arXiv subjects

Md. Ismiel Hossen Abir

Publications and source records attributed to Md. Ismiel Hossen Abir.

2 recordsLinked to original sources

arXiv2026

A Conceptual Hybrid Framework for Post-Quantum Security: Integrating BB84 QKD, AES, and Bio-inspired Mechanisms

Quantum computing is a significant risk to classical cryptographic, especially RSA, which depends on the difficulty of factoring large numbers. Classical factorization methods, such as Trial Division and Pollard's Rho, are inefficient for large keys, while Shor's quantum algorithm can break RSA efficiently in polynomial time. This research studies RSA's vulnerabilities under both classical and quantum attacks and designs a hybrid security framework to ensure data protection in the post-quantum era. The conceptual framework combines AES encryption for classical security, BB84 Quantum Key Distribution (QKD) for secure key exchange with eavesdropping detection, quantum state comparison for lightweight authentication, and a bio-inspired immune system for adaptive threat detection. RSA is vulnerable to Shor's algorithm, BB84 achieves full key agreement in ideal conditions, and it detects eavesdropping with high accuracy. The conceptual model includes both classical and quantum security methods, providing a scalable and adaptive solution for Post-Quantum encryption data protection. This work primarily proposes a conceptual framework. Detailed implementation, security proofs, and extensive experimental validation are considered future work.

cs.OH↗

arXiv2025

A CNN-Based Malaria Diagnosis from Blood Cell Images with SHAP and LIME Explainability

Malaria remains a prevalent health concern in regions with tropical and subtropical climates. The cause of malaria is the Plasmodium parasite, which is transmitted through the bites of infected female Anopheles mosquitoes. Traditional diagnostic methods, such as microscopic blood smear analysis, are low in sensitivity, depend on expert judgment, and require resources that may not be available in remote settings. To overcome these limitations, this study proposes a deep learning-based approach utilizing a custom Convolutional Neural Network (CNN) to automatically classify blood cell images as parasitized or uninfected. The model achieves an accuracy of 96%, with precision and recall scores exceeding 0.95 for both classes. This study also compares the custom CNN with established deep learning architectures, including ResNet50, VGG16, MobileNetV2, and DenseNet121. To enhance model interpretability, Explainable AI techniques such as SHAP, LIME, and Saliency Maps are applied. The proposed system shows how deep learning can provide quick, accurate and understandable malaria diagnosis, especially in areas with limited resources.

cs.CV↗