Professor, Chitkara University, Punjab, India
Ms. Poorva Nayak is a distinguished physicist and academic leader with nearly two decades of experience in higher education and research. Currently serving as the Pro Vice Chancellor and Dean at Chitkara University, Punjab, India, she has significantly contributed to the fields of neuromarketing, navigation systems, software engineering, business intelligence, and data mining. She is dedicated to academic excellence, innovation, and fostering a collaborative research environment.
Profile
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📚 Education:
- Ph.D. in Computer Science and Engineering: Punjabi University Patiala, Punjab, India (2010)
- M.Phil. in Computer Science: Madurai Kamaraj University, Madurai, Tamil Nadu, India (2006)
- Master of Computer Applications: Punjabi University, Patiala, Punjab, India (2004)
- B.A (Hons) School in Economics: Punjabi University, Patiala, Punjab, India (2001)
🔬 Work Experience:
- Research Fellow: INTI International University, Malaysia (15 December 2023 to 31 Dec 2025)
- Professor & Dean: Chitkara University, Punjab, India (Jan 2011 to Date)
- Associate Professor: RIMT, Mandi Gobindgarh, Punjab, India (Jul 2006 to Jan 2011)
- Lecturer: S.G.H.C.M.T., Patiala, Punjab, India (Aug 2005 to Jul 2006)
- Lecturer: Lyallpur Khalsa College, Jalandhar, Punjab, India (Jul 2004 to Aug 2005)
🏢 Administrative Experience:
- Pro Vice Chancellor (Computer Applications): Chitkara University, Punjab (2017 to date)
- Dean, Department of Computer Applications: Chitkara University, Punjab (2017 to Dec 2023)
- Coordinator, Research and Development: Chitkara University, Punjab (2012 to 2017)
- Coordinator, Undergraduate Studies: Regional Institute of Management and Technology, Mandi Gobindgarh, Punjab (2006 to 2010)
🎓 Research Interests:
Ms. Poorva Nayak’s research interests span neuromarketing, navigation systems, software engineering, business intelligence, and data mining. She has a profound interest in developing innovative solutions and methodologies to enhance these fields.
📜 Publications Top Notes:
Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques The Journal of Supercomputing, 2019. Cited by 120.
Deep feature extraction and classification of android malware images Sensors, 2020. Cited by 80.
Assessing OpenStreetMap data using intrinsic quality indicators: an extension to the QGIS processing toolbox Future Internet, 2017. Cited by 77.
Dominant feature selection and machine learning‐based hybrid approach to analyze android ransomware Security and Communication Networks, 2021. Cited by 59.
A study of neuromarketing techniques for proposing cost-effective information-driven framework for decision making Materials Today: Proceedings, 2022. Cited by 57.
Systematic literature review on software effort estimation using machine learning approaches 2017 International Conference on Next Generation Computing and Information Systems, 2017. Cited by 53.
Classification and analysis of android malware images using feature fusion technique IEEE Access, 2021. Cited by 50.