Angeliki Antoniou | AI | Best Researcher Award

Published on by Academic Achievements

Assoc. Prof. Dr. Angeliki Antoniou | AI | Best Researcher Award

Assoc. Prof. Dr. Angeliki Antoniou | AI | Associate Professor at University of West Attica | Greece

Assoc. Prof. Dr. Angeliki Antoniou is a distinguished scholar in the field of Human-Computer Interaction (HCI), Educational Technologies, and Digital Cultural Heritage, currently serving at the University of West Attica, Department of Archival, Library and Information Studies, Greece. She earned her Doctor of Informatics (Ph.D.) from the University of Peloponnese, focusing on adaptive educational technologies for museums, and holds an MSc in Human-Computer Interaction with Ergonomics from University College London (UCL). Additionally, she possesses undergraduate degrees in Psychology from the University of Kent and Early Childhood Education from the National and Kapodistrian University of Athens, illustrating her interdisciplinary foundation that bridges education, psychology, and informatics. Professionally, Assoc. Prof. Dr. Angeliki Antoniou has accumulated extensive teaching and research experience across institutions such as the University of Peloponnese and the University of West Attica, where she has led courses in cognitive psychology, human-computer interaction, and digital learning environments. Her research interests include user-centered design, cognitive modeling, serious games, digital storytelling, and technology-enhanced museum learning. She has successfully contributed to and coordinated several international and national projects on cultural heritage technologies, and her work is well-cited in high-impact academic journals indexed in Scopus and IEEE. Assoc. Prof. Dr. Angeliki Antoniou’s research skills encompass experimental design, usability evaluation, qualitative and quantitative analysis, and the development of adaptive systems for education and culture. She has received academic recognition for her leadership in interdisciplinary research, along with honors for her contributions to digital culture and innovation in educational informatics. In conclusion, Assoc. Prof. Dr. Angeliki Antoniou exemplifies academic excellence, innovative vision, and global impact through her scholarly research, educational leadership, and enduring contributions to the advancement of digital cultural heritage and human-computer interaction.

Profile: Google Scholar

Featured Publications 

  1. Lykourentzou, I., Antoniou, A., Naudet, Y., & Dow, S. P. (2016). Personality matters: Balancing for personality types leads to better outcomes for crowd teams. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. Citations: 158

  2. Theodoropoulos, A., & Antoniou, A. (2022). VR games in cultural heritage: A systematic review of the emerging fields of virtual reality and culture games. Applied Sciences, 12(17), 8476. Citations: 108

  3. Antoniou, A., & Lepouras, G. (2010). Modeling visitors’ profiles: A study to investigate adaptation aspects for museum learning technologies. Journal on Computing and Cultural Heritage (JOCCH), 3(2), 1–19. Citations: 84

  4. Lykourentzou, I., Claude, X., Naudet, Y., Tobias, E., Antoniou, A., & Lepouras, G. (2013). Improving museum visitors’ quality of experience through intelligent recommendations: A visiting style-based approach. Workshop Proceedings of the 9th International Conference on Intelligent Environments. Citations: 76

  5. Antoniou, A., Lepouras, G., Bampatzia, S., & Almpanoudi, H. (2013). An approach for serious game development for cultural heritage: Case study for an archaeological site and museum. Journal on Computing and Cultural Heritage (JOCCH), 6(4), 1–19. Citations: 69

  6. Katifori, A., Perry, S., Vayanou, M., Antoniou, A., Ioannidis, I. P., & McKinney, S. (2020). “Let them talk!” Exploring guided group interaction in digital storytelling experiences. Journal on Computing and Cultural Heritage (JOCCH), 13(3), 1–30. Citations: 67

  7. Antoniou, A., Katifori, A., Roussou, M., Vayanou, M., Karvounis, M., & Kyriakidi, M. (2016). Capturing the visitor profile for a personalized mobile museum experience: An indirect approach. Proceedings of the Digital Heritage International Congress. Citations: 60

 

Rajani Kumari Vaddepalli | Data Engineering | Excellence in Research

Published on by Academic Achievements

Mrs. Rajani Kumari Vaddepalli | Data Engineering | Excellence in Research

Mrs. Rajani Kumari Vaddepalli | Data Engineering | Senior Data Engineer at Callaway Golf | United States

Mrs. Rajani Kumari Vaddepalli is a distinguished Senior Data Engineer whose contributions span multiple advanced domains including artificial intelligence, blockchain, data engineering, and machine learning. With a scholarly focus on ethical AI design, adaptive systems, and data interoperability, she has made significant academic and industry contributions. She is recognized for her impactful research, thought leadership, and commitment to developing innovative technologies that address real-world challenges in finance, healthcare, retail, and smart governance. Her work is not only technically rigorous but also driven by a passion for responsible innovation, making her a respected figure within the data science community.

Academic Profile:

Google Scholar

Education:

Mrs. Vaddepalli earned her Master’s in Computer Science, with a specialization in data-centric AI systems and automated machine learning frameworks. Her academic training laid a strong foundation for her later work in applied data science, equipping her with the theoretical and practical skills required to lead complex data projects. Through her academic journey, she developed a keen interest in fairness, explainability, and adaptability of intelligent systems, all of which are reflected in her professional research endeavors. Her academic qualifications continue to support her evolving role as a researcher and practitioner in advanced computational technologies.

Experience:

As a Senior Data Engineer at a globally recognized organization, Mrs. Vaddepalli has consistently demonstrated leadership and technical excellence. Her role involves architecting scalable data systems, implementing AI-driven pipelines, and overseeing intelligent automation in cloud environments. Her experience spans cross-functional teams and international collaborations, where she has contributed to diverse projects focusing on federated learning, real-time analytics, and secure data sharing. She has mentored junior researchers, led technical workshops, and played a pivotal role in delivering data solutions aligned with both business goals and ethical standards. Her professional footprint reflects a balanced blend of strategic thinking and hands-on innovation.

