Yasir Nawaz | Machine Learning | Research Excellence Award

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Dr. Yasir Nawaz | Machine Learning | Research Excellence Award

Dr. Ankit Agrawal is a cardiology fellow at the University of Arkansas for Medical Sciences with 943 citations, h-index 18, and 33 i10-index. His research spans structural cardiology, transcatheter valve therapies, pericardial diseases, cardiovascular imaging, meta-analyses, and outcomes research, emphasizing evidence-based strategies to improve cardiovascular care and patient safety.

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Featured Publications

Didier Torres Guzmán | Machine Learning | Best Researcher Award

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Dr. Didier Torres Guzmán | Machine Learning | Best Researcher Award

Professor | National Autonomous University of Mexico | Mexico

Dr. Didier Torres Guzmán is a distinguished researcher whose work focuses on biomedical signal processing, neuroimaging, and the application of machine learning to clinical diagnostics. His research contributions have advanced the understanding and analysis of neurological and physiological conditions, particularly through the development of innovative computational biomarkers and signal processing techniques. Notably, he has explored tortuosity and discrete compactness biomarkers for machine learning-based classification of mild cognitive impairment, providing new tools for early and accurate detection of cognitive decline. In addition, his studies on discrete neuroimaging metrics have enabled the identification of structural brain alterations associated with COVID-19, highlighting the relevance of his work to pressing global health challenges. Dr. Torres Guzmán has also contributed to non-invasive physiological monitoring, including methods for estimating heart and respiratory rates through face video processing and novel approaches for ECG signal morphology analysis using tortuosity estimation. His work consistently demonstrates a combination of methodological rigor, interdisciplinary application, and translational potential, bridging computational techniques with practical healthcare solutions. The originality and impact of his research are reflected in his publications in high-quality peer-reviewed journals and book chapters, where he collaborates with international researchers across biomedical engineering, signal processing, and clinical disciplines. Through these contributions, Dr. Torres Guzmán has established himself as a leading figure in his field, whose work not only advances scientific knowledge but also has tangible implications for improving patient care, diagnostic accuracy, and the integration of artificial intelligence in biomedical research, making him a highly deserving candidate for recognition with the Best Researcher Award.

Profile: ORCID | Scopus

Featured Publications

Torres Guzmán, D., Pinzón Vivas, J. D., & Barbará Morales, E. (2026). Tortuosity and discrete compactness biomarkers for machine learning-based classification of mild cognitive impairment. Biomedical Signal Processing and Control.

Delgado-Castillo, D., Barbará-Morales, E., Hevia-Montiel, N., Arámbula-Cosío, F., & Torres Guzmán, D. (2025). Discrete neuroimaging metrics for identifying structural alterations in COVID-19-related brain atrophy. International Journal of Online and Biomedical Engineering (iJOE).

Ruíz-Espinosa, G., Jimenez-Angeles, L., Torres Guzmán, D., Rojas-Arce, J. L., & Marmolejo-Saucedo, J. A. (2024). A comparison of algorithms to estimate heart and respiratory rate from face video processing. In Book chapter.

Pacheco González, L. E., Torres Guzmán, D., & Barbará-Morales, E. (2024). A novel method for ECG signal morphology analysis using tortuosity estimation. Biomedical Signal Processing and Control.

Narendra V Ganganagowdar | Machine Learning | Best Researcher Award

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Dr. Narendra V Ganganagowdar | Machine Learning | Best Researcher Award

Professor at Manipal Institute of Technology | India

Dr. Narendra V. Ganganagowdar is a seasoned academic and researcher with over 26 years of experience in computer science and engineering. He is a Professor at MIT Manipal, with expertise in computer graphics, image processing, artificial intelligence, and soft computing techniques. He has contributed significantly to research, teaching, consultancy, and academic leadership, mentoring numerous students, securing grants, and publishing extensively in indexed journals and conferences. Dr. Ganganagowdar is an active member of multiple professional organizations and serves in advisory roles at the department and institutional level.

Professional Profile:

Education: 

Dr. Narendra V. Ganganagowdar completed his Ph.D. in Computer Science and Engineering from MIT Manipal, Manipal University. He earned his M.Tech. in Computer Science and Engineering from JNNCE, Shimoga (VTU Belgaum), and his B.E. in Computer Science and Engineering from STJIT Ranebennur (Karnataka University, Dharwad). His academic training laid the foundation for his expertise in advanced computing technologies and engineering education.

Experience:

Dr. Narendra V. Ganganagowdar’s academic career spans multiple roles, including Professor at MIT Manipal, Associate Professor, and Assistant Professor. He began his career as a Lecturer at STJIT Ranebennur and BIET Davangere, and progressed through senior roles contributing to teaching, research, and administration. He has organized workshops, FDPs, and short-term programs, served as a resource person in various technical talks, and evaluated Ph.D. theses at multiple universities. Additionally, he has provided consultancy in projects such as automation for managing country labels and NLP applications in healthcare, and secured research grants exceeding Rs. 80 lakhs from government and industry sources.

Research Interests:

Dr. Narendra V. Ganganagowdar research focuses on computer graphics, algorithms, image processing, computer vision, artificial intelligence, and soft computing techniques. Dr. Ganganagowdar’s work integrates programming languages like C, C++, Python, MATLAB, and tools such as OpenGL, Weka, MySQL, and platforms across Windows and Unix/Linux environments. His interests extend to solving real-world problems through computational intelligence, improving machine learning pipelines, and applying AI techniques in healthcare and other domains.

