All Research Areas

PRISM Lab Research Domain

Machine Learning

Research, development, and scholarly output from this specialized PRISM Lab domain.

1 related publicationResearch overview

About this domain

Research Overview

Machine Learning (ML) is a branch of artificial intelligence that enables computational systems to learn patterns from data and make intelligent decisions with minimal human intervention. By leveraging statistical methods, optimization techniques, and computational intelligence, machine learning systems can automatically improve performance through experience. Research in machine learning spans areas such as supervised learning, unsupervised learning, reinforcement learning, deep learning, predictive analytics, pattern recognition, and intelligent automation. Applications include healthcare diagnostics, cybersecurity, natural language processing, computer vision, recommendation systems, predictive maintenance, and smart decision-making systems. Current research directions focus on privacy-preserving machine learning, federated learning, explainable AI (XAI), trustworthy machine learning, edge intelligence, and scalable intelligent systems for solving real-world interdisciplinary challenges.

Research output

Related Publications

Peer-reviewed journals and conference papers connected with this research domain.

Peer-reviewed research

Journal Publications

1 publication
012025

ForkRL: Deep Reinforcement Learning-Based Forking Prevention in Blockchain-Enabled Federated IoMT

Authors: Shubham Kant Ajay, Rohit Sharma, Satendra Kumar

IEEE Transactions on Consumer Electronics