Research Interest:

Mrs. Vaddepalli’s research interests lie at the intersection of data engineering and artificial intelligence. Her work explores schema drift adaptation, ethical generative AI models, energy-efficient blockchain systems, and explainable machine learning. She is particularly focused on developing culturally adaptive algorithms that enhance interpretability and trust across global user bases. Her research addresses critical gaps in fairness, bias detection, and model transparency—especially in regulated sectors such as finance and healthcare. Her interdisciplinary approach ensures that her work remains relevant, timely, and socially impactful, with continuous contributions to both academic and applied research fields.

Award:

Throughout her academic and professional career, Mrs. Rajani Kumari Vaddepalli has built a portfolio that reflects both depth and versatility. Her achievements include publishing in internationally reputed, peer-reviewed journals, contributing to major AI and data science conferences, and being actively involved in collaborative global projects. Her inclusion in citation databases such as Scopus underscores the academic reach of her work. Additionally, her professional memberships in organizations such as IEEE and ACM further demonstrate her standing in the research community. Her commitment to advancing responsible AI practices and contributing to the broader technological landscape makes her a fitting nominee for this award.

Selected Publications:

  • Toward a Greener Blockchain for Document Verification: Balancing Energy Efficiency and Security with Hybrid Consensus Models – 4 citations

  • Moving Beyond Generic Solutions: Crafting Industry-Tailored Ethical Frameworks for Unbiased Generative AI in B2B Sales – 4 citations

  • Bridging the Interoperability Gap in Healthcare AI: Adaptive Federated Learning for Secure, Cross-Platform Data Harmonization – 3 citations

  • Automated Feature Engineering and Hidden Bias: A Framework for Fair Feature Transformation in Machine Learning Pipelines – 3 citations

Conclusion:

Mrs. Rajani Kumari Vaddepalli is an exemplary candidate for this award, owing to her deep research expertise, technical accomplishments, and impactful contributions to both academia and industry. Her ability to merge theoretical innovation with practical application distinguishes her as a leader in the field of data science. Through high-quality publications, active collaborations, and a strong ethical orientation, she continues to shape emerging technologies in meaningful ways. Her potential for future leadership in AI research, especially in areas of responsible innovation and scalable systems, positions her as a deserving nominee for academic recognition on an international platform.

 

Perepi Rajarajeswari | Computer science | Best Researcher Award

Published on by Academic Achievements

Dr. Perepi Rajarajeswari | Computer science | Best Researcher Award

Associate professor at Vellore Institute of Technology, India

Dr. Perepi Rajarajeswari, an accomplished academician and researcher, holds an impressive academic background, with a PhD in Computer Science and Engineering from Jawaharlal Nehru Technological University, Hyderabad. She is currently an Associate Professor in the Department of Software Systems, School of Computer Science and Engineering at Vellore Institute of Technology (VIT), Tamil Nadu. With vast teaching experience in diverse computer science disciplines, Dr. Rajarajeswari has made notable contributions to fields like Blockchain technology, Software Engineering, Data Mining, Artificial Intelligence, and Internet of Things, among others. Over the years, she has garnered respect for her knowledge and expertise in both teaching and research.

Profile:

Google scholar

Education:

Dr. Rajarajeswari’s academic journey began with a Bachelor’s degree (B.Tech) in Computer Science from Sri Venkateswara University, Tirupati, in 2000. She then completed her Master of Technology (M.Tech) in Computer Science at Jawaharlal Nehru Technological University, Hyderabad, in 2008. Dr. Rajarajeswari earned her Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University, Hyderabad, in 2017. Her educational background has equipped her with a solid foundation in the ever-evolving field of computer science.

Experience:

Dr. Rajarajeswari has a distinguished career as an educator and researcher. She began her career as a lecturer at Madanapalle Institute of Technology and Science in 2000. Over the years, she has progressively advanced in academia. From Assistant Professor to Associate Professor, she has worked at various reputed institutions, including Madanapalle Institute of Technology and Science, Aditya College of Engineering, Kingston Engineering College, and Sreenivasa Institute of Technology and Management Studies. Since 2022, Dr. Rajarajeswari has been serving as an Associate Professor at VIT, contributing significantly to both research and academic development. Her wide-ranging experience in teaching and research has made her a pivotal figure in her academic community.

Research Interests:

Dr. Rajarajeswari’s research interests are multi-disciplinary and encompass cutting-edge areas in computer science and engineering. Her expertise spans Blockchain technology, Software Engineering, Software Architecture, Data Mining, Artificial Intelligence, Cloud Computing, and the Internet of Things. She is particularly passionate about exploring the intersections of these technologies, such as Mobile Cloud Computing and Cyber-Physical Systems, and their real-world applications. Her focus on advanced computational techniques aims to address complex problems in fields such as healthcare, smart systems, and secure architectures.

Awards:

Dr. Rajarajeswari’s work has been recognized by various academic and professional organizations. While specific awards are not detailed, her commitment to excellence in education, research, and innovation has earned her the respect of peers and students alike. Her contributions to sponsored projects and her active participation in research have placed her at the forefront of her field.

Publications:

Dr. Rajarajeswari has authored several influential publications in reputed journals and conferences. Some of her key publications include:

  1. “Thermomagnetic Bioconvection Flow in a Semi trapezoidal Enclosure Filled with a Porous Medium Containing Oxytactic Micro-Organisms: Modeling Hybrid Magnetic Biofuel Cells,” ASME Journal of Heat and Mass Transfer, SCIE Journal, 2025.

  2. “Finite Element Numerical Simulation of Free Convection Heat Transfer in a Square Cavity Containing an Inclined Prismatic Obstacle with Machine Learning Optimization,” Heat Transfer-Wiley, 2025.

  3. “Magneto-convective flow in a differentially heated enclosure containing a non-Darcy porous medium with thermal radiation effects—a Lattice Boltzmann simulation,” Journal of the Korean Physical Society, 2025.

  4. “Deep Learning Techniques for Lung Cancer Recognition,” Engineering, Technology & Applied Science Research, 2024.

  5. “Prediction of Heart Attack Risk and Detection of Sleep Disorders Using Deep Learning Approach,” International Research Journal of Multidisciplinary Scope, 2024.

  6. “Object Oriented Design Approach for the Implementation of Secure Aircraft Management System Based on Machine Learning,” Nanotechnology Perceptions, 2024.

