Francesco Agnelli | Graph Neural Networks | Best Researcher Award

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Dr. Francesco Agnelli | Graph Neural Networks | Best Researcher Award

Dr. Francesco Agnelli, University of Milan, Italy

Francesco Agnelli is an Italian researcher and PhD candidate at the University of Milan, where he delves into the frontiers of deep learning and artificial intelligence. Born in 1998 in Cantù, Italy, Francesco began his academic journey with a stellar Bachelor’s and Master’s degree in Mathematics, both earned cum laude at the University of Insubria. His academic focus evolved from Morse Theory to applied mathematics and computational intelligence, leading to his cutting-edge research on Graph Neural Networks (GNNs). Francesco's work bridges advanced machine learning methods with real-world problems like affective computing and graph isomorphism. With industry experience at Power Reply and teaching stints in local schools, he combines theory with practical impact. Francesco also shares his knowledge as a tutor and speaker in major academic and tech platforms, including an NVIDIA webinar and the 2024 ECCV Conference. He continues to contribute to the intersection of mathematics, AI, and neural computation.

Professional Profile 

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Summary of Suitability for the 'Research for Best Researcher Award'

Francesco Agnelli stands out as an exceptional candidate for the 'Research for Best Researcher Award' due to his impressive academic background, comprehensive research experience, and contributions to cutting-edge fields in mathematics and computer science, particularly through his work with Graph Neural Networks (GNNs).

Francesco holds a Master’s degree in Mathematics with honors (110/110 cum laude) from Università degli Studi dell'Insubria, where his academic pursuits focused on computational mathematics and machine learning. His work has specifically contributed to the application of GNNs in the domains of graph isomorphism and affective computing, demonstrating his ability to innovate within highly specialized research areas. His ongoing Ph.D. studies at Università degli studi di Milano further highlight his dedication to advancing human understanding through deep learning and multi-modal input integration.

Education 

Francesco Agnelli holds a Master’s degree in Mathematics with highest honors (110/110 cum laude) from the University of Insubria, where he focused on computational mathematics and machine learning. His thesis explored the application of Graph Neural Networks to the graph isomorphism problem. During his studies, he completed an Erasmus semester at KU Leuven, Belgium, earning top grades in advanced courses like Wavelets and Applications (29/30), Life Insurance (30/30), and Machine Learning (29/30). He also earned a Bachelor’s degree in Mathematics, again with 110/110 cum laude, from the same university. Francesco participated in a 24 CFU course for teacher training and holds a Scientific High School diploma from Liceo Scientifico Enrico Fermi with a score of 96/100. Now pursuing a PhD at the University of Milan, Francesco investigates the use of GNNs in affective computing, incorporating multimodal inputs and foundation models to advance human-centered AI.

Professional Experience

Francesco Agnelli is currently a PhD student at the University of Milan, conducting research at PhuseLab on deep learning and human understanding. His work integrates Graph Neural Networks and foundation models to process multimodal affective data. Previously, Francesco worked as an IT Consultant at Power Reply in Milan, where he supported the Eni Multicard CRM project. His tasks included transitioning the system from Siebel to Salesforce, performing data analysis, and implementing technical corrections using tools like SOQL and Excel. Earlier, he served as a Mathematics and Science teacher at Istituto Comprensivo Como Lora, where he engaged with younger students to foster scientific curiosity. In parallel, he has tutored university-level courses like Mathematical Analysis and Computational Mathematics. His technical fluency spans Python (especially PyTorch), Matlab, SOQL, and Java. Francesco’s diverse experiences reflect a strong ability to bridge academic rigor with real-world application in both corporate and educational environments.

Awards and Recognition

Francesco Agnelli has received consistent recognition throughout his academic and professional journey. He graduated cum laude in both his Bachelor’s and Master’s degrees in Mathematics, reflecting outstanding academic excellence. As a department representative and member of multiple university commissions at the University of Insubria, Francesco was honored for his leadership and advocacy in academic governance. He was also selected as a university tutor, mentoring students in Mathematical Analysis and Computational Mathematics. His expertise in artificial intelligence earned him an invitation to speak at the prestigious NVIDIA webinar on "Enhancing Visual Understanding With Generative AI". Furthermore, he was a volunteer at the renowned ECCV 2024 Conference, showcasing his commitment to engaging with the AI research community. These accolades affirm his growing impact in academia and the AI research landscape, positioning him as a promising thought leader in the field of Graph Neural Networks and beyond.

