Faculty Members Publications

Ghose, P., Parvin, M., Akter, S., Rakib, S. H., & Bhuiyan, M. R. I. (2025). Gravitating towards technology-based emerging financial crime: A PRISMA-based systematic review. International Journal of Innovative Research and Scientific Studies, 8(2), 3387–3402. https://www.ijirss.com (Scopus Indexed- Q2)

Haider, R., Parvin, M., Sabil, S., Arafat, M. S., Ahad, M. F. R., & Amin, R. (2026). Generative AI and Quantum-Inspired Optimization: Redefining Portfolio Risk Management and Real-Time Capital Allocation in Volatile Markets. Journal of Informatics Education and Research, 6(1). https://doi.org/10.52783/jier.v6i1.4540

The abstract of this paper has been accepted at the upcoming international conference, Beyond Nature and Culture: Planetary Precarity in Literary-Cultural Linguistic Representations, organised by Daffodil University. 

The abstract of this paper has been accepted at the upcoming BELTA-EWU International Conference 2026. 

The emergence of new and improved technological advances created severe problems in the security state of the educational certification system. Throughout this paper, a proposal has been made to improve security. Here, Blockchain technology has been introduced as reliable secure storage for the educational certification system, providing an additional facility to the users. That is the validation and authentication of the student’s academic records. Moreover, for security purposes, Blockchain technology can replace the traditional academic certification system and contribute to a new model for sharing student information. After completion of data inclusion and hashing, the blocks will be inserted into the Blockchain network. This proposed model enhances document security and fraud reduction and additionally reduces a significant amount of authentication time almost up to double the current speed. With this system, we will get a certification process in which all data will be digitalized and secured in an unbreakable database with proper authentication and with a noticeable amount of time efficiency.

Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Genetic algorithm has been utilized to identify the most significant biomarkers. The XGBoost classifier is then trained on the selected features and high ROC-AUC scores of 0.864 and 0.911 have been obtained for the premenopausal and postmenopausal populations, respectively. Lack of explainability is one major limitation of current AI systems. The stochastic nature of the ML algorithms raises concerns about the reliability of the system as it is difficult to interpret the reasons behind the decisions. To increase the trustworthiness and accountability of the diagnostic system as well as to provide transparency and explanations behind the predictions, explainable AI has been incorporated into the ML framework. SHAP is employed to quantify the contributions of the selected biomarkers and determine the most discriminative features. Merging SHAP with the ML models enables clinicians to investigate individual decisions made by the model and gain insights into the factors leading to that prediction. Thus, a hybrid decision support system has been established that can eliminate the bottlenecks caused by the black-box nature of the ML algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy obtained from the proposed system outperforms the existing methods as well as the state-of-the-art ROMA algorithm by a substantial margin which signifies its potential to be an effective tool in the differential diagnosis of OC.

Global food security is seriously threatened by plant diseases, which annually cause large losses in agricultural productivity. Early diagnosis and accurate classification of plant diseases are required for disease management programs to be implemented promptly and efficiently. In the area of plant disease classification, Convolutional Neural Networks (CNN) have demonstrated encouraging results in recent years. In this study, we propose a CNN based approach for plant disease classification using a MobileNetV2 based model and transfer learning. The proposed model leverages the MobileNetV2 architecture, known for its lightweight and efficient design, making it well-suited for resource-constrained environments. The pre-trained MobileNetV2 model is modified using transfer learning to accommodate the goal of classifying plant diseases. The model benefits from the characteristics that have been learned from a large-scale dataset through the use of pre-trained weights, leading to improved generalization and reduced training time. We use a standard plant disease dataset with a filtering method as a preprocessing strategy in extended trials to assess the efficiency of the proposed approach. The performance of the model is compared using several cutting-edge techniques, including VGG16, AlexNet and InceptionV3. The experimental findings show that the suggested model performs competitively in classifying plant diseases, surpassing other approaches with an accuracy of 98.56%.

