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All Journal Bulletin of Electrical Engineering and Informatics
Polin, Johora Akter
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Electrical & Electronics Engineering

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A novel approach to analyzing the impact of AI, ChatGPT, and chatbot on education using machine learning algorithms Hasan, Nahid; Polin, Johora Akter; Ahmmed, Md. Rayhan; Sakib, Md. Mamun; Jahin, Md. Farhan; Rahman, Md. Mahfuzur
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7158

Abstract

Artificial intelligence (AI) is one of the most common and essential technologies in this modern era, especially in the education and research sectors. It mimics machine-processed human intellect. In modern times, ChatGPT is one of the most effective and beneficial tools developed by OpenAI. Provides prompt answers and feedback to help academics and researchers. Using ChatGPT has various advantages, including improving methods of instruction, preparing interactive lessons, assessment, and advanced problem-solving. Threats against ChatGPT, however, include diminishing creativity, and analytical thinking. Additionally, students would adopt unfair procedures when submitting any tests or assignments online, which would increase their dependency on AI systems rather than thinking analytically. In this study, we have demonstrated arguments on both sides of AI technology. We believe that our study would provide a depth of knowledge and more informed discussion. Data is collected via an offline platform and then machine learning algorithms such as K-nearest neighbour (K-NN), support vector machine (SVM), naive bayes (NB), decision tree (DT), and random forest (RF) are used to analyze the data which helps to improve teaching and learning techniques where SVM shows best performance. The results of the study would offer several significant learning and research directions as well as ensure safe and responsible adoption.
Depression detection in social media comments data using machine learning algorithms Vasha, Zannatun Nayem; Sharma, Bidyut; Esha, Israt Jahan; Al Nahian, Jabir; Polin, Johora Akter
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4182

Abstract

Depression is the next level of negative emotions. When a person is in a sad mood or going through a difficult situation and it is not leaving him and giving him pain continuously and he is unable to bear it anymore, that situation is called depression. The last stage of depression occurs in suicide. According to the World Health Organization (WHO), Currently, 4.4% of people in the world are currently suffering from depression. In 2021, fourteen thousand people committed suicide all over the world and the rating of suicide is increasing day by day. So, our study is to find depressed people by their comments, posts, or texts on social media. We collected almost 10,000 data from Facebook posts, comments, and YouTube comments. Data mining and machine learning (ML) algorithms make our work easier and play a big role in easily detecting a person’s emotions. We applied six classifiers to predict depression non-depression and found the best accuracy on a support vector machine (SVM).
Co-Authors Ahmmed, Md. Rayhan Al Nahian, Jabir Esha, Israt Jahan Hasan, Nahid Jahin, Md. Farhan Rahman, Md. Mahfuzur Sakib, Md. Mamun Sharma, Bidyut Vasha, Zannatun Nayem