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All Journal International Journal of Electrical and Computer Engineering
Masuma Parvin
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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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.
Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective Lamisha Haque Poushy; Salauddin Ahmed Bhuiyan; Masuma Parvin; Refath Ara Hossain; Nazmun Nessa Moon; Jarin Nooder; Ashrarfi Mahbuba
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5553-5561

Abstract

This research focuses on the education-based online learning platform. Due to the coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".
Co-Authors Ashrarfi Mahbuba Fernaz Narin Nur Iftakhar Mohammad Talha Imrus Salehin Jarin Nooder Lamisha Haque Poushy Md. Mehedi Hasan Mohd. Saifuzzaman Nazmun Nessa Moon Refath Ara Hossain Salauddin Ahmed Bhuiyan Susanta Chandra Debnath