Chandrashekar Gudada | Computer Science | Best Researcher Award

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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 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.

Jamshir Qureshi | Cybersecurity | Best Researcher Award

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Mr. Jamshir Qureshi | Cybersecurity | Best Researcher Award 

Vice President, at Purdue University Global, United States.

Summary

Jamshir Qureshi is a seasoned Solution Architect based in Dallas/Fort Worth, Texas, with over two decades of experience in FinTech and regulated industries. Specializing in developing resilient, secure, and scalable enterprise architectures, he has led transformative projects in modern application platforms, cloud-native solutions, and cybersecurity frameworks. His extensive knowledge extends to healthcare information systems and compliance-driven solutions, supporting operational excellence in highly regulated environments. In addition to his technical contributions, Jamshir actively engages in thought leadership, authoring works on AI-driven innovations and their impact on digital transformation. His recent paper, “AI-Powered Cloud-Based E-Commerce,” presented at the IACIS 2024 Conference, showcases his commitment to integrating AI into cloud technologies for digital business growth. Recognized for his leadership in AI-powered cybersecurity solutions, he also served as a judge for the Globee Awards in Cybersecurity. Jamshir is passionate about mentoring and driving continuous improvement across teams and aligning strategies with business priorities.

Professional Profile

Education

🎓 Jamshir Qureshi holds a Master of Science in Information Technology from Purdue University Global, USA. This advanced degree has equipped him with the knowledge and technical prowess to tackle complex architectural and technological challenges in today’s fast-evolving digital landscape. Beyond his formal education, Jamshir is dedicated to continuous learning, demonstrated by his professional certifications. He is an AWS Certified Solutions Architect – Professional, showcasing his commitment to cloud excellence and proficiency in designing and implementing solutions on Amazon Web Services. His memberships in prominent organizations like the National Society of Leadership and Success (NSLS) and the American Association for the Advancement of Science reflect his passion for professional development and leadership in technology. These credentials, combined with his diverse experiences, have empowered Jamshir to build solutions that meet rigorous standards in highly regulated industries, including FinTech and healthcare.

Experience

💼 Over his 20+ year career, Jamshir Qureshi has built a strong foundation in enterprise architecture, focusing on scalable, secure, and resilient systems across various global firms. Currently, he serves as Vice President of Software Engineering at MUFG Bank in Dallas, Texas, where he drives technology initiatives and leads engineering teams. His career includes significant roles, such as Architect at Charles Schwab (2016-2021) and Lead Software Engineer at CVS Caremark (2011). He has also worked with prestigious organizations like Target and OM Software Ltd., spanning locations from India to South Africa and the U.S. Jamshir’s expertise in cloud-native platforms, microservices, DevOps practices, and cybersecurity has allowed him to implement successful strategies that align with business objectives, compliance standards, and operational excellence. His leadership in regulated industries has been recognized as transformative, focusing on innovation and team mentorship.

Research Interests

🔬 Jamshir’s research interests center on the transformative applications of artificial intelligence (AI) and cloud technology in FinTech, e-commerce, and healthcare systems. His research addresses emerging trends in AI-driven solutions, exploring how AI impacts human cognition, emotion, and healthcare. His paper “AI-Powered Cloud-Based E-Commerce,” presented at the IACIS 2024 Conference, delves into how AI integration with cloud technologies can enhance digital business strategies. His interest in AI deepfakes, illustrated in his work “Deciphering Deception: The Impact of AI Deepfakes on Human Cognition and Emotion,” explores the ethical and psychological implications of deepfake technology. Jamshir is also intrigued by the role of AI in healthcare, analyzing both risks and benefits in publications such as “AI Deepfakes in Healthcare: A Double-Edged Sword.” His work underscores his dedication to leveraging AI for innovation while recognizing the ethical considerations tied to this technology.

Awards

🏆 Jamshir Qureshi has received notable recognition for his contributions to technology and innovation. He was honored as a judge for the prestigious Globee Awards in Cybersecurity, reflecting his expertise in AI-powered cybersecurity solutions. His leadership in advancing secure, resilient, and scalable architectures in highly regulated environments has set a standard for excellence. Jamshir’s work on integrating AI into enterprise systems has garnered industry attention, positioning him as a thought leader in cloud-based e-commerce and AI-driven digital transformation. Additionally, his influence extends to the academic and professional communities, where he has shared his expertise through publications and conference presentations. His accolades underscore his commitment to enhancing technology-driven solutions and his ability to shape the future of cybersecurity, artificial intelligence, and enterprise architecture.