  7. “A Deep Learning Computational Approach for the Classification of COVID-19 Virus,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022.

Her works have been cited by numerous scholars, contributing significantly to advancing research in computational intelligence, data mining, and machine learning.

Conclusion:

Dr. Perepi Rajarajeswari’s academic achievements and research contributions underscore her dedication to advancing the field of Computer Science and Engineering. Her diverse experience, coupled with her deep understanding of contemporary technological issues, places her as a leader in her domain. With a passion for teaching and a commitment to solving real-world problems, Dr. Rajarajeswari continues to inspire students and researchers alike. Through her ongoing work in research and development, she is poised to make further impactful contributions in the fields of AI, Blockchain, Cloud Computing, and more.

Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

Published on by Academic Achievements

Mr. Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

Mr. Ahmed Ghazi BLAIECH | Artificial intelligence-Associate professor at Higher Institute of Applied Sciences and Technology of Sousse, Tunisia

Ahmed Ghazi Blaiech is a distinguished academic and researcher in the field of computer science, currently serving as an Assistant Professor at the High Institute of Applied Sciences and Technology of Sousse (ISSATSo), University of Sousse. With extensive experience in artificial intelligence, machine learning, and real-time computing, he has made significant contributions to the development of innovative deep learning models and neural networks. His research focuses on medical imaging, embedded systems, and FPGA-based accelerators. Over the years, he has been instrumental in fostering cutting-edge technological advancements through both research and academic mentoring.

Profile:

Orcid | Scopus | Google Scholar

Education:

Ahmed Ghazi Blaiech has an extensive academic background in computer science and informatics systems. He obtained his Habilitation thesis in Engineering of Informatics Systems from the National Engineering School of Sfax (ENIS) in 2022. Prior to that, he earned his PhD in Engineering of Informatics Systems in 2015 from the same institution, graduating with first-class honors. He also holds a Master’s degree in Safety and Security of Industrial Systems with a specialization in Real-Time Computer Science from the High Institute of Applied Sciences and Technology of Sousse. His foundational academic journey began with a Licence degree in Computer Science from the same institute in 2006.

Experience:

Dr. Blaiech has accumulated over a decade of teaching and research experience in academia. Since 2017, he has been an Assistant Professor at ISSATSo, contributing to various undergraduate and postgraduate courses. Before this, he served as an Assistant in Computer Science at ISSATSo (2016-2017) and at the High Institute of Computer Science and Multimedia of Gabes, University of Gabes (2011-2015). He also worked as a contractual assistant at the Faculty of Sciences of Monastir, University of Monastir (2008-2011). In addition to his teaching roles, he has actively led numerous research initiatives and coordinated academic programs.

Research Interests:

Dr. Blaiech’s research interests span multiple domains within artificial intelligence, machine learning, and real-time computing. His work is particularly focused on deep learning applications in medical imaging, embedded systems, and hardware-accelerated computing using FPGA-based architectures. He has also contributed to the advancement of intelligent pervasive systems and neural networks for real-time applications. His research outputs have been widely recognized in high-impact journals, showcasing innovative methodologies in biomedical signal processing, image synthesis, and classification techniques.

Awards and Recognitions:

Throughout his career, Dr. Blaiech has received several accolades for his contributions to the field of computer science. He holds multiple prestigious certifications, including the Huawei Certified ICT Associate (HCIA) in Artificial Intelligence and the Microsoft Technology Associate (MTA) for Python programming. He has also been recognized for his mentorship and coaching in AI-related competitions, playing a crucial role in fostering innovation among students and researchers.

Publications:

Dr. Blaiech has authored numerous research papers in high-impact journals, contributing to advancements in artificial intelligence and medical imaging. Some of his notable publications include:

📌 “CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features” – Biomedical Signal Processing and Control, 2022. DOI 📖
📌 “An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality” – Applied Intelligence, 2022. DOI 📖
📌 “Comparison by multivariate auto-regressive method of epileptic seizures prediction for real patients and virtual patients” – Biomedical Signal Processing and Control, 2021. DOI 📖
📌 “Innovative deep learning models for EEG-based vigilance detection” – Neural Computing and Applications, 2020. DOI 📖
📌 “A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network” – Computational Intelligence and Neuroscience, 2019. DOI 📖
📌 “A New Hardware Architecture for Self-Organizing Map Used for Colour Vector Quantization” – Journal of Circuits, Systems, and Computers, 2019. DOI 📖
📌 “A Survey and Taxonomy of FPGA-based Deep Learning Accelerators” – Journal of Systems Architecture, 2019. DOI 📖

Conclusion:

Dr. Ahmed Ghazi Blaiech’s contributions to the field of artificial intelligence and medical computing have been impactful in both research and academia. His dedication to technological innovation, particularly in neural networks and real-time computing, has positioned him as a leader in the domain. His extensive research output, coupled with his teaching and mentoring experience, underscores his significant role in advancing knowledge and fostering the next generation of AI researchers. Through his work, he continues to drive progress in medical imaging, deep learning applications, and FPGA-based architectures, making a lasting impact in his field.

Harry Moongela | Computer Science | Young Scientist Award

Published on by Academic Achievements

Dr. Harry Moongela | Computer Science | Young Scientist Award

Wits University, South Africa

Harry Moongela is a skilled IT professional with a strong background in Information Technology, including teaching, research, and development roles. He currently serves as a Postdoctoral Researcher at the University of Pretoria in South Africa, where he focuses on Artificial Intelligence and Critical Thinking research projects. Fluent in English and with basic knowledge of German, Harry has a rich academic and professional background that spans over a decade in various IT roles, ranging from academic positions to industry-specific consultancy.

Profile

Scopus

Education🎓

Harry holds a PhD in Information Technology from the University of Pretoria (2022), following a Master’s Degree in Information Systems from Rhodes University (2017). His undergraduate studies in Computer Science & Information Technology were completed at the University of Namibia in 2013. Additionally, Harry holds several certifications, including Cisco CCNA and a Certificate in Basic Telkom Network from Huawei University, providing him with a broad technical skill set.

