Publications

@inproceedings{10.1007/978-3-031-78195-7_4,

title = {SSMT: Few-Shot Traffic Forecasting with Single Source Meta-transfer},

author = {Kishor Kumar Bhaumik and Minha Kim and Fahim Faisal Niloy and Amin Ahsan Ali and Simon S. Woo},

editor = {Apostolos Antonacopoulos and Subhasis Chaudhuri and Rama Chellappa and Cheng-Lin Liu and Saumik Bhattacharya and Umapada Pal},

isbn = {978-3-031-78195-7},

year  = {2025},

date = {2025-01-01},

booktitle = {Pattern Recognition},

pages = {46–61},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Traffic forecasting in Intelligent Transportation Systems (ITS) is vital for intelligent traffic prediction. Yet, ITS often relies on data from traffic sensors or vehicle devices, where certain cities might not have all those smart devices or enabling infrastructures. Also, recent studies have employed meta-learning to generalize spatial-temporal traffic networks, utilizing data from multiple cities for effective traffic forecasting for data-scarce target cities. However, collecting data from multiple cities can be costly and time-consuming. To tackle this challenge, we introduce Single Source Meta-Transfer Learning (SSMT) which relies only on a single source city for traffic prediction. Our method harnesses this transferred knowledge to enable few-shot traffic forecasting, particularly when the target city possesses limited data. Specifically, we use memory-augmented attention to store the heterogeneous spatial knowledge from the source city and selectively recall them for the data-scarce target city. We extend the idea of sinusoidal positional encoding to establish meta-learning tasks by leveraging diverse temporal traffic patterns from the source city. Moreover, to capture a more generalized representation of the positions we introduced a meta-positional encoding that learns the most optimal representation of the temporal pattern across all the tasks. We experiment on five real-world benchmark datasets to demonstrate that our method outperforms several existing methods in time series traffic prediction. Our code is available at https://github.com/Kishor-Bhaumik/SSMT.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3706599.3720245,

title = {Internet Disconnection as a Risk to Cross-Border Human Rights},

author = {Zinnat Sultana and Miss. Rokeya Akter and Tanveer Ehsanur Rahman and Hasan Shahid Ferdous and Teresa Wulandari and M Ashraful Amin and Syed Ishtiaque Ahmed and Sharifa Sultana},

url = {https://doi.org/10.1145/3706599.3720245},

doi = {10.1145/3706599.3720245},

isbn = {9798400713958},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {CHI EA '25},

abstract = {One of the biggest movements organized by Gen-Z is the July-August 2024 quota reformation movement in Bangladesh. During this movement, the government in power of Bangladesh shut down the internet nationwide for ten consecutive days to suppress the voices of people and disconnect them from the rest of the world. Our ongoing project investigates the movement and analyzes this crisis. We are currently conducting interviews with Bangladeshi people living outside of Bangladesh to understand the trouble they faced due to the internet shutdown and found that their human rights were violated in multiple ways. The participants informed us about how propaganda spread, which was impossible to verify due to the internet shutdown, resulting in more chaos and confusion during that time and possibly leading to more damage to the nation and its resources. We present the findings and discuss possible directions for HCI.},

keywords = {Bangladesh, Cross-border, Ethics, Human Rights, ICTD, Immigrants, July Uprising, Justice},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1117/12.3079561,

title = {Land use land cover segmentation using synthetic aperture radar data on Dhaka division, Bangladesh},

author = {Sadia Khan and Mir Sayad B. Almas and M. Ashraful Amin and Amin A. Ali and A. K. M. Mahbubur Rahman},

editor = {Wolfgang Osten and Xudong Jiang and Kemao Qian},

url = {https://doi.org/10.1117/12.3079561},

doi = {10.1117/12.3079561},

year  = {2025},

date = {2025-01-01},

booktitle = {Eighth International Conference on Machine Vision and Applications (ICMVA 2025)},

volume = {13734},

pages = {1373407},

publisher = {SPIE},

organization = {International Society for Optics and Photonics},

keywords = {Bangladesh Agriculture, Deep Learning, Developing country, Land Use Land Cover, Remote Sensing, Synthetic Aperture Radar},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-76809-5_13,

