CFP last date
20 March 2024
Reseach Article

Comparative Analysis of Student Psychology Prediction-Recommendation Two Phase Strategy

by Bhakti Ratnaparkhi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 7
Year of Publication: 2016
Authors: Bhakti Ratnaparkhi
10.5120/ijca2016912110

Bhakti Ratnaparkhi . Comparative Analysis of Student Psychology Prediction-Recommendation Two Phase Strategy. International Journal of Computer Applications. 153, 7 ( Nov 2016), 38-42. DOI=10.5120/ijca2016912110

@article{ 10.5120/ijca2016912110,
author = { Bhakti Ratnaparkhi },
title = { Comparative Analysis of Student Psychology Prediction-Recommendation Two Phase Strategy },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 153 },
number = { 7 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume153/number7/26419-2016912110/ },
doi = { 10.5120/ijca2016912110 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:58:32.787553+05:30
%A Bhakti Ratnaparkhi
%T Comparative Analysis of Student Psychology Prediction-Recommendation Two Phase Strategy
%J International Journal of Computer Applications
%@ 0975-8887
%V 153
%N 7
%P 38-42
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Big data analysis includes many theories and methods for prediction system. Statistical methods such as Person’s correlation, Regression analysis and Rough Set Theory etc are being used for predicting facts. Also theory like collaboration filtering uses word’s filtering to predict and provide recommendations. We have studied all these methods and selected most appropriate method for student’s psychology prediction. In our proposed work we have used Rough sets to extract the rules for prediction of student’s psychology. Rough Set is a comparatively recent method that has been effective in various fields such as medical, geological and other fields where intelligent decision making is required. Our experiments with rough sets in predicting student’s psychology produced attractive results.

References
  1. Maria Augusta S. N. Nunes, “Towards To Psychological-Based Recommenders Systems: A Survey on Recommender Systems”, Scientia Plena Vol. 6, Num. 8 2010.
  2. Manos Papagelis, DimitrisPlexousakis, IoannisRousidis and Elias Theoharopoulos,“Qualitative Analysis of User-based and Item-based Prediction Algorithms for Recommendation Systems”.
  3. Shuai Zhang, Sally I. Mcclean, “A Predictive Model for Assistive Technology Adoption for People With Dementia”, Ieee Journal Of Biomedical And Health Informatics, Vol. 18, No. 1, January 2014.
  4. Yang Guo, GuohuaBai, Yan Hu, “Using Bayes Network for Prediction Of Type-2 Diabetes”, 2012, Ieee, 7th International Conference For Internet Technology And Secured Transactions (Icitst).
  5. AymanKhedr,“Business Intelligence Framework To Support Chronic Liver Disease Treatment”, International Journal Of Computers & Technology Volume 4 No. 2, March-April, 2013, Issn 2277-3061.
  6. Samuel and Omisore, “Hybrid Intelligent System for the Diagnosis of Typhoid Fever”, J ComputEngInfTechnol 2013, 2:2, Journal of Computer Engineering & Information Technology.
  7. “Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method”, International Journal of Computer Applications (0975 – 8887) Volume 24– No.3, June 2011.
  8. “Finding Locally Frequent Diseases Using Modified Apriori Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 10, October 2013.
  9. “Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 2, March 2013.
  10. “Lung cancer differential diagnosis based on the computer assisted radiology: The state of the art”
  11. “The Application of Machine Learning Technique for Malaria Diagnosis”
  12. “Performance Evaluation of Levenberg-Marquardt Technique in Error Reduction for Diabetes Condition Classification”, International Conference on Computational Science, ICCS 2013.
  13. “An Investigation into the Feasibility of Detecting Microscopic Disease Using Machine Learning”, Keynote Lecture of IEEE International Conference on Bioinformatics and Biomedicine November 2-4, 2007, Silocon Valley, California, USA.
  14. ArturasKaklauskas, EdmundasKazimierasZavadskas, VaidotasTrinkunas, Laura Tupenaite, Justas Cerkauskas, PauliusKazokaitis, “Recommender system to research students’ study efficiency”, Procedia - Social and Behavioral Sciences 51 ( 2012 ) 980 – 984.
  15. SakchaiTangwannawit and MonteanRattanasiriwongwut, “Comparing the Strengths and Difficulties Questionnaire (SDQ) and Behavior Consideration Assessment Using SVM Techniques”, DOI: 10.7763/IPEDR. 2014. V70. 16.
  16. Bhakti Ratnaparkhi, Prof. Dr.J. S. Umale, “State of the art of Prediction and Recommender System”, International Journal of Computer Applications (0975 – 8887) Volume 108 – No. 11, December 2014.
  17. Umang Gupta, Niladri Chatterjee, “Personality Traits Identification using Rough sets based Machine Learning”, IEEE 2013 International Symposium on Computational and Business Intelligence.
  18. Bhakti Ratnaparkhi, Dr. J. S. Umale, “Improved student psychology prediction & recommendation strategy using 2 state data analysis”, IEEE Global Conference on Communication Technologies 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Student’s psychology prediction recommendation rough set theory