Experience💼

Harry’s professional career began with roles such as a Web Developer and IT Technician. He has since advanced to academic roles, serving as a Lecturer, Course Coordinator, and Researcher at the University of Pretoria. He has also held various IT consultancy and development positions, including at Derivco Pty Ltd and Kanuga Auto Parts SA. Harry has extensive experience in IT teaching, course design, software development, and system administration.

Research Interests🔬

Harry’s research interests lie in the intersection of Artificial Intelligence, Critical Thinking, and Information Technology. His work includes publishing academic research in AI applications, computer networks, and the integration of innovative technologies in IT systems. His goal is to continue advancing research in AI to solve real-world problems and improve educational systems in IT.

Awards🏆

Harry has received multiple accolades throughout his academic journey, including being named Best IT Student at the University of Namibia (2010-2013), Best Postgraduate Student at Rhodes University (2017), and being a member of the prestigious Future Africa Futures Literacy Masterclass (2023). These awards reflect his dedication to excellence in both his studies and professional life.

Publications📚

Harry has contributed to various conference and journal publications in the field of Information Technology. Some of his notable works include research on AI in education, network security, and critical thinking in IT systems.
For detailed reading, please refer to the following articles:

A framework for using social media for organisational learning: An empirical study of South African companies”

  • Authors: Moongela, H., Hattingh, M.
  • Journal: African Journal of Science, Technology, Innovation and Development
  • Year: 2024
  • Volume: 16
  • Issue: 6
  • Pages: 761–773
  • Citations: 0

“Healthcare Supply Chain Efficacy as a Mechanism to Contain Pandemic Flare-Ups: A South Africa Case Study”

  • Authors: Maramba, G., Smuts, H., Hattingh, M., Mawela, T., Enakrire, R.
  • Journal: International Journal of Information Systems and Supply Chain Management
  • Year: 2023
  • Volume: 17
  • Issue: 1
  • Article ID: 333713
  • Citations: 3

“Perceptions of social media on students’ academic engagement in tertiary education”

  • Authors: Moongela, H., McNeill, J.
  • Conference: ACM International Conference Proceeding Series
  • Year: 2017
  • Part: F130806
  • Article ID: a23
  • Citations: 2

Conclusion🚀

Harry Moongela’s combination of academic excellence, significant research contributions, leadership in both research and teaching, and dedication to professional development make him an outstanding candidate for the Best Researcher Award. His wide-ranging expertise in information technology and commitment to advancing knowledge through research and teaching solidify his qualifications for this prestigious recognition.

Rashmi S | Machine Learning Techniques | Best Researcher Award

Published on by Academic Achievements

Mrs. Rashmi S | Machine Learning Techniques | Best Researcher Award

Rashmi S – Machine Learning Techniques | Senior Research Fellow at JSS Science and Technology University, India

Rashmi S. is an accomplished Ph.D. research scholar specializing in Computer Vision and Machine Intelligence. Her academic focus is particularly on medical image analysis, with a concentration on radiographic image annotation using AI and deep learning techniques. With approximately five years of experience in the tech industry as a Core Java Developer, Rashmi brings a unique blend of software development expertise and advanced research skills. She is currently working at the Pattern Recognition & Image Processing Lab at JSS Science and Technology University, Mysuru. Rashmi is driven by the ambition to enhance healthcare systems through innovative AI solutions, and her research contributions aim to create more accurate, automated systems for interpreting medical imagery.

Profile Verification

Google Scholar

Education

Rashmi S. completed her Bachelor of Engineering (B.E.) in Computer Science and Engineering from SJCE, Mysore, graduating with a CGPA of 9.05. She then pursued her Master’s degree in Computer Engineering (M.Tech) from the same institution, achieving an outstanding CGPA of 9.77. Currently, she is pursuing her Ph.D. in Computer Science and Engineering at JSS S&TU, where she is expected to submit her thesis in September 2024. Her academic journey has been marked by a strong commitment to research excellence, particularly in Machine Learning and Deep Learning, both of which she applies in her medical image analysis research.

Experience

Rashmi S. has held various roles in both academic and industry settings, which have enriched her research and technical skills. She began her career in software engineering, working with Cisco Video Technology in Bengaluru, where she was involved in the development of Java-based software for Set-Top Boxes. She later moved on to Oracle India Pvt. Ltd. as an Application Engineer, working on software maintenance and the development of Oracle Projects Fusion, a project management tool. Rashmi’s academic career includes positions as a Junior Research Fellow and Senior Research Fellow at JSS Science and Technology University, where she currently conducts her doctoral research. Her professional journey in both the software industry and academia gives her a unique edge in developing and implementing cutting-edge research in healthcare.

Research Interests

Rashmi S. is primarily focused on Machine Learning, Deep Learning, and Image Processing, especially in the context of medical image analysis. Her research interests revolve around improving diagnostic tools through AI-powered systems. Specifically, her work addresses cephalometric landmark annotation in radiographs using both traditional machine learning algorithms and deep learning techniques. Rashmi has explored applications of EEG signal processing and computer vision in healthcare, striving to develop solutions that can automate the annotation of medical images for more accurate diagnoses. Her research aims to bridge the gap between artificial intelligence and clinical practices, potentially revolutionizing medical imaging and diagnostic procedures.

Awards

Rashmi S. has received several prestigious awards throughout her academic and professional career. She was awarded the UGC-NET Junior Research Fellowship in November 2021, which has enabled her to pursue her doctoral research in depth. She was also recognized with the Senior Research Fellowship by the University Grants Commission in February 2024. Additionally, Rashmi has been the recipient of several scholarships, including the MHRD & GATE Scholarships during her undergraduate and postgraduate studies. Her commitment to research excellence has also earned her multiple accolades for her academic performance, including being recognized for her outstanding contributions to machine learning in the medical field.

Publications

“Cephalometric Skeletal Structure Classification Using Convolutional Neural Networks and Heatmap Regression“, co-authored with P. Murthy, V. Ashok, and S. Srinath, published in SN Computer Science (2022). This study leverages convolutional neural networks (CNNs) and heatmap regression for advanced skeletal structure classification in cephalometric radiographs, with a focus on enhancing the accuracy of diagnostic tools in orthodontics.

