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Suresh Joseph, K.
- Agent Based Test Automation for New Generation Web Applications
Abstract Views :175 |
PDF Views:6
Authors
Affiliations
1 Department of Computer Science and Engineering, Dr. Pauls Engineering College, Anna University, Villupuram, Tamil Nadu, IN
2 Department of Computer Science, Pondicherry University, Kalapet, Pondicherry, IN
1 Department of Computer Science and Engineering, Dr. Pauls Engineering College, Anna University, Villupuram, Tamil Nadu, IN
2 Department of Computer Science, Pondicherry University, Kalapet, Pondicherry, IN
Source
Software Engineering, Vol 2, No 5 (2010), Pagination: 74-78Abstract
Now a days new generation Interactive and attractive dynamic web applications are developed by Silverlight. It is developed my Microsoft on Dot Net 3.5 Frame work using extended Application Markup Language (XAML). User Interface Test Automation for Silverlight Applications (UITASA) plays a vital role in software industry; especially User Interface Test Automation (UITA) in new technology like Silverlight is a challenging task because of its high security and low accessibility. Agent Technology is intermediate software that provides a better bridge between User Interface Test Automation and Silverlight applications. There are different software agents for each group of controls to do User Interface Test Automation. For example button control agent will take care of all buttons in User Interface Test Automation.Keywords
User Interface, Agents Test Automation, Silverlight Web Applications.
- Face Searching and Matching with Iris Recognition by Diagonal Square Matrix
Abstract Views :204 |
PDF Views:3
Authors
Affiliations
1 Department of CSE, Dr. Pauls Engineering College, Villupuram, Tamilnadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
1 Department of CSE, Dr. Pauls Engineering College, Villupuram, Tamilnadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
Source
Biometrics and Bioinformatics, Vol 2, No 7 (2010), Pagination: 105-112Abstract
Face Searching and Matching is a challenging task in the field of Image processing. This paper presents a novel approach in digital image of face searching and matching with iris recognition. In the field of security Face and iris identification is very important. In this paper we concentrated face searching and matching with iris recognition. The given key image is converted into gray scale image and after that a matrix is computed with gray scale values of the key image. Then we are collecting the diagonal key elements for diagonal searching key sequence. Using Pair wise sequence alignment we are trying to match the key with available images in the large data base of collection of faces. Initially we discussed various techniques used in digital image searching and matching in this paper. This new algorithm Diagonal matrix is a new algorithm for all face images searching and matching. There are several algorithms for face image matching. But still needs more optimization for image matching. Using this new approach we can match criminal photo from a large database. Face Image recognition, feature extraction and pattern matching needs improvements in Image processing. There are several methods for Face image searching and matching, but we need new optimized technique for image searching and matching. This new Diagonal matrix approach is tried to give optimized solution in Face digital image matching.
Keywords
Image Retrieval, Face and Iris Detection and Matching, Face Recognition, Affine Invariant, Face Searching and Matching, Diagonal Searching and Pair Wise Alignment.- Preventive and Protective Methods to Avoid Spreading Viruses from USB Drives using Windows Registry
Abstract Views :211 |
PDF Views:5
Authors
Affiliations
1 Department of CSE, Dr. Pauls Engineering College, Villupuram, Affiliated to Anna University of Technology, Chennai, Tamil Nadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
1 Department of CSE, Dr. Pauls Engineering College, Villupuram, Affiliated to Anna University of Technology, Chennai, Tamil Nadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
Source
Automation and Autonomous Systems, Vol 3, No 1 (2011), Pagination: 12-18Abstract
Protecting and securing from viruses is always a challenging task. Lot of viruses occupy space in disk and degrades the performance. USB Hard Disk and Flash Drive play a major role in spreading viruses. In this paper we discussed about preventive measures to avoid spreading viruses from USB Hard Disk and Flash Drive to system. Initially we discussed about stand alone system protection and then Anti – virus software for virus detection. Third is taking no action while connecting USB drive and manual deletion of viruses. Finally we discussed Preventive measures to avoid spreading viruses from USB Hard Disk and Flash Drive.Keywords
Preventive Measures from Viruses Spreading, USB Hard Disk and Flash Drive Virus Prevention.- Design and Architecture for Moonlight Web Applications Test Automation
