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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 18 Issue 2

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Volume 10 Issue 4 April - June 2016

Article

Computer Forensics: An Overview

K.V.N. Rajesh* , K.V.N. Ramesh**

* HOD, Department of Information Technology, Vignan’s Institute of Information Technology, Visakhapatnam, India.

** Project Manager, Tech Mahindra, Visakhapatnam, India.

Rajesh, K. V. N., and Ramesh, K. V. N. (2016). Computer Forensics: An Overview. i-manager’s Journal on Software Engineering, 10(4), 1-5. https://doi.org/10.26634/jse.10.4.6056

Abstract

Computer Forensics involves identification of computer crimes and finding solutions for them by using analytical and investigative techniques. These analytical and investigative techniques involve the acquisition, analysis and documentation of computer data related to crimes. This paper describes the computer forensics, security issues in computer and network, types of computer crimes, examples of computer crimes, tools of computer forensics, cyber laws, courses and career in computer forensics.

References

[1]. Prosise, C., Mandia, K., and Pepe, M. (2003). Incident Response & Computer Forensics. (p.11). McGraw- Hill/Osborne.

[2]. Schneier, B., and Kelsey, J. (1999). “Secure audit logs to support Computer Forensics”. ACM Transactions on Information and System Security (TISSEC), Vol. 2, No. 2, pp. 159-176.

[3]. Vacca, J. R. (2005). Computer Forensics: Computer Crime Scene Investigation (Networking Series). Charles River Media, Inc.

[4]. Kruse II, W. G., and Heiser, J. G. (2001). Computer Forensics: Incident Response Essentials. Pearson Education.

[5]. Marcella Jr, A., and Greenfield, R. S. (Eds.), (2002). Cyber Forensics: A Field Manual for Collecting, Examining, and Preserving Evidence of Computer Crimes. CRC Press.

[6]. Rogers, M. K., and Seigfried, K. (2004). “The Future of Computer Forensics: a Needs Analysis Survey ”. Computers & Security, Vol. 23, No. 1, pp. 12-16.

[7]. Yasinsac, A., Erbacher, R. F., Marks, D. G., Pollitt, M. M., and Sommer, P. M. (2003). “Computer Forensics Education”. IEEE Security & Privacy, Vol. 4, pp. 15-23.

[8]. Rogers, M. K., Goldman, J., Mislan, R., Wedge, T., and Debrota, S. (2006). “Computer Forensics Field Triage Process Model”. In Proceedings of the Conference on Digital Forensics, Security and Law (p. 27). Association of Digital Forensics, Security and Law.

[9]. Nelson, B., Phillips, A., and Steuart, C. (2015). Guide to Computer Forensics and Investigations. Cengage Learning.

[10]. Caloyannides, M. A. (2001). Computer Forensics and Privacy. Artech House Publishers.

[11]. Meyers, M., and Rogers, M. (2004). “Computer Forensics: The Need for Standardization and Certification”. International Journal of Digital Evidence, Vol. 3, No. 2, pp. 1- 11.

[12]. Yasinsac, A., and Manzano, Y. (2001). “Policies to Enhance Computer and Network Forensics”. In Proceedings of the 2001 IEEE Workshop on Information Assurance and Security, pp. 289-295.

[13]. Vacca, J. R. (2002). Computer Forensics: Computer Crime Scene Investigation. Charles River Media, Inc.

[14]. Rogers, M. (2003). The Role of Criminal Profiling in the Computer Forensics Process. Computers & Security, Vol. 22, No. 4, pp. 292-298.

[15]. Fahey, A. L. (2008). Computer Forensics, E-Discovery and Incident Response Methods and Systems. U.S. Patent Application No. 12/318,083.

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Research Paper

Data Mining Approach For Advancement of “Association Rule Mining” Using “R Programming”

Rahul Kumar Vij* , Parveen Kalra, C. S. Jawalkar *

PG Scholar, Department of Production and Industrial Engineering, PEC University of Technology, Chandigarh, India.

Professor, Department of Production and Industrial Engineering, PEC University of Technology, Chandigarh, India.

