Mir Maruf Ahmed

@aiub.edu

Computer Science & Engineering
American International University-Bangladesh (AIUB)

Mir Maruf Ahmed


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https://researchid.co/maruf420
Mir Maruf Ahmed graduated from the American International University-Bangladesh (AIUB) with a Bachelor of Science degree in Computer Science and Engineering. A passion has marked his academic journey for learning new skills and technologies. He is a researcher focused on Machine Learning (ML), Artificial Intelligence (AI) and Data Science, with a particular interest in exploring neural networks and their applications in Deep Learning, including Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs).

Throughout his academic career, Maruf has earned multiple honors and certifications, including being listed on the Dean’s List Honors for academic excellence. His areas of expertise include Cyber Security, IT support, Networking, Web Development, UI/UX Design, Software Quality Assurance and API Testing. He aims to contribute to the cybersecurity and digital media industries through his creativity, knowledge, and technical skills.

EDUCATION

1. Degree: Bachelor of Science
Major: Computer Science & Engineering
Passing Year: 2023
CGPA: 3.63 out of 4.00
Institute: American International University - Bangladesh (AIUB)

2. Degree: Higher Secondary School Certificate
Major: Science
Passing Year: 2018
CGPA: 4.50 out of 5.00
Institute: Dhaka Residential Model College (DRMC)

3. Degree: Secondary School Certificate
Major: Science
Passing Year: 2016
CGPA: 5.00 out of 5.00
Institute: Tejgaon Government High School (TGHS)

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Engineering, Computer Science, Numerical Analysis
7

Scopus Publications

32

Scholar Citations

2

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Advanced Heart Attack Prediction Using a Stacked Ensemble Machine Learning Model and Diverse Data Integration
    Sultanul Arifeen Hamim, Rakin S. Aftab, M. Ahmed, Farzana Faiza, M. F. Mridha
    International Journal of Intelligent Systems and Applications, 2025
    Heart attacks continue to be one of the primary causes of death globally, highlighting the critical need for advanced predictive models to improve early diagnosis and timely intervention. This study presents a comprehensive machine learning (ML) approach to heart attack prediction, integrating multiple datasets from diverse sources to construct a robust and accurate predictive model. The research employs a stacking ensemble model, which combines the strengths of individual ML algorithms to improve overall performance. Extensive data preprocessing steps were carefully undertaken to preserve the dataset's integrity and maintain its quality. The results demonstrate a superior accuracy of 97.48%, significantly outperforming state-of-the-art approaches. The high level of accuracy indicates the model’s potential effectiveness in the clinical setting for early detection of heart attack and prevention. However, the proposed model is influenced by the quality and diversity of the integrated datasets, which could affect its generalizability across broader populations. Challenges encountered during the model's development include optimizing hyperparameters for multiple classifiers, ensuring data preprocessing consistency, and balancing computational efficiency with model interpretability. The results underscore the pivotal contribution of advanced ML approaches in revolutionizing the management of cardiovascular attack. By addressing the complexities and variabilities inherent in heart attack prediction, the work provides a pathway towards more effective and personalized cardiovascular disease management strategies, demonstrating the transformative potential of ML in healthcare.
  • Harnessing Convolutional Neural Networks for Potato Leaf Disease Detection: A Proposed Model
    Mir Maruf Ahmed, Rakin Sad Aftab, Sultanul Arifeen Hamim, Md. Abdullah-Al-Jubair, Dip Nandi
    Studies in Computational Intelligence, 2025
  • Leaf disease detection using convolutional neural networks: a proposed model using tomato plant leaves
    Neural Computing and Applications, 2024
  • APPLICATIONS OF IOT-ENABLED SMART MODEL: A MODEL FOR ENHANCING FOOD SERVICE OPERATION IN DEVELOPING COUNTRIES
    Journal of Applied Engineering and Technological Science, 2024
  • Deep Facial Recognition: Unraveling Kinship Patterns Among Strangers Using CNN
    Md Masum Billah, Rakin Sad Aftab, Mir Maruf Ahmed, Mohammad Shorif Uddin
    2024 IEEE Conference on Computing Applications and Systems Compas 2024, 2024
    This study explores the application of deep facial recognition technology to identify kinship patterns among strangers using convolutional neural networks (CNNs). Utilizing the VGGFace2 dataset, a deep CNN model was developed and evaluated to determine its effectiveness in inferring familial relationships based on facial features. The model achieved an impressive accuracy of 95%, demonstrating its potential for accurately recognizing kinship. This research highlights the promising applications of facial feature analysis in various domains, including forensic science and social network research. Additionally, it addresses both technological and ethical considerations, contributing to the responsible development and application of facial recognition technology for establishing familial ties. The comprehensive evaluation provided in this study underscores the potential and implications of facial recognition technology in determining kinship, while also identifying areas for future research and improvement.
  • SkinScanNet: A CNN-Based Model with Explainable AI for Reliable and Transparent Skin Cancer Detection
    Rakin Sad Aftab, Sultanul Arifeen Hamim, Mir Maruf Ahmed, S M Abdullah Shafi, Md. Mazid-Ul-Haque
    2024 27th International Conference on Computer and Information Technology Iccit 2024 Proceedings, 2024
    Early and accurate detection of skin cancer, particularly melanoma, is critical for improving survival rates and treatment outcomes. This study introduces SkinScanNet, a CNN-based deep learning model that classifies skin lesions into benign and malignant categories. The model demonstrates robust performance, achieving a test accuracy of around 94%, with precision, recall, and F1-scores of 95.45%, 89.94%, and 92.56%, respectively. Gradient-weighted class activation mapping (Grad-CAM++), an explainable artificial intelligence (XAI) technique, is integrated into the model to generate class-discriminative heatmaps, highlighting key regions influencing predictions. This enhances transparency, enabling clinicians to validate and interpret model decisions. The system addresses key challenges in artificial intelligence (AI)-driven diagnostics by combining high accuracy with interpretability, offering a reliable and explainable tool for real-world clinical applications. Future directions include expanding dataset diversity and optimizing the model for broader generalizability and reduced false negatives.
  • Tomato Disease Classification using Convolutional Neural Networks
    2023 4th International Conference on Data Analytics for Business and Industry Icdabi 2023, 2023