Research Skill On Graph Neural Networks

Francesco Agnelli’s research skills are deeply rooted in computational mathematics, with a specialized focus on Graph Neural Networks (GNNs). His academic evolution—from Morse Theory to graph isomorphism problems—demonstrates a solid foundation in abstract mathematics and its translation into real-world computing tasks. In his PhD at the University of Milan, he explores the fusion of GNNs with affective computing, particularly multimodal input processing and fine-tuning of foundation models. His technical toolkit includes Python (PyTorch), Matlab, and data query languages like SOQL. Francesco exhibits high proficiency in integrating theoretical algorithms with deep learning frameworks, often experimenting with cross-domain solutions. He has hands-on experience working with numerical analysis, approximation methods, and neural architectures, allowing him to simulate and interpret graph-structured data effectively. His research reflects a forward-thinking and collaborative approach to AI that bridges data, emotions, and decision-making through intelligent systems grounded in strong mathematical logic.

  Publication Top Notes

  • Title: KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis

  • Authors: Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti

  • Journal: Array

  • DOI: 10.1016/j.array.2025.100392

  • Year: 2025

  • Citation: Agnelli, F., Facchi, G., Grossi, G., & Lanzarotti, R. (2025). KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis. Array. https://doi.org/10.1016/j.array.2025.100392

Vijaya G | Computer Science | Women Researcher Award

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Dr. Vijaya G | Computer Science | Women Researcher Award

Dr. Vijaya G, Sri Krishna College of Engineering and Technology, Coimbatore, India

Dr. G. Vijaya is an accomplished educator, researcher, and administrator with over 18 years of experience in academia and operations management. Currently a Professor at Sri Krishna College of Engineering and Technology, she has previously held leadership roles, including Principal In-Charge and Head of Department. Her expertise spans curriculum development, faculty training, research supervision, and program implementation. She has been instrumental in introducing innovative courses, securing research funding, and mentoring students in cutting-edge areas like Artificial Intelligence, Machine Learning, and Cybersecurity. With multiple certifications, including Fortinet Certified Associate in Cybersecurity and NPTEL mentorship, she has made a significant impact on student learning and institutional growth. Dr. Vijaya has been recognized with several awards, including the Honorary Doctorate (D.Litt.) from the University of South America and the IEAE Young Achiever Award. Her dedication to research and education has positioned her as a leader in Computer Science and Engineering.

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Summary of Suitability for the Research for Women Researcher Award

Dr. G. Vijaya M.E. Ph.D. stands out as a highly qualified and accomplished educator with over 18 years of experience across multiple domains, including administration, operations management, teaching, curriculum development, and research. Her extensive academic and professional journey has consistently focused on empowering students, particularly women, and making significant contributions to the educational landscape.

Dr. Vijaya has been deeply involved in initiatives that promote the advancement of women in engineering and technology. As the Principal In-Charge at Bapatla Women’s Engineering College and later as a professor at Sri Krishna College of Engineering and Technology, she has taken leadership roles that directly impact female students. She has been instrumental in spearheading projects that aim to enhance the skill sets of women, such as the Pradhan Mantri Kaushal Vikas Yojana, which focuses on rural self-employment, and the various Faculty Development Programs she has initiated.

🎓 Education

Dr. G. Vijaya holds a Doctorate (Ph.D.) in Computer Science and Engineering from Annamalai University, specializing in Artificial Intelligence and Machine Learning applications. Her Master’s degree (M.E.) in Computer Science has provided her with a strong foundation in software development, data structures, and emerging technologies. She completed her Bachelor’s degree in Computer Science and Engineering, excelling in her academic pursuits and demonstrating a passion for research from an early stage. Throughout her educational journey, she has been an active participant in technical workshops, conferences, and training programs. She has also completed various certifications, including NPTEL courses in Python for Data Science and Soft Skills Development. Her academic background has enabled her to bridge the gap between theoretical knowledge and real-world applications, allowing her to develop innovative solutions and contribute to cutting-edge research in Artificial Intelligence, Cybersecurity, and Computational Biology.