title = {MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women},

author = {Mohammad Arshad Hossain Ratul and Tunisha Yanoor Bristy and Noorjahan Sayeed and Ashraful Islam and Beenish Moalla Chaudhry},

editor = {Vincent G. Duffy},

isbn = {978-3-031-76809-5},

year  = {2025},

date = {2025-01-01},

booktitle = {HCI International 2024 – Late Breaking Papers},

pages = {173–188},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-76818-7_18,

title = {ProbinShasthoBondhu: A User-Centered mHealth App for Enhancing Elderly Health Management in Bangladesh},

author = {Sumyia Afnan Mukta and Mahfuja Khanam and Kalpika Paul and Azrin Ahmed and Ashraful Islam and Asif Mahmood},

editor = {Margherita Antona and Constantine Stephanidis and Qin Gao and Jia Zhou},

isbn = {978-3-031-76818-7},

year  = {2025},

date = {2025-01-01},

booktitle = {HCI International 2024 – Late Breaking Papers},

pages = {256–272},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Current global demographic changes require the expansion of healthcare technologies, as seniors are more vulnerable to chronic diseases and mental illness, impacting their strength, mobility, and physical and mental well-being. As a result, seniors using smartphones require specific apps for physical and mental health, as well as emergency medical care. This work presents a methodical approach to designing a specific healthcare and fitness app dedicated to seniors while focusing on a user-centered design (UCD) strategy as well as studying their characteristics through surveys. The development of `ProbinShasthoBondhu', a Bangla mobile health (mHealth) app aimed at Bangladeshi seniors, is presented here. It includes a variety of personalized features, such as various exercises like cardio and yoga, meditation techniques, medication management tools, and an emergency SOS button to quickly seek medical assistance. To test the usability and user experience of ProbinShasthoBondhu, several usability scales were used, including the System Usability Scale (SUS) and User Experience Questionnaire Short version (UEQ-S). The results show a favorable overall SUS score of 60.3, indicating good usability. This study adds to the expanding literature on healthcare technologies for seniors by demonstrating the ability of UCDs to meet their specific requirements and ensure that mHealth apps will be more effective in promoting their health and well-being.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-93061-4_19,

title = {Designing HepCare App: Promoting Hepatitis Prevention and Awareness in Bangladesh},

author = {Mst. Shikha Khatun and Anikah Hossain and Sumaiya Akter and Md Abu Sayed and Ashraful Islam and M. Ashraful Amin},

editor = {June Wei and George Margetis},

isbn = {978-3-031-93061-4},

year  = {2025},

date = {2025-01-01},

booktitle = {Human-Centered Design, Operation and Evaluation of Mobile Communications},

pages = {300–318},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Hepatitis remains a major public health concern in Bangladesh due to a lack of awareness, inadequate access to healthcare, and low vaccination rates. In response to these challenges, the `HepCare' app has been designed, aiming to promote hepatitis prevention and awareness across Bangladesh. The app is expected to play a crucial role in reducing the country's high rates of viral hepatitis. The User-Centered Design (UCD) methodology is used in this study to create a mobile health (mHealth) application (app) `HepCare' that is tailored specifically for Bangladeshi people. The `HepCare' app seeks to offer comprehensive information on any type of hepatitis, including prevention techniques, and modes of transmission. So the research objectives include conducting a needfinding analysis and developing low and high-fidelity prototypes of the `HepCare' app. Key functionalities of the `HepCare' app include ``About the Types of Hepatitis'', ``Prevention Guidelines From Online Resources'', ``People at Risk'', ``Chatbox'', ``Guidelines for Living With Hepatitis'', ``Symptoms Tracker'', ``Medication Reminder'', ``Doctor's Appointment Reminder'', ``Pregnancy and Hepatitis'', and ``Advertisement and Awareness''. Interactive elements, like ``Chatbox'', encourage participation and lessen stigma. Moreover, 91.2% of participants expressed willingness to use and recommend the `HepCare' app during a user study. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, becoming a vital resource in the fight against hepatitis in Bangladesh, empowering individuals with the information and resources.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-93061-4_21,

