Psychotic Motivation for Improving Student Performance Based On Pattern Learner Features Using Deep Neural Classifier for Bipolar Disorder Students

Abstract

Bipolar disorder is a depressive fact that makes manic illness pressures in young ages due to the non-intensive nature of brain functions, energy levels, mood-outs, and health disorders. These abnormalities may affect student performance under the learning strategies of students. Improvement of bipolar disorder affected student performance needs more data analysis forums that lead to high dimensional nature of features. The problem is that nonrelation feature analysis depends on the nature of student fitness that creates low prediction during classifications for students' motivation. To resolve this problem, a Psychotic motivation is proposed for improving student performance based on Pattern Learner Features (PLF) using Intra Segment Recurrent Deep Neural Network (ISRDNN) for bipolar disorder students. The proposed system makes student academic data's with physical fitness data collection progressive approach to predict important features to classify the result. Bipolar Disorder Influence Rate (BDIR) is usedto spill the progressive student defectives and the learning capabilities for classification result. With Intra Segment Activation Function (ISAF), the recurrent neural network is optimized to classify the result. This classifier improved the student's academic performance based on psychological motivation recommendations. Results prove that the accuracy of the proposed system produces high results compared to the previous system.