Education

Dr. Chandrashekar V. Gudada has built a strong academic foundation in computer science, beginning with a B.Sc. in P.E.Cs. and an M.Sc. in Computer Science from Gulbarga University, Kalaburagi. He further advanced his scholarly journey by earning a Ph.D. in Computer Science from Rani Channamma University, Belagavi, where his doctoral thesis focused on the recognition and classification of historical Kannada handwritten scripts. His education reflects not only subject expertise but also the development of research skills in artificial intelligence, image processing, and machine learning. Additionally, he enhanced his knowledge through specialized certifications from ISRO in remote sensing, digital image analysis, and geoprocessing using Python, demonstrating his commitment to continuous learning. This blend of formal education and technical training has enabled him to pursue cutting-edge interdisciplinary research addressing both academic and societal challenges.

Experience

Dr. Chandrashekar brings over a decade of academic and teaching experience, beginning his career as a Guest Lecturer at Gulbarga University, Kalaburagi. He later transitioned into a full-time academic role and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence, Kalaburagi. In this capacity, he has been instrumental in teaching, mentoring, and guiding students while also spearheading funded research initiatives such as the DeepLeuko project on leukemia detection using AI. His professional journey also includes responsibilities in academic governance, having served as a member of the Board of Studies and Board of Examination. He has contributed to institutional quality improvement through roles in NAAC internal committees and as Deputy Chief Superintendent for examinations. His professional career highlights his dedication to both research excellence and academic leadership.

Research Interest

Dr. Chandrashekar’s research interests lie at the intersection of artificial intelligence, machine learning, image processing, and digital signal analysis. His doctoral work explored the recognition and digitization of historical Kannada handwritten manuscripts, contributing significantly to the preservation of linguistic heritage through computational techniques. Expanding his scope, he has advanced research in medical image and signal processing, with projects applying AI to detect heart diseases and classify blood smear images for leukemia diagnosis. He is also engaged in agricultural informatics, employing deep learning to identify pests and enhance crop protection. His multidisciplinary interests emphasize the application of technology to solve real-world challenges across healthcare, agriculture, and linguistics. With over 20 scholarly contributions, his work reflects a blend of innovation, practical relevance, and cross-domain applicability in computer science research.

Awards and Honors

Throughout his career, Dr. Chandrashekar has received recognition for his research and academic contributions through conference presentations, publications, and active roles in professional bodies. His involvement in IEEE and Springer-supported international conferences, such as SoCPaR and ICCCI, has given him platforms to present original research to global audiences. He has contributed book chapters in Springer’s Advances in Intelligent Systems and Computing series, which highlights the value of his scholarly work. Additionally, he holds memberships in reputed professional organizations such as ACM, IAENG, IRED, and MIR Lab, underlining his recognition as an active member of the international research community. His reviewer roles for reputed journals further reflect academic acknowledgment of his expertise. These honors collectively illustrate his growing influence and professional recognition in computer science research.

Research Skill

Dr. Chandrashekar possesses strong research skills spanning artificial intelligence, deep learning, pattern recognition, and biomedical data analysis. He has proficiency in machine learning algorithms, image processing techniques, and feature extraction methods such as GLCM, HOG, and LBP, applied in both language digitization and healthcare solutions. His technical expertise includes geoprocessing, digital image analysis, and remote sensing, enhanced by ISRO-certified training programs. He is also adept at developing and implementing deep learning models for complex tasks such as disease detection, agricultural pest recognition, and script identification in Dravidian languages. With experience in publishing high-quality research, presenting at international conferences, and collaborating across disciplines, he demonstrates a balanced skill set combining theoretical innovation with practical application. His capacity for interdisciplinary problem-solving underscores his strength as a researcher and innovator.

Publication Top Notes

Title: Age-type identification and recognition of historical Kannada handwritten document images using HOG feature descriptors
Authors: P Bannigidad, C Gudada
Year: 2018
Citations: 23

Title: Restoration of degraded Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniques
Authors: P Bannigidad, C Gudada
Year: 2017
Citations: 23

Title: Restoration of degraded historical Kannada handwritten document images using image enhancement techniques
Authors: P Bannigidad, C Gudada
Year: 2016
Citations: 19

Title: Identification and classification of historical Kannada handwritten document images using LBP features
Authors: B Parashuram, G Chandrashekar
Year: 2018
Citations: 15

Title: Historical Kannada handwritten character recognition using machine learning algorithm
Authors: P Bannigidad, C Gudada
Year: 2020
Citations: 8

Title: Restoration of degraded non-uniformally illuminated historical Kannada handwritten document images
Authors: P Bannigidad, C Gudada
Year: 2018
Citations: 7

Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCM
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 3

Title: Historical Kannada handwritten scripts recognition system using line segmentation with LBP features
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 3

Title: Heart sound analysis with machine learning using audio features for detecting heart diseases
Authors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N Ail
Year: 2024
Citations: 2

Title: Digitization and recognition of historical Kannada handwritten manuscripts using text line segmentation with LBP features
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 2

Title: Historical Kannada Handwritten Character Recognition using K-Nearest Neighbour Technique
Authors: P Bannigidad, C Gudada
Year: 2019
Citations: 2

Title: Use of audio transfer learning to analyse heart sounds for detecting heart diseases
Authors: S Satyanarayana, K Srikanta, Murthy, G Chandrashekar, M Satish Kumar
Year: 2024
Citations: 1

Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture Features
Authors: S Mallappa, C Gudada, PM Santhoshi
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection
Authors: SSS Murthy, S Mallappa, G Chandrashekar
Year: 2025

Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble Classifiers
Authors: C Gudada, S Mallappa
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart Diseases
Authors: S Sathyanarayanan, S Murthy, S Mallappa, C Gudada
Year: 2025

Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCM features
Authors: B Parashuram, G Chandrashekar
Year: 2019

Title: Age-Type Identification and Classification of Historical Kannada Handwritten Scripts using Line Segmentation with HOG feature Descriptors
Authors: P Bannigidad, C Gudada
Year: 2019

Title: Ensemble of Deep and Texture Features for Script Identification from Camera Based Dravidian Languages
Authors: S Kumar, C Gudada
Year: 2025

Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart Diseases
Authors: C Gudada
Year: 2025

Conclusion

In summary, Dr. Chandrashekar V. Gudada is an accomplished academic and researcher whose contributions span computer science, artificial intelligence, healthcare applications, and cultural preservation. His educational achievements, professional experiences, and active involvement in funded projects demonstrate both scholarly depth and societal relevance. With over two decades of combined research and teaching exposure, he has established himself as a capable leader, innovator, and mentor in higher education. His professional memberships, reviewer roles, and participation in global academic forums underscore his recognition at an international level. By combining cutting-edge research with community-focused contributions, he exemplifies the qualities of a well-rounded researcher. Looking ahead, his potential to expand global collaborations, publish in high-impact journals, and engage in academic leadership positions positions him as a strong candidate for recognition and prestigious awards.