In recent years, social media platforms, particularly twitter, have emerged as crucial sources of public opinion and sentiment. Analyzing sentiment on twitter data presents a significant challenge due to the platform's inherent characteristics, such as brevity, informality, and the prevalence of slang and emojis. This research paper proposes a method for twitter sentiment analysis by leveraging the power of a transformer-based model called RoBERTa. The proposed strategy employs RoBERTa due to its exceptional performance in various natural language processing tasks. Our system captures intricate contextual information and semantic nuances in tweets, making it well-suited for sentiment analysis on this challenging platform. To build an effective sentiment analysis system, the architecture is fine-tuned using a large corpus of twitter data, annotated with sentiment labels. Additionally, we explore various strategies to handle the unique characteristics of twitter data, including tokenization, handling hashtags, user mentions, and URLs, as well as the incorporation of emojis and emoticons. We compare the performance of our model with three other standard machine learning and deep learning models, such as Decision Tree (DT), Support Vector Machine (SVM), and Long Short Term Memory (LSTM) in order to show that our model is superior at correctly analyzing twitter sentiment. The model showcases an exceptional accuracy of 96.78%, highlighting its effectiveness in understanding and classifying sentiment within the context of tweets.

In the burgeoning landscape of e-commerce, the ability to accurately classify product texts is paramount for enhancing user experience and driving business success. Traditional approaches to text classification often struggle with the nuances and complexities inherent in e-commerce product descriptions. In this paper, we propose a novel approach utilizing Gated Recurrent Unit (GRU) to address these challenges and improve product understanding in e-commerce text classification tasks. Our model leverages the inherent sequential nature of product descriptions, effectively capturing long-range dependencies and semantic relationships within the text. We use a standard dataset in extended trials to demonstrate the superiority of our GRU-based approach over conventional methods in terms of classification accuracy and robustness across diverse product categories. Furthermore, we conduct comprehensive analyses to gain insights into the inner workings of our model and its ability to learn meaningful representations of e-commerce text data. The performance of the model is compared using several cutting-edge techniques, including Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) in order to show that our model is superior at correctly classifying e-commerce texts. The experimental findings show that the suggested model performs competitively in classifying e-commerce texts, surpassing other approaches with an accuracy of 98.35%. Our findings underscore the potential of GRU-based approaches for advancing the state-of-the-art in e-commerce text classification, offering promising avenues for future research and practical applications in the domain.

Fahim, M.M.U., Huq, Z.Z. & Mosharrof, M.F. Does environmental, social, and governance disclosure impact credit access and quality of banks? Moderating role of corporate governance. International Journal of Disclosure and Governance (2026). DOI: https://doi.org/10.1057/s41310-026-00361-3  (Scopus-Indexed, Q2, ABDC-B, ABS-2) Link: https://rdcu.be/e4fNJ

Title: Organizational Culture and Performance: A Perspective of Hofstede and Schein’s Cultural Dimensions

Position: Co-researcher 

Research Grant: The project will be funded by the Center for Research and Training, East West University (EWUCRT), 2026

Status: Proposal Under Revision Following Review Feedback 

1. Anik, R H.; Rahman, A.; and Noume, U S.; Impact of Talent Management Practices on Sustainable Organizational Performance: A Study on the Telecommunication Industry of Bangladesh; Journal of Innovation in Business Studies,Volume-03, Issue-01, December-2023.

2. Noume, U. S., et al., Does Flexible Work Schedule effect on Bangladeshi Retail Workers' Job Performance and Job Satisfaction? A Case Study. Independent Business Review ( IBR, 2026)

3. Noume, U. S., et al., Catalyst for Change: Digital Leadership Reshaping Work in Bangladesh's Higher Education. ASBM Journal of Management, 2026.

4. Sarker, M. A. R., Noume, U. S., et al., OHS: Guarding Well-being in Bangladesh's Retail Revolution. ASBM Journal of Management, 2026.