Abstract Views :209 |
PDF Views:4
Authors
Affiliations
1 Dept. of CSE, Dr. Pauls Engineering College, Anna University of Technology-Chennai, Villupuram, Tamilnadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
1 Dept. of CSE, Dr. Pauls Engineering College, Anna University of Technology-Chennai, Villupuram, Tamilnadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
Source
Automation and Autonomous Systems, Vol 2, No 9 (2010), Pagination: 81-88Abstract
The main objectives of Test Automation are minimizing cost, time and man power. Initially web pages are developed by HTML pages. But now web applications are developed by new technologies like Moonlight, Silverlight, JAVAFX, FLEX, etc. Accessing and automating of Silverlight and moonlight controls are not so easy because of their security and technology. The manually testing of complex software becomes more difficult and challenging task. Moonlight is a new Micro Soft .NET technology to develop rich interactive and attractive Internet web applications with the collaboration of Novel Corporation with Linux support. Testing these kinds of applications are not so easy, especially the User interface test automation is very difficult for these kinds of web applications. In this paper we propose a framework for moonlight web applications test automation. It has the capability to decrease the overall cost of testing and improve software quality, but most testing organizations have not been able to achieve the full potential of test automation. Sometimes test automation programs run into a number of common pitfalls because of its design and architecture failure. In this paper we first discussed some of the key benefits of software test automation, and then examine the most common techniques used to implement software test automation of Moonlight web applications Test Automation and their potentials. Finally, The Architecture design and implementation of Test Automation for Moonlight web applications.Keywords
Graphical User Interface Test Automation, Moonlight Web Applications Automation.- Financial forecasting Using Decision Tree (reptree&C4.5) and Neural Networks (K*) for Handling the Missing Values
Abstract Views :346 |
PDF Views:3
Authors
Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, IN
2 Department of Computer Science, Pondicherry University, IN
1 School of Computer Science and Engineering, Lovely Professional University, IN
2 Department of Computer Science, Pondicherry University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1473-1477Abstract
Missing values are a widespread problem in data analysis. The purpose of this paper is to design a model to handle the missing values in predicting financial health of companies. Forecasting business failure is an important and challenge task for both academic researchers and business practitioners. In this study, we compare the classification of accuracy in decision tree methods (REP tree, C4.5) and with ANN method (K*) to handle the missing values.Keywords
Bankruptcy Prediction, Missing Values, Decision Tree (REPTree, C4.5), ANN (K*).References
- Wei-Yang Lin, Ya-Han Hu and Chih-Fong Tsai, “Machine Learning in Financial Crisis Prediction: A Survey”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 42, No. 4, pp. 421-436, 201
- Damrongrit Setsirichok, Theera Piroonratana, Waranyu Wongsere, Touchpong Usavanarong, Nuttawut Paulkhaolarn, Chompunut Kanjanakorn, Monchan Sirikong, Chanin Limwongse and Nachol Chaiyaratana, “Classification of Complete Blood Count and Haemoglobin Typing Data by a C4.5 Decision Tree, A Naive Bayes Classifier and Multilayer Perception for Thalassaemica Screening”, Biomedical Signal Processing and Control, Vol. 7, No. 2, pp. 202-212, 2012.
- Amita Karmaker and Stephen Kwek, “Incorporating An EM- Approach for Handling Missing Attribute Values in Decision Tree Induction”, Proceedings of IEEE 5th International Conference on Hybrid Intelligent Systems, pp. 1-6, 2005
- Taghi M. Khoshgoftaar, Andres Follcco, Jason Van Hulse and Lofton Bullard, “Software Quality Imputation in the Presence of Noisy Data”, Proceedings of IEEE International Conference on Information Reuse and Integration, pp. 484-489, 2006.
- John. G. Cleary and Leonard E. Trigg, “K* An Instance based Learner using Entropic Distance Measure”, Proceedings of International Conference on Machine Learning, pp. 108-114, 1995.
- Elaze Zibanezhad, Daryush Foroghi and Amirthassan Monadjemi, “Applying Decision Tree to Predict Bankruptcy”, Proceedings of IEEE International Conference on Computer Science and Automation Engineering, Vol. 4, pp. 165-169, 2011.
- J. Jayanthi, K. Suresh Joseph and J. Vaishnavi, “Bankruptcy Prediction using SVM and Hybrid SVM Survey”, International Journal of Computer Applications, Vol. 34, No. 7, pp. 39-45, 2011.
- Qin Zheng and Jiang Yanhui, “Financial Distress Prediction based on Decision Tree Models”, Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 1-6, 2007.