Assistant Professor, Department of Production and Industrial Engineering, PEC University of Technology, Chandigarh, India.

Vij, R. K., Kalra, P., and Jawalkar, C. S. (2016). Data Mining Approach For Advancement of “Association Rule Mining” Using “R Programming. i-manager’s Journal on Software Engineering, 10(4), 6-12. https://doi.org/10.26634/jse.10.4.6057

Abstract

Over the years, data study was focused on finding proofs using rough datasets which made data mining to jump into, to show its strengths. This field gave the researchers a pictorial view of trends and patterns that can help in predictive maintenance. Algorithms available in past are unable to handle rough datasets; especially when major issues were processing data in advance and making an understandable model. Association rules were made to predict the correlation between the attributes. The current study mainly focuses on the trends followed by the “car evaluation dataset attributes” to give a suitable rating for a car. Arules library in “R” is used to reflect the same as well as some preprocessing is done. The results are then compared with “Step Induction Association Rule Mining” i.e. advancement done as well as orange classifier. The results clearly reflect that rules formed can help in predictive analysis of a car by knowing its features and specifications and the step induction rules give more clarity about the predictions.

References

[1]. Agrawal, R., Imieliki, T. and Swami, A., (1993). “Mining association rules between sets of items in large databases”. ACM SIGMOD Record, Vol. 22, No. 2, pp.207-216.

[2]. Wikipedia (2016). Association Rule Learning. Retrieved from https: // en.wikipedia. org /w/ index. php? title=Association_rule_learning & old id=704096918 (accessed February 20, 2016).

[3]. Zupan, Blaz, et al. (1997). “Machine learning by function decomposition”. ICML.

[4]. Bohanec, M. and Rajkovic, V., (1988). “Knowledge acquisition and explanation for multi-attribute decision th making”. In 8 Intl Workshop on Expert Systems and their Applications, pp. 59-78.

[5]. Aranganayagi, S. and Thangavel, K., (2009). “Improved k-modes for categorical clustering using weighted dissimilarity measure”. International Journal of Information and Mathematical Sciences, Vol. 5, No. 2, pp. 182-188.

[6]. Dai, J., Xu, Q. and Wang, W., (2011). “A comparative study on strategies of rule induction for incomplete data based on rough set approach”. International Journal of Advancements in Computing Technology, Vol. 3, No. 3, pp. 176-183.

[7]. Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M. and Baesens, B., (2007). “Classification with Ant Colony Optimization”. Evolutionary Computation, IEEE Transactions on, Vol. 11, No. 5, pp. 651-665.

[8]. Moshkovich, H.M., Mechitov, A.I. and Olson, D.L., (2002). “Rule induction in data mining: Effect of ordinal scales”. Expert Systems with Applications, Vol. 22, No. 4, pp. 303-311.

[9]. Wong, A.K., Chiu, D.K. and Huang, W., (2001). “A discrete-valued clustering algorithm with applications to biomolecular data”. Information Sciences, Vol. 139, No. 1, pp. 97-112.

[10]. Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

[11]. Zhao, Y., (2015). “Association Rule Mining with R”. Australian Customs, Canberra, Australia, pp. 1-30.

[12]. Zhao, Y., (2012). R and Data Mining: Examples and Case Studies. Academic Press.

[13]. Hahsler, M., Buchta, C., Gruen, B., Hornik, K. and Hahsler, M.M., (2014). Package 'arules'.

[14]. Hahsler, M., Chelluboina, S. and Hahsler, M.M., (2011). “Package 'arulesViz'”. R-project CRAN.

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Review Paper

A Review of Techniques for Real time Authentication

Sabarinathan E.* , E. Manoj**

* Research Scholar, Erode, Tamilnadu, India.

** UG Scholar, Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India.

Sabarinathan, E., and Manoj, E. (2016). A Review of Techniques for Real time Authentication. i-manager’s Journal on Software Engineering, 10(4), 13-29. https://doi.org/10.26634/jse.10.4.6058

Abstract

The Development of multimedia applications makes digital media to bring about conveniences to the people by easy processing of data. At the same time, it enables the illegal attackers to attack the works. For the protection of data, there has been growing interest in developing effective techniques to discourage the unauthorized duplication of digital data. Among Cryptography and Steganography, only the watermarking provides the copyright protection and authentication. This paper is a comprehensive review of diverse image processing methods and enormous number of interrelated solicitations in various disciplines, including various watermarking techniques. In this paper, different existing techniques are discussed along with their drawbacks, also explaining their future scope.