💼 Professional Experience 

Dr. G. Vijaya has an extensive career in academia, spanning multiple prestigious institutions. She started as an Assistant Professor at Kalasalingam University and later progressed to Associate Professor at Bapatla Women’s Engineering College. She has served as Principal In-Charge, overseeing institutional development, faculty training, and curriculum enhancement. Currently a Professor at Sri Krishna College of Engineering and Technology, she is actively involved in mentoring students, leading research initiatives, and implementing AI-driven educational advancements. Her expertise includes academic administration, accreditation coordination (NBA, NAAC), and research proposal development. She has successfully secured government funding for AICTE-sponsored projects, focusing on ethical AI and smart technology applications. Additionally, she has played a key role in increasing departmental pass percentages and establishing industry-academic collaborations. With a passion for leadership, she has served as a Board of Studies (BoS) member, IEEE Coordinator, and Department Advisory Committee (DAC) Coordinator.

🏅 Awards and Recognition

Dr. G. Vijaya’s contributions to academia and research have been widely recognized. She was awarded an Honorary Doctorate (D.Litt.) from the University of South America for her exceptional work in Computer Science. She also received the IEAE Young Achiever Award for her research excellence. She has been certified as a Fortinet Certified Associate in Cybersecurity and recognized as a mentor for the IEEE Sustainable Solution for Humanity 2024. Her book, Futuristic Trends in Computing Technologies and Data Sciences, has been published by Iterative International Publishers. She has received multiple Certificates of Appreciation for achieving outstanding student results in AI and Machine Learning courses. Additionally, she secured research funding from AICTE, CSIR, and TNSCST for projects on AI ethics and smart agriculture. Her dedication to education, leadership in accreditation processes, and mentorship in research have made her a distinguished figure in Computer Science.

🌍 Research Skills On Computer Science

Dr. G. Vijaya specializes in Artificial Intelligence, Machine Learning, Deep Learning, and Cybersecurity. She has extensive experience in AI-driven predictive analytics, ethical AI integration, and IoT-enabled smart solutions. Her research contributions extend to Computational Biology, Natural Language Processing, and Blockchain for secure computing. She has successfully guided research projects in AI ethics, data security, and cloud computing. She has authored high-impact research papers and collaborated with industry experts on AI applications. As a Board of Studies (BoS) member, she has helped shape Computer Science curricula to incorporate cutting-edge technologies. She has mentored students in AI-based problem-solving competitions and secured research grants for AICTE-sponsored projects. Her ability to integrate AI methodologies with real-world applications has positioned her as a leading researcher in the field. She continues to contribute to emerging trends in AI governance, cybersecurity, and data-driven decision-making.

📖 Publication Top Notes

  1. Optimization and analysis of microwave-assisted extraction of bioactive compounds from Mimosa pudica L. using RSM & ANFIS modeling

    • Authors: V. Ganesan, V. Gurumani, S. Kunjiappan, T. Panneerselvam, …
    • Citations: 43
    • Year: 2018

  2. An adaptive preprocessing of lung CT images with various filters for better enhancement

    • Authors: G. Vijaya, A. Suhasini
    • Citations: 36
    • Year: 2014

  3. A novel noise reduction method using double bilateral filtering

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 17
    • Year: 2010

  4. Automatic detection of lung cancer in CT images

    • Authors: G. Vijaya, A. Suhasini, R. Priya
    • Citations: 15
    • Year: 2014

  5. Drivers Drowsiness Detection using Image Processing and I-Ear Techniques

    • Authors: S. Ananthi, R. Sathya, K. Vaidehi, G. Vijaya
    • Citations: 13
    • Year: 2023

  6. A simple algorithm for image denoising based on ms segmentation

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 13
    • Year: 2010

  7. Bilateral filtering using modified fuzzy clustering for image denoising

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 9
    • Year: 2010

  8. Image Denoising based on Soft Computing Techniques

    • Authors: G. Vijaya, V. Vasudevan
    • Citations: 4
    • Year: 2011

  9. A review analysis of attack detection using various methodologies in network security

    • Authors: P. R. Kanna, S. Gokulraj, K. Karthik, G. Vijaya, G. S. Kumar, G. Rajeshkumar
    • Citations: 3
    • Year: 2022

