Open AccessBehavioral Pattern Based Psychotic Analysis for Improved Student Performance using Fuzzy Set
Author(s) -
S. Peerbasha,
M. Mohamed Surputheen
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d5292.118419
Subject(s) - behavioral pattern , set (abstract data type) , class (philosophy) , fuzzy logic , mood , pattern analysis , computer science , psychology , reading (process) , artificial intelligence , social psychology , software engineering , political science , law , programming language
The problem of bipolar disorder has been well studied and analyzed. To perform the detection of presence of BD, there are number of approaches available and the result of detection has been used in several ways. In order to improve the performance in BD detection and utilize the result in gauging the performance of students, a behavioral pattern base psychotic analysis model has been presented in this paper. The method maintains the behaviors, habits and interests of different students in different period of time. The student behaviors includes mood change, depression, sudden laughs, uninterested, short temper, lack of concentration, adamant, frustration, energy, sleep and so on. Such behaviors has been tracked for number of students for prolong period and stored in the behavior set. By reading the behavior set and with the identified samples of BD, the method generates set of behavioral patterns. The behavioral pattern has been generated for three different classes like lower, medium and high. For each class of behavioral pattern, the method generates set of fuzzy rules. Using the fuzzy rule, each student has been analyzed for their behavioral pattern in different time window. Based on the patterns, the method estimates BDCW (Bipolar Disorder Class Weight). Based on the weight measure, the presence of BD has been identified and classified under different class. Identified results have been used to generate academic pattern and helps to generate analysis result to improve the student performance. The proposed approach improve the performance of student development, monitoring and health development.