Lipismita Panigrahi
Assistant Professor
Dr. Lipismita Panigrahi is from Odisha and is Indian by birth. She is working as an Assistant Professor in the School of Computer Applications at KIIT University, Bhubaneswar, Odisha, India. She received her master’s degree with honors in computer applications from S.O.A. University, Odisha, India, in 2012 and earned her Ph.D. in the Department of Computer Applications from NIT Raipur, India, in 2020. She has over nine years of teaching and research experience. Her research interests include Digital Image Processing and Analysis, Data Mining, AI, and Machine Learning. She has various significant publications to her credit in leading journals indexed in SCI, Scopus, and national and international conferences. She has one patent and author of one edited book. She is an active member of IEEE. She reviewed several papers for IEEE conferences and is an active reviewer for many reputed journals, like Expert Systems with Applications and Ultrasound Medicine and Biology. She was named the 2017 Chhattisgarh Young Scientist Award winner.
Machine Learning, Artificial Intelligence, Image Processing, Data Mining
"Patent
Panigrahi, L., Jha, Avaneesh, “Novel Compilation System For Accepting Non-English Based Programming Languages”.
Books :
Reference BOOK
Panigrahi L., Biswal S., Bhoi A.K., Kalam A.and Barsocchi, P, “Machine Learning and AI Techniques in Interactive Medical Image Analysis"" published by IGI Global., USA, SCOPUS for possible indexing. (ISBN13: 9781668446713, ISBN10: 1668446715, EISBN13: 9781668446737).
International / National Journals
Panigrahi, L., Verma, K., & Singh, B. K. (2019). Evaluation of Image Features Within and Surrounding Lesion Region for Risk Stratification in Breast Ultrasound Images. IETE Journal of Research, 1-12.
Panigrahi, L., Verma, K., & Singh, B. K. (2019). Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution. Expert Systems with Applications, 115, 486-498.
Panigrahi, L., Verma, K., & Singh, B. K. (2018). Hybrid segmentation method based on multi-scale Gaussian kernel fuzzy clustering with spatial bias correction and region-scalable fitting for breast US images. IET Computer Vision, 12(8), 1067-1077.
Singh, B. K., Verma, K., Panigrahi, L., & Thoke, A. S. (2017). Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm. Expert Systems with Applications, 90, 209-223.
Panigrahi, L., Das, K., & Mishra, D. (2014). Missing value imputation using hybrid higher order neural classifier. Indian Journal of Science and Technology, 7(12), 2007-2014."