“Extended Template Matching Method for Region of Interest Extraction in Cephalometric Landmarks Annotation“, co-authored with S. Srinath, R. Rakshitha, and B.V. Poornima, presented at the 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical… This paper introduces an extended template matching method aimed at improving the extraction of regions of interest (ROIs) in cephalometric image annotation, a crucial step for automatic landmark detection.

“Lateral Cephalometric Landmark Annotation Using Histogram Oriented Gradients Extracted from Region of Interest Patches“, co-authored with S. Srinath, K. Patil, P.S. Murthy, and S. Deshmukh, published in Journal of Maxillofacial and Oral Surgery (2023). This research presents a novel approach for lateral cephalometric landmark annotation by extracting histogram-oriented gradients from ROIs, advancing the methods for more precise orthodontic assessments.

“A Novel Method for Cephalometric Landmark Regression Using Convolutional Neural Networks and Local Binary Pattern“, co-authored with V. Ashok, presented at the 5th International Conference on Computer Vision and Image Processing (2021). This paper explores a novel technique for landmark regression in cephalometric images using a combination of CNNs and local binary patterns, enhancing the automation of cephalometric analysis.

“Landmark Annotation Through Feature Combinations: A Comparative Study on Cephalometric Images with In-depth Analysis of Model’s Explainability“, co-authored with S. Srinath, S. Murthy, and S. Deshmukh, published in Dentomaxillofacial Radiology (2024). This comparative study examines various feature combinations for landmark annotation and provides an explainability analysis of the models used, aiming to make machine learning-based medical imaging more transparent and understandable.

“Recognition of Indian Sign Language Alphanumeric Gestures Based on Global Features“, co-authored with B.V. Poornima, S. Srinath, and R. Rakshitha, presented at the 2023 IEEE International Conference on Distributed Computing, VLSI… This paper investigates the use of global features for recognizing Indian Sign Language gestures, contributing to the development of gesture recognition systems in communication technologies.

“ISL2022: A Novel Dataset Creation on Indian Sign Language“, co-authored with R. Rakshitha, S. Srinath, and S. Rashmi, presented at the 2023 10th International Conference on Signal Processing and Integrated…. This paper presents the creation of the ISL2022 dataset, a significant step toward improving machine learning models for Indian Sign Language recognition, highlighting the importance of datasets in advancing language recognition research.

“Cephalometric Landmark Annotation Using Transfer Learning: Detectron2 and YOLOv8 Baselines on a Diverse Cephalometric Image Dataset“, co-authored with S. Srinath, S. Deshmukh, S. Prashanth, and K. Patil, published in Computers in Biology and Medicine (2024). This work leverages transfer learning techniques, using Detectron2 and YOLOv8 models, to annotate cephalometric landmarks on a diverse dataset, pushing the envelope for automated medical image analysis.

“Crack SAM: Enhancing Crack Detection Utilizing Foundation Models and Detectron2 Architecture“, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Journal of Infrastructure Preservation and Resilience (2024). This research explores advanced crack detection techniques, using foundation models and Detectron2, to improve the detection of cracks in infrastructure.

“Enhancing Crack Pixel Segmentation: Comparative Assessment of Feature Combinations and Model Interpretability”, co-authored with R. Rakshitha, S. Srinath, N. Vinay Kumar, and B.V. Poornima, published in Innovative Infrastructure Solutions (2024). This paper focuses on crack pixel segmentation, offering insights into the comparative performance of various feature combinations and the interpretability of machine learning models used in infrastructure monitoring.

Conclusion

Rashmi S. has demonstrated exceptional skill and dedication to the field of Computer Vision and Machine Intelligence. With her substantial industry experience and strong academic background, Rashmi has contributed significantly to AI research in healthcare. Her work has the potential to revolutionize medical image analysis, offering more efficient and accurate diagnostic tools. Through her awards, publications, and ongoing research, Rashmi S. stands as an exemplary candidate for the Best Researcher Award, with the promise of continuing to make groundbreaking advancements in her field.

Zhiqiang He | Artificial Intelligence | Best Researcher Award

Published on by Academic Achievements

Dr. Zhiqiang He | Artificial Intelligence | Best Researcher Award 

Ph.D. at The university of Electro-Communications, China

Zhiqiang He is an emerging researcher specializing in reinforcement learning and artificial intelligence (AI), with a focus on developing and optimizing control algorithms for complex systems. He has made significant contributions to both academic research and industrial applications, demonstrating expertise in designing innovative AI solutions for real-world problems. His educational background in control science and engineering, combined with practical experiences at leading tech companies, has shaped his career and led to several impactful publications in renowned journals. Zhiqiang’s accomplishments, recognized through various academic awards and industry achievements, make him a strong candidate for the “Best Researcher Award.”

Profile

ORCID

Education

Zhiqiang pursued his Master of Science in Control Science and Engineering at Northeastern University (NEU), Shenyang, China, from September 2019 to June 2022, where he maintained a commendable GPA of 3.29/4. During his master’s program, he specialized in the development of reinforcement learning algorithms, which formed the cornerstone of his research. Prior to this, he earned his Bachelor of Science in Automation at East China Jiaotong University (ECJTU), Nanchang, China, from September 2015 to June 2019, with a GPA of 3.42/4. His undergraduate studies laid a strong foundation in automation and control systems, providing the technical skills and knowledge that fueled his passion for AI and intelligent decision-making.

Experience

Throughout his academic journey, Zhiqiang actively engaged in research and industry roles that enriched his experience in the field of AI. He served as a team leader at the Institute of Deep Learning and Advanced Intelligent Decision-Making at NEU, where he worked on the development of reinforcement learning algorithms. Leading projects from September 2020 to June 2021, he conducted research on model-based reinforcement learning, optimized algorithm performance, and supervised students in their projects. Additionally, his early experience as a team leader at the Jiangxi Province Advanced Control and Key Optimization Laboratory involved applying reinforcement learning to control problems from 2016 to 2019, where he gained hands-on skills in analyzing system behaviors and establishing Markov Decision Process (MDP) models.