  10. Deep learning-based computer-aided diagnosis system

  • Authors: G. Vijaya
  • Citations: 2
  • Year: 2022

Le Yao | Computer Science | Best Researcher Award

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Prof. Le Yao | Computer Science | Best Researcher Award

Prof. Le Yao, Hangzhou Normal University, China

Le Yao is an accomplished Associate Professor at the School of Mathematics, Hangzhou Normal University, China. With a strong background in control science and engineering, he specializes in data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. His research focuses on deep learning, interpretable modeling, and causal analysis for industrial applications. Le Yao has been actively involved in multiple funded projects supported by NSFC and the China Postdoctoral Science Foundation. He has an impressive academic record, with numerous high-impact publications in IEEE Transactions and other renowned journals. Recognized for his contributions, he has received prestigious awards, including the National Scholarship for Ph.D. and Outstanding Dissertation Awards. His innovative work bridges the gap between theoretical advancements and practical applications in industrial processes, making significant contributions to smart manufacturing and intelligent systems.

Professional Profile

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Summary of Suitability for the ‘Research for Best Researcher Award’

Le Yao is an exceptional candidate for the ‘Research for Best Researcher Award,’ given his impressive academic journey, extensive research contributions, and leadership in the field of industrial data-driven modeling. His work focuses on crucial areas such as soft sensor modeling, quality prediction, fault diagnosis, and causal analysis, with significant contributions to process control in industrial settings. His innovations in deep learning, causal analysis, and interpretable process modeling have greatly advanced the application of machine learning techniques to complex, large-scale industrial systems.

Notably, his research on scalable and distributed parallel modeling for big process data, combined with his exploration of probabilistic modeling and causal discovery methods, reflects a profound understanding of both theoretical and practical aspects of industrial systems. His ability to fuse domain knowledge with data-driven techniques has led to breakthroughs in process quality prediction and fault detection, impacting industries significantly. Furthermore, Le Yao has successfully secured competitive research funding from prestigious sources, such as the National Natural Science Foundation of China (NSFC) and the China Postdoctoral Science Foundation, demonstrating his capability to lead high-level research initiatives.

🎓 Education

Le Yao holds a Ph.D. in Control Science and Engineering from Zhejiang University (2019), where he specialized in big process data modeling, quality prediction, and process monitoring. His doctoral studies were pivotal in advancing soft sensor modeling techniques for industrial applications. Prior to his Ph.D., he earned an M.S. (2015) from Jiangnan University, where he focused on soft sensor modeling and system identification. His bachelor’s degree (2012) was also from Jiangnan University, where he developed a strong foundation in control science and engineering. Throughout his academic journey, Le Yao has consistently demonstrated excellence, securing prestigious scholarships and honors. His multidisciplinary expertise enables him to develop innovative solutions for industrial automation, smart manufacturing, and data-driven decision-making. His research contributions have influenced numerous industrial applications, bridging the gap between academic advancements and real-world implementations.

💼 Professional Experience 

Le Yao is currently an Associate Professor at Hangzhou Normal University (2022–present), where he leads research on deep learning, causal analysis, and interpretable modeling for industrial systems. Prior to this, he served as a Postdoctoral Researcher (2019–2022) at Zhejiang University’s Institute of Industrial Process Control, focusing on deep learning-driven process modeling and process knowledge fusion. During his postdoctoral tenure, he was awarded research grants from NSFC and the China Postdoctoral Science Foundation. His expertise spans scalable and distributed parallel modeling, soft sensor applications, and quality prediction in large-scale industrial systems. Le Yao’s research integrates advanced computational techniques with practical industrial challenges, driving innovation in smart manufacturing. His leadership in industrial data analytics and AI-driven process control has positioned him as a key contributor to the field, influencing both academic research and industry practices.

