Research & Publications
Engineered solutions at the intersection of Federated Learning, Privacy-Preserving AI, and Medical Diagnosis. Focusing on efficiency metrics and scalable architecture.
FVCM-Net: Privacy-Preserving Lung Cancer Detection from CT Images
Federated learning-based ensemble deep learning model with explainable HiRes-CAM and SHAP attribution for decentralized lung cancer detection. Ensures data privacy while maintaining high-fidelity diagnostic performance across multiple medical institutions.
Deep Learning for Identification of Arsenic-Induced Skin Diseases
Attention-based CNN (ARS-CNNSA) for arsenic-induced skin condition classification, outperforming VGG16 and InceptionV3 in real-world clinical datasets.
Clustering as a Catalyst for Big Data Classification (CC-BC)
Clustering-driven classification framework leveraging unsupervised instance similarity to reduce labeling dependency and enhance ensemble classifiers in high-volume environments.
My Education
United International University
Bsc in Computer Science & Engineering
GPA
3.67 / 4.00
Graduated
OCT 2024
Core Focus Areas
Academic Excellence Scholarship
United International University
AI & Automation Unpacked Hackathon
IBM TechZone
Problem Solving Intermediate
HackerRank Certified