Chandan Kumar

About

I am working as an Associate Professor at Amity University Jharkhand, Ranchi, India. Earlier I worked as an Assistant Professor (Sl. Gr.) at the School of Computing of Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh. I was the Professional Consultant at the Institutions of Eminence (IoE) Secretariat of the University Grants Commission (UGC), New Delhi from July 2019 to October 2021. I also served as a Lecturer at the Department of Computer Applications of NIT Jamshedpur from April 2011 to May 2018. I completed my Ph.D. from NIT Jamshedpur in 2017 under the supervision of Dr. Dilip Kumar Yadav. I completed the Post Doctoral Fellowship (PDF) from University of Technology Sydney, Australia under the supervision of Dr. Mukesh Prasad. My research areas are Software Engineering, AI/ML/DL, LLM, Predictive Analysis, and Scientometrics Analyis. My Ph.D. thesis was on Software Quality and Reliability Modeling Using Causal Relationships of Software Metrics.

Employment

• Amity University Jharkhand Associate Professor
  2025 - Present
• Amrita Vishwa Vidyapeetham Assistant Professor (Sl. Gr.)
  2021 - 2025
• University Grants Commission Consultant
  2019 - 2021
• National Institute of Technology Jamshedpur Teaching/Assistant Professor
  2011 - 2018

Education

• National Institute of Technology Jamshedpur Ph.D.
  2012 - 2018

Projects & Funding

Projects & funding information is unavailable.

Publications (45)

• Causal modeling in software defect prediction: bridging expert knowledge and novice insight
  Automated Software Engineering 2026 DOI: 10.1007/s10515-026-00625-w
  +Save
• Predicting Cardiovascular Disease Using a Quantum Support Vector Machine
  Applied Artificial Intelligence 2026 DOI: 10.1080/08839514.2026.2684103
  +Save
• XAI-driven requirement risk classification using transformer-based models
  International Journal of System Assurance Engineering and Management 2026 DOI: 10.1007/s13198-026-03178-z
  +Save
• Software reliability growth modeling integrating imperfect debugging, learning effects, and uncertain testing environments
  Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability 2026 DOI: 10.1177/1748006X261432523
  +Save
• DeiTFake: Deepfake detection model using DeiT multi-stage training
  Array 2026 DOI: 10.1016/j.array.2026.100734
  +Save
• CodeSmellSynergy hybrid ensemble learning framework for detecting python code smells
  Discover Computing 2026 DOI: 10.1007/s10791-026-10055-7
  +Save
• A machine learning ensemble for software fault prediction using weighted voting regression
  Life Cycle Reliability and Safety Engineering 2026 DOI: 10.1007/s41872-026-00414-0
  +Save
• Solving System of Equations over Algebras using Gröbner Bases and Integro-Differential Structures
  Mathematics Open 2026 DOI: 10.1142/S2811007226500148
  +Save
• Multimodal Alzheimer’s Disease Classification From Heterogeneous Datasets Using Multihead Cross‐Attention CNN–BiLSTM With BAM
  Applied Computational Intelligence and Soft Computing 2026 DOI: 10.1155/acis/8280381
  +Save
• A comprehensive review of DDoS attack prevention, detection, and mitigation in IoT and SDN-IoT networks
  Discover Internet of Things 2026 DOI: 10.1007/s43926-026-00341-7
  +Save