Top Noted Publications

📚 Below are some of Jamshir Qureshi’s notable publications, illustrating his contributions to AI and digital transformation:

  • “AI-Powered Cloud-Based E-Commerce: Driving Digital Business Transformation Initiatives”
    • Published in: IACIS 2024 Conference
    • Summary: This paper explores how artificial intelligence (AI) integrated with cloud technologies can drive digital business transformation in e-commerce. It discusses the potential of AI in enhancing business operations, customer experience, and overall business agility, with an emphasis on cloud-native platforms, scalability, and resilience. The paper provides insights into how AI applications can reshape business strategies and models in the digital age.
    • Link: IACIS 2024 Conference Paper
  • “Deciphering Deception: The Impact of AI Deepfakes on Human Cognition and Emotion”
    • Published in: Journal of Applied Artificial Intelligence, 2024
    • Summary: This paper delves into the psychological and cognitive implications of AI-generated deepfakes. It explores how deepfakes influence human perception, trust, and emotions, addressing the ethical concerns and risks associated with their use. The paper discusses both the dangers and potential applications of deepfakes in various sectors, including media and healthcare.
    • Link: Journal of Applied Artificial Intelligence
  • “Artificial Intelligence (AI) Deepfakes in Healthcare Systems: A Double-Edged Sword? Balancing Opportunities and Navigating Risks”
    • Published in: Preprints, 2024
    • Summary: This paper examines the dual nature of AI deepfakes in healthcare. While deepfakes offer transformative potential in areas like medical imaging and patient simulation, they also pose significant risks related to misinformation, privacy, and ethical concerns. The paper explores strategies for navigating these challenges while harnessing the benefits of AI in healthcare.
    • Link: Preprints – AI Deepfakes in Healthcare
  • “How Artificial Intelligence Technology Can Be Used to Treat Diabetes”
    • Published in: Preprints, 2024
    • Summary: This paper investigates how AI technologies can be applied in the treatment of diabetes, focusing on data analytics, predictive modeling, and personalized medicine. It reviews AI-driven advancements in monitoring glucose levels, improving diagnostic accuracy, and designing tailored treatment plans to enhance patient outcomes.
    • Link: Preprints – AI in Diabetes Treatment
  • “How Artificial Intelligence and Machine Learning Can Impact Market Design”
    • Published in: OPAST Publishers, 2024
    • Summary: This paper explores the role of AI and machine learning in transforming market design. It discusses how these technologies can improve market efficiency, enhance decision-making processes, and optimize resource allocation. The paper highlights the future of AI-driven market solutions, particularly in areas such as pricing strategies, demand forecasting, and dynamic pricing models.
    • Link: OPAST Publishers – AI and Market Design

Conclusion

Jamshir Qureshi is a strong candidate for the Best Researcher Award, given his extensive background, contributions to AI and cybersecurity, and demonstrated thought leadership in highly regulated industries. His career reflects an impressive blend of industry expertise, technical knowledge, and commitment to emerging research areas like AI ethics, cybersecurity, and market design. While a more focused publication strategy and deeper academic collaborations could enhance his research impact, his contributions already make a significant case for recognition. His dedication to innovation and the ethical challenges of technology use strengthens his candidacy, aligning well with the goals of a Best Researcher Award.

 

Atif Rehman | AI & Cybersecurity | Best Researcher Award

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Mr. Atif Rehman | AI & Cybersecurity | Best Researcher Award

Vice President at NUST, Pakistan

Summary:

Mr. Atif Rehman is a graduate in Computational Sciences and Engineering with a special focus on control systems, having earned his Master’s degree from the National University of Science and Technology (NUST), Islamabad. He completed his undergraduate studies in Mathematics from the International Islamic University, Islamabad. His academic journey is marked by his commitment to integrating advanced mathematical techniques with engineering applications, particularly in the fields of nonlinear control systems and optimization. Mr. Rehman has contributed to the development of novel optimization algorithms aimed at improving control system performance, notably through his work on Grey Wolf Optimization for robust nonlinear controller design. He is passionate about using his knowledge to develop sustainable, efficient solutions in various domains, including healthcare and transportation.

Professional Profile:

👩‍🎓Education:

  • Master of Science in Computational Sciences and Engineering
    • Institution: National University of Science and Technology (NUST), Islamabad
    • Duration: September 2021 – August 2023
    • CGPA: 3.70 / 4.0
    • Principal Subjects: Linear Control Systems, Nonlinear Control Systems, Adaptive/Closed-loop Control, Sliding Mode Control, Advanced Machine Learning, Deep Learning
    • Research Thesis: “Improved Grey Wolf Optimization-Based Robust Nonlinear Controller Design for Prostate Cancer”
    • Focus: This research bridged the theoretical knowledge of control systems with their practical applications, specifically targeting health technology for prostate cancer treatments.
  • Bachelor of Science in Mathematics
    • Institution: International Islamic University (IIU), Islamabad
    • Duration: September 2017 – August 2021
    • CGPA: 3.61 / 4.0
    • Principal Subjects: Calculus, Linear Algebra, Real Analysis, Numerical Methods, Partial Differential Equations, Fluid Mechanics, Discrete Structures

🏢 Professional Experience:

Mr. Atif Rehman has a strong academic background that bridges both theoretical and applied mathematics, computational sciences, and engineering. His advanced studies in computational sciences and engineering with a focus on control systems, along with his extensive research in optimization techniques, provide him with the necessary skills to contribute significantly to the development of efficient systems. His research endeavors in robust nonlinear controller design and optimization algorithms demonstrate his capacity for both theoretical advancements and practical solutions.

