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Rajni Bhalla
Lovely Professional University
Computer Science & IT Electrical & Electronics Engineering

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A Comparative Analysis of Factor Effecting the Buying Judgement of Smart Phone Rajni Bhalla; Amandeep Amandeep; Prateek Jain
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (221.099 KB) | DOI: 10.11591/ijece.v8i5.pp3057-3066

Abstract

Smart phone has various utilizations to various clients as per their necessities. With sensational rise in the usage of smart phone the individuals are considering different factors while purchasing a smart phone. This paper has put endeavor to reveal the fundamental factors which effect clients in picking up of the smart phone. A sample of 512 responses was taken through questionnaire. An organized questionnaire was planned with five point Likert scale was utilized to meeting respondent’s .Factor analysis and descriptive statistical tools were applied to extricate the basic variables influence cell phone acquiring choice. The result shows that the most important factors are physical attributes, apps and sounds while the less importance is given to other factors such as convenience, price which can also vary by age, service and gender. The future scope of this paper lies in the fact that whether age, occupation, gender makes any difference in purchasing decision of smart phone.
Opinion mining framework using proposed RB-bayes model for text classication Rajni Bhalla; Amandeep Bagga
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 1: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (27.586 KB) | DOI: 10.11591/ijece.v9i1.pp477-484

Abstract

Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.
Co-Authors Amandeep Amandeep Amandeep Bagga Prateek Jain