Seok-Woo Jang | Computer Vision | Best Researcher Award

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Prof. Seok-Woo Jang | Computer Vision | Best Researcher Award

Associate Professor at Anyang University South Korea

Dr. Seok-Woo Jang is an Associate Professor in the Department of Software at Anyang University, Korea. With extensive experience in computer science and software engineering, he has contributed significantly to the fields of image processing, artificial intelligence, and human-computer interaction. His research spans biometrics, computer vision, and information security. Over the years, he has actively participated in numerous research projects and published widely in internationally recognized journals. Dr. Jang’s academic journey and professional experience highlight his dedication to advancing technology through innovative research and education.

profile

scopus

Education

Dr. Seok-Woo Jang obtained his Ph.D. in Computer Science from Soongsil University, Seoul, Korea, in 2000. His doctoral dissertation focused on “Shot Transition Detection by Compensating Camera Operations,” showcasing his early expertise in image and video processing. He completed his Master’s degree in Computer Science from the same institution in 1997, researching velocity extraction of moving objects through cluster analysis. His academic foundation was laid with a Bachelor’s degree in Computer Science from Soongsil University in 1995.

Experience

Dr. Jang’s professional career spans over two decades in academia and research. He has been a Professor at Anyang University since 2009, contributing to software education and research. Prior to that, he was a Research Professor at Sungkyunkwan University from 2008 to 2009. His industry and research experience include roles as a Senior Researcher at the Korea Institute of Construction Technology and a Principal Researcher at the Institute of Industrial Technology Research at Soongsil University. He has also conducted post-doctoral research at the University of Massachusetts, Boston, and the University of North Carolina at Charlotte. His teaching experience includes lecturing at Soongsil University and Sungkyul University.

Research Interests

Dr. Jang’s research focuses on multiple domains, including 2D/3D image processing, human-computer interaction, biometrics, information security, and pattern recognition. He is particularly interested in digital video data indexing, computer vision, object tracking, and image surveillance. His work also extends to developing innovative techniques for harmful content detection and deep learning-based solutions in software engineering and AI-driven image analysis.

Awards

Dr. Jang has received numerous awards for his contributions to research and academia. He was awarded the Best Paper Award at the International Conference on Small and Medium Business in 2018 for his work on harmful content extraction using learning algorithms. In 2016, he received the Best Researcher Award at Anyang University. He also won the Best Paper Award at the International Conference on Digital Policy and Management in 2013 for his work on dynamic camera switching. His achievements have been recognized internationally, including being listed in Marquis Who’s Who in the World in 2008.

Publications

Dr. Jang has authored numerous peer-reviewed publications. Some of his notable works include:

“Detection of Ventricular Fibrillation Using Wavelet Transform and Phase Space Reconstruction from ECG Signals” – Journal of Mechanics in Medicine and Biology, 2021.

“Pupil Detection and Gaze Tracking Using a Deformable Template” – Multimedia Tools and Applications, 2020.

“Robust Hand Pose Estimation Using Visual Sensor in IoT Environment” – The Journal of Supercomputing, 2019.

“Harmful Content Detection Based on Cascaded Adaptive Boosting” – Journal of Sensors, 2018.

“A Monitoring Method of Semiconductor Manufacturing Processes Using Internet of Things-based Big Data Analysis” – International Journal of Distributed Sensor Networks, 2017.

“Learning-based Detection of Harmful Data in Mobile Devices” – Mobile Information Systems, 2016.

“An Adaptive Camera-Selection Algorithm to Acquire Higher-Quality Images” – Cluster Computing, 2015.

Conclusion

Dr. Seok-Woo Jang is a highly deserving candidate for the Best Researcher Award. His extensive academic credentials, innovative research projects, influential publications, and numerous awards establish him as a leading researcher in his field. His contributions to computer vision, biometrics, and artificial intelligence continue to push the boundaries of technology, making a lasting impact on both academia and industry.

Prof. Mohammed Almulla | Machine Learning | Best Researcher Award

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Prof. Mohammed Almulla | Machine Learning | Best Researcher Award

VP Academic Affairs, Kuwait University, Kuwait

Professor Mohammed Ali Almulla is a distinguished Kuwaiti academic and researcher. With a career spanning over three decades, he has significantly contributed to the field of computer science, particularly in areas such as web services, emotion recognition, and fuzzy logic techniques. He is currently a Professor at Kuwait University and has held various leadership positions, including Chairman of the Department of Computer Science. His work has garnered recognition through several publications, research awards, and innovations in the realm of machine learning, artificial intelligence, and medical expert systems. Professor Almulla is known for his comprehensive research approach and deep engagement with emerging technologies, bridging academia and industry. His scholarly contributions are frequently cited, underlining his influence within the academic community.