title = {Designing a Mobile App for a National Liver Cancer Registry in Bangladesh},

author = {Nuha Najibah and Nahin Fatima and Md Alimuzzaman Farsi and Md Abu Sayed and Ashraful Islam and M. Ashraful Amin and Md. Fazal Karim},

editor = {June Wei and George Margetis},

isbn = {978-3-031-93061-4},

year  = {2025},

date = {2025-01-01},

booktitle = {Human-Centered Design, Operation and Evaluation of Mobile Communications},

pages = {329–346},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Liver cancer is a critical global health concern, particularly in developing countries and low- and middle-income countries (LMIC) such as Bangladesh, where the health infrastructure and public awareness are limited. The increasing incidence of liver cancer in Bangladesh is mainly driven by risk factors such as Hepatitis B and C, liver cirrhosis, and nonalcoholic fatty liver disease. Bangladesh faces significant challenges in combating liver cancer, including a lack of systematic data collection, limited healthcare resources, and low public awareness. A national liver cancer registry could address these challenges by tracking patient data, risk factors, and treatment outcomes. Mobile technology offers a promising solution, as a mobile registry app would enable real-time data entry, improve accessibility in rural areas, and integrate with existing health systems. Therefore, a mobile app `LiverCare Registry' is created that serves as a platform for doctors, researchers, and patients to update medical records, conduct research, and track treatments, respectively. It also includes a chatbox feature for peer communication, enabling patients to connect with others in similar conditions for support. However, the core feature of the app is data collection through a comprehensive liver cancer registration form. The features of the app were initially decided by performing need-finding methods. Then, a low-fidelity prototype was created and evaluated by surveying people to ensure that the app meets the requirements of the people. The survey showed that of the participants who provided their information to us, 92.3% of them were ready to accept and willing to use our app. In addition, they liked the features of the app and felt that this app could actually help them maintain a healthy lifestyle. Then a high-fidelity prototype was created which is yet to be launched as a final product.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-93061-4_22,

title = {Early-Stage Development of a Handwritten Prescription Interpretation App for Bangladeshi Citizens},

author = {Bushra Rahman and Afsana Rubyat and Mohammad Faiyaz Uz Zaman and Nabarun Halder and Ashraful Islam and M. Ashraful Amin},

editor = {June Wei and George Margetis},

isbn = {978-3-031-93061-4},

year  = {2025},

date = {2025-01-01},

booktitle = {Human-Centered Design, Operation and Evaluation of Mobile Communications},

pages = {347–366},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {In Bangladesh, handwritten prescriptions remain the conventional method of medical documentation, posing major obstacles to accurately interpreting medication information. Due to the diverse styles of handwriting, these handwritten prescriptions are often difficult to understand, which can put the patient's health at risk. This study presents a novel approach to digitizing handwritten prescriptions using the User-Centered Design (UCD) methodology to develop a mobile application for Bangladeshi citizens. This innovative solution helps Bangladeshi citizens improve access to clear and reliable medication information, reduce errors, and encourage safer healthcare practices. Research objectives include conducting design requirements, need-finding analysis, and developing a high-fidelity prototype. A survey involving a total of 155 participants, in which the insights of the participants were recorded for the features and design requirements of the app to ensure that the app meets the essential requirements of potential users. This process led to the design of a low-fidelity paper-based prototype alongside a high-fidelity digital version. The participants expressed their difficulties in reading handwritten prescriptions independently and their inability to understand the names of the medications and the dosage instructions. Participants preferred features such as daily medication reminders, medication names and dosages, automatic scanning of prescription labels, chat, and voice commands to interact with the Relational Agent, and guidelines for using the app. Using these insights, our high-fidelity prototype, ``PresCa'', incorporates key features such as prescription scanning, scanning guidelines, report summary, Relational Agent, check reminder, and configure medicine reminder.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-93061-4_15,