🏅 Awards and Recognition

Le Yao has been recognized with numerous prestigious awards for his academic and research contributions. He received the 2020 Outstanding Dissertation Award from the Chinese Institute of Electronics and was named an Outstanding Graduate by Zhejiang University and Zhejiang Province in 2019. His research excellence has been acknowledged through multiple National Scholarships for Ph.D. students (2017, 2018). His work has been featured in top-tier conferences, earning him Best Paper Finalist awards at IEEE DDCLS (2018) and China Process Control Conferences (2016, 2017, 2018). These accolades reflect his outstanding contributions to industrial process modeling, soft sensing, and causal analysis. His innovative approaches to quality prediction and fault diagnosis have significantly impacted the field, earning him recognition from both academic institutions and industry leaders. Le Yao’s commitment to excellence continues to drive his research endeavors, making him a prominent figure in data-driven industrial applications.

🌍 Research Skills On Computer Science

Le Yao’s research expertise spans multiple domains, including data-driven process modeling, soft sensor development, quality-related fault diagnosis, and industrial causal analysis. He specializes in deep learning techniques for process optimization and interpretable modeling to enhance decision-making in industrial environments. His work on scalable and distributed parallel modeling has introduced novel methodologies for handling big process data efficiently. His causal analysis research integrates process knowledge with data-driven approaches, improving anomaly detection and fault diagnosis. He has developed advanced deep learning models incorporating hierarchical extreme learning machines and probabilistic latent variable regression. His research contributions have been implemented in real-world industrial applications, optimizing quality prediction and process control. With a strong foundation in control engineering, statistics, and artificial intelligence, Le Yao continues to advance the field by bridging theoretical research with industrial needs.

📖 Publication Top Notes

  • Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application

    • Authors: L Yao, Z Ge
    • Citation: 295
    • Year: 2017
    • Journal: IEEE Transactions on Industrial Electronics, 65 (2), 1490-1498

  • Big data quality prediction in the process industry: A distributed parallel modeling framework

    • Authors: L Yao, Z Ge
    • Citation: 108
    • Year: 2018
    • Journal: Journal of Process Control, 68, 1-13

  • Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure

    • Authors: B Shen, L Yao, Z Ge
    • Citation: 102
    • Year: 2020
    • Journal: Control Engineering Practice, 94, 104198

  • Scalable semisupervised GMM for big data quality prediction in multimode processes

    • Authors: L Yao, Z Ge
    • Citation: 90
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (5), 3681-3692

  • Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data

    • Authors: L Yao, Z Ge
    • Citation: 80
    • Year: 2016
    • Journal: IEEE Transactions on Automation Science and Engineering, 14 (1), 126-138

  • Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2019
    • Journal: Engineering Applications of Artificial Intelligence, 81, 450-465

  • Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis

    • Authors: L Yao, Z Ge
    • Citation: 62
    • Year: 2017
    • Journal: Control Engineering Practice, 61, 72-80

  • Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction

    • Authors: L Yao, Z Ge
    • Citation: 61
    • Year: 2020
    • Journal: IEEE Transactions on Industrial Informatics, 17 (6), 3782-3792

  • Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach

    • Authors: L Yao, Z Ge
    • Citation: 60
    • Year: 2017
    • Journal: IEEE Transactions on Instrumentation and Measurement, 66 (8), 1985-1993

  • Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data

    • Authors: W Shao, L Yao, Z Ge, Z Song
    • Citation: 53
    • Year: 2018
    • Journal: IEEE Transactions on Industrial Electronics, 66 (8), 6362-6373

Ahlem Ayari | Computer Science | Best Researcher Award

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Dr. Ahlem Ayari | Computer Science | Best Researcher Award

Dr. Ahlem Ayari, Higher Institute of Management of Sousse, Tunisia 

Ahlem Ayari is a dedicated PhD student at the National Engineering School of Sousse (ENISo), specializing in cloud computing, edge computing, and high-performance computing. She has extensive experience as a lecturer and trainer in computer science, particularly in database management, object-oriented programming, and web development. With a strong foundation in Java, C, JavaScript, and other programming languages, Ahlem has contributed to various software and web-based projects. Her research focuses on distributed systems, artificial intelligence, and machine learning algorithms. She has actively participated in international conferences and academic collaborations, highlighting her expertise in deploying e-health applications on cloud and edge computing environments. Ahlem is passionate about advancing technology in healthcare and has successfully developed innovative platforms that integrate deep learning algorithms for medical data analysis. She remains committed to contributing to technological advancements and mentoring the next generation of computer scientists through her academic and research pursuits.