Profile

Education

Professor Almulla completed his B.Sc. (1986), M.Sc. (1990), and Ph.D. (1995) in Computer Science from McGill University, Canada. His Ph.D. thesis, titled “Analysis of the Use of Semantic Trees in Automated Theorem Proving”, laid the foundation for his future research endeavors. With a deep understanding of theoretical and applied computer science, he has focused on machine learning, fuzzy systems, and semantic analysis. His education from McGill University, a globally recognized institution, has helped him build a solid academic foundation. Additionally, he possesses a comprehensive grasp of Arabic and English, enabling him to communicate and collaborate across cultures and academic circles.

Experience

Professor Almulla’s career at Kuwait University started in 1986 when he began as an Instructor. He progressed to Assistant Professor (1995–2006), then Associate Professor (2006–2021), and is currently serving as a Professor (2021–present). He has also been actively involved in departmental administration, having served as Chairman (2015–2020) and Graduate Program Director (2010–2013). Under his leadership, the Department of Computer Science achieved ABET accreditation, an outstanding accomplishment. His role as Acting Chairman in Mathematics and Computer Science in various periods further exemplifies his leadership skills. His dedication to advancing higher education and research has been integral to the development of the computer science field in Kuwait.

Awards and Honors

Professor Almulla’s academic excellence has been recognized through several Incentive Rewards for Unfunded Research in 2014, 2015, and 2017, with impactful papers published in journals such as Knowledge-Based Systems. His work on service trust behaviors, web services ranking, and fuzzy techniques has earned him significant recognition. He was also honored with Distinctive Teaching Awards in both the College of Computer Science and Engineering (2011/2012) and the Faculty of Science (2001/2002). These awards underscore his excellence in teaching, his commitment to innovative research, and his positive impact on student education.

Research Focus

Professor Almulla’s research focuses on a wide array of cutting-edge topics in computer science. Key areas of expertise include machine learning, fuzzy systems, service trust behaviors, and medical expert systems. His recent work explores emotion recognition systems, federated learning, and web services ranking. In addition, he has contributed to advancements in semantic similarity, automated theorem proving, and healthcare applications. With an eye toward the future, his research continues to bridge the gap between theoretical models and real-world applications, particularly in healthcare and artificial intelligence.

Publication top Notes

  • A Trust-based Global Expert System for Disease Diagnosis Using Hierarchical Federated Learning 🏥🤖
  • A Novel CLIPS-based Medical Expert System for Migraine Diagnosis and Treatment Recommendation 💡🧠
  • On the Effect of Prior Knowledge in Text-Based Emotion Recognition 🧠💬
  • A Multimodal Emotion Recognition System Using Deep Convolution Neural Networks 🖥️🔍
  • Location-based Expert System for Diabetes Diagnosis and Medication Recommendation 🏥💊
  • Measuring Semantic Similarity between Services Using Hypergraphs 🧠🌐
  • Specification and Recognition of Service Trust Behaviors 💻✅
  • Next-Generation Sequencing in Familial Breast Cancer Patients from Lebanon 🧬🎗️
  • A New Framework for the Verification of Service Trust Behaviors 🛡️💡
  • GeoCover: An Efficient Sparse Coverage Protocol for RSU Deployment over Urban VANETs 🚗🌍
  • A New Fuzzy Hybrid Technique for Ranking Real World Web Services 🌐🔍

 

 

 

Mahad Rashid | Machine Learning | Best Researcher Award

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Mr Mahad Rashid | Machine Learning | Best Researcher Award

Senior Analytics Consultant at WorkSafe VIC, Australia

Mahad Rashid is an enthusiastic and skilled Analytics Consultant with a Master’s degree in Data Science from Deakin University. With over six years of experience in data analysis, machine learning, and software engineering, Mahad has a proven track record of developing data-driven solutions to enhance business operations and customer experiences. Proficient in Python, SAS, SQL, and data visualization tools like Power BI and Qlik, he has worked in diverse roles, including Senior Analytics Consultant at WorkSafe Victoria and Research Engineer at the Applied Artificial Intelligence Institute. Mahad is passionate about leveraging data to drive innovation and deliver impactful results. His expertise spans machine learning, deep learning, and statistical analysis, with a strong focus on MLOps and AI deployment. Mahad is also an effective communicator, capable of translating complex technical concepts for non-technical stakeholders.

