Autor/Autorin: Nandihal Dr. Priya
Dr.Priya Nandihal is an Associate Professor in the department of Artificial Intelligence and Machine Learning at Global Academy of Technology, Bangalore. She has 11 years of teaching experience. She received her Ph.D from Visvesvaraya Technical University, Belgaum, Karnataka, India. Her main research work focuses on Image Processing and AI/ML.
A total of four contributory works were developed during the overall tenure of the work taken up in this thesis and was explained in four separate chapters, viz., chapter 3, 4, 5 and 6 respectively. The four proposed works presented as four detailed chapters are: Contribution 1: Microarray Image Denoising Based on Markov Random Fields in the spatial domain.Contribution 2: Adaptive MinMax Threshold Algorithm for Microarray Image based De-noising methods which is based on the Non-sub-sampled Contourlet Transforms approach.Contribution 3: An image noise type identification scheme for microarray images utilizing deep learning.Contribution 4: Determination of Noise Type in Microarray Images using Deep Learning, based on Iterative Parameters Generated in a Denoising Scheme using Markov Random Field.
Nandihal, Dr. PriyaDr.Priya Nandihal is an Associate Professor in the department of Artificial Intelligence and Machine Learning at Global Academy of Technology, Bangalore. She has 11 years of teaching experience. She received her Ph.D from Visvesvaraya Technical University, Belgaum, Karnataka, India. Her main research work focuses on Image Processing and AI/ML.
Über den Autor
Priya Nandihal è professore associato presso il dipartimento di Intelligenza artificiale e apprendimento automatico della Global Academy of Technology di Bangalore. Ha 11 anni di esperienza di insegnamento. Ha conseguito il dottorato di ricerca presso la Visvesvaraya Technical University, Belgaum, Karnataka, India. Il suo lavoro di ricerca principale si concentra sull'elaborazione delle immagini e sull'AI/ML.
Klappentext
A total of four contributory works were developed during the overall tenure of the work taken up in this thesis and was explained in four separate chapters, viz., chapter 3, 4, 5 and 6 respectively. The four proposed works presented as four detailed chapters are: Contribution 1: Microarray Image Denoising Based on Markov Random Fields in the spatial domain.Contribution 2: Adaptive MinMax Threshold Algorithm for Microarray Image based De-noising methods which is based on the Non-sub-sampled Contourlet Transforms approach.Contribution 3: An image noise type identification scheme for microarray images utilizing deep learning.Contribution 4: Determination of Noise Type in Microarray Images using Deep Learning, based on Iterative Parameters Generated in a Denoising Scheme using Markov Random Field.