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Scopus

Suitability for the Research for Best Researcher Award – Ahlem Ayari

Ahlem Ayari demonstrates a strong academic background and technical expertise, making her a promising candidate for the Research for Best Researcher Award. She is currently a PhD student at the National Engineering School of Sousse (ENISo), Tunisia, specializing in advanced computing fields such as distributed systems, high-performance computing, artificial intelligence, and machine learning. Her research contributions, particularly in cloud and edge computing for e-health applications, highlight her commitment to solving real-world challenges through innovative technology.

Her experience extends beyond academia, as she has actively contributed to teaching and training at multiple institutions, including Higher Institute of Management (ISG) and The Maghreb Institute of Economic Sciences and Technology (IMSET). She has taught courses in database management, programming, UML, MERISE, and cloud computing, demonstrating her ability to disseminate knowledge effectively. Additionally, her industry experience as a web developer strengthens her profile by bridging the gap between theoretical research and practical application.

🎓 Education

  • 2023-2024: PhD Student in Computer Science, “Mars” Laboratory, National Engineering School of Sousse (ENISo), Sousse University. Supervisor: Prof. Mohamed Nazih Omri, Co-supervisor: Hassen Hamdi.
  • 2021-2023: Master in Business Computing, Higher Institute of Management (ISG), Sousse University. Supervisor: Hassen Hamdi.
  • 2017-2020: Bachelor’s Degree in Business Computing, Higher Institute of Management (ISG), Sousse University. Supervisor: Kamel Garrouch.
  • 2017: Mathematics Baccalaureate, Ibn Rachik Kairouan.

Ahlem Ayari’s academic journey reflects her commitment to interdisciplinary learning, combining computer science and business computing. Her doctoral research focuses on cloud and edge computing, aiming to optimize computational efficiency and data security in medical applications. Throughout her studies, she has acquired expertise in artificial intelligence, database management, software engineering, and statistical analysis, contributing to her proficiency in designing and implementing complex computational systems.

💼 Professional Experience 

  • Trainer, Maghreb Institute of Economic Sciences and Technology (IMSET) (2024)
    • Taught courses on E-commerce, UML, MERISE, and SQL database management.
    • Conducted practical training in software development and IT systems.
  • Assistant Master, Higher Institute of Management (ISG), Sousse (2024)
    • Lectured in object-oriented programming (Java), cloud computing, big data, and C2I (Certificate in Computer Science and Internet).
  • Trainer, Higher Institute of Management (ISG), Sousse (2024)
    • Conducted LaTeX training for CV, report, and presentation creation.
  • Web Developer, Access Leader (March 2020 – September 2020)
    • Developed a web application using Angular for truck sales management.

🏅 Awards and Recognition

  • 12th International Conference of ISG Sousse (2023): Recognized for research on deploying e-health applications in edge and cloud computing environments.
  • 28th International Conference on Knowledge-Based and Intelligent Information Engineering Systems, Seville, Spain (2024): Presented innovative research on IoMT-based e-health applications.
  • Academic Distinction: Awarded recognition for excellence in cloud computing and AI research.
  • Best Research Presentation Award: Acknowledged for outstanding work in high-performance computing.

🌍 Research Skills On Computer Science

Ahlem Ayari possesses advanced research skills in distributed computing, cloud and edge computing, big data analytics, and AI-driven applications. Her expertise extends to designing and implementing machine learning models for real-time data processing. She is proficient in various programming languages (Java, C, Python) and frameworks (Angular, Symfony, Node.js). Her research methodologies involve deep learning algorithms for e-health applications and optimizing computational infrastructures. Ahlem actively contributes to international conferences, presenting innovative solutions in high-performance computing.

