I'm a passionate AI & ML researcher currently pursuing my MSc in Computer Science with a major in Data Science. My research blends practical innovation with deep technical knowledge — targeting impactful domains like medical imaging, agriculture, and audio-based mental health analysis.

🔬 Key expertise areas:

• Deep Learning | Transfer Learning | Vision Transformers (ViT)
• Hybrid CNN–ViT and GAN architectures
• Federated Learning & Feature Selection (BGWO, RFE)
• Graph Neural Networks & Audio Feature Synthesis
• XAI (Explainable AI) in medical, agricultural, and educational domains
• Research-backed dataset development & AI integration

📝 Published in Elsevier, Springer Nature, and top-tier conferences.
🌱 Awarded for Scholarly Excellence by Daffodil International University.

• 🧠 Brain & Lung Disease Detection (Medical Imaging)
• 🌾 Leaf Disease Identification in Smart Farming
• 🎧 Audio Intelligence for Mental Stability Prediction
• 🧩 Attention-Based Hybrid CNN–ViT Architectures
• ⚙️ Feature Fusion & Optimization
• 🔍 Explainable AI (XAI) for Transparency & Fairness
• 📊 AI for Academic Performance Analysis
• 🌐 Decentralized Intelligence via Federated Learning
• 🧪 GAN-based Audio Feature Synthesis
• 🗂️ Dataset Curation & Annotation Tools

🚧 Project showcase coming soon! Stay tuned for:

• 🔍 GAN + GNN for Audio–Mental State Mapping
• 🍃 Potato & Soursop Leaf Disease Detection (YOLOv5/8, ViT)
• 🧠 Brain & Lung Imaging Classification with Transfer Learning
• 📱 Smart Irrigation System with IoT + ML
• 🎓 Dropout Prediction Model with Explainable Outputs
• 🧠 Adaptive ViT with Dual Attention Mechanism

📌 View My Google Scholar

Published in international journals and conferences (Q1–Q4 indexed)
Topics include: medical image classification, agricultural AI, lightweight CNN/ViT architectures, and XAI.

• 🔭 I'm currently working on ViT + GAN + GNN hybrid models
• 🧠 I focus on real-world AI that's transparent, explainable, and ethical
• 🌱 I'm also writing research guides to help young AI enthusiasts