title = {Initial Design of a Serious Game for Reducing Risks of Prenatal Excessive Gestational Weight Gain in Bangla},

author = {Tabriji Islam and Khandaker Umme Salma and Newaz Ben Alam and Ratna Saha and Ashraful Islam and M. Ashraful Amin},

editor = {June Wei and George Margetis},

isbn = {978-3-031-93061-4},

year  = {2025},

date = {2025-01-01},

booktitle = {Human-Centered Design, Operation and Evaluation of Mobile Communications},

pages = {227–246},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Excessive gestational weight gain (GWG) poses substantial health concerns for both the mother and the child. In Bangladesh, the issue is further complicated by the difference between urban and rural areas. Urban women often suffer from obesity during and after pregnancy, whereas rural women suffer from malnutrition and accompanying syndromes. In particular, excessive GWG poses a bigger health risk than nutritional inadequacies. Traditional approaches to encouraging good habits during pregnancy are often hampered by expense, accessibility, and limited reach in remote locations. This study introduces ``NutriMom'', a serious game-inspired mobile health application (mHealth) to reduce excessive GWG among Bangladeshi women. The study had three goals: (i) to perform design requirements and need-finding analysis, (ii) to create a high-fidelity NutriMom prototype, and (iii) to evaluate the usability of the proposed prototype. A survey of 59 pregnant women in different trimesters highlighted significant factors contributing to excessive GWG, especially social taboos, and lack of nutritional education. Participants expressed dissatisfaction with limited access to healthcare providers (HCPs), particularly in remote areas. Based on these findings, the NutriMom prototype was designed to include several key characteristics: gamified learning about healthy diets and physical exercise, virtual chat support with HCPs, and reminders about sleep, hydration, and food intake. The target audience voted that the `Daily Quiz' and `Physical Exercise Planner' features from the `Gamified Learning' category were most appreciated, with 31.6% of combined votes. NutriMom received a mean system usability scale score (SUS) of 79.81% (n = 26). NutriMom merges gamification and mHealth to provide interactive tools and personalized recommendations for pregnant women. Future iterations will include longitudinal studies and the integration of machine learning models, further improving functionality.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10883365,

title = {Design and Evaluation of CareLine: mHealth Service App for Elder Citizens Connecting with Caregivers Within Bangladesh},

author = {Nusrat Jahan Shawon and Nizar Ahmed and Fabia Jannat and Moinul Alam and Ashraful Islam},

doi = {10.1109/ICMCSI64620.2025.10883365},

year  = {2025},

date = {2025-01-01},

booktitle = {2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)},

pages = {1517-1523},

keywords = {Surveys;Bridges;Heuristic algorithms;Digital transformation;Pressing;Older adults;Particle swarm optimization;Informatics;Mobile computing;Bangladesh;caregiving;caregivers;elderly;localized services;mHealth;mobile app},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-981-96-8011-5_10,

title = {IoV Cyberattack Detection via TabTransformer on CAN Bus Communications},

author = {Jahanggir Hossain Setu and Armun Alam and Nabarun Halder and Asif Mahmood and Ashraful Islam and M. Ashraful Amin},

editor = {Hao-Shang Ma and Hwa-Young Jeong and Yu-Wei Chan and Hsuan-Che Yang},

isbn = {978-981-96-8011-5},

year  = {2025},

date = {2025-01-01},

booktitle = {Innovative Computing 2025, Volume 4},

pages = {59–65},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {The Internet of Vehicles (IoV) represents a transformative evolution in transportation by linking vehicles through the Internet to improve communication and operational efficiency. This interconnected network relies heavily on the Controller Area Network (CAN) which facilitates communication among Electronic Control Units (ECUs) within vehicles. However, this increased connectivity also introduces vulnerabilities making IoV systems susceptible to various cyberattacks. This study paper focuses on identifying cyberattacks on IoV systems using advanced Machine Learning (ML) techniques. The TabularTransformer (TabTransformer) model was utilized to analyze the CICIoV2024 dataset. To address class imbalance inherent in the dataset, two resampling techniques were implemented: Synthetic Minority Oversampling - Edited Nearest Neighbor (SMOTEENN) and Synthetic Minority Oversampling-Tomek links (SMOTETomek). The comparative study evaluated the performance of the TabTransformer under different class distributions, including a baseline scenario. The results indicate that the SMOTEENN technique improved model performance, achieving a precision of 89%, recall of 85%, and F1-score of 86%.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-981-96-8011-5_11,

