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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Nazmun Nessa Moon
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.
Sentiment analysis on Bangla conversation using machine learning approach Mahmudul Hassan; Shahriar Shakil; Nazmun Nessa Moon; Mohammad Monirul Islam; Refath Ara Hossain; Asma Mariam; Fernaz Narin Nur
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.pp5562-5572

Abstract

Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.
Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019 Nazmun Nessa Moon; Refath Ara Hossain; Israt Jahan; Shahriar Shakil; Shihab Uddin; Mahmudul Hassan; Fernaz Narin Nur
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.pp5501-5510

Abstract

The novel coronavirus disease 2019 (COVID-19) current pandemic is a worldwide health emergency like no other. It is not the only COVID-19 infection in infants, children, and adolescents that is causing concern among their families and professionals; there are also other serious issues that must be carefully detected and addressed. Major things are identified due to COVID-19, some elements are affecting children’s healthcare in direct or indirect ways, affecting them not just from a medical standpoint but also from social, psychological, economic, and educational perspectives. All these factors may have affected children’s mental development, particularly in rural settings. As Bangladesh faces a major challenge such as a lack of public mental health facilities, especially in rural areas. So, we discovered a method to predict the mental development condition of rural children that they are facing at this time of COVID-19 using machine learning technology. This research work can predict whether a rural child is mentally developed or mentally hampered in Bangladesh and this prediction gives nice feedback.
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?".
IFSG: Intelligence agriculture crop-pest detection system using IoT automation system Imrus Salehin; S. M. Noman; Baki Ul-Islam; Israt Jahan Lopa; Prodipto Bishnu Angon; Ummya Habiba; Nazmun Nessa Moon
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp1091-1099

Abstract

The agricultural and technological combination is blessed for modern world life. Internet of things (IoT) is essential for comfort and development to our agriculture side. In our study, we detected the various pest using different types of sensors and this information has automatically sent to the farmer's mobile for the alert. All these sensors had a central database. Those sensors collect all the data and display the results compared to the central data. The High-image sensor will be able to detect all the rays emitted from the plant and another one is the gas sensor which is able to detect all the gases coming from the diseased plant. We mainly use sound sensor, MQ138, CMOSOV-7670, AMG-8833 for a better automation system. We test it with real-time environment conditions (40°C≤TA≤14°C). Crop pest detection automatic process is more efficient than the other detection process according to testing output. As a result, far-reaching changes in the agricultural sector are possible. To reduce extra cost and increasing more farming ability we need to IoT and Agriculture combinations more.
Co-Authors A S M Farhan Al Haque Ashrarfi Mahbuba Asma Mariam Baki Ul-Islam Farnaz Narin Nur Hasan Fernaz Narin Nur Iftakhar Mohammad Talha Imrus Salehin Israt Jahan Israt Jahan Lopa Jarin Nooder Lamisha Haque Poushy Mahmudul Hassan Masuma Parvin Md. Azizul Hakim Md. Mehedi Hasan Mohammad Monirul Islam Mohd. Saifuzzaman Prodipto Bishnu Angon Refath Ara Hossain S. M. Noman Salauddin Ahmed Bhuiyan Shahriar Shakil Shihab Uddin Susanta Chandra Debnath Ummya Habiba