Nicko Magnaye | Computer Science | Excellence in Research Award Published on 10/10/202510/10/2025 by Best Paper Awards Prof. Nicko Magnaye | Computer Science | Excellence in Research Award Scholarship Coordinator | Mindoro State University | Philippines Mr. Nicko A. Magnaye is an emerging academic and researcher from Mindoro State University whose contributions are steadily growing in the fields of computing studies, artificial intelligence, and applied information technologies. His research interests span across a variety of impactful areas including artificial intelligence applications, smart farming systems, web-based platforms, digital transformation tools, and information security awareness. Despite being in the early stages of his research career, his academic portfolio reflects a promising trajectory, with 19 citations, an h-index of 2, and an i10-index of 1, which indicate active engagement in scholarly activities. Between 2021 and 2023, he has co-authored and authored several publications in international journals, showing consistent involvement in research dissemination and knowledge sharing. His projects, such as e-Carp, SaBaTech, and FruitTech, illustrate how he effectively integrates technology with practical solutions to address real-world challenges faced by communities, particularly in agriculture and security. He has also contributed to research on web-based rental systems, AI applications for senior citizens, and information systems for education, highlighting his multidisciplinary approach and technical versatility. Mr. Magnaye’s research work often involves collaboration with other scholars and students, reflecting his commitment to teamwork, mentorship, and institutional development. His innovative applications of AI and information systems toward solving local issues demonstrate a strong orientation toward community-driven research and sustainable development. While his academic impact metrics are still developing, the quality and relevance of his work position him as a strong contender for recognition in the “Emerging Researcher” or “Early Career” category of the Excellence in Research Award. With continued publications, broader collaborations, and increased citation impact, Mr. Magnaye has the potential to establish himself as a significant contributor to the advancement of applied computing and technological innovation. Profiles: Google Scholar | Scopus | ORCID | LinkedIn | ResearchGate Featured Publications Sopera, S. K., Alaban, J. S., Briones, Z., & Magnaye, N. A. (2023). Artificial intelligence (AI) on learning process. International Journal of Integrative Research, 1(9), 557–570. Monteverde, A. L., Maderazo, J. J. S., Cruz, K. C. M., & Magnaye, N. A. (2023). Web-based rental house smart finder using rapid application development basis for evaluation of ISO 205010. International Journal of Metaverse, 1(1), 1–4. Kim, N. A. M., Mendeja, L. D., Dulce, N. R., Martinez, V. U., & Magnaye, N. A. (2023). A development using the rapid application model of peTrace: Peter’s poultry supply sales and monitoring management system. International Journal of Metaverse, 1(1), 21–31. Magnaye, N. A. (2023). A case study of Windows 11 operating system for inexperienced users. Intelligent Control and System Engineering, 239–239. Magnaye, N. A. (2022). Anticipatory assessment through information security awareness: Approaching of local government units in a province of Oriental Mindoro. Journal of Positive School Psychology, 6(3), 3655–3664.
Mr. Nicko A. Magnaye is an emerging academic and researcher from Mindoro State University whose contributions are steadily growing in the fields of computing studies, artificial intelligence, and applied information technologies. His research interests span across a variety of impactful areas including artificial intelligence applications, smart farming systems, web-based platforms, digital transformation tools, and information security awareness. Despite being in the early stages of his research career, his academic portfolio reflects a promising trajectory, with 19 citations, an h-index of 2, and an i10-index of 1, which indicate active engagement in scholarly activities. Between 2021 and 2023, he has co-authored and authored several publications in international journals, showing consistent involvement in research dissemination and knowledge sharing. His projects, such as e-Carp, SaBaTech, and FruitTech, illustrate how he effectively integrates technology with practical solutions to address real-world challenges faced by communities, particularly in agriculture and security. He has also contributed to research on web-based rental systems, AI applications for senior citizens, and information systems for education, highlighting his multidisciplinary approach and technical versatility. Mr. Magnaye’s research work often involves collaboration with other scholars and students, reflecting his commitment to teamwork, mentorship, and institutional development. His innovative applications of AI and information systems toward solving local issues demonstrate a strong orientation toward community-driven research and sustainable development. While his academic impact metrics are still developing, the quality and relevance of his work position him as a strong contender for recognition in the “Emerging Researcher” or “Early Career” category of the Excellence in Research Award. With continued publications, broader collaborations, and increased citation impact, Mr. Magnaye has the potential to establish himself as a significant contributor to the advancement of applied computing and technological innovation. Profiles: Google Scholar | Scopus | ORCID | LinkedIn | ResearchGate Featured Publications Sopera, S. K., Alaban, J. S., Briones, Z., & Magnaye, N. A. (2023). Artificial intelligence (AI) on learning process. International Journal of Integrative Research, 1(9), 557–570. Monteverde, A. L., Maderazo, J. J. S., Cruz, K. C. M., & Magnaye, N. A. (2023). Web-based rental house smart finder using rapid application development basis for evaluation of ISO 205010. International Journal of Metaverse, 1(1), 1–4. Kim, N. A. M., Mendeja, L. D., Dulce, N. R., Martinez, V. U., & Magnaye, N. A. (2023). A development using the rapid application model of peTrace: Peter’s poultry supply sales and monitoring management system. International Journal of Metaverse, 1(1), 21–31. Magnaye, N. A. (2023). A case study of Windows 11 operating system for inexperienced users. Intelligent Control and System Engineering, 239–239. Magnaye, N. A. (2022). Anticipatory assessment through information security awareness: Approaching of local government units in a province of Oriental Mindoro. Journal of Positive School Psychology, 6(3), 3655–3664.
Sopera, S. K., Alaban, J. S., Briones, Z., & Magnaye, N. A. (2023). Artificial intelligence (AI) on learning process. International Journal of Integrative Research, 1(9), 557–570. Monteverde, A. L., Maderazo, J. J. S., Cruz, K. C. M., & Magnaye, N. A. (2023). Web-based rental house smart finder using rapid application development basis for evaluation of ISO 205010. International Journal of Metaverse, 1(1), 1–4. Kim, N. A. M., Mendeja, L. D., Dulce, N. R., Martinez, V. U., & Magnaye, N. A. (2023). A development using the rapid application model of peTrace: Peter’s poultry supply sales and monitoring management system. International Journal of Metaverse, 1(1), 21–31. Magnaye, N. A. (2023). A case study of Windows 11 operating system for inexperienced users. Intelligent Control and System Engineering, 239–239. Magnaye, N. A. (2022). Anticipatory assessment through information security awareness: Approaching of local government units in a province of Oriental Mindoro. Journal of Positive School Psychology, 6(3), 3655–3664.
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.