📖 Publication Top Notes

E-health Application In IoMT Environment Deployed in An Edge And Cloud Computing Platforms

  • Author: A., Ayari, Ahlem, H., Hassen, Hamdi, K.A., Alsulbi, Khlil Ahmad

Dr. SRINIVASA NAVEEN KUMAR G | Data Science | Best Researcher Award

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Dr. SRINIVASA NAVEEN KUMAR G | Data Science | Best Researcher Award

Dr. SRINIVASA NAVEEN KUMAR G | Data Science | Best Researcher Award

Dr. Srinivasa Naveen Kumar G is an Associate Professor and Dean of Data Science at Malla Reddy University, Hyderabad. He has over 16 years of teaching experience and a Ph.D. from Lincoln University College, Malaysia, specializing in Image and Video Processing. An accomplished academic leader, Dr. Kumar has organized numerous workshops, hackathons, and international conferences, contributing significantly to the fields of Data Science, Machine Learning, and Computer Vision. As an active member of professional bodies like IEEE and CSI, he is committed to academic excellence and innovation. His passion for research is evident in his numerous Scopus-indexed publications and workshops designed to bridge the gap between theoretical and practical knowledge.

Professional Profile

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Summary of Suitability for the Award

Dr. Srinivasa Naveen Kumar G is a distinguished academic professional with a robust background in teaching and research, making him a strong candidate for the “Research for Best Researcher Award.” With over 16 years of teaching experience and a noteworthy academic journey, culminating in a Ph.D. from Lincoln University College, Malaysia, his expertise in Image and Video Processing, Data Science, Machine Learning, and Computer Vision stands out. Serving as an Associate Professor and Dean of the Data Science Department at Malla Reddy University, Hyderabad, Dr. Kumar has demonstrated a strong commitment to education and research.

🎓 Education

Dr. Kumar completed his Ph.D. in 2020 from Lincoln University College, Malaysia, where he focused on Image and Video Processing. He earned his M.Tech in Digital Electronics and Communication Systems from JNTU, Hyderabad, in 2008, and his B.Tech in Electronics & Communication Engineering from JNTUK, Kakinada, in 2006. His academic journey is marked by a dedication to understanding complex systems, with a strong emphasis on digital communication and electronics. Throughout his studies, he demonstrated exceptional skills in applying theoretical knowledge to real-world challenges, laying a strong foundation for his research and teaching career. His academic background has equipped him with expertise in Data Science, Machine Learning, and related fields.

💼   Professional Experience

With over 16 years of teaching experience, Dr. Kumar currently serves as Associate Professor and Dean of Data Science at Malla Reddy University, Hyderabad, a role he has held since July 2021. Before that, he worked as an Associate Professor at Malla Reddy College of Engineering & Technology, Secunderabad, from 2008 to 2021. His extensive experience includes teaching both undergraduate and postgraduate courses, such as Python Programming, Image & Video Processing, and Data Analytics. He has a keen interest in research areas like Image and Video Processing, Machine Learning, and Computer Vision. Dr. Kumar is a leader in organizing academic events, workshops, and conferences, fostering a culture of continuous learning and innovation among students and faculty.

🏅  Awards and Recognition

Dr. Kumar has been recognized for his academic leadership and contributions to Data Science and Engineering. His accolades include organizing high-profile international conferences like the Scopus-indexed Springer ICISSC series and leading national-level student technical fests such as “Technosplurge.” He is a sought-after organizer, having successfully coordinated events like Salesforce Development workshops and 24-hour hackathons. His work in bridging academic research with industry practices has gained widespread acclaim. Dr. Kumar is also an esteemed member of IEEE and CSI, reflecting his commitment to professional excellence and continuous learning. His contributions have elevated his institution’s profile in the global academic community.

🌍   Research Skills

Dr. Kumar is proficient in Data Science, Machine Learning, Computer Vision, and Image & Video Processing. He has developed expertise in Python, Java, and various data analytics tools, applying his skills to solve complex problems in digital communication and systems design. His research focuses on innovative solutions for real-time data analysis and intelligent system development. As an organizer of Scopus-indexed conferences, he stays updated with cutting-edge research trends, ensuring his work is relevant and impactful. His skills extend to coding theory, digital system design, and programming, making him a versatile researcher and educator dedicated to advancing the field of Data Science.

📖 Publication Top Notes

  • Title: Video shot boundary detection and key frame extraction for video retrieval
    Cited by: 18
  • Title: Detection of Shot Boundaries and Extraction of Key Frames for Video Retrieval
    Cited by: 17*
  • Title: Key frame extraction using rough set theory for video retrieval
    Cited by: 16
  • Title: High-performance video retrieval based on spatio-temporal features
    Cited by: 15
  • Title: Yoga pose recognition with real-time correction using deep learning
    Cited by: 10