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.
Published In:IJCSN Journal Volume 5, Issue 5
Date of Publication : October 2016
Pages : 818-823
Figures :04
Tables :--
Bhakti Ratnaparkhi : Department of Information Technology,
Pimpri Chinchwad College of Engineering,
Pune.
Student’s Psychology, Prediction,
Recommendation, Rough Set Theory
Various techniques used for prediction and recommender
system were studied in order to develop system for
prediction of student psychology and provide remedial
actions to improve their academic performance with
improved accuracy in prediction and recommender
system. Maslow motivation theory was also studied which
will be used for predicting current state of student’s
psychology and also to provide suggestions for
improvements. Our own 2-phase model was proposed and
results were obtained through simulation.
[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, Dimitris Plexousakis,
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.