title = {Cross-Dataset Framework for Diabetes Prediction with KAN and MLP},

author = {Armun Alam and Jahanggir Hossain Setu and Syed Tangim Pasha and Nabarun Halder and Ashraful Islam and M. Ashraful Amin},

editor = {Hao-Shang Ma and Hwa-Young Jeong and Yu-Wei Chan and Hsuan-Che Yang},

isbn = {978-981-96-8011-5},

year  = {2025},

date = {2025-01-01},

booktitle = {Innovative Computing 2025, Volume 4},

pages = {66–72},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {Diabetes mellitus, a chronic condition caused by insufficient insulin production or poor cellular response to insulin, leads to elevated blood glucose levels and, if untreated, can severely affect organs, e.g., the heart, kidneys, and eyes. Diabetes is preventable and associated with lifestyle factors. Given the global rise in diabetes cases, effective early prediction is necessary. This study evaluates the effectiveness of advanced Machine Learning (ML) algorithms, i.e., Kolmogorov-Arnold Network (KAN) and Multi-Layer Perceptron (MLP), for diabetes classification across three datasets: Indian PIMA, DiaHealth Bangladesh, and Taiwan datasets. eXtreme Gradient Boosting (XGBoost) based feature selection technique identified core attributes for the model consistency, while KAN and MLP were trained on different dataset combinations and tested on a third, covering three configurations. Results indicate that KAN achieved higher accuracy (up to 74%) and F1-Score (up to 65%) in certain configurations, surpassing MLP in most cases. Although moderate precision and recall highlight potential data imbalance, KAN demonstrates promising results for diabetes prediction.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{11003364,

title = {Analyzing Machine Learning Models for Multiclass Classification of Human Stress Levels: Comparison Between Balanced and Imbalanced Data},

author = {Nabarun Halder and Armun Alam and Jahanggir Hossain Setu and Asif Mahmood and Ashraful Islam and M Ashraful Amin},

doi = {10.1109/KST65016.2025.11003364},

year  = {2025},

date = {2025-01-01},

booktitle = {2025 17th International Conference on Knowledge and Smart Technology (KST)},

pages = {104-109},

keywords = {Support vector machines;Accuracy;Sensitivity;Psychology;Human factors;Nearest neighbor methods;Data models;Reliability;Random forests;Strain;Human Stress;Machine Learning;SMOTE},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3706599.3720240,

title = {Improving User Engagement and Learning Outcomes in LLM-Based Python Tutor: A Study of PACE},

author = {Muhtasim Ibteda Shochcho and Mohammad Ashfaq Ur Rahman and Shadman Rohan and Ashraful Islam and Hasnain Heickal and AKM Mahbubur Rahman and M. Ashraful Amin and Amin Ahsan Ali},

url = {https://doi.org/10.1145/3706599.3720240},

doi = {10.1145/3706599.3720240},

isbn = {9798400713958},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {CHI EA '25},

abstract = {Large Language Models (LLMs) are increasingly being adopted for educational applications, but sometimes, limited internet access and budget constraints restrict their accessibility. Small Language Models (SLMs) have emerged as viable alternatives, capable of providing effective tutoring in resource-constrained contexts. This paper introduces PACE (Python AI Companion for Enhanced Engagement), a system leveraging SLMs to deliver step-by-step guidance and adaptive feedback for teaching Python. An evaluation with varying levels of learners showed PACE’s effectiveness, achieving a System Usability Scale (SUS) score of 77.28. While participants were generally satisfied with its clarity and personalized feedback, they identified some areas for improvement, such as loss of context during lengthy conversations. This study examines (1) the PACE system’s effectiveness in programming education according to learners, (2) learners’ trust in PACE versus traditional resources, and (3) design recommendations to enhance engagement and learning outcomes. PACE contributes to advancing cost-effective, scalable programming education.},