Professional Profile

Scopus

Education 🎓

Mahad Rashid holds a Master of Data Science from Deakin University, Burwood (2019–2021), where he honed his skills in data analysis, machine learning, and AI. Prior to this, he completed his Bachelor of Software Engineering from the National University of Sciences and Technology, Pakistan (2014–2018), gaining a strong foundation in programming, software development, and data structures. Throughout his academic journey, Mahad demonstrated a keen interest in applying data science to solve real-world problems. He has also pursued additional certifications, including Getting Started with SAS Programming and Git and GitHub on Coursera (2024), showcasing his commitment to continuous learning and professional development.

Experience 💼

Mahad Rashid has a rich professional background spanning over six years. As a Senior Analytics Consultant at WorkSafe Victoria (2023–Present), he excels in data extraction, cleaning, and machine learning using Python and SQL. He also mentors junior consultants and creates insightful dashboards using Power BI. Previously, as a Research Engineer at the Applied Artificial Intelligence Institute, Deakin (2021–2023), he developed and deployed ML models, applied MLOps principles, and collaborated on AI research. Earlier, as a Software Engineer at CureMD, Pakistan (2018–2019), he designed machine learning applications and advanced data visualizations. Mahad’s expertise lies in leveraging data to drive innovation, improve decision-making, and deliver impactful solutions across industries.

Awards and Honors 🏆

While specific awards and honors are not explicitly mentioned in the provided profile, Mahad Rashid’s contributions to data science and AI research are evident through his publications and professional achievements. His work on high-voltage lithium cathode materials, published in ACS Applied Energy Materials (2024), highlights his involvement in cutting-edge research. Additionally, his role in mentoring junior consultants and leading analytics projects at WorkSafe Victoria underscores his recognition as a skilled and reliable professional. Mahad’s commitment to continuous learning, evidenced by his certifications in SAS and Git, further reflects his dedication to excellence in the field of data science.

Research Focus 🔍

Mahad Rashid’s research focuses on applying machine learning, deep learning, and AI to solve complex problems across various domains. His work includes developing and deploying ML models for classification, regression, and image detection, as well as applying MLOps principles to streamline data science workflows. He has also contributed to research on high-voltage lithium cathode materials using Bayesian optimization and first-principles studies, showcasing his interdisciplinary expertise. Mahad is passionate about leveraging data-driven approaches to improve business operations, enhance customer experiences, and drive innovation. His research interests extend to data visualization, statistical analysis, and AI-driven decision-making, with a strong emphasis on delivering practical, impactful solutions.

Publication Top Notes 📚

  1. High-Voltage, High Capacity Aluminum-Rich Lithium Cathode Materials: A Bayesian Optimization and First-Principles Study – ACS Applied Energy Materials, 2024.

Conclusion 🌟

Mahad Rashid is a highly skilled and passionate data science professional with a strong academic background and extensive industry experience. His expertise in data analysis, machine learning, and AI, combined with his ability to communicate complex ideas effectively, makes him a valuable asset to any organization. Mahad’s commitment to innovation, continuous learning, and delivering impactful results positions him as a leader in the field of data science and analytics.

 

Duaa Mehiar | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr Duaa Mehiar | Artificial Intelligence | Best Researcher Award

Assistant Professor, Middle East University, Jordan

Duaa Mehiar is an expert in Artificial Intelligence and Robotics, currently part of the Department of IT at Middle East University. She has a strong background in developing intelligent robotic systems, optimizing robotic swarm behaviors, and exploring AI applications in education and healthcare. Duaa is passionate about the intersection of technology and practical solutions, particularly in enhancing robot autonomy and improving human-robot interactions. She has contributed significantly to research in robotic optimization and AI systems, with numerous publications in international journals.

Professional Profile

Google Scholar

Orcid

Scopus

Strengths for the Award

Duaa Mehiar is an accomplished researcher in the fields of Artificial Intelligence (AI) and Robotics. With a PhD in AI from the University of Malaya, her extensive academic background, demonstrated by her impactful research on optimization methods in robot swarm behaviors, makes her a prime candidate for the Best Researcher Award. Her published works in high-impact journals and international conferences, including contributions to cloud-based frameworks for social robotics and AI-based systems for education, have received recognition in academia. Duaa’s research on dynamic task distribution in drone swarms and deep fake image detection also highlights her broad expertise in both practical applications and cutting-edge AI research.

Areas for Improvement

Although Duaa has made significant strides in her research, further collaboration with interdisciplinary teams in the fields of neuroscience and psychology could enhance the human-robot interaction aspects of her work, especially with virtual agents. Expanding the scope of her research to explore more industry-based applications of her findings, particularly in education and healthcare, would also make her work more impactful. Additionally, more emphasis on real-world robotic deployments could demonstrate her research’s practical outcomes.