In the industry, Zhiqiang took on roles that deepened his technical expertise. He was an intern at Baidu, Beijing, China, where he pioneered the development of the Expert Data-Assisted Multi-Agent Proximal Policy Optimization (EDA-MAPPO) algorithm, an innovative approach to multi-agent cooperative adversarial AI. Later, as a reinforcement learning algorithms engineer at InspirAI in Hangzhou, he led the development of AI strategies for popular card games, showcasing his ability to apply AI solutions to commercial projects and enhance algorithmic performance.

Research Interest

Zhiqiang’s research interests are centered on reinforcement learning, AI, and control systems. He focuses on designing algorithms that improve the efficiency and accuracy of AI models in decision-making tasks. His work involves exploring new methods for multi-agent reinforcement learning, optimizing algorithms for real-time applications, and addressing challenges in intelligent control. By bridging theoretical research with practical applications, he aims to push the boundaries of AI, making it more adaptable and applicable to various industries. His dedication to advancing reinforcement learning techniques aligns with the future trajectory of AI research, where automation and intelligent decision-making are key drivers of innovation.

Awards

Zhiqiang has received recognition for his academic excellence and research contributions throughout his career. He was honored as an “Outstanding Graduate” by East China Jiaotong University in 2019, acknowledging his academic achievements and leadership potential. In addition, he secured the Third Prize in the 15th “Challenge Cup” Jiangxi Division in 2017 and the Second Prize in the International Mathematical Modeling Competition for American College Students in 2018, demonstrating his problem-solving skills and competitive spirit. His active engagement in professional development is further highlighted by his certifications in network technology and programming languages, which add to his multidisciplinary skill set.

Publications

He Z, Qiu W, Zhao W, et al. Understanding World Models through Multi-Step Pruning Policy via Reinforcement Learning. Information Sciences, 2024: 121361. – Cited by 32 articles.

Chen P, He Z, Chen C, et al. Control strategy of speed servo systems based on deep reinforcement learning. Algorithms, 2018, 11(5): 65. – Cited by 15 articles.

Wang J, Zhang L, He Z, et al. Erlang planning network: An iterative model-based reinforcement learning with multi-perspective. Pattern Recognition, 2022, 128: 108668. – Cited by 27 articles.

Zhang L, He Z, Zhao Y, et al. Reinforcement Learning-based Control of Robotic Manipulators. Journal of Robotics, 2023, 12(3): 112-121. – Cited by 19 articles.

He Z, Zhao W, Zhang L, et al. Multi-Agent Deep Reinforcement Learning in Dynamic Environments. Artificial Intelligence Review, 2022, 55(2): 456-472. – Cited by 24 articles.

Chen C, He Z, Qiu W, et al. Optimal Control for Nonlinear Systems Using Reinforcement Learning. Control Theory and Applications, 2021, 59(4): 553-566. – Cited by 18 articles.

Conclusion

Zhiqiang He’s contributions to AI and reinforcement learning, coupled with his practical experience and research output, position him as a promising researcher in the field. His work not only advances the academic understanding of intelligent control but also finds applications in industry, where AI solutions are critical to technological development. By consistently pushing for excellence in his projects, he demonstrates qualities that make him a deserving candidate for the “Best Researcher Award.” His trajectory reflects a commitment to innovation, making him an asset to the research community and a potential leader in future AI advancements.

Preeti Sharma | Deep Learning | Women Researcher Award

Published on by Academic Achievements

Mrs . Preeti Sharma | Deep Learning | Women Researcher Award 

Assistant Professor , DIT University, Dehradun, Uttrakhand , India

Preeti Sharma is a dedicated researcher and educator currently pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun. With a distinguished academic background including gold medals and high honors in her MTech and MCA degrees, Preeti has demonstrated excellence in her field. She is passionate about advancing the field of artificial intelligence and machine learning, focusing on generative adversarial networks (GANs) and deepfake detection.

Profile

Google Scholar

Education 

Preeti Sharma is pursuing a Ph.D. in Computer Science and Engineering at the University of Petroleum and Energy Studies, Dehradun, with her thesis submitted. She holds an MTech in Computer Science and Engineering from Uttarakhand Technical University, where she graduated as a gold medalist with an impressive 85%. Preeti completed her M.C.A. from M.D.U. (Campus), Rohtak, with a strong academic record of 82%.

Experience 

Preeti Sharma currently serves as a Junior Research Fellow and Teaching Assistant at the University of Petroleum and Energy Studies, Dehradun, where she has been contributing since April 2021. Prior to this, she was a Non-Teaching Staff member at the same university from September 2015 to March 2021. She also gained valuable experience as a Guest Lecturer at Arihant Institute of Technology, Haldwani, and an intern at the National Informatics Center (NIC).

Research Interests 

Preeti Sharma’s research interests include the application of Generative Adversarial Networks (GANs) in image and deepfake detection, robust CNN models, and advancements in digital forensics. Her work explores innovative methods for deepfake detection and image forgery using GAN-based models, contributing significantly to the field of multimedia tools and applications.

Awards 

Preeti Sharma has been recognized for her exceptional research and presentations. She received a certification for the best oral presentation at the International Young Researcher Conclave (IYRC-2024). Her paper on generative adversarial networks won first prize in the Research Conclave IYRC 2024 at UPES.

Publications 

  • Sharma, P., Kumar, M., Sharma, H.K. et al. Generative adversarial networks (GANs): Introduction, Taxonomy, Variants, Limitations, and Applications. Multimedia Tools and Applications (2024). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. Robust GAN-Based CNN Model as Generative AI Application for Deepfake Detection, EAI Endorsed Trans IoT, vol. 10 (2024).
  • Sharma, P., Kumar, M., & Sharma, H.K. A generalized novel image forgery detection method using a generative adversarial network. Multimedia Tools and Applications (2023). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. A GAN-based model of deepfake detection in social media. Procedia Computer Science, 218, 2153-2162 (2023).
  • Sharma, P., Kumar, M., & Sharma, H.K. Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimedia Tools and Applications, 82(12), 18117-18150 (2023).
  • Sharma, P., Kumar, M., & Sharma, H.K. A Guide to Digital Forensic: Theoretical to Software-Based Investigations. Perspectives on Ethical Hacking and Penetration Testing, IGI Global (2023). Link
  • Sharma, P., Kumar, M., & Sharma, H.K. CNN-based Facial Expression Recognition System Using Deep Learning Approach. Conference on Computational Intelligence and Information Retrieval CIIR (2021).
  • Sharma, P. Real Time Tracking System for Object Tracking using the Internet of Things (IoT). Conference on Computational Intelligence and Information Retrieval CIIR (2021).
  • Sharma, P. Leach and Improved Leach: A Review. International Journal of Advanced Research in Computer Science, Vol 10 (2019).

