Satishkumar Mallappa | Computer Science | Best Researcher Award Published on 04/09/202504/09/2025 by Best Paper Awards Dr. Satishkumar Mallappa | Computer Science | Best Researcher Award Assistant Professor at Sri Sathya Sai University for Human Excellence | India Dr. Satishkumar Mallappa is an accomplished academic and researcher in computer science with expertise in machine learning, deep learning, computer vision, and document image analysis. He holds a Ph.D. in Computer Science from Gulbarga University and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence, Karnataka. His research contributions cover medical imaging, agricultural automation, multilingual script identification, and cloud computing security, with numerous publications in reputed journals and conferences including IEEE, Springer, and Scopus-indexed platforms. He has actively participated in international collaborations, delivered invited talks, and served as a reviewer for conferences, demonstrating academic leadership and recognition. Committed to student development and community engagement, he continues to contribute significantly to advancing knowledge and innovation, making him a strong candidate for the Best Researcher Award. Professional Profile Google Scholar Education Dr. Satishkumar Mallappa has pursued his academic journey with consistent excellence in the field of computer science. He earned his Ph.D. in Computer Science from Gulbarga University, focusing on advanced computational methods and intelligent systems. Prior to this, he completed his M.Phil. and M.Sc. in Computer Science from the same university with strong academic performance, laying a solid foundation for his teaching and research career. His early education, marked by a Bachelor’s degree in Computer Science, reflects his long-standing commitment to technological innovation and problem-solving. This strong academic background has equipped him with both theoretical knowledge and practical research skills, enabling him to contribute meaningfully to diverse domains including machine learning, deep learning, image processing, and computational intelligence in real-world applications. Experience Dr. Satishkumar Mallappa has a rich academic career with extensive teaching and research experience in computer science. He is currently serving as Assistant Professor at the Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence, Karnataka. Over the years, he has worked in various teaching capacities at Gulbarga University and its postgraduate centers, mentoring students in core computing subjects and guiding research initiatives. His experience spans both academic instruction and applied research, making him a versatile professional. He has delivered invited talks, conducted workshops, and engaged in curriculum development to support higher education. Additionally, he has contributed as a reviewer for reputed conferences, further strengthening his professional standing. His experience demonstrates a balance of pedagogy, research engagement, and academic leadership. Research Interest Dr. Satishkumar Mallappa’s research interests span a wide range of computer science applications with strong focus areas in machine learning, deep learning, computer vision, document image analysis, and pattern recognition. His work extends into medical imaging, where he develops intelligent models for disease detection and analysis, as well as agricultural applications involving pest identification and automation. He is equally passionate about script identification and natural language computing for multilingual contexts, addressing challenges unique to Indian and Dravidian languages. His research also contributes to cloud computing security and data protection. Through collaborations and conference engagements, he has successfully integrated advanced algorithms with practical applications. His interests reflect a blend of innovation and societal impact, aimed at solving real-world challenges through computational intelligence. Awards and Honors Dr. Satishkumar Mallappa has earned recognition for his scholarly contributions through prestigious awards and honors. He received a Best Paper Award for his work on bilingual and multilingual script identification, highlighting the significance of his research in computational linguistics and image analysis. His publications in reputed international journals and conference proceedings, including IEEE and Scopus-indexed platforms, stand as testaments to his research excellence. He has been invited as a speaker at seminars and conferences, where he shared insights on artificial intelligence, machine learning, and