1. Sarker, M.A.R; Ferdousi, F. and Noume, U.S. (2025). ‘‘An overview of AI usages in HRM: Evidence from Bangladesh’’, 10^th International Management Conference, 21-22^nd February 2025, ASBM University, India. Book chapter published by Bloomsbury India.

2. Noume, U. S., et al. Unveiling Exploitation Accountability in Bangladesh's Ready-Made Garment Sector, 11^th International Management Conference, 6-7^th February 2026, ASBM University, India. Book chapter published by Bloomsbury India.

3. Noume, U. S., et al., Does Flexible Work Schedule effect on Bangladeshi Retail Workers' Job Performance and Job Satisfaction? A Case Study. International Conference on Economics, Business and Technology Management ( ICEBTM, 14-15 November 2025). Journal Article published in Independent Business Review 2025

4. Sarker, M. A. R., Ferdousi, F., Noume, U. S., et al., Workforce Diversity for Enhancing Production Efficiency in Textile Sectors of Bangladesh. 3rd International Conference on Sustainable Ecological Engineering Design for Society, 2025.

5. Sarker, M. A. R., Ferdousi, F., Noume, U. S., et al. The impact of AI on workforce planning & talent acquisition., 11^th International Management Conference, 6-7^th February 2026, ASBM University, India.

6. Noume, U. S., et al., Catalyst for Change: Digital Leadership Reshaping Work in Bangladesh's Higher Education. 11^th International Management Conference, 6-7^th February 2026, ASBM University, India.

7. Sarker, M. A. R., Noume, U. S., et al. OHS: Guarding Well-being in Bangladesh's Retail Revolution. 11^th International Management Conference, 6-7^th February 2026, ASBM University, India.

1. Sarker, M.A.R; Ferdousi, F. and Noume, U.S. (2025). ‘‘An overview of AI usages in HRM: Evidence from Bangladesh’’ in Navigating the Future: Management Strategies for Sustainable Transformation. Bloomsbury India, 21 & 22 February 2025. 

2. Noume, U. S., et al. Unveiling Exploitation Accountability in Bangladesh's Ready-Made Garment Sector. Management Renaissance: Profit Meets Purpose, Book chapter published by Bloomsbury India, 6 & 7 February 2025.

1. EWU IQAC Training Program on Teaching, Learning, and Research. Training Provider: East West University, Bangladesh (November 2024 – February 2025)

2. Training in Research Methods. Training Provider: Center for Research and Training, East West University (April – August 2025)

1. Seminar by Ross Wainwright, Partnership Director, OnCampus UK (Cambridge Education Group) – Academic pathways, scholarships and admission insights for UK studies (8 April 2025, Dhaka)

2. National Level IN-PERSON Erasmus+ Info Session and Higher Education Opportunities (7 May 2025)

3. National Level IN-PERSON Erasmus+ Info Session and Higher Education Opportunities (12 November 2025)

Fahim, M.M.U., Huq, Z.Z., Mosharrof, M.F. (2026). Does Environmental, Social and Governance Disclosure Impact Credit Access and Quality of Banks? Moderating role of Corporate Governance. International Journal of Disclosure and Governance (Q2) Scopus Indexed 

• Testing EKC Hypothesis using Bangladesh Annual Time Series Data

Text classification is known to be a supervised machine learning technique used in one or more predefined categories to classify sentences or text archives. To be a perfect phrase to convey one's feelings or to be significant, a Bengali sentence must have three properties i.e. Compatibility, Proximity and Expectancy. In this paper, we have proposed a method that is able to detect whether a Bengali sentence has compatibility, proximity and expectancy using Long Short Term Memory network. Our model is trained with word embedding layer and LSTM layer for the detection of Compatibility of a sentence but POS tagging is included for assuring the syntactic structure in case of Proximity and Expectancy detection. The model is tested on around 75000 Bengali simple sentences. The proposed framework achieves an accuracy of 97.5 percent, 85.5 percent and 97 percent for Compatibility, Proximity and Expectancy respectively. The result analysis proves that our model gives better performance.