Publications Top Noted:

  1. A federated learning-based crop yield prediction for agricultural production risk management, Year: 2022, Citation: 75

  2. A trusted IoT data sharing and secure oracle based access for agricultural production risk management, Year: 2023, Citation: 55

  3. Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision, Year: 2011, Citation: 52

  4. A Blockchain Based Decentralized Identifiers for Entity Authentication in Electronic Health Records, Year: 2022, Citation: 50

  5. An intelligent computer vision system for vegetables and fruits quality inspection using soft computing techniques, Year: 2019, Citation: 33

Conclusion:

Dr. Narendra V. Ganganagowdar exemplifies dedication, innovation, and excellence in machine learning and computer science education. His work integrates cutting-edge technologies with practical applications, particularly in agriculture and healthcare, addressing key societal challenges. Through mentorship, research leadership, and consultancy, he has fostered a collaborative and impactful academic environment. His expertise in AI and soft computing continues to inspire students and peers alike. Recognition through the Best Researcher Award under the Global Network & Technology Excellence Awards celebrates his outstanding contributions to technology, education, and societal advancement.

Muhammad Imran Khan | Machine Learning | Young Scientist Award

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Muhammad Imran Khan | Machine Learning | Young Scientist Award

Dr. Muhammad Imran Khan, International Islamic University Islamabad Pakistan, Pakistan.

Publication profile

Scopus

Education And Experiance

  • 📘 Ph.D. in Applied Mathematics (Expected August 2024): International Islamic University Islamabad, Pakistan.
  • 📗 M.Sc. in Computational Mathematics (2019): COMSATS University Islamabad, Pakistan.
  • 📙 Bachelor’s in Applied Mathematics (2016): University of Sargodha, Pakistan.
  • 📒 FSc (2012): Federal Board of Intermediate and Secondary Education, Islamabad, Pakistan.
  • 📕 Metric (2010): Sargodha Board of Intermediate and Secondary Education.

Suitability For The Award

Dr. Muhammad Imran Khan is an outstanding candidate for the Young Scientist Award, characterized by his profound academic journey, versatile skill set, and commitment to advancing mathematical research. His focus on applied mathematics, specifically in the area of partial differential equations (PDEs) and computational methods, positions him as a promising young researcher. His proficiency in machine learning, deep learning, and advanced scientific software highlights his ability to integrate modern computational tools into mathematical problem-solving, making him an asset to the scientific community.

Professional Development 

Muhammad Imran Khan 🔬 thrives on leveraging mathematics to address real-world challenges. His proficiency spans advanced numerical analysis, machine learning, and deep learning 🧠, alongside extensive experience with scientific software tools such as DUNE PDELab and ANSYS 🔧. Skilled in Python and C++, he applies computational methods to explore innovative solutions for diverse fields. Muhammad actively advocates for mathematical research 📊, engaging with decision-makers and fostering collaboration to enhance knowledge dissemination. He envisions a future where mathematics drives practical advancements, supporting both academic growth and societal progress 🚀.

Research Focus 

Awards and Honors

  • 🏅 Merit-Based Scholarship: For outstanding academic performance during M.Sc. at COMSATS University.
  • 🏆 Best Research Poster Award: Recognized at a national mathematics conference for innovative work on PDE applications.
  • 🎖️ Distinction in FSc: Achieved top honors in Federal Board examinations.
  • 🌟 Programming Excellence Certificate: Awarded for proficiency in Python and C++ during Ph.D. coursework.
  • 📜 Recognition of Contribution: For active participation in research collaboration projects at International Islamic University Islamabad.

Publoication Top Notes

  • Integrated Artificial Intelligence and Non-Similar Analysis for Forced Convection of Radially Magnetized Ternary Hybrid Nanofluid of Carreau-Yasuda Fluid Model Over a Curved Stretching Surface (2024) 🧠
  • Advanced Intelligent Computing ANN for Momentum, Thermal, and Concentration Boundary Layers in Plasma Electro Hydrodynamics Burgers Fluid (2024) – Cited by: 0 🤖
  • Analysis of Nonlinear Complex Heat Transfer MHD Flow of Jeffrey Nanofluid Over an Exponentially Stretching Sheet via Three Phase Artificial Intelligence and Machine Learning Techniques (2024) 🔥
  • Modeling and Predicting Heat Transfer Performance in Bioconvection Flow Around a Circular Cylinder Using an Artificial Neural Network Approach (2024) 🌡️
  • Advanced Computational Framework to Analyze the Stability of Non-Newtonian Fluid Flow Through a Wedge with Non-Linear Thermal Radiation and Chemical Reactions (2024) – Cited by: 1 🧪
  • Computational Intelligence Approach for Optimising MHD Casson Ternary Hybrid Nanofluid Over the Shrinking Sheet with the Effects of Radiation (2023) – Cited by: 17 ⚡
  • Artificial Neural Network Simulation and Sensitivity Analysis for Optimal Thermal Transport of Magnetic Viscous Fluid Over Shrinking Wedge via RSM (2023) – Cited by: 20 🔍