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All Journal International Journal of Electrical and Computer Engineering
Md. Mehedi Hasan
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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Analysis of student sentiment during video class with multi-layer deep learning approach Imrus Salehin; Nazmun Nessa Moon; Iftakhar Mohammad Talha; Md. Mehedi Hasan; Farnaz Narin Nur Hasan; Md. Azizul Hakim; A S M Farhan Al Haque
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3981-3993

Abstract

The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
Natural language processing based advanced method of unnecessary video detection Nazmun Nessa Moon; Imrus Salehin; Masuma Parvin; Md. Mehedi Hasan; Iftakhar Mohammad Talha; Susanta Chandra Debnath; Fernaz Narin Nur; Mohd. Saifuzzaman
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5411-5419

Abstract

In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.
Co-Authors A S M Farhan Al Haque Farnaz Narin Nur Hasan Fernaz Narin Nur Iftakhar Mohammad Talha Imrus Salehin Masuma Parvin Md. Azizul Hakim Mohd. Saifuzzaman Nazmun Nessa Moon Susanta Chandra Debnath