keywords = {Education, Learning, LLM, PACE, Python, SLM, Tutor, Tutoring},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10980496,

title = {Emotion Recognition in Bangla Text: An Ensemble Approach with Data Augmentation using BanglaBERT and MultiBERT},

author = {Nabarun Halder and Armun Alam and Jahanggir Hossain Setu and Ashraful Islam and M. Ashraful Amin},

doi = {10.1109/ICCAE64891.2025.10980496},

year  = {2025},

date = {2025-01-01},

booktitle = {2025 17th International Conference on Computer and Automation Engineering (ICCAE)},

pages = {6-11},

keywords = {Emotion recognition;Accuracy;Translation;Automation;Text recognition;Data augmentation;Transformers;Natural language processing;Data models;Ensemble learning;Emotion Recognition;Data Augmentation;Bangla NLP;BanglaBERT;MultiBERT;Transformers;Back Translation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-87784-1_23,

title = {A Theoretical Framework for Decentralized Intrusion Detection in Smart Networks Using Blockchain and Machine Learning},

author = {Moinul Alam and Mostafa Hasan and Arvil Nath Akash and Md Junayed Hossain and Ashraful Islam and Sanzar Adnan Alam},

editor = {Leonard Barolli},

isbn = {978-3-031-87784-1},

year  = {2025},

date = {2025-01-01},

booktitle = {Advanced Information Networking and Applications},

pages = {245–256},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {This paper presents a theoretical analysis of a blockchain-based Intrusion Detection System (IDS) designed to improve cybersecurity through decentralized threat intelligence sharing. The proposed IDS framework integrates the Isolation Forest algorithm, a machine learning model, with blockchain technology to enable anomaly detection and automated threat response across a network. Using the Isolation Forest algorithm, the system effectively identifies anomalies in network traffic, while smart contracts on the blockchain allow for autonomous actions, such as node quarantine and malicious IP blocking. This approach aims to reduce the frequency of false positives and enhance response times, providing an efficient solution for scalable and collaborative cybersecurity applications. By examining the potential of combining machine learning with blockchain and smart contracts, this paper contributes to the advancement of adaptive IDS solutions that leverage decentralized architectures for enhanced threat detection and rapid response.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{11077493,

title = {Performance Analysis of Machine Learning Models for Detecting Anomalous Traffic in Internet of Medical Things},

author = {Nabarun Halder and Tunisha Yanoor Bristy and Mohammad Arshad Hossain Ratul and Md Zahangir Alam and Mehedi Hassan and Ashraful Islam and M. Ashraful Amin},

doi = {10.1109/AIRC64931.2025.11077493},

year  = {2025},

date = {2025-01-01},

booktitle = {2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC)},

pages = {482-487},

keywords = {Radio frequency;Atmospheric modeling;Internet of Medical Things;Telecommunication traffic;Feature extraction;Real-time systems;Safety;Reliability;Computer crime;Robots;IoMT;Machine Learning;Cyberattack;Medical Devices;Cybersecurity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{11077542,

title = {Integrating Supervised and Self-Supervised Models for an Enhanced PCOS Detection: A Data-Driven Approach with Machine Learning Perspective},

author = {Tasmia Tahmida and Syed Tangim Pasha and Mehedi Hassan and Ashraful Islam and M. Ashraful Amin},

doi = {10.1109/AIRC64931.2025.11077542},

year  = {2025},

date = {2025-01-01},

booktitle = {2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC)},

pages = {499-503},

keywords = {Training;Accuracy;Atmospheric modeling;Autoencoders;Supervised learning;Static VAr compensators;Machine learning;Data models;Vectors;Robots;Polycystic Ovary Syndrome;KagglePCOS Dataset;Supervised Learning;Self-Supervised Learning;Autoencoder;SimCLR;BYOL},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyb,

title = {Using Transformers for Emotion Recognition in Bangla Text: A Comparative Study of MultiBERT and BanglaBERT with Data Augmentation},

