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All Journal Bulletin of Electrical Engineering and Informatics Language Literacy: Journal of Linguistics, Literature, and Language Teaching
Islam, Mirajul
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Humanities Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Hybrid deep learning: a comparative study on ai algorithms in natural language processing for text classification Mahmudul Hasan, Md.; Kumar Das, Rajesh; Hassan, Mocksidul; Razia, Sultana; Ferdous Ani, Jannatul; Akter Khushbu, Sharun; Islam, Mirajul
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7617

Abstract

The objective of this research project is to assess the effectiveness of various machine learning algorithms, including deep learning and combination approaches, in performing tasks such as categorizing products into specific categories using data from an e-commerce platform named "OTHOBA." In this study, a dataset consisting of 19,087 data samples is used to evaluate the effectiveness of seven supervised machine learning models. Among these models are three based on deep learning: long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and 1D convolutional (Conv1D), as well as a multi-layer model that combines Conv1D and LSTM approaches. The task at hand is the classification of product categories. The LSTM-based model demonstrates the highest accuracy rate of 96.23% among the deep learning models, while the logistic regression (LR) models achieve the highest accuracy scores of 97.00% for product category classification. Overall, the proposed models and techniques show significant progress in natural language processing (NLP) research for text classification, specifically in English, and have practical applications for e-commerce sites.
EFL TEACHERS' PERCEPTIONS OF AI'S IMPACT ON ACADEMIC INTEGRITY AND PEDAGOGY IN BANGLADESHI UNIVERSITIES Islam, Mirajul; Hasan, Md. Mahadhi; Mahmud, Rashed
Language Literacy: Journal of Linguistics, Literature, and Language Teaching Vol 8, No 2: December 2024
Publisher : Universitas Islam Sumatera Utara (UISU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/ll.v8i2.10082

Abstract

The objectives of the research are to identify Bangladeshi EFL teachers’ perceptions regarding the role of AI in language teaching in the way it affects academic integrity, the pedagogical issues, and how these can affect the efficacy of the existing institutional policies in English teaching profession. This study used a descriptive quantitative research design, which is based on an online questionnaire survey with 115 EFL teachers teaching in 22 private universities in Bangladesh. The results found that 69.6% of teachers were concerned about the ethical implications of AI, most prominently 69.6% about the alignment with academic dishonesty. In addition, 65.2% of the teachers revealed challenges in merging AI into class pedagogy, with the foremost reason being a lack of institutional support and training provision to integrate the lessons into class lessons. Indicatively, 74.8% of respondents stated that current institutional policies do not enable dealing with the challenges posed by AI in education. The study recommends the immediate requirements for extensive AI-related training programs, institutional regulations, and instruments to attain academic integrity in EFL classrooms. This research builds on and adds to the growing literature on AI in education while capturing the Bangladeshi EFL context. Future research can be conducted on the threats of AI-integration in EFL teaching and learning. 
Co-Authors Akter Khushbu, Sharun Ferdous Ani, Jannatul Hasan, Md. Mahadhi Hassan, Mocksidul Kumar Das, Rajesh Mahmud, Rashed Mahmudul Hasan, Md. Razia, Sultana