Conclusion

Preeti Sharma’s profile shows a strong foundation in research and technical expertise, with notable contributions to GANs and deepfake detection. Her academic achievements, innovative patents, and recognition in the field underscore her qualifications. To strengthen her candidacy for the Research for Women Researcher Award, she could emphasize the broader impact of her research and highlight her leadership or mentorship roles. Overall, her qualifications and achievements make her a strong contender for the award.

Natasha Christabelle Santosa | Artificial Intelligence | Best Researcher Award

Published on by Academic Achievements

Mrs Natasha Christabelle Santosa | Artificial Intelligence | Best Researcher Award

Mrs Natasha Christabelle Santosa , Tokyo Institute of Technology , Japan

Natasha Christabelle Santosa is a dedicated artificial intelligence researcher with a passion for advancing machine learning technologies. Fluent in four languages, she has honed her expertise over two years of part-time work and PhD studies. Natasha is currently a research assistant at Tokyo Institute of Technology, where she investigates dynamic ontology applications in scientific paper recommendations. Her experience spans diverse areas including natural language processing, information retrieval, and computer vision. She is actively seeking opportunities in Tokyo, preferably in remote or hybrid roles, to leverage her skills in a global or English-Japanese environment.

Publication Profile

Google Scholar

Strengths for the Award

  1. Diverse Expertise: Natasha has a strong background in AI, machine learning, and data analysis, covering the full machine learning cycle from data construction to model deployment. Her experience spans various domains, including information retrieval, natural language processing, and computer vision.
  2. Advanced Research: Her PhD research at Tokyo Institute of Technology on dynamic ontology for scientific paper recommendations shows a commitment to advancing AI methodologies and practical applications. Her work on graph neural networks for paper recommendations, published in reputable journals, highlights her ability to tackle complex problems in cutting-edge research.
  3. Multilingual Capabilities: Being quadrilingual (Indonesian, Javanese, English, and intermediate Japanese) enhances her ability to collaborate in diverse environments, particularly beneficial in global research settings.
  4. Recognition and Funding: Receiving the prestigious Japanese government MEXT scholarship for both master’s and PhD studies underscores her exceptional academic capabilities and potential.

Areas for Improvement

  1. Broader Impact: While her research is advanced, expanding her work to include more interdisciplinary applications or collaborations could broaden its impact and applicability.
  2. Professional Experience: Gaining more industry experience or leading larger-scale projects could further enhance her practical skills and visibility in the field.
  3. Networking and Outreach: Increasing her presence in international conferences and workshops could provide additional opportunities for collaboration and recognition.

Education

Natasha is pursuing a PhD in Artificial Intelligence at Tokyo Institute of Technology, with an expected completion in September 2024. Her research focuses on scientific paper recommendation using dynamic ontology and neural networks. She holds a Master’s in Artificial Intelligence from the same institution, with a thesis on ontology-based personalized recommendation systems. Her academic journey began with a Bachelor’s in Computer Science from Gadjah Mada University, where she graduated with honors, focusing on adaptive neuro-fuzzy inference systems for cancer diagnosis.

Experience

Natasha’s professional experience includes part-time research roles at Tokyo Institute of Technology and the Advanced Institute of Science and Technology. At Tokyo Tech, she explores dynamic ontology for scientific paper recommendations. Previously, at AIST, she worked on using graph neural networks for end-to-end paper recommendations, contributing to a preprint publication. Her roles involved extensive research and practical applications in machine learning, enhancing her expertise across various domains including NLP and computer vision.

Research Focus

Natasha’s research concentrates on enhancing scientific paper recommendation systems through dynamic ontology and neural network approaches. Her PhD work involves developing advanced methods to assist in paper writing, while her earlier research explored ontology-based personalized recommendations. She has applied her skills in machine learning, data analysis, and graph neural networks to improve information retrieval and recommendation systems, aiming to advance the field of AI with innovative solutions.

Publications Top Notes

📄 N. C. Santosa, X. Liu, H. Han, J. Miyazaki. 2023. S3PaR: Section-Based Sequential Scientific Paper Recommendation for Paper Writing Assistance. In Knowledge Based Systems [in press]

📄 N. C. Santosa, J. Miyazaki, H. Han. 2021. Automating Computer Science Ontology Extension with Classification Techniques. In IEEE Access, Vol. 9, pp.161815-161833.

📄 N. C. Santosa, J. Miyazaki, H. Han. 2021. Flat vs. Hierarchical: Classification Approach for Automatic Ontology Extension. In Proceedings of Data Engineering and Information Management (DEIM).

Conclusion

Natasha Christabelle Santosa is a highly qualified candidate for the Best Researcher Award due to her extensive expertise in AI, strong research contributions, and multilingual capabilities. Her innovative work on scientific paper recommendations and advanced machine learning techniques demonstrates her potential to make significant contributions to the field. By addressing areas for improvement, such as expanding her interdisciplinary impact and gaining further industry experience, she can enhance her profile and increase her chances of receiving the award.

Jie Li | Artificial Intelligence | Best Researcher Award

Published on by Academic Achievements

Assoc Prof Dr. Jie Li | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Jie Li, Chongqing University of Science & Technology, China

Profile

scopus

Dr. Jie Li is an Associate Professor at the School of Computer Science and Engineering, Chongqing University of Science and Technology. With a PhD from Chongqing University (2011), she has held roles as an assistant researcher at the Chongqing Green Intelligent Technology Research Institute and a post-doctoral fellow at Chongqing Qingshan Industrial Company Limited. Her research has led to numerous patents and influential publications in top journals like IEEE Transactions. Dr. Li has also been involved in significant university-enterprise cooperative projects, highlighting her leadership and innovation in artificial intelligence and machine learning.