emerging technologies, further solidifying his recognition in the academic community. Additionally, his role as a reviewer for international conferences demonstrates his contribution to peer knowledge evaluation and quality research dissemination. These achievements reflect his standing as a respected researcher and educator. Research Skills Dr. Satishkumar Mallappa possesses strong research skills that blend technical expertise with innovative problem-solving. He is proficient in applying advanced machine learning and deep learning techniques to diverse domains such as medical diagnostics, agricultural pest control, image classification, and multilingual script identification. His ability to design and implement ensemble models, hybrid feature extraction methods, and deep neural architectures highlights his technical depth. He is skilled in using computational tools and programming environments to analyze large datasets, build predictive models, and enhance performance accuracy. His extensive publication record in reputed journals showcases his ability to conduct impactful research and communicate findings effectively. Furthermore, his collaborative approach, evident through international research partnerships and conference participation, demonstrates his capacity to contribute to multidisciplinary research environments. Publication Top Notes Title: A study on Image Segmentation and its MethodsAuthors: S Kumar, R SrinivasYear: 2013Citations: 14 Title: Script Identification from Camera Based Tri-Lingual DocumentAuthors: G Mukarambi, S Mallappa, BV DhandraYear: 2017Citations: 9 Title: Scrambling and Descrambling of Document Image for Data Security in Cloud ComputingAuthors: N Salimath, S Mallappa, N Padhy, J SheetlaniYear: 2019Citations: 6 Title: Skin Cancer Classification using VGG-16 and Googlenet CNN ModelsAuthors: SB Ummapure, R Tilekar, S MallappaYear: 2023Citations: 4 Title: Script Identification of Camera Based Bilingual Document Images Using SFTA FeaturesAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2019Citations: 4 Title: Hybridization of Texture Features for Identification of Bi-lingual Scripts from Camera Images at Word LevelAuthors: S Mallappa, BV Dhandra, G MukarambiYear: 2023Citations: 3 Title: Heart Sound Analysis with Machine Learning Using Audio Features for Detecting Heart DiseasesAuthors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N AilYear: 2024Citations: 2 Title: Camera-based Tri-lingual Script Identification at Word Level using a Combination of SFTA and LBP FeaturesAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2020Citations: 2 Title: Use of Audio Transfer Learning to Analyse Heart Sounds for Detecting Heart DiseasesAuthors: S Swaminathan, S Murthy, C Gudada, SK MallappaYear: 2024Citations: 1 Title: Script Identification from Camera Captured Indian Document Images with CNN ModelAuthors: S Mallappa, BV Dhandra, G MukarambiYear: 2023Citations: 1 Title: Camera-Based Bi-lingual Script Identification at Word Level using SFTA FeaturesAuthors: G Mukarambi, BV Dhandra, S MallappaYear: 2019Citations: 1 Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture FeaturesAuthors: S Mallappa, C Gudada, PM SanthoshiYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the DetectionAuthors: SSS Murthy, S MallappaYear: 2025 Title: A Deep Learning Model based White Blood Cell Image ClassificationAuthors: S Ummapre, S MallappaYear: 2025 Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble ClassifiersAuthors: C Gudada, S MallappaYear: 2025 Title: Multi-fruit Classification Using a New FruitNet-11 Based on Deep Convolutional Neural NetworkAuthors: Raghavendra, S MallappaYear: 2022 Title: Script Identification at Line-level using SFTA and LBP Features from Bi-lingual and Tri-lingual Documents Captured from the CameraAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2020 Title: Hybrid Method for Elimination of Uneven Illumination from Camera-based Document ImagesAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2020 Title: Camera-based Document Image Scrambling for Security in Cloud Computing EnvironmentAuthors: N Salimath, S Mallappa, J SheetlaniYear: 2019 Title: Script Identification System for Camera Based Multilingual Document ImagesAuthor: S Mallappa Conclusion Dr. Satishkumar Mallappa stands out as a dedicated academician and researcher whose contributions span impactful areas of computer science, including machine learning, deep learning, medical imaging, and computational linguistics. His academic journey, professional experience, and extensive research portfolio highlight a career committed to advancing knowledge and addressing real-world challenges. Recognized through awards, publications, and international collaborations, he has established himself as an innovative thinker and active contributor to the global research community. His teaching and leadership roles further demonstrate his ability to inspire and mentor future scholars. With a strong foundation of expertise, recognized achievements, and forward-looking research interests, he is well-positioned to continue making significant contributions to academia, industry, and society, making him highly deserving of recognition such as the Best Researcher Award.