References

[1]. Sabarinathan E, and Manoj E, (2015). “FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm”. In: i-manager’s Journal on Pattern Recognition, Vol. 2, No. 2, pp. 19-28.

[2]. Arathi, C., and Chandra, M, (2012). “Authentication of Images Through Lossless Watermarking Technique with the Aid of Elliptic Curve Cryptography (ECC)”. In: International Journal of Computer Applications, Vol. 57, No. 6, pp. 17-25.

[3]. Baisa, G, (2011). “Wavelet Based Color Image Watermarking Scheme Giving High Robustness and Exact Correlation”. In: International Journal of Emerging Trends in Engineering and Technology, Vol. 1, No. 1, pp. 23-30.

[4]. Devapriya, M., and Ramar, K, (2010). “Statistical Image Water marking in DWT with Capacity Improvement”. In: Global Journal of Computer Science and Technology, Vol. 10, No. 2, pp. 20-24.

[5]. Dinu C, (2011). “Improved embedding for prediction based reversible watermarking”. In: IEEE Transactions on Information Forensics and security, Vol. 6, No. 3, pp. 873- 882.

[6]. Jih, P., Che, W., Wei, J., and Hung, H, (2010). “Watermarking technique based on DWT associated with embedding rule”. In: International Journal of Circuits, Systems and Signal Processing, Vol. 4, No. 2, pp. 72-82.

[7]. Anumol, T. J, Binson, V. A, and Soumya, R, (2013). “FPGA Implementation of Low Power, High Speed, Area Efficient Invisible Image Watermarking Algorithm for Images”. In: International Journal of Scientific And Engineering Research, Vol. 4, No. 8.

[8]. Sandeep, K., and Rajiv B, (2013). “Enhanced Technique for Watermarking using MFHWT”. In: International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 5, pp. 1199-1202.

[9]. Umaamaheshvari, A., and Thanushkodi, K, (2012). “A novel watermarking technique based on visual cryptography”. In: International Journal of Advanced Research in Computer Engineering and Technology, Vol. 1, No. 7, pp. 70-74.

[10]. Yang, C., and Tsai, M., (2010). “Improving histogram-based reversible data hiding by interleaving predictions”. In: IET Image Process, Vol. 4, No. 4, pp. 223- 234.

[11]. Bo, O., Rongrong, N., and Gang. C, (2010). “A high payload histogram-based reversible watermarking using linear prediction”. In: Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 446-449.

[12]. Pan, W., Coatrieux, G., Cuppens, N., Cuppens, F., and Roux, C, (2010). “An Additive and Lossless Watermarking Method Based on Invariant Image nd Approximation and Haar Wavelet Transform”. In: 32 Annual International Conference of the IEEE EMBS Buenos Aires, pp. 4740-4743.

[13]. Sujatha, S., and Mohamed. S, (2010). “Feature Based Watermarking Algorithm by Adopting Arnold Transform”. In: Proceedings of Springer International Conference on Information and Communication Technologies ICT, pp.78-82.

[14]. Karthigaikumar, P., and Baskaran. K, (2010a). Microelectronics. In: Elsevier. Available from: Elsevier books.

[15]. Karthigaikumar. P., Anumol., and Baskaran, (2011). “FPGA Implementation of High Speed Low Area DWT Based Invisible Image Watermarking Algorithm”. In: International Journal of System Architecture, Vol. 57, No. 4, pp. 404-411.

[16]. Karthigaikumar, P., and K Baskaran, (2010b). “An ASIC implementation of a low power invisible robust watermarking processor”. In: International Journal of System Architecture, Vol. 57, No. 4, pp. 404-411.

