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Faculty Profile

Dr. Siji Jose Pulluparambil

Assistant Professor

Dr. Siji Jose Pulluparambil serves as an Assistant Professor in the Department of Artificial Intelligence and Data Science since 2024. She has successfully completed her Ph.D. in Computer Science from Srinivas University, Mangalore. She holds an MCA from Nirmala College of Information Technology, Chalakudy (2010), where she was recognized as the University First Rank holder, and a B.Sc. in Mathematics from Carmel College, Mala (2004–2007). With more than 10 years of teaching experience, she has taught a wide range of subjects including Introduction to Artificial Intelligence and Data Science, Data Structures, Software Testing, Software Engineering, Compilers, Algorithms, Discrete Mathematics, DBMS, Computer Organization, Operations Research, Professional Business Skills, and Office Automation Tools. Her research interests span Machine Learning, Artificial Intelligence, and Deep Learning. She has presented papers at national and international conferences, participated in seminars, workshops, and faculty development programmes, and published extensively in reputed journals. In addition, she has authored books: Learn C – The Complete C Programming Concepts and Computer Fundamentals and HTML and published book chapters.

Journals and Conference Publications

Siji Jose Pulluparambil,Dr.Netravathi P.S(2023), A Comparitive Study on Medical Image Denoising Methods,Indian Journal of Natural Sciences(Web of science),ISSN- 0976-0997
Siji Jose Pulluparambil,Fredy Varghese,Laiby Thomas(2022), Learn C –The Complete C Programming Concepts, ISBN-979-88-883377-4-5
Siji Jose Pulluparambil, Stanely John,(2022), Analysis of optimization Techniques in Machine Learning Algorithms, International Conference (VIGYAAN) on “Advanced and Emerging Trends in Computer Science”.
Siji Jose Pulluparambil, Fredy Varghese(2021),A Study on Internet of Things - Based Energy Management System, Frame work and Challenges in Smart Cities, NaiVigyan-A Collection of Multidisciplinary Research Papers –Volume 2 Issue 2 pp 26-28.
Siji Jose Pulluparambil, Fredy Varghese (2021), Bitcoin-Applications In Modern Era and Its Methodology-An Overview Vigyaan 2021-2022(Proceedings of Sixth National Conference on Emerging Trends In Cloud Computing.ISBN-978-81-949892-5-7 .
Siji Jose Pulluparambil, Fredy Varghese(2021) ,A Comprehensive Study on the Current Trends and Future Scope of Machine Learning in Education Sector, Vigyaan 2020-2021(Proceddings of Sixth National Conferenceon Technological Advancements in Computational Intelligence, ISBN-978-81-949892-0-2
Ms.Siji Jose Pulluparambil,Dr.Subrahmanya Bhat(2021), Survey of Applications of Deep Learning in Medical image Processing, INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS (IJRAR), E-ISSN 2348-1269,P-ISSN 2349-5138
Ms.Siji Jose Pulluparambil,Dr.Subrahmanya Bhat(2021), Application of Machine Learning in Google Services- A Case Study, International Journal of Case Studies in Business, IT, and Education (IJCSBE), ISSN: 2581-6942
Ms.Siji Jose,Dr.Subrahmanya Bhat(2021), Medical Image Processing: Detection And Prediction Of Pcos – A Systematic Literature Review, International Journal of Health Sciences and Pharmacy (IJHSP), ISSN: 2581-6411.
S. J. Pulluparambil, A. S, D. Sanson, F. J. Thiruthanathil and G. K. R, "Audio-Based Emotion Analysis Using Deep Learning Techniques," 2025 2nd International Conference on Trends in Engineering Systems and Technologies (ICTEST), Ernakulam, India, 2025, pp. 1-6, doi: 10.1109/ICTEST64710.2025.11042455.
Siji Jose Pulluparambil and Subrahmanya Bhat B. “Detection and Prediction of Polycystic Ovary Syndrome Using Attention-Based CNN-RNN Classification Model”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.2 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160270
Pulluparambil SJ, Subrahmanya Bhat. Exploring the feature prioritization and data sampling of PCOS diagnosis via densely connected attention based squeeze deep learning detection model. J Steroid Biochem Mol Biol. 2025 Dec 31;258:106933. doi: 10.1016/j.jsbmb.2025.106933.