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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Habib, Md. Tarek
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Tomato pest recognition using convolutional neural network in Bangladesh Polin, Johora Akter; Hasan, Nahid; Habib, Md. Tarek; Rahman, Atiqur; Vasha, Zannatun Nayem; Sharma, Bidyut
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6073

Abstract

The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.
A study on social media addiction analysis on the people of Bangladesh using machine learning algorithms Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.5680

Abstract

Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
Machine learning based COVID-19 study performance prediction Rahman, Md. Ataur; Rahman, Md. Sadekur; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1130-1139

Abstract

COVID-19 has impacted education worldwide. In this troublesome situation, it is hard enough for an institution to predict a student’s performance. Students’ performance prediction has always been a complex task and this pandemic situation has led this task to be more complex. The main focus of this work is to come up with a machine learning model based on a classical machine learning technique to predict the change in students’ performance due to COVID-19. Initially, some relevant features are selected, based on which the data are collected from students of some private universities in Bangladesh. After the entire data set is formed, we preprocessed the dataset to remove redundancy and noise. These preprocessed data are used for testing and training using the proposed model. The model is extensively evaluated in this way using three separate classical machine learning techniques, namely linear regression, k-nearest neighbors (k-NN), and decision tree. Finally, the results of the entire experiment follow, demonstrating the power of the machine learning model in such an application. It is observed that the proposed model with linear regression exhibits the best performance with an R2 error of 0.07% and an accuracy of 99.84%.
Co-Authors Atiqur Rahman Firoz, Mehedi Hasan, Mahady Hasan, Nahid Islam, Mohammad Monirul Md. Sadekur Rahman, Md. Sadekur Mim, Minjun Nahar Polin, Johora Akter Rahman, Md. Ataur Sharma, Bidyut Vasha, Zannatun Nayem