Strengths for the Award:

  1. Significant Research Contributions: Dr. Jie Li has made substantial contributions to artificial intelligence, machine learning, and fault diagnosis. Her work, published in top-tier journals like IEEE Transactions, demonstrates high-impact research in these fields.
  2. Extensive Patent Portfolio: With over 40 invention patents applied for and 18 authorized, Dr. Li’s innovative approaches are translating into practical technologies and solutions, showcasing her role as a leading inventor and researcher.
  3. Leadership in Projects: She has successfully led 16 national and provincial research projects and 7 enterprise-level projects. Her leadership in university-enterprise cooperative projects further underscores her ability to bridge academia and industry effectively.
  4. Academic and Industry Impact: Her book “Artificial Intelligence” has received industry praise, and her publications, totaling over 40 papers, reflect a broad and impactful research portfolio.

Areas for Improvement:

  1. Broader Citation Metrics: While Dr. Li has a respectable citation count, expanding her citation index could enhance her visibility and recognition in the global research community. Increasing collaboration with international researchers might help achieve this.
  2. Research Dissemination: Although Dr. Li has published extensively, further dissemination through high-impact conferences and workshops could elevate her work’s visibility and influence, potentially leading to more collaborative opportunities.
  3. Diverse Research Areas: Diversifying her research focus beyond her core areas could open new avenues for innovation and impact. Exploring emerging trends in AI and machine learning might strengthen her research portfolio.

Education🎓

Dr. Jie Li completed her PhD in Computer Science at Chongqing University in December 2011. Her doctoral studies laid the foundation for her extensive research in artificial intelligence and machine learning. During her academic career, she has broadened her expertise through postdoctoral research and academic visits to prestigious institutions like Tsinghua University and the University of Rhode Island. These experiences have enriched her academic perspective and research capabilities, significantly contributing to her professional achievements.

Experience💼

Dr. Jie Li began her career as an assistant researcher at the Chongqing Green Intelligent Technology Research Institute from February 2012 to April 2014. She later worked as a post-doctoral fellow at Chongqing Qingshan Industrial Company Limited from April 2017 to January 2020. Her academic tenure at Chongqing University of Science and Technology includes significant roles, such as being rated as an associate professor in September 2019. Additionally, she has led numerous national and provincial research projects and has been actively involved in university-enterprise cooperation initiatives.

Research Focus🔬

Dr. Jie Li’s research encompasses Deep Learning, Machine Learning, Fault Diagnosis, and Artificial Intelligence. Her work focuses on advancing these fields through innovative algorithms and practical applications. She has led and participated in several high-impact projects funded by national and provincial bodies. Her research has significantly contributed to the development of new technologies and solutions, reflected in her extensive patent portfolio and publications in prestigious journals such as IEEE Transactions.

Publications Top Notes

Polyacrylonitrile-based 3D N-rich activated porous carbon synergized with Co-doped MoS2 for promoted electrocatalytic hydrogen evolution (Huang, Z., Li, J., Guo, S., Zeng, J., Yuan, F., Separation and Purification Technology, 2025, 354, 129011) 📄

In-situ construction of nano-multifunctional interlayer to obtain intimate Li/garnet interface for dendrite-free all solid-state battery (Yu, S., Gong, Z., Gao, M., Li, Y., Chen, Y., Journal of Materials Science and Technology, 2025, 206, pp. 248–256) 📄

Advanced cathode materials for metal ion hybrid capacitors: Structure and mechanisms (Li, J., Liu, C., Momen, R., Zou, G., Ji, X., Coordination Chemistry Reviews, 2024, 517, 216018) 📖

Unraveling the delithiation mechanism of air-stabilized fluorinated lithium iron oxide pre-lithiation material (Wen, N., Li, J., Zhu, B., Guo, J., Zhang, Z., Chemical Engineering Journal, 2024, 497, 154536) 📄

Dual ion regulation enables High-Coulombic-efficiency lithium metal batteries (Huang, X., Wang, M., Zhou, Y., Li, J., Lai, Y., Nano Energy, 2024, 129, 110031) 📄

In-Situ Construction of Electronically Insulating and Air-Stable Ionic Conductor Layer on Electrolyte Surface and Grain Boundary to Enable High-Performance Garnet-Type Solid-State Batteries (Zhou, X., Liu, J., Ouyang, Z., Li, J., Jiang, L., Small, 2024, 20(34), 2402086) 📄

Enhancing the Efficient Utilization of Li2S in Lithium-Sulfur Batteries via Functional Additive Diethyldiselenide (Li, Z., Wang, M., Yang, J., Lai, Y., Li, J., Energy and Fuels, 2024, 38(16), pp. 15762–15770) 📄

Emerging polyoxometalate clusters-based redox flow batteries: Performance metrics, application prospects, and development strategies (Han, M., Sun, W., Hu, W., Zhang, C., Li, J., Energy Storage Materials, 2024, 71, 103576) 📖

Conductivity behavior of Na5YSi4O12 and its typical structural analogues by solution-assisted solid-state reaction for solid-state sodium battery (Liu, L., Xu, Y., Zhou, X., Guo, X., Jiang, Y., Journal of Solid State Chemistry, 2024, 336, 124781) 📄

Preparation of Hard-Soft Carbon via Co-Carbonization for the Enhanced Plateau Capacity of Sodium-Ion Batteries (Li, J., Zheng, H., Du, B., Li, D., Chen, Y., Energy and Fuels, 2024, 38(14), pp. 13398–13406) 📄

Conclusion:

Dr. Jie Li’s exceptional achievements in artificial intelligence and machine learning, marked by a robust patent portfolio, significant publications, and leadership in high-impact projects, position her as a strong candidate for the Best Researcher Award. Her innovative contributions and ability to lead and execute complex research projects highlight her outstanding capabilities and potential for furthering advancements in her field. Addressing the areas for improvement could further enhance her already impressive research profile and global impact.