[17]. Tamilvanan, and Selvakumar, (2014). “FPGA Implementation of Digital Watermarking System”. In: International Journal of Computer Science and Mobile Computing, Vol. 3, No. 4.

[18]. Rohollah Mazrae Khoshki, Sami Oweis, and Shumei Wang, (2014). “FPGA Hardware Based Implementation of an Image Watermarking System”. In: International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, No. 5.

[19]. Nallagarla Ramamurthy, and Varadarajan, (2012). “Robust Digital Watermarking Scheme with Neural Network and Fuzzy Logic Approach”. In: International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 9.

[20]. Sameh Oueslati, Adnene Cherif and Bassel Solaimane, (2011). “Adaptative Image Watermarking Scheme Based on Neural Network”. In: International Journal of Engineering Science and Technology, Vol. 3 No. 1.

[21]. BibiIsac V, and Santhi, (2011). “A Study on Digital Image and Video Watermarking Schemes using Neural Networks”. In: International Journal of Computer Applications, Vol. 12, No. 9.

[22]. Nanda yalamali, and Manjunath Asuti, (2012). “An Adaptive Image Watermarking Algorithm based on Neural Networks”. In: International Journal of Scientific & Engineering Research, Vol. 3, No. 7.

[23]. Remya, and Sumithra, (2013). “Development of a New Watermarking Algorithm for Telemedicine Applications”. In: International Journal of Engineering Research and Applications (IJERA), ISSN: 2248-9622, Vol. 3, No. 1, pp. 962-968.

[24]. Manoj, and Gireendranath, (2012). “Novel Invisible and Blind Watermarking Scheme for Copyright Protection of Digital Images”. In : International Journal of Engineering Research & Technology, Vol. 1, No. 7.

[25]. Shinfeng, and Shih, (2010). “Improving the robustness of DCT-based image watermarking against JPEG compression”. In: Elsevier, Computer Standards & Interfaces, pp.54-60.

[26]. Tomaz, and Andrej, (2012). “Block–wise authentication method for digital images”. In: Journal of Electrical Engineering, Vol. 63, No. 5, pp. 289-295, ISSN(Online), ISSN (Print) 1335-3632, DOI: 10.2478/v10187- 012-0042-6.

[27]. Veera, (2010). “A novel technique for image steganography based on Block-DCT and Huffman Encoding ” . 06/2010; abs/ 1006.1186(3). DOI: 10.5121/ijcsit.2010.2308.

[28]. Gouenou, et al. (2013). “Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting”. In: IEEE, in: Press.

[29]. Rajendra, and Narawade, (2012). “Reduced Pixel Shifting and Improved Histogram Shifting Based Reversible Watermarking for Cartoon Images”. In: International Journal of Computer Science and Telecommunications, Vol. 3, No. 4.

[30]. Shyang, Zhenfei, Zhe and Hao, (2011). “Reversible data hiding based on multilevel histogram modification and sequential recovery”. In: Elsevier, International Journal of Electronics and Communications, pp.814- 826.

[31]. Kiran and Kanwal Garg, (2015). “Watermark Embedding and Extraction using Histogram Shifting and Butterworth Filtering”. IJARCSSE, Vol. 5, No. 5, pp. 1481- 1487.

[32]. Umaamaheshvari, A., and Thanushkodi K, (2013). “Robust image watermarking based on block based error correction code”. In: International Journal of Advanced Research in Computer Engineering and Technology, Vol. 1, No. 7, pp. 70-74.

[33]. Sujatha, and Mohamed, (2012). “A Novel DWT Based Blind Watermarking for Image Authentication”. In: International Journal of Network Security, Vol. 14, No. 4, pp. 223-228.

[34]. Hazem, and Nedal, (2010). “A robust Blind color image watermarking based on wavelet-tree bit host difference selection”. ACM Digital Library, Vol. 90, No. 8, pp. 2498-2512. 2010, DOI:10.1016/j.sigpro.2010.02.017.

[35]. Roseline, and Sumalatha, (2012). “Fuzzy Based Chaotic and Logistic Method for Digital Watermarking Systems”. In: International Journal of Scientific & Engineering Research, Vol. 3, No. 6, ISSN 2229-5518.