author = {N. Halder and T. P. Proma and J. H. Setu and A. Noor and A. Islam and M. A. Amin},

year  = {2024},

date = {2024-12-18},

booktitle = {23rd IEEE International Conference on Machine Learning and Applications (ICMLA)},

publisher = {IEEE},

address = {USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {ECGInsight: A Web Application-Based Approach to Myocardial Infarction Detection From ECG Image Reports Utilizing ResNet},

author = {J. H. Setu and S. T. Pasha and N. Halder and S. Sikder and A. Islam and M. Z. Alam},

year  = {2024},

date = {2024-12-18},

urldate = {2024-12-01},

booktitle = {23rd IEEE International Conference on Machine Learning and Applications (ICMLA)},

publisher = {IEEE},

address = {USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyk,

title = {Segment Anything Model 2 (SAM 2) for Accurate Flood Detection in Remote Sensing Imagery},

author = {Nusrat Sultana and Anika Islam and Yousra Tabassum and Jahanggir Hossain Setu and Asif Mahmud and Ashraful Islam and M. Ashraful Amin},

year  = {2024},

date = {2024-12-01},

booktitle = {2024 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2024)},

pages = {1-5},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyj,

title = {Comparative Analysis of Machine Learning Models for Diabetes Prediction: A Cross-Dataset Study of Bangladeshi and Pima Indian Populations},

author = {Mahmudul Islam and Syed Tangim Pasha and Jahanggir Hossain Setu and Nabarun Halder and Ashraful Islam and M. Ashraful Amin},

year  = {2024},

date = {2024-12-01},

booktitle = {2024 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2024)},

pages = {1-5},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyi,

title = {Recognizing Medicine Names from Bangladeshi Handwritten Prescription Images Using TrOCR},

author = {Mohammad Faiyaz Uz Zaman and Bushra Rahman and Afsana Rubyat and Nabarun Halder and Asif Mahmud and Ashraful Islam and M. Ashraful Amin},

year  = {2024},

date = {2024-12-01},

booktitle = {2024 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2024)},

pages = {1-5},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyf,

title = {SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer Learning},

author = {Kishor Bhaumik and Minha Kimb and Fahim Faisal Niloy and Amin Ahsan Ali and Simon Woo},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

booktitle = {27th International Conference on Pattern Recognition},

publisher = {IEEE},

address = {KolKata, India},

organization = {ICPR},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeye,

title = {MIXAD: Memory-Induced Explainable Time Series Anomaly Detection},

author = {Minha Kim and Kishor Bhaumik and Amin Ahsan Ali and Simon Woo},

year  = {2024},

date = {2024-12-01},

booktitle = {27th International Conference on Pattern Recognition},

publisher = {IEEE},

address = {KolKata, India},

organization = {ICPR},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyd,

title = {How Good are LM and LLMs in Bangla Newspaper Article Summarization?},

author = {Faria Sultana and Md Tahmid Hasan Fuad and Md Fahim and Rahat Rizvi Rahman and Meheraj Hossain and M Ashraful Amin and AKM Mahabubur Rahman and Amin Ahsan Ali},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

booktitle = {27th International Conference on Pattern Recognition},

publisher = {IEEE},

address = {KolKata, India},

organization = {ICPR},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyc,

title = {TinyLLM Efficacy in Low-Resource Language},

author = {Farhan Noor Dehan and Md Fahim and AKM Mahabubur Rahman and M Ashraful Amin and Amin Ahsan Ali},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

booktitle = {27th International Conference on Pattern Recognition},

publisher = {IEEE},

address = {KolKata, India},

organization = {ICPR},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyl,

title = {Improving the Performance of Transformer-based Models Over Classical Baselines in Multiple Transliterated Languages},

author = {Fahim Ahmed and Md Fahim and AKM Mahabubur Rahman and M Ashraful Amin and Amin Ahsan Ali},