Mahmud, Sakib and Zubayer, Sayed Arafat and Eshtiak, Ahmed and Ahmed, Ibrahim and Nasim, Mahmudul Hossain, Economic Impact of COVID-19 on the Vulnerable Population of Bangladesh (December 3, 2020). http://dx.doi.org/10.2139/ssrn.3741890

1. Md. Asif Khan Rifat, Ahmedul Kabir, and Armana Huq, “An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction,” Procedia Computer Science, vol. 246, pp. 1905–1914, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.704. ‌[Presented at the 28th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2024)]

1. Md. Asif Khan Rifat, Yeasmin Sultana, B M Mainul Hossain, “Vulnerabilities Assessment of Financial and Government Websites: A Developing Country Perspective,” International Journal of Information Engineering and Electronic Business (IJIEEB), vol.15, no.5, pp. 42-53, 2023, doi: https://doi.org/10.5815/ijieeb.2023.05.05.

1. Rahman, M. S., Islam, M. R., Sarker, M. M., & Siddique, M. A. B. (2026). Exploring the Climate Impacts of Residential Electricity Consumption: Evidence from Bangladesh. Asian Journal of Education and Social Studies, 52(10), 365–390. https://doi.org/10.9734/ajess/2026/v52i103376         

2. Hemat, M. W., & Rahman, M. S. (2024). Microfinance in Afghanistan: Trends, Impacts, and Challenges. Asia-Pacific Journal of Rural Development, 33(1), 129-148. https://doi.org/10.1177/10185291241235445 

3. Rahman, M. S., Sultana, S., Sultana, M., & Afzal, M. N. I. (2024). An Investigation on the Effectiveness and Trade Direction of ASEAN Trade Agreements. Financial Markets, Institutions and Risks, 8(2), 98-118. https://doi.org/10.61093/fmir.8(2).98-118.2024      

4. Afzal, M.N.I., Rokon-Uz-Zaman, M., Al Musa, M.Z., Rahman, M. S., Sultana, S., & Manni, U.H. (2022). Can Government Incentive on Remittance Encourages Remitters in Bangladesh? A Policy Study. ICRRD Journal, 3(2), 131-142. https://doi.org/10.53272/icrrd.v3i2.3 

5. Khan, M.G., Sultana, S., Rahman, M. S., Afzal, M.N.I. (2021). Impact of Covid-19 on the Aviation Industry: A Comparison between South Asian and Other Asian Regions. SUST Journal of Social Sciences, 32(2), 48-70. https://doi.org/10.5281/zenodo.19998519

• Uddin, M. H., Rahman, Md. S., Sajuti, T. T., Akter, A. (2024). Assessing the Role of Food and Nutrition Security Policies of the CMCs in Helping the Poor and Attaining the Relevant SDGs. Centre on Integrated Rural Development for Asia and the Pacific (CIRDAP). https://cirdap.org/wp-content/uploads/2025/03/Final-Rural-Development-Report-2023-Book.pdf 

Barkat, A. (2022). Determinants of Perceived Insecurity among Victims of Crime in Bangladesh, Bangladesh Journal of Political Economy, 38(2): 175-198. https://bea-bd.org/site/article-details/774

"Impact of Household Chores Situation on Female University Teachers in the Context of Bangladesh” [Single Authored]

Contemporary Issues in Business and Economics, August 2022 (1^st) Chapter 24. Publisher: RED’SHINE PUBLICATION 232, Bilton road, Perivale, Greenford Passcode: UB6 7HL London, UK. DOI: 10.25215/1387734229

“Respectability or Career: The Crisis of Female Academicians in Bangladesh” [1^st Author]

Gender Work and Organization 2025 Conference (GWO 2025), Nantes, France

From Invisibility to Visibility: Shwapna Bhowmick, A Tale of Meaningful Change [1^st Author]

ICCBM 2025; BRAC University in partnership with Emerald Publication