[36]. Maruthu Perumal, and Vijayakumar, (2012). “Sorted Pixel Value Difference on Fuzzy Watermarking Scheme”. In: Global Journals Inc. Vol. 12, No. 4.

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[38]. Manikandan, R., Uma, M., and Mahalakshmi Preethi, S, (2012). “Reversible Data Hiding for Encrypted Image”. In: Journal of Computer Applications, Vol. 5, No. 1, pp. 104-110

[39]. A. Mohamed Zuhair., and Mohamed Yousuf, (2010). “FPGA based image security and authentication in digital camera using invisible watermarking technique”. In: International Journal of Engineering Science and Technology, Vol. 2, No. 6.

[40]. Ramezani, H., Keynia, F., Ramezani. F, (2013). “A Novel Image Steganography in Contourlet domain using Genetic Algorithm”. In: International Journal of Future Computer and Communication, Vol. 2, No. 4, pp. 359-363.

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Review Paper

Mining Fuzzy Association Rules Using Various Algorithms: A Survey

O. Gireesha* , O. Obulesu**

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.

** Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.

Gireesha, O., and Obulesu, O. (2016). Mining Fuzzy Association Rules Using Various Algorithms: A Survey. i-manager’s Journal on Software Engineering, 10(4), 30-36. https://doi.org/10.26634/jse.10.4.6059

Abstract

The discovery of Association Rules (AR) acquire an imperative rule in Data Mining, which tries to find correlation among the attributes in a database. Classical Association Rules are meant for Boolean data and they suffer from a sharp boundary problem in handling quantitative data. Thereby, Fuzzy Association Rules (FAR) with fuzzy minimum support and confidence is introduced. In Fuzzy Association Rule Mining (FARM), determining fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence, FARM can be viewed as a multi-objective optimization problem. In this paper, different algorithms for FARM are discussed with merits and demerits.

References

[1]. R. Srikant, and R. Agrawal, (1996). “Mining Quantitative Association Rules in Large Relational Tables”. ACMSIGMOD,International Conference on Management of Data, pp.1-12, Montreal, Canada.

[2]. Chan, C.C. Keith, and A.U. Wai-Ho, (1997). “Mining fuzzy association rules”. In: Proceedings of the Sixth International Conference on Information and Knowledge Management, ACM.

[3]. M. Kuok, A.W. C. Fu, and M. H. Wong, (1998). “Mining Fuzzy Association Rules in Databases”. ACM SIGMOD Record, Vol. 27, No. 1, pp. 41-46.

[4]. W. Au, and K. Chan, (1998). “An Effective Algorithm for Discovering Fuzzy Rules in Relational Databases”. Proceeding of the 1998 IEEE International Conference on Fuzzy Systems, pp. 1314-1319, Anchorage, Alaska.

[5]. T. P. Hong, C. S. Kuo, and C. S. Chi, (2001). “Trade-off between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data”. International Journal of Uncertainty, Fuzziness and Knowledge-Based System, Vol. 9, No. 5, pp. 587- 604.

[6]. K. Hirota, and W. Pedrycz, (1999). “Fuzzy Computing for Data Mining”. Proceedings of the IEEE, Vol. 87, No. 9, pp. 1575.

[7]. W. Zhang, “Mining Fuzzy Quantitative Association rules”. Proceeding of the 11^th International Conference on Tools with A.I., pp. 99-102, Chicago, IL, USA.

[8]. J. Han, J. Pei, and Y. Yin, (2000). “Mining frequent patterns without candidate generation”. The 2000 ACM SIGMOD International Conference on Management of Data, Vol. 29, No. 2, pp. 1-12.

[9]. H. Ishibuchi, T. Nakashima, and T. Murata, (2001). “Fuzzy Data Mining: Effect of Fuzzy Discretization”. Proceeding of 2001 IEEE International Conference on Data Mining, pp.241- 248.

[10]. S. Mitra, S. Pal, and P. Mitra, (2002). “Data Mining in Soft Computing Framework: A Survery”. IEEE Transactions on Neural Networks, Vol. 13, No. 1.