year  = {2024},

date = {2024-10-09},

urldate = {2024-10-09},

booktitle = {European Conference on Artificial Intelligence},

publisher = {IOS Press},

address = {Santiago de Compostela},

organization = {ECAI},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeym,

title = {Lightweight Recurrent Neural Network for Image Super-resolution},

author = {Mir Sazzat Hossain and AKM Mahbubur Rahman and Md. Ashraful Amin and Amin Ahsan Ali},

year  = {2024},

date = {2024-10-01},

booktitle = {IEEE Int'l Conf on Image Processing},

publisher = {IEEE},

address = {Abu Dhabi},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyn,

title = {Empowering Multiclass Classification and Data Augmentation of Arabic News Articles Through Transformer Model},

author = {Jahanggir Hossain Setu and Nabarun Halder and Sankar Sikder and Ashraful Islam and Md Zahangir Alam},

year  = {2024},

date = {2024-06-01},

urldate = {2024-06-01},

booktitle = {2024 International Joint Conference on Neural Networks (IJCNN)},

pages = {1-7},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey_28,

title = {Securing Bluetooth Technology Against The BrakTooth Vulnerabilities Through Hybrid Machine Learning Model},

author = {Jahanggir Hossain Setu and Md Sazzad Hossain and Nabarun Halder and Ashraful Islam and M Ashraful Amin},

year  = {2024},

date = {2024-05-01},

booktitle = {3rd International Conference on Computational Modelling, Simulation and Optimization},

address = {THAILAND},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey_27,

title = {Performance Analysis of Ensemble and DNN Models for Decoding Mental Stress Utilizing ECG-Based Wearable Data Fusion},

author = {Noor Masrur and Nabarun Halder and Sami Rashid and Ashraful Islam and Tarem Ahmed},

year  = {2024},

date = {2024-05-01},

booktitle = {IEEE 12th International Black Sea Conference on Communications and Networking},

address = {Georgia},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyz,

title = {User-Centered Design and Validation of mHealth App for Providing Vital Assistance and Emergency Healthcare Support in Bangladesh},

author = {Abu Musa Sakib and Sanzar Adnan Alam and Ashraful Islam},

year  = {2024},

date = {2024-05-01},

booktitle = {6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications},

address = {Turkey},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyy,

title = {GorbhoKotha: a mHealth App for Maternal Support in Bangladesh Utilizing User-Centered Design Principles},

author = {Sarah Binte Mahbub and Nafis Islam and Mehedi Hasan Surem and Ayatullah Arefin and Md Rashid Shabab and Ashraful Islam},

year  = {2024},

date = {2024-05-01},

booktitle = {6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications},

address = {Turkey},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyx,

title = {Mycological Examination of Microscopic Fungi Images with Deep Learning and Gradient Weighted Class Activation Mapping Visualization},

author = {Md. Safirur Rashid and Md. Samin Morshed and Muhammad Usama Islam and Sami Rashid and Asif Mahmud and Ashraful Islam},

year  = {2024},

date = {2024-05-01},

booktitle = {IEEE 2024 International Advances in Science & Engineering 

Technology (ASET) Multi-Conferences},

publisher = {IEEE},

address = {Dubai},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyw,

title = {Design and Evaluation of the PustiPothika mHealth App: A Machine Learning-Enabled Solution for Daily Nutritional Monitoring and Recommendations in Bangladesh},

author = {Nabarun Halder and Yousra Tabassum and Anika Islam and Nusrat Sultana and J. M. Sadik-Ul Islam Smaron and Ashraful Islam},

year  = {2024},

date = {2024-05-01},

booktitle = {IEEE 2024 International Advances in Science & Engineering Technology (ASET) Multi-Conferences},

publisher = {IEEE},

address = {Dubai},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyv,

title = {Performance Evaluation of Machine Learning and Deep Learning Models for Predicting Type-2 Diabetes on Balanced and Imbalanced Data},

author = {Nayema Khatun and Nabarun Halder and Ashraful Islam and Md Zahangir Alam},

year  = {2024},

date = {2024-05-01},

booktitle = {IEEE 2024 International Advances in Science & Engineering Technology (ASET) Multi-Conferences},

publisher = {IEEE},

address = {Dubai},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}