Hadiur Rahman Nabil

A B.Sc. graduate in Computer Science and Engineering from American International University-Bangladesh (AIUB). Currently, I serve as a Research Assistant at the Advanced Machine Intelligence Research (AMIR) Lab, where I focus on Deep Learning applications in medical imaging.

My research interests lie at the intersection of Deep Learning and Computer Vision, with a particular emphasis on developing efficient and interpretable models for medical image analysis. I am passionate about leveraging artificial intelligence to enhance healthcare diagnostics and improve patient outcomes. Additionally, I am exploring emerging areas in Generative AI and Large Language Models (LLMs), investigating their potential applications in healthcare and beyond.

Hadiur Rahman Nabil

Recent News

2025

January 2025

Looking forward to new research collaborations and opportunities in artificial intelligence and computer vision.

2024

December 2024

Served as a peer reviewer for the Indonesian Journal of Electrical Engineering and Computer Science , contributing to the academic peer review process and advancement of knowledge in the field.

November 2024

Research manuscript accepted for publication in the proceedings of the International Conference on Computer and Information Technology (ICCIT 2024). Additionally, a conference paper was successfully accepted at the IEEE Decision Aid Sciences and Applications Conference (DASA'24).

September 2024

Two scholarly publications were accepted for publication in peer-reviewed academic venues, contributing to the advancement of knowledge in the field of artificial intelligence and computer vision.

May 2024

Successfully completed undergraduate studies at American International University-Bangladesh with the successful defense of thesis research focused on deep learning applications in medical image analysis.

January 2024

Published inaugural research paper in IEEE Access (Q1 journal). Concurrent with this achievement, received promotion to Research Assistant position at the Advanced Machine Intelligence Research Laboratory, acknowledging research contributions and potential.

Old News

August 2023

Commenced academic research career as Research Intern at the Advanced Machine Intelligence Research Laboratory, focusing on applications of deep learning to problems in medical imaging and diagnostics.

Education

2020 - 2024

B.Sc. in Computer Science and Engineering

American International University-Bangladesh (AIUB)

Relevant Coursework: Data Structures, Algorithms, Research Methodology, Data Science, Data Warehousing & Data Mining, Operating Systems, Artificial Intelligence, Computer Vision & Pattern Recognition.

Professional Experience

2024 - Present

Research Assistant

Advanced Machine Intelligence Research (AMIR) Lab

  • Conducted research on deep learning applications in medical imaging
  • Implemented deep learning algorithms for image diagnosis
  • Collaborated with interdisciplinary teams on various research projects
  • Published research findings in peer-reviewed journals and conferences
2023 - 2023

Research Intern

Advanced Machine Intelligence Research (AMIR) Lab

  • Conducted research on deep learning applications in medical imaging
  • Collaborated with interdisciplinary teams on various research projects
  • Published research findings in peer-reviewed journals and book Chapters

Research & Publications

View Complete Publication List on Google Scholar

Journal Generative adversarial networks (GANs) in medical imaging: Advancements, applications, and challenges

IEEE Access (Q1), 2024

This extensive study explores the vast array of applications of GANs in medical imaging, scrutinizing them within recent research. We analyze prevalent datasets, pre-processing techniques, and provide an in-depth discussion of GAN algorithms, elucidating their respective strengths and limitations.

Read Paper

Journal TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation

IEEE Open Journal of the Computer Society (Under Review)

Journal Gallbladder Classification incorporating Multi Scale Feature Extraction with XAI

Scientific Reports (Under Review)

Journal Harnessing deep learning for plant disease analysis: Current trends, challenges, and future prospects

Smart Agricultural Technology (Under Review)

Journal Advancements in deep learning architectures for aerial imagery

Intelligent Systems with Applications (Accepted)

Journal Generative AI with sentiment Analysis

Multimedia Tools and Applications (Under Review)

Conference NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

2024 International Conference on Decision Aid Sciences and Applications (DASA)

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture.

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Conference Insights into zooplankton abundance dynamics in tropical temporary ponds using machine learning and explainable AI

International Conference on Innovations in Science, Engineering and Technology 2024 (ICISET)

This paper contributes a robust ensemble and explainable AI-based framework for accurately predicting zooplankton abundance, aiding sustainable management of tropical temporary pond ecosystems.

Read Paper

Conference Explainable AI-driven vision transformers for assessing fruit freshness via transfer learning

27th International Conference on Computer and Information Technology (ICCIT) (Accepted)

Book Chapter Banana leaf spot disease detection using deep learning-based algorithms

Machine Vision in Plant Leaf Disease Detection for Sustainable Agriculture (Accepted)

Technical Skills

Programming Languages

  • C++
  • Python
  • Java
  • C#
  • R
  • PHP
  • HTML & CSS

Libraries & Frameworks

  • TensorFlow
  • PyTorch
  • ASP.Net

Tools & Platforms

  • GitHub & Git
  • LaTeX
  • MATLAB
  • UML
  • Figma
  • Postman
  • Visual Studio

Databases

  • MySQL

Academic Projects

Cardiovascular Disease Risk Prediction

Cardiovascular Disease Risk Prediction

This project designed a predictive model using R Language to assess the risk of cardiovascular diseases. Utilized statistical methods and machine learning techniques to predict potential risk factors.

GitHub
Clustering Analysis of Heart Stroke Data

Clustering Analysis of Heart Stroke Data

Performed clustering analysis using K-Means and hierarchical clustering to classify heart stroke data and evaluate clustering performance.

GitHub
Sentiment Analysis with BERT

Sentiment Analysis with BERT

This project performs sentiment analysis on Yelp reviews using a pre-trained BERT Multilingual Uncased Model with web scraping and visualization.

GitHub
Travel Management System

Travel Management System

This project developed a travel management system for handling customer travel itineraries. Built using C# and ASP.Net to manage travel bookings, cancellations, and payment integration.

GitHub
Train Station Management System

Train Station Management Systrem

This project developed a train station management system to automate the process of scheduling and managing trains.

GitHub
Rice Leaf Spot Disease Detection

RLSDD-YOLO: Rice Leaf Spot Disease Detection

Object detection on rice leaf images to identify and classify rice leaf spot disease using YOLO. The project includes dataset annotation and comprehensive evaluation metrics.

GitHub

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