[11]. G. Chen, and Q. Wei, (2002). “Fuzzy Association Rules and the Extended Mining Algorithm”. Information Sciences, Vol. 147, pp.201-228.

[12]. Y. C. Hu, R. S. Chen, and G. H. Tzeng, (2003). “Discovering fuzzy Association Rules using Fuzzy Partition Methods”. Knowledge Based Systems, Vol. 16, pp. 137-147.

[13]. Tzung-Pei Hong, Chan - Sheng Kuo, and Shyue - Liang Wang, (2004). “A fuzzy AprioriTid mining algorithm with reduced computational time”. Applied Soft Computing, Vol. 5, No. 1, pp. 1-10.

[14]. S. Papadimitriou, and S. Mavroudi, (2005). “The fuzzy frequent pattern tree” In: The WSEAS International Conference on Computers, pp. 1-7.

[15]. E Ramaraj, K Rameshkumar, and N Venkatesan, (2008). “A better performed transaction reduction algorithm for mining frequent itemset from large voluminous database”. Proceedings of the 2^nd National Conference, INDIACOM-2008, Vol. 5, pp. 1-10.

[16]. Reza Sheibani, and Amir Ebrahimzadeh, (2008). “An Algorithm For Mining Fuzzy Association Rules”. Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. 1, pp. 486-490.

[17]. Ashish Mangalampalli, and Vikram Pudi, (2010). “FPrep: Fuzzy Clustering driven Efficient Automated Preprocessing for Fuzzy Association Rule Mining”. IEEE Intl Conference on Fuzzy Systems (FUZZ-IEEE).

[18]. K. Suriya Prabha, and R. Lawrance, (2012). “Mining Fuzzy Frequent itemset using Compact Frequent Pattern (CFP) tree Algorithm”. International Conference on Computing and Control Engineering (ICCCE 2012).

[19]. Sunita Soni, and O.P. Vyas, (2012). “Fuzzy Weighted Associative Classifier: A Predictive Technique For Health Care Data Mining”. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 2, No. 1.

[20]. J. Preethi, (2013). “Temporal outlier detection using fuzzy logic and evolutionary computation”. International Conference on Optical Imaging Sensor and Security (ICOSS), pp. 2-3.

[21]. Anubha Sharma, and Nirupama Tiwari, (2014). “A Survey of Fuzzy Based Association Rule Mining to Find Cooccurrence Relationships”. IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 16, No. 1, pp. 83-87.

[22]. Urvi A. Chaudhary, and Mahesh Panchal, (2014). “Mining Multilevel Fuzzy Association Rule from Transaction Data”. International Journal of Computer Science and Mobile Computing, Vol. 3, No. 2, pp. 773-778.

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Research Paper

Accomplishing Bi-Directional Vertical Architectural Scalability with Software Stability

M.E. Fayad* , Shivanshu K. Singh, Rafael Capilla*

* Professor, Department of Computer Engineering, Charles W. Davidson College of Engineering, San Jose State University, San Jose, USA.

Software/Data Engineering and DevOps, Athos, San Francisco, California, USA.

* Associate Professor, University Rey Juan Carlos of Madrid (Spain), USA.

Fayad, M. E., Singh, S. K., and Capilla, R. (2016). Accomplishing Bi-Directional Vertical Architectural Scalability with Software Stability. i-manager’s Journal on Software Engineering, 10(4), 37-42. https://doi.org/10.26634/jse.10.4.6060

Abstract

While moving away from traditional approaches to build software and design software architecture, the authors realized that it is sensible to migrate to a platform of better fundamental approach. This refers to the way one looks at the analysis and design of any software. This helps one to weave into the system's architecture itself like different qualities such as adaptability, extensibility, scalability and stability over time, than worrying about them at a much later stage. Such architecture will compel developers to find fixes to address the issues related to such quality factors; this approach usually culminates in creating projects that incur very high costs and unprecedented consequences that are bad and unwanted.

References

[1]. M. E Fayad, (2002). “Accomplishing Software Stability.” Communications of the ACM, Vol. 45, No. 1.

