Publications
Peer-reviewed research bridging AI theory with applied engineering.
CrypTon: A Hybrid Quantum-Classical Framework Integrating BB84 QKD with AES for Secure Communication
Co-authors: Vishesh Goyal, Dr. Raguru Jaya Krishna
Quantum computers are coming for classical encryption. CrypTon is an IEEE-published research framework that integrates Quantum Key Distribution (BB84 protocol) with AES encryption to build a hybrid cryptographic system that is secure against quantum attacks, without modifying the cipher itself. Implemented in Qiskit and Python, the system achieved 97.3% eavesdropper detection accuracy across 1000 trials, proving that quantum-secured keys can be seamlessly plugged into existing AES infrastructure.
Identifying Mango Leaf Diseases with Advanced Deep Learning Approaches and Convolutional Neural Networks - MangoMed AI
Co-authors: Tanishta, Vishesh Goyal, Dr. Megha P. Arakeri
India produces 50% of the world's mangoes. Yet diseases like Anthracnose and Bacterial Canker routinely destroy 10–39% of yields because early detection requires expert eyes that most farmers don't have access to. MangoMedAI is an IEEE-published deep learning system that detects and classifies 8 mango leaf diseases with 98.97% accuracy and an F1 score of 99.10%, using a fine-tuned EfficientNet-B0 model trained on 12,046 leaf images. Built with FastAI and PyTorch, it outperforms multiple existing approaches in both accuracy and deployment efficiency.
PRIORIS: Dynamic Adapting Scheduling for HPC — Eliminating Job Failure through Robust Resource Allocation
Co-authors: Vishesh Goyal, Dr. Pavithra N.
Every large-scale computing cluster, from cloud engines to supercomputers, runs on a job scheduler. Most of them are decades-old algorithms that don't know what's happening in the system right now. PRIORIS is an IEEE-published adaptive job scheduling framework for High Performance Computing environments that replaces static scheduling and failure prediction with real-time resource awareness, dependency-driven job promotion, and starvation prevention. Evaluated on 5000 synthetic jobs, it reduced makespan by 24.7% and average wait time by 31.5% compared to the standard First-Come-First-Served baseline.
AI-Powered Personalized Learning Platform: NLP-Driven Article-Centric Chatbot with Sentiment Analysis
Co-authors: Dr. Pavithra N., Dr. Sapna R., Dr. Preethi, Dr. Manasa C. M., Dr. Ashwitha A., Vishesh Goyal
Most AI learning tools answer from a giant pre-trained knowledge base, which means they hallucinate, drift off-topic, and can't be controlled. This IEEE-published system takes a different approach: upload any article, and the chatbot answers only from that content. Built with NLP, TF-IDF vectorisation, cosine similarity, and an SVM-based sentiment classifier, the platform achieved 90% question-answering accuracy and 90.84% precision, with zero reliance on large language models. Designed specifically for educational environments where transparency and controlled knowledge sources matter.