Education

  • Ph.D. in Artificial Intelligence – University of Malaya, 2016–2021
    Thesis: Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory for Robot Exploration and Communication
  • MSc in Computer Science – Al Balqa Applied University, Jordan, 2007–2009
    Thesis: The Multi-Robot Cooperative System for Objects Detection
  • B.Sc. in Computer Science – Al Zaytoona University, Jordan, 1998–2002
    Graduation Project: Production System for Oriental Arabs using Oracle Language

Experience

Duaa Mehiar has a wealth of experience in the field of robotics and AI. She has trained teachers and school supervisors on integrating AI into education, including using metaverse and robots. Duaa was involved in numerous training programs with institutions like EduTech and Ishraq Institute. She has contributed to robotic research and development, from swarm optimization algorithms to practical applications like controlling robots using various protocols. Her expertise includes IoT projects and robot building using Arduino technology.

Awards and Honors

Duaa has received multiple honors for her work in robotics and AI. She was nominated as a trainer for the Ministry of Education in Dubai and Ras Al Khaima for teaching robotic theory and practice. She has also been part of the Arab Robotics Association and has served as a judge for the Arab Robotics Competition. Her research in AI and robotics has been recognized by international platforms, earning her citations and respect within the scientific community.

Research Focus

Duaa’s research primarily focuses on AI and robotics, particularly in optimization algorithms for swarm robots, human-robot interaction, and AI applications in education and healthcare. She has developed quantum-behaved swarm optimization methods for robot exploration and communication. Her interests include improving robotic autonomy, integrating robots in educational environments, and exploring AI systems in healthcare, such as asthma management and rehabilitation. She is dedicated to advancing AI technologies to benefit society.

Publication Top Notes

  • Revolutionizing Social Robotics: A Cloud-Based Framework for Enhancing the Intelligence and Autonomy of Social Robots 🤖
  • Towards Renewable Urban Landscapes: Exploring Photovoltaic Panel Integration – A Case Study 🌍
  • Chatbots in Classrooms: Tailoring Education and Boosting Engagement 🎓
  • QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments 🤖
  • Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections 🎥
  • Report on Optimization for Efficient Dynamic Task Distribution in Drone Swarms Using QRDPSO Algorithm 🚁
  • Linguistic and Gender Factors in User Engagement with Arabic LLM-Based Virtual Agents for Rehabilitation 🧑‍💻
  • Real-Time Student Attention Evaluation and Engagement Recommendation System Using AI-Based Behavior Analysis 📚
  • Personalized Alarming System for Asthma Management Based on Lung Functionality 🫁
  • Reducing Interrupts Among Robots in Quantum-Behaved Swarm Exploration with MR-LEACH 🤖
  • Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory for Robot Exploration and Communication 🔍
  • Multi-Robot Cooperative System for Object Detection 🤖
  • QRDPSO Equation: A New Optimization Method for Swarm Robot 🏎
  • Multi-Agent Cooperative System for Object Detection 🛠
  • Multi-Robot System for Search and Rescue Operations 🚑

Conclusion

Duaa Mehiar stands out for her contributions to the fields of AI and Robotics, with a focus on innovative solutions to enhance autonomous robotic systems and optimize AI-driven tasks. She demonstrates strong academic potential, with a portfolio of research that is not only technically sound but also socially relevant, particularly in education and healthcare. For the Best Researcher Award, her continued growth in interdisciplinary collaboration and real-world applications would solidify her place as a leading researcher in her field.

Prasad Mutkule | Machine Learning | Best Researcher Award

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Mr Prasad Mutkule | Machine Learning | Best Researcher Award

Assistant Professor, Sanjivani College of Engineering, Kopargaon, India

Prasad Mutkule is an accomplished academic and researcher serving as an Assistant Professor at Sanjivani College of Engineering, Kopargaon. He holds expertise in Machine Learning, Data Science, and Artificial Intelligence, contributing significantly to academia and industry. His work spans developing advanced algorithms for healthcare and agriculture, with a strong emphasis on practical applications. Prasad is known for his dedication to research and education, mentoring students and fostering innovation.

PROFILE

Google Scholar

Orcid

Scopus

STRENGTHS FOR THE AWARD

  1. ACADEMIC BACKGROUND:
    • Advanced academic qualifications, including a pursuing Ph.D. in Computer Engineering, focusing on cutting-edge research.
    • High academic performance in Master’s and Bachelor’s programs, showcasing a strong foundation in computer engineering.
  2. RESEARCH CONTRIBUTIONS:
    • A significant number of publications in reputed journals and conferences.
    • Research topics include brain tumor segmentