Kuljeet Singh
Papers
2
Total Citations
35
H-Index
2
About
Kuljeet Singh is a rising researcher at the intersection of artificial intelligence and cybersecurity, with a focus on meta-learning and intrusion detection systems. His most impactful work, "Meta-Health: Learning-to-Learn (Meta-learning) as a Next Generation of Deep Learning Exploring Healthcare Challenges and Solutions for Rare Disorders," published in 2023 with 31 citations, introduces a transformative approach to tackling rare diseases by applying meta-learning techniques that enable models to adapt quickly from limited data—a critical breakthrough for medical domains where data scarcity is a major barrier. In his 2025 paper, Singh proposes a novel univariate feature selection method using ANOVA F-test combined with machine learning to enhance intrusion detection frameworks for robotic systems, addressing the growing cybersecurity vulnerabilities in industries like manufacturing, healthcare, and logistics. With 4 recent citations, this work underscores his ability to bridge theoretical advances with practical security solutions. Singh’s contributions demonstrate a dual commitment to advancing healthcare AI and fortifying critical infrastructure, positioning him as a promising voice in both fields. His research not only pushes the boundaries of deep learning but also offers tangible tools for real-world challenges.
Research Focus
Key Achievements
Top Papers
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- 2