RECENT SCHOLAR PUBLICATIONS

  • Enhancing Medical Image Analysis with Advanced Optimization Techniques: A Comparative Study of Machine Learning Model Optimizers
    SA Hamim, RS Aftab, MM Ahmed, M Abdullah-Al-Jubair, MF Mridha
    Nature-Inspired Approaches to Engineering and Healthcare Solutions, 247-264 , 2026
    2026.0
  • Harnessing Convolutional Neural Networks for Potato Leaf Disease Detection: A Proposed Model
    MM Ahmed, RS Aftab, SA Hamim, M Abdullah-Al-Jubair, D Nandi
    Machine Vision in Plant Leaf Disease Detection for Sustainable Agriculture … , 2025
    2025.0
    Citations: 1
  • SkinScanNet: A CNN-Based Model with Explainable AI for Reliable and Transparent Skin Cancer Detection
    RS Aftab, SA Hamim, MM Ahmed, SMA Shafi, M Mazid-Ul-Haque
    2024 27th International Conference on Computer and Information Technology … , 2024
    2024.0
  • Leaf disease detection using convolutional neural networks: a proposed model using tomato plant leaves
    MM Billah, A Sultana, R Sad Aftab, MM Ahmed, M Shorif Uddin
    Neural Computing and Applications 36 (32), 20043-20053 , 2024
    2024.0
    Citations: 20
  • Deep Facial Recognition: Unraveling Kinship Patterns Among Strangers Using CNN
    MM Billah, RS Aftab, MM Ahmed, MS Uddin
    2024 IEEE International Conference on Computing, Applications and Systems … , 2024
    2024.0
    Citations: 2
  • Applications of IOT-enabled smart model: A model for enhancing food service operation in developing countries
    A Sultana, MM Billah, M Ahmed, RS Aftab, M Kaosar, MS Uddin
    Journal of Applied Engineering and Technological Science (JAETS) 5 (2), 1123 … , 2024
    2024.0
    Citations: 8
  • Tomato Disease Classification using Convolutional Neural Networks
    MI Nakib, S Ghose, MM Ahmed, MA Mahomuda, MF Mridha
    2023 4th International Conference on Data Analytics for Business and … , 2023
    2023.0
    Citations: 1
  • Advanced Heart Attack Prediction Using a Stacked Ensemble Machine Learning Model and Diverse Data Integration
    SA Hamim, RS Aftab, M Ahmed, F Faiza, MF Mridha

