Design and Implementation of Students Grievance and Database

Abstract

Tracking student data and complaints is critical to track student performance in the classroom even while studying. This research study aims to address the grievances of the students and presents the website as a portal built using Javascript, HTML, Python, and MySQL, where students may file concerns, and the department handling the case is notified. The student has complete access to the complaint's history and may see if it has been examined, investigated, sent, rejected, or handled. The proposed technique is totally transparent, and if a complaint remains unanswered for several days, the system will automatically transmit it to the person specified further up the hierarchy. Any correctional system should be concerned about language quality since it may be used to spread incorrect information by making comments about people's gender, color, or religion. Employing the state-of-the-art technologies like deep learning and machine learning helps to detect hate speech. After training 11,325 tweets and analyzing the outcomes using evaluation metrics including F1 score, recall, and precision. Bi-LSTM, LSTM, and SVM models are utilized. By reaching the metrics values of 0.884, 0.84, and 0.86, the LSTM model outscores the other models.