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Jayanthi, J.
- A Novel Relevance Metric Prediction Algorithm For a Personalized Web Search
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Authors
Affiliations
1 Department of Computer Science and Engineering, Sona College of Technology, IN
1 Department of Computer Science and Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 3, No 4 (2013), Pagination: 596-604Abstract
Software metrics are the key performance indicators, using which the performance of a system can be assessed quantitatively. Metrics can also be applied for personalized web search which can be used to retrieve relevant results for each individual user depending on their unique profile. Although personalized search based on user profile has been under research for many years and various metrics have been proposed, it is still uncertain whether personalization is unswervingly effective on different queries for different user profiles. We present a framework for personalized search which retrieves result based on user profile and query type. Also we evaluate the performance of proposed system using relevance evaluation metrics.Keywords
Personalized Web Search, P-Click, G-Click, Profile Convergence.
- Financial forecasting Using Decision Tree (reptree&C4.5) and Neural Networks (K*) for Handling the Missing Values
Abstract Views :323 |
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
- Student Prediction System for Placement Training Using Fuzzy Inference System
Abstract Views :237 |
PDF Views:3
Authors
Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, IN
1 School of Computer Science and Engineering, Lovely Professional University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1443-1446Abstract
Proposed student prediction system is most vital approach which may be used to differentiate the student data/information on the basis of the student performance. Managing placement and training records in any larger organization is quite difficult as the student number are high; in such condition differentiation and classification on different categories becomes tedious. Proposed fuzzy inference system will classify the student data with ease and will be helpful to many educational organizations. There are lots of classification algorithms and statistical base technique which may be taken as good assets for classify the student data set in the education field. In this paper, Fuzzy Inference system has been applied to predict student performance which will help to identify performance of the students and also provides an opportunity to improve to performance. For instance, here we will classify the student's data set for placement and non-placement classes.Keywords
Classification, Fuzzy Inference System, MATLAB.References
- Indriana Hidayah, Adhistya Erna Permanasari and Ning Ratwastuti, “Student Classification for Academic Performance Prediction using Neuro Fuzzy in a Conventional Classroom”, Proceedings of IEEE Conference Information Technology and Electrical Engineering, pp. 1-5, 2013.
- Satwanti Devi, Sanjay Kumar and Govind Singh Kushwaha, “An Adaptive Neuro Fuzzy Inference System for Prediction of Anxiety of Students”, Proceedings of 8th International Conference on Advanced Computational Intelligence, pp. 7-13, 2016.
- M. Alkhozae, “Students Performance Prediction System using Multi Agent Data Mining”, International Journal of Data Mining and Knowledge Management Process, Vol. 4, No. 5, pp. 1-20, 2014.
- Q. Qiu and G. Sapiro, “Learning Transformations for Clustering and Classification”, Journal of Machine Learning Research, Vol. 16, pp. 187-225, 2015.
- M. Sharma, “Data Mining : A Literature Survey”, International Journal of Emerging Research in Management and Technology, Vol. 9359, No. 2, pp. 1-4, 2014.
- Mashael A. Al-Barrak and Muna Al-Razgan, “Predicting Students Final GPA Using Decision Trees: A Case Study”, International Journal of Information and Education Technology, Vol. 6, No. 7, pp. 528-533, 2016.
- S.K. Yadav, “Data Mining : A Prediction for Performance Improvement of Engineering Students using Classification”, World of Computer Science and Information Technology Journal, Vol. 2, No. 2, pp. 51-56, 2012.
- B. Minaei-bidgoli, D.A. Kashy, G. Kortemeyer and W.F. Punch, “Predicting Student Performance: An Application of Data Mining Methods with An Educational Web-Based System”, Proceedings of 33rd Annual Frontiers in Education, pp. 1-6, 2003.
- Bashir Khan, Malik Sikandar Hayat 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.