[2]. Mohamed E. Fayad, Ralph E. Johnson, and Douglas C. Schmidt, (1999). Building Application Frameworks: Object-Oriented Foundations of Framework Design. Wiley, John & Sons, Incorporated.

[3]. M. E. Fayad, (2002). “How to Deal with Software Stability.” Communications of the ACM, Vol. 45, No. 4.

[4]. M. E. Fayad and S. Wu, (2002). “Merging Multiple Conventional Models into One Stable Model ”. Communications of the ACM, Vol. 45, No. 9.

[5]. M. E. Fayad. H. Sanchez, et. al. Knowledge Maps, (Book, In progress).

[6]. M. E. Fayad. Stable Analysis Patterns, (2-volume Book, In progress).

i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 18 Issue 2

Most Read

Most Cited

Volume 10 Issue 4 April - June 2016

Computer Forensics: An Overview

K.V.N. Rajesh* , K.V.N. Ramesh**

* HOD, Department of Information Technology, Vignan’s Institute of Information Technology, Visakhapatnam, India. ** Project Manager, Tech Mahindra, Visakhapatnam, India.

Rajesh, K. V. N., and Ramesh, K. V. N. (2016). Computer Forensics: An Overview. i-manager’s Journal on Software Engineering, 10(4), 1-5. https://doi.org/10.26634/jse.10.4.6056

Abstract

References

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Data Mining Approach For Advancement of “Association Rule Mining” Using “R Programming”

Rahul Kumar Vij* , Parveen Kalra**, C. S. Jawalkar ***

Vij, R. K., Kalra, P., and Jawalkar, C. S. (2016). Data Mining Approach For Advancement of “Association Rule Mining” Using “R Programming. i-manager’s Journal on Software Engineering, 10(4), 6-12. https://doi.org/10.26634/jse.10.4.6057

Abstract

References

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A Review of Techniques for Real time Authentication

Sabarinathan E.* , E. Manoj**

* Research Scholar, Erode, Tamilnadu, India. ** UG Scholar, Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India.

Sabarinathan, E., and Manoj, E. (2016). A Review of Techniques for Real time Authentication. i-manager’s Journal on Software Engineering, 10(4), 13-29. https://doi.org/10.26634/jse.10.4.6058

Abstract

References

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Mining Fuzzy Association Rules Using Various Algorithms: A Survey

O. Gireesha* , O. Obulesu**

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India. ** Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.

Gireesha, O., and Obulesu, O. (2016). Mining Fuzzy Association Rules Using Various Algorithms: A Survey. i-manager’s Journal on Software Engineering, 10(4), 30-36. https://doi.org/10.26634/jse.10.4.6059

Abstract

References

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Accomplishing Bi-Directional Vertical Architectural Scalability with Software Stability

M.E. Fayad* , Shivanshu K. Singh**, Rafael Capilla***

* Professor, Department of Computer Engineering, Charles W. Davidson College of Engineering, San Jose State University, San Jose, USA. ** Software/Data Engineering and DevOps, Athos, San Francisco, California, USA. *** Associate Professor, University Rey Juan Carlos of Madrid (Spain), USA.

Fayad, M. E., Singh, S. K., and Capilla, R. (2016). Accomplishing Bi-Directional Vertical Architectural Scalability with Software Stability. i-manager’s Journal on Software Engineering, 10(4), 37-42. https://doi.org/10.26634/jse.10.4.6060

Abstract

References

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* HOD, Department of Information Technology, Vignan’s Institute of Information Technology, Visakhapatnam, India.

** Project Manager, Tech Mahindra, Visakhapatnam, India.

Rahul Kumar Vij* , Parveen Kalra, C. S. Jawalkar *

* Research Scholar, Erode, Tamilnadu, India.

** UG Scholar, Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, India.

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.

** Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.

M.E. Fayad* , Shivanshu K. Singh, Rafael Capilla*

* Professor, Department of Computer Engineering, Charles W. Davidson College of Engineering, San Jose State University, San Jose, USA.

Software/Data Engineering and DevOps, Athos, San Francisco, California, USA.

* Associate Professor, University Rey Juan Carlos of Madrid (Spain), USA.