Dr. Satishkumar Mallappa is an accomplished academic and researcher in computer science with expertise in machine learning, deep learning, computer vision, and document image analysis. He holds a Ph.D. in Computer Science from Gulbarga University and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence, Karnataka. His research contributions cover medical imaging, agricultural automation, multilingual script identification, and cloud computing security, with numerous publications in reputed journals and conferences including IEEE, Springer, and Scopus-indexed platforms. He has actively participated in international collaborations, delivered invited talks, and served as a reviewer for conferences, demonstrating academic leadership and recognition. Committed to student development and community engagement, he continues to contribute significantly to advancing knowledge and innovation, making him a strong candidate for the Best Researcher Award. Professional Profile Google Scholar Education Dr. Satishkumar Mallappa has pursued his academic journey with consistent excellence in the field of computer science. He earned his Ph.D. in Computer Science from Gulbarga University, focusing on advanced computational methods and intelligent systems. Prior to this, he completed his M.Phil. and M.Sc. in Computer Science from the same university with strong academic performance, laying a solid foundation for his teaching and research career. His early education, marked by a Bachelor’s degree in Computer Science, reflects his long-standing commitment to technological innovation and problem-solving. This strong academic background has equipped him with both theoretical knowledge and practical research skills, enabling him to contribute meaningfully to diverse domains including machine learning, deep learning, image processing, and computational intelligence in real-world applications. Experience Dr. Satishkumar Mallappa has a rich academic career with extensive teaching and research experience in computer science. He is currently serving as Assistant Professor at the Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence, Karnataka. Over the years, he has worked in various teaching capacities at Gulbarga University and its postgraduate centers, mentoring students in core computing subjects and guiding research initiatives. His experience spans both academic instruction and applied research, making him a versatile professional. He has delivered invited talks, conducted workshops, and engaged in curriculum development to support higher education. Additionally, he has contributed as a reviewer for reputed conferences, further strengthening his professional standing. His experience demonstrates a balance of pedagogy, research engagement, and academic leadership. Research Interest Dr. Satishkumar Mallappa’s research interests span a wide range of computer science applications with strong focus areas in machine learning, deep learning, computer vision, document image analysis, and pattern recognition. His work extends into medical imaging, where he develops intelligent models for disease detection and analysis, as well as agricultural applications involving pest identification and automation. He is equally passionate about script identification and natural language computing for multilingual contexts, addressing challenges unique to Indian and Dravidian languages. His research also contributes to cloud computing security and data protection. Through collaborations and conference engagements, he has successfully integrated advanced algorithms with practical applications. His interests reflect a blend of innovation and societal impact, aimed at solving real-world challenges through computational intelligence. Awards and Honors Dr. Satishkumar Mallappa has earned recognition for his scholarly contributions through prestigious awards and honors. He received a Best Paper Award for his work on bilingual and multilingual script identification, highlighting the significance of his research in computational linguistics