- Ming-Hua Chen, “Pattern Recognition of Business Failure by Auto Associative Artificial Neural Networks in Considering the Missing Values”, Proceedings of IEEE International Computer Symposium, pp. 711-715, 2010.
- Maytal Saar-Tsechansky and Foster Provost, “Handling Missing Values when Applying Classification Models”, Journal of Machine Learning Research, Vol. 8, pp. 1625-1657, 2007.
- P. Ravi kumar and V. Ravi, “Bankruptcy Prediction in Banks and Firms Via Statistical and Intelligent Technique- A Review”, European Journal of Operational Research, Vol. 180, No. 1, pp. 1-28 ,2007.
- Nikolaos Mallios, Elpiniki Papageorgion and Michael Samarinas, “Comparison of Machine Learning Technique using the WEKA Environment for Prostate Cancer Therapy Plan”, Proceedings of IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 151-155, 2011.
- Brijesh Kumar Baradwaj and Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, pp. 63-70, 2011.
- Abdullah AL-Malaise, Areej Malibari and Mona Alkhozae, “Students Performance Prediction System using Multi Agent Data Mining Technique”, International Journal of Data Mining and Knowledge Management Process, Vol. 4, No. 5, pp. 1-20, 2014.
- Kamal Bunkar, Rajessh Kumar, Umesh Kumar and Singhand Bhupendra Pandya, “Data Mining: Prediction for Performance Improvement of Graduate Students using Classification”, Proceedings of 9th International Conference on Wireless and Optical Communications Networks, pp. 1-5, 2012.
- S. Venkata Krishna Kumar and S. Padmapriya, “An Efficient Recommender System for Predicting Study Track to Students using Data Mining Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, No. 9, pp. 7996-7999, 2014.
- Dorina Kabakchieva, “Predicting Student Performance by using Data Mining Methods for Classification”, Cybernetics and Information Technologies, Vol. 13, No. 1, pp. 61-72, 2013.
- Bashir Khan, Malik Sikandar Hayat Khiyal and Muhammad Daud Khattak, “Final Grade Prediction of Secondary School Student using Decision Tree”, International Journal of Computer Applications, Vol. 115, No. 21, pp. 32-36, 2015.
- G. Naga Raja Prasad and A. Vinaya Babu, “Mining Previous Marks Data to Predict Students Performance in Their Final Year Examinations”, International Journal of Engineering Research and Technology, Vol. 2, No. 2, pp. 1-4, 2013
- Jyoti Namdeo and Naveenkumar Jayakumar, “Predicting Students Performance using Data Mining”, International Journal of Advance Research in Computer Science and Management Studies, Vol. 2, No. 2, pp. 367-373, 2014.
- Md. Hedayetul Islam Shovon and Mahfuza Haque, “Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 7, pp. 353-355, 2012
- Face Searching and Matching Using Gray Scale Diagonal Square Matrix
Abstract Views :103 |
PDF Views:0
Authors
Affiliations
1 Department of CSE, Dr. Pauls Engineering College, Villupuram, Tamilnadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
1 Department of CSE, Dr. Pauls Engineering College, Villupuram, Tamilnadu, IN
2 Department of Computer Science, Pondicherry University, Pondicherry, IN
Source
International Journal of Advanced Networking and Applications, Vol 2, No 2 (2010), Pagination: 602-607Abstract
This paper presents a novel approach in face digital image searching and matching. The given key image is converted into gray scale image and after that a matrix is computed with gray scale values of the key image. Then we are collecting the diagonal key elements for diagonal searching key sequence. Using Pair wise sequence alignment we are trying to match the key with available images in the large data base of collection of faces. Initially we discussed various techniques used in digital image searching and matching in this paper. This new algorithm Diagonal matrix is a new algorithm for all face images searching and matching. Face recognition is very essential in the field of criminology. Face Image searching and matching are very difficult task in image processing; there are several algorithms for face image matching. But still needs more optimization for image matching. Using this new approach we can match criminal photo from a large database. Face Image recognition, feature extraction and pattern matching needs improvements in Image processing. There are several methods for Face image searching and matching, but we need new optimized technique for image searching and matching. This new Diagonal matrix approach is tried to give optimized solution in Face digital image matching.Keywords
Image Retrieval, Edge Detection and Matching, Face Recognition, Affine Invariant, Image Searching and Matching, Diagonal Searching and Pair Wise Alignment.Full Text