annotation, Ontology, Statistical Models, Regional Contexts, Visual Topics.

For More Details: http://aircconline.com/ijcax/V3N4/3416ijcax01.pdf

Volume Link: http://airccse.org/journal/ijcax/vol3.html

REFERENCES

[1] Einarsson S. H, (2004), “Data structures for intermediate search results in the Eff2 image retrieval system,” Reykjavík University, technical report.
[2] A. Tousch, S. Herbin, J. Audlibert, (2012) “Semantic Hierarchies for Image Annotation: A Survey,” Elsevier Ltd., Pattern Recognition, vl.40, pp. 333-345.
[3] Long F, Zhang H and Dagan Feng D, (2003), “Fundamentals of content-based image retrieval, in Multimedia Information Retrieval and Management Technological Fundamentals and Applications,” Springer-Verlag, pp. 1-26.
[4] D. Zhang, Md.M. Islam, G. Lu, (2011) “A review on automatic image annotation techniques,” Elsevier Ltd., Pattern Recognition, vol. 45.
[5] Li X, Chen S, Shyu M and Furht B, (2002), “An Effective Content-Based Visual Image Retrieval System,” in 26th IEEE Computer Society International Computer Software and Applications Conference (COMPSAC), Oxford, pp. 914- 919.
[6] Rui Y., Huang Th. S. and Chang Sh., (1999), “Image Retrieval: Current Techniques, Promising Directions, and Open Issues,” Journal of Visual Communication and Image Representation, vol. 10, pp. 39–62.
[8] S. J. Hwang, K. Grauman, and F. Sha. (2014), Semantic kernel forests from multiple taxonomies. Neural Information Processing Systems.
[9] S. J. Hwang, K. Grauman, and F. Sha. (2014),“Analogy-preserving semantic embedding for visual object categorization”. International Conference on Machine Learning, pages 639–647.
[10] Mohammad Mehdi Farhangi, Mohsen Soryani, Mahmood Fathy, (2012): Improvement the Bag of Words Image Representation Using Spatial Information. ACITY (2): 681-690.
[11] Guang-Hai Liu, Lei Zhang, Ying-Kun Hou, Zuo-Yong Li, Jing-Yu Yang, (2010) “Image retrieval based on multi-texton histogram”, Pattern Recognition Journals of science direct, Volume 43, Issue 7, July, Pages 2380–2389.
[12] HengameDeljooi, Ahmad R. Eskandari, (2014) “A Novel Semantic Statistical Model for Automatic Image Annotation Using the Relationship between the Regions Based on Multi-Criteria Decision Making”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 4, No. 1,
Feburary, pp. 37~51.
[13] HengameDeljooi, SomayyeJafaraliJassbi,(2015) “A Multi Criteria Decision Making Based Approach for Semantic Image Annotation”, International Journal of Computer-Aided Technologies (IJCAx) Vol.2, No.1.

DESIGN AND DEVELOPMENT OF CUSTOM CHANGE MANAGEMENT WORKFLOW TEMPLATES AND HANDLERS FOR VEHICLE DESIGN RELEASE.

Vijay B. Tatipamde¹ and Dr.Vilas M. Nandedkar²

¹ SY M.Tech (Mechanical-PLM) student, Shri Guru Gobind Singhji Institute of
Engineering and Technology, Vishnupuri, Nanded, India
² Professor, Production Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, India

 

ABSTRACT

A large no. of automobile companies finding a convinient way to manage design changes with the use of various PLM techniques. Change in any product is something that should occur on timely basis to match up with customer requirement and cost reduction. The change made in the vehicle designs directly affects various concerned agencies. Automobile Vehicle structures contains thousands of parts and if there is any change is occurring in child parts then it becomes important to track that impacted part, propose a solution on that part and release a new assembly structure with feasible changes such that all efforts need to be done for cost reduction.

KEYWORDS: Workflow, Change Management, Custom-handlers, Teamcenter Unified, Assembly Structure, EBOM

For More Details: http://aircconline.com/ijcax/V3N3/3316ijcax03.pdf

Volume Link: http://airccse.org/journal/ijcax/vol3.html

REFERENCES

[1] Joze Tavcar, Joze Duhovnik, (2005)”Engineering change management in individualand mass production” , Robotics and Computer-Integrated Manufacturing 21 205–215
[2] Eduard-Ionel Ionescua, Alexandrina Meru, Rodica Dragomiroiua (2013), “Towards a new approach to supporting top managers in SPI organizational change management” – International Conference on Health and Social Care Information Systems and Technologies.
[3] Aynar and Ducellier(2008) “From workflow specification to implementation”, International Design Conference Dubrovnik – Croatia, May 19 – 22, 2008.
[4] Anna Wasmer, Gunter Staub, Regine W. Vroom, “An industry approach to shared, crossorganizational engineering change handling – The road towards standards for product data processing”, Computer-Aided Design, Vol. 43-5 (2011), 533-545
[5] Eduard-Ionel Ionescua, Alexandrina Meru, Rodica Dragomiroiua, “Role of Managers in Management of Change”, Procedia Economics and Finance, Sibiu-Romania, Vol. 16 (2014)
[6] Rob Dekkers, C. M. Chang, Jochen Kreutzfeldt, “The interface between product design and engineering and manufacturing: A review of the literature and empirical evidence”, International Journal of Production Economics, Vol. 144-1 (2013), 316-333
[7] Teamcenter Online Help (2008)
[8] Vildan Kocar, Ali Akgunduz , “ADVICE: A virtual environment for Engineering Change
Management”, Computers in Industry, Vol. 61-1 (2010), 15-28
[9] Kamel Rouibah, Kevin R. Caskey, Change management in concurrent engineering from a parameter perspective”, Computers in Industry, Vol. 50-1 (2003), 15-34