and image analysis. His publications in reputed international journals and conference proceedings, including IEEE and Scopus-indexed platforms, stand as testaments to his research excellence. He has been invited as a speaker at seminars and conferences, where he shared insights on artificial intelligence, machine learning, and emerging technologies, further solidifying his recognition in the academic community. Additionally, his role as a reviewer for international conferences demonstrates his contribution to peer knowledge evaluation and quality research dissemination. These achievements reflect his standing as a respected researcher and educator. Research Skills Dr. Satishkumar Mallappa possesses strong research skills that blend technical expertise with innovative problem-solving. He is proficient in applying advanced machine learning and deep learning techniques to diverse domains such as medical diagnostics, agricultural pest control, image classification, and multilingual script identification. His ability to design and implement ensemble models, hybrid feature extraction methods, and deep neural architectures highlights his technical depth. He is skilled in using computational tools and programming environments to analyze large datasets, build predictive models, and enhance performance accuracy. His extensive publication record in reputed journals showcases his ability to conduct impactful research and communicate findings effectively. Furthermore, his collaborative approach, evident through international research partnerships and conference participation, demonstrates his capacity to contribute to multidisciplinary research environments. Publication Top Notes Title: A study on Image Segmentation and its MethodsAuthors: S Kumar, R SrinivasYear: 2013Citations: 14 Title: Script Identification from Camera Based Tri-Lingual DocumentAuthors: G Mukarambi, S Mallappa, BV DhandraYear: 2017Citations: 9 Title: Scrambling and Descrambling of Document Image for Data Security in Cloud ComputingAuthors: N Salimath, S Mallappa, N Padhy, J SheetlaniYear: 2019Citations: 6 Title: Skin Cancer Classification using VGG-16 and Googlenet CNN ModelsAuthors: SB Ummapure, R Tilekar, S MallappaYear: 2023Citations: 4 Title: Script Identification of Camera Based Bilingual Document Images Using SFTA FeaturesAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2019Citations: 4 Title: Hybridization of Texture Features for Identification of Bi-lingual Scripts from Camera Images at Word LevelAuthors: S Mallappa, BV Dhandra, G MukarambiYear: 2023Citations: 3 Title: Heart Sound Analysis with Machine Learning Using Audio Features for Detecting Heart DiseasesAuthors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N AilYear: 2024Citations: 2 Title: Camera-based Tri-lingual Script Identification at Word Level using a Combination of SFTA and LBP FeaturesAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2020Citations: 2 Title: Use of Audio Transfer Learning to Analyse Heart Sounds for Detecting Heart DiseasesAuthors: S Swaminathan, S Murthy, C Gudada, SK MallappaYear: 2024Citations: 1 Title: Script Identification from Camera Captured Indian Document Images with CNN ModelAuthors: S Mallappa, BV Dhandra, G MukarambiYear: 2023Citations: 1 Title: Camera-Based Bi-lingual Script Identification at Word Level using SFTA FeaturesAuthors: G Mukarambi, BV Dhandra, S MallappaYear: 2019Citations: 1 Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture FeaturesAuthors: S Mallappa, C Gudada, PM SanthoshiYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the DetectionAuthors: SSS Murthy, S MallappaYear: 2025 Title: A Deep Learning Model based White Blood Cell Image ClassificationAuthors: S Ummapre, S MallappaYear: 2025 Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble ClassifiersAuthors: C Gudada, S MallappaYear: 2025 Title: Multi-fruit Classification Using a New FruitNet-11 Based on Deep Convolutional Neural NetworkAuthors: Raghavendra, S MallappaYear: 2022 Title: Script Identification at Line-level using SFTA and LBP Features from Bi-lingual and Tri-lingual Documents Captured from the CameraAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2020 Title: Hybrid Method for Elimination of Uneven Illumination from Camera-based Document ImagesAuthors: BV Dhandra, S Mallappa, G MukarambiYear: 2020 Title: Camera-based Document Image Scrambling for Security in Cloud Computing EnvironmentAuthors: N Salimath, S Mallappa, J SheetlaniYear: 2019 Title: Script Identification System for Camera Based Multilingual Document ImagesAuthor: S Mallappa Conclusion Dr. Satishkumar Mallappa stands out as a dedicated