MOST CITED SCHOLAR PUBLICATIONS

  • Leaf disease detection using convolutional neural networks: a proposed model using tomato plant leaves
    MM Billah, A Sultana, R Sad Aftab, MM Ahmed, M Shorif Uddin
    Neural Computing and Applications 36 (32), 20043-20053 , 2024
    2024.0
    Citations: 20
  • Applications of IOT-enabled smart model: A model for enhancing food service operation in developing countries
    A Sultana, MM Billah, M Ahmed, RS Aftab, M Kaosar, MS Uddin
    Journal of Applied Engineering and Technological Science (JAETS) 5 (2), 1123 … , 2024
    2024.0
    Citations: 8
  • Deep Facial Recognition: Unraveling Kinship Patterns Among Strangers Using CNN
    MM Billah, RS Aftab, MM Ahmed, MS Uddin
    2024 IEEE International Conference on Computing, Applications and Systems … , 2024
    2024.0
    Citations: 2
  • Harnessing Convolutional Neural Networks for Potato Leaf Disease Detection: A Proposed Model
    MM Ahmed, RS Aftab, SA Hamim, M Abdullah-Al-Jubair, D Nandi
    Machine Vision in Plant Leaf Disease Detection for Sustainable Agriculture … , 2025
    2025.0
    Citations: 1
  • Tomato Disease Classification using Convolutional Neural Networks
    MI Nakib, S Ghose, MM Ahmed, MA Mahomuda, MF Mridha
    2023 4th International Conference on Data Analytics for Business and … , 2023
    2023.0
    Citations: 1
  • Enhancing Medical Image Analysis with Advanced Optimization Techniques: A Comparative Study of Machine Learning Model Optimizers
    SA Hamim, RS Aftab, MM Ahmed, M Abdullah-Al-Jubair, MF Mridha
    Nature-Inspired Approaches to Engineering and Healthcare Solutions, 247-264 , 2026
    2026.0
  • SkinScanNet: A CNN-Based Model with Explainable AI for Reliable and Transparent Skin Cancer Detection
    RS Aftab, SA Hamim, MM Ahmed, SMA Shafi, M Mazid-Ul-Haque
    2024 27th International Conference on Computer and Information Technology … , 2024
    2024.0
  • Advanced Heart Attack Prediction Using a Stacked Ensemble Machine Learning Model and Diverse Data Integration
    SA Hamim, RS Aftab, M Ahmed, F Faiza, MF Mridha

Publications

Journal Publications:

[1] Billah, M.M., Sultana, A., Sad Aftab, R. et al. Leaf disease detection using convolutional neural networks: a proposed model using tomato plant leaves. Neural Comput & Applic 36, 20043–20053 (2024).

[2] Sultana, A., Billah, M. M., Ahmed, M. M., Aftab, R. S., Kaosar, M., & Uddin, M. S. (2024). Applications of IoT-Enabled Smart Model: A Model For Enhancing Food Service Operation in Developing Countries. Journal of Applied Engineering and Technological Science (JAETS), 5(2), 1123–1141.


Conference Publications:

[1] M. I. Nakib, S. Ghose, M. M. Ahmed, M. A. Mahomuda and M. F. Mridha, "Tomato Disease Classification using Convolutional Neural Networks," 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI), Bahrain, 2023, pp. 495-498, doi: 10.1109/

[2] M. M. Billah, R. Sad Aftab, M. M. Ahmed and M. Shorif Uddin, "Deep Facial Recognition: Unraveling Kinship Patterns Among Strangers Using CNN," 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), Cox's Bazar, Bangladesh, 2024, pp. 1-9, doi: 10.1109/

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

MOBDOG and AJSE [AIUB Journal of Science and Engineering]

Industry, Institute, or Organisation Collaboration

Advanced Machine Intelligence Research Lab

INDUSTRY EXPERIENCE

1. Trainee Officer (3 Months) (7 July 2024 – Continuing)

Area of Expertise: API testing and control, Maintenance Data Analysis and Reporting, Troubleshooting and Debugging.

2. Junior Security Engineer (5 Months) (1 Oct 2023 - 29 Feb 2024)
eTech Solution Ltd.
Area of Expertise: Cyber Security, Network Security, Python, SQL and Data Protection.

3. Customized Plugin Theme Developer [AJSE] (4 Months) (26 Mar 2023 - 27 Jul 2023)
American International University Bangladesh (AIUB)
Area of Expertise: HTML, CSS, PHP, JavaScript, Frontend development, UI and UX Design.