His master’s thesis on designing a robust nonlinear controller for prostate cancer treatment using Grey Wolf Optimization reflects his interest in applying computational techniques to real-world problems. Additionally, his undergraduate studies in mathematics provided him with a robust understanding of the fundamental principles of calculus, linear algebra, and numerical methods, which laid the groundwork for his future research in control systems and optimization.

Research Interests:

Mr. Rehman’s research interests lie at the intersection of control systems, optimization techniques, and machine learning. Specifically, he is keen on the following areas:

  • Deep Reinforcement Learning: Applying reinforcement learning to optimize control systems.
  • Machine Learning: Using machine learning for system modeling and predictive control.
  • Adaptive Control Systems: Developing control strategies that adjust to changing system parameters over time, such as in biomedical applications where system parameters may vary across individuals or conditions.
  • Optimization Techniques: Implementing advanced optimization algorithms like Improved Grey Wolf Optimization, Genetic Algorithms, and reinforcement learning-based optimization to solve complex control problems.

Author Metrics:

  • Publications:
    1. Improved Grey Wolf Optimization-Based Robust Nonlinear Controller Design for Prostate Cancer
    2. Optimized Nonlinear Robust Controller Along with Model-Parameter Estimation for Blood Glucose Regulation in Type-1 Diabetes
    • His works primarily focus on optimization techniques, machine learning, and adaptive control, showing substantial contributions to both academia and practical applications.
  • Citations: His research in robust controller design and adaptive systems has garnered attention in related academic circles, contributing to advancements in both theoretical studies and practical solutions for control systems in biomedical applications.

Top Noted Publication:

Artificial Intelligence-Based Robust Nonlinear Controllers Optimized by Improved Grey Wolf Optimization Algorithm for Plug-In Hybrid Electric Vehicles in Grid-to-Vehicle Applications

  • Authors: S. Saleem, I. Ahmad, S.H. Ahmed, A. Rehman
  • Journal: Journal of Energy Storage
  • Publication Year: 2024
  • Citations: 15
  • Summary: This study presents an AI-driven, robust nonlinear controller design optimized using an Improved Grey Wolf Optimization (IGWO) algorithm for enhancing the energy management and performance of plug-in hybrid electric vehicles (PHEVs) in grid-to-vehicle systems.

Advancing Optimized Nonlinear Control Strategies for Cancerous Tumor Dynamics

  • Authors: A. Rehman, R. Ghias, S.H.A. Shah, S. Saleem, I. Ahmad
  • Conference: 2023 2nd International Conference on Emerging Trends in Electrical, Control, and Instrumentation Engineering
  • Publication Year: 2023
  • Citations: 3
  • Summary: This paper explores the development of optimized nonlinear control strategies tailored to manage and mitigate the complex dynamics of tumor growth in cancer patients.

A Novel Approach to Nonlinear Control in Tuberculosis Transmission Dynamics

  • Authors: S.H. Ahmed, A. Rehman, I. Ahmad
  • Conference: 2023 2nd International Conference on Emerging Trends in Electrical, Control, and Instrumentation Engineering
  • Publication Year: 2023
  • Citations: 3
  • Summary: This research presents a unique methodology for applying nonlinear control theory to model and manage tuberculosis transmission, offering insights into effective intervention strategies.

Advance Optimized Nonlinear Control Strategies for Managed Pressure Drilling

  • Authors: A. Rehman, R. Ghias, I. Ahmad, H.I. Sherazi
  • Journal: IEEE Access
  • Publication Year: 2024
  • Citations: 2
  • Summary: The study introduces enhanced nonlinear control techniques optimized using IGWO for managing pressure during drilling operations, which is crucial for operational safety and efficiency in the oil and gas industry.

IGWO-Based Robust Nonlinear Control Design for Androgen Suppression Therapy in Prostate Tumor Patients

  • Authors: A. Rehman, I. Ahmad, A.U. Jabbar
  • Journal: Biomedical Signal Processing and Control
  • Publication Year: 2024
  • Summary: This paper outlines the design of a robust nonlinear control system optimized by IGWO for regulating androgen suppression therapy in prostate cancer treatment, showcasing significant improvements in treatment strategies through adaptive control techniques.

Conclusion:

Mr. Atif Rehman’s robust academic foundation, innovative research contributions in AI, cybersecurity, and optimization techniques, and dedication to applying his knowledge to practical problems make him a strong candidate for the Best Researcher Award. By addressing areas for improvement, such as expanding his publication record and participating in international collaborations, he could further solidify his reputation as an influential researcher. His trajectory suggests a promising future marked by continued advancements and interdisciplinary contributions.