academician and researcher whose contributions span impactful areas of computer science, including machine learning, deep learning, medical imaging, and computational linguistics. His academic journey, professional experience, and extensive research portfolio highlight a career committed to advancing knowledge and addressing real-world challenges. Recognized through awards, publications, and international collaborations, he has established himself as an innovative thinker and active contributor to the global research community. His teaching and leadership roles further demonstrate his ability to inspire and mentor future scholars. With a strong foundation of expertise, recognized achievements, and forward-looking research interests, he is well-positioned to continue making significant contributions to academia, industry, and society, making him highly deserving of recognition such as the Best Researcher Award.
Chandrashekar Gudada | Computer Science | Best Researcher Award Published on 01/09/202501/09/2025 by Best Paper Awards Dr. Chandrashekar Gudada | Computer Science | Best Researcher Award Assistant Professor at Sri Sathya Sai University for Human Excellence, India Dr. Chandrashekar V. Gudada is a distinguished researcher in computer science with expertise in artificial intelligence, handwritten script recognition, biomedical signal analysis, and deep learning applications. He holds a Ph.D. from Rani Channamma University, Belagavi, and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence. With over 20 publications in reputed international journals, conferences, and book chapters, his research spans historical language digitization, disease detection using AI, and agricultural innovations. He leads a funded project on leukemia detection through deep learning and contributes as a reviewer for leading journals while actively participating in academic committees, faculty development programs, and symposia. As a member of professional societies including ACM, IAENG, and MIR Lab, he exemplifies strong research leadership, academic service, and commitment to advancing technology for societal benefit. Professional Profile Google Scholar 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 descriptorsAuthors: P Bannigidad, C GudadaYear: 2018Citations: 23 Title: Restoration of degraded Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniquesAuthors: P Bannigidad, C GudadaYear: 2017Citations: 23 Title: Restoration of degraded historical Kannada handwritten document images using image enhancement techniquesAuthors: P Bannigidad, C GudadaYear: 2016Citations: 19 Title: Identification and classification of historical Kannada handwritten document images using LBP featuresAuthors: B Parashuram, G ChandrashekarYear: 2018Citations: 15 Title: Historical Kannada handwritten character recognition using machine learning algorithmAuthors: P Bannigidad, C GudadaYear: 2020Citations: 8 Title: Restoration of degraded non-uniformally illuminated historical Kannada handwritten document imagesAuthors: P Bannigidad, C GudadaYear: 2018Citations: 7 Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCMAuthors: P Bannigidad, C GudadaYear: 2019Citations: 3 Title: Historical Kannada handwritten scripts recognition system using line segmentation with LBP featuresAuthors: P Bannigidad, C GudadaYear: 2019Citations: 3 Title: Heart sound analysis with machine learning using audio features for detecting heart diseasesAuthors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N AilYear: 2024Citations: 2 Title: Digitization and recognition of historical Kannada handwritten manuscripts using text line segmentation with LBP featuresAuthors: P Bannigidad, C GudadaYear: 2019Citations: 2 Title: Historical Kannada Handwritten Character Recognition using K-Nearest Neighbour TechniqueAuthors: P Bannigidad, C GudadaYear: 2019Citations: 2 Title: Use of audio transfer learning to analyse heart sounds for detecting heart diseasesAuthors: S Satyanarayana, K Srikanta, Murthy, G Chandrashekar, M Satish KumarYear: 2024Citations: 1 Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture FeaturesAuthors: S Mallappa, C Gudada, PM SanthoshiYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the DetectionAuthors: SSS Murthy, S Mallappa, G ChandrashekarYear: 2025 Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble ClassifiersAuthors: C Gudada, S MallappaYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart DiseasesAuthors: S Sathyanarayanan, S Murthy, S Mallappa, C GudadaYear: 2025 Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCM featuresAuthors: B Parashuram, G ChandrashekarYear: 2019 Title: Age-Type Identification and Classification of Historical Kannada Handwritten Scripts using Line Segmentation with HOG feature DescriptorsAuthors: P Bannigidad, C GudadaYear: 2019 Title: Ensemble of Deep and Texture Features for Script Identification from Camera Based Dravidian LanguagesAuthors: S Kumar, C GudadaYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart DiseasesAuthors: C GudadaYear: 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.
Dr. Chandrashekar V. Gudada is a distinguished researcher in computer science with expertise in artificial intelligence, handwritten script recognition, biomedical signal analysis, and deep learning applications. He holds a Ph.D. from Rani Channamma University, Belagavi, and currently serves as Assistant Professor at Sri Sathya Sai University for Human Excellence. With over 20 publications in reputed international journals, conferences, and book chapters, his research spans historical language digitization, disease detection using AI, and agricultural innovations. He leads a funded project on leukemia detection through deep learning and contributes as a reviewer for leading journals while actively participating in academic committees, faculty development programs, and symposia. As a member of professional societies including ACM, IAENG, and MIR Lab, he exemplifies strong research leadership, academic service, and commitment to advancing technology for societal benefit. Professional Profile Google Scholar
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 descriptorsAuthors: P Bannigidad, C GudadaYear: 2018Citations: 23 Title: Restoration of degraded Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniquesAuthors: P Bannigidad, C GudadaYear: 2017Citations: 23 Title: Restoration of degraded historical Kannada handwritten document images using image enhancement techniquesAuthors: P Bannigidad, C GudadaYear: 2016Citations: 19 Title: Identification and classification of historical Kannada handwritten document images using LBP featuresAuthors: B Parashuram, G ChandrashekarYear: 2018Citations: 15 Title: Historical Kannada handwritten character recognition using machine learning algorithmAuthors: P Bannigidad, C GudadaYear: 2020Citations: 8 Title: Restoration of degraded non-uniformally illuminated historical Kannada handwritten document imagesAuthors: P Bannigidad, C GudadaYear: 2018Citations: 7 Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCMAuthors: P Bannigidad, C GudadaYear: 2019Citations: 3 Title: Historical Kannada handwritten scripts recognition system using line segmentation with LBP featuresAuthors: P Bannigidad, C GudadaYear: 2019Citations: 3 Title: Heart sound analysis with machine learning using audio features for detecting heart diseasesAuthors: S Swaminathan, SM Krishnamurthy, C Gudada, SK Mallappa, N AilYear: 2024Citations: 2 Title: Digitization and recognition of historical Kannada handwritten manuscripts using text line segmentation with LBP featuresAuthors: P Bannigidad, C GudadaYear: 2019Citations: 2 Title: Historical Kannada Handwritten Character Recognition using K-Nearest Neighbour TechniqueAuthors: P Bannigidad, C GudadaYear: 2019Citations: 2 Title: Use of audio transfer learning to analyse heart sounds for detecting heart diseasesAuthors: S Satyanarayana, K Srikanta, Murthy, G Chandrashekar, M Satish KumarYear: 2024Citations: 1 Title: Enhancing Script Identification in Dravidian Languages using Ensemble of Deep and Texture FeaturesAuthors: S Mallappa, C Gudada, PM SanthoshiYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the DetectionAuthors: SSS Murthy, S Mallappa, G ChandrashekarYear: 2025 Title: Feature-Driven Acute Lymphoblastic Leukemia Detection From Blood Smears Using Machine Learning Ensemble ClassifiersAuthors: C Gudada, S MallappaYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart DiseasesAuthors: S Sathyanarayanan, S Murthy, S Mallappa, C GudadaYear: 2025 Title: Identification and Classification of Historical Kannada Handwritten Scripts based on their Age-Type using Line Segmentation with GLCM featuresAuthors: B Parashuram, G ChandrashekarYear: 2019 Title: Age-Type Identification and Classification of Historical Kannada Handwritten Scripts using Line Segmentation with HOG feature DescriptorsAuthors: P Bannigidad, C GudadaYear: 2019 Title: Ensemble of Deep and Texture Features for Script Identification from Camera Based Dravidian LanguagesAuthors: S Kumar, C GudadaYear: 2025 Title: Machine Learning Approach Using HOG and LBP Features of Spectrograms-Based Heart Sounds Analysis for the Detection of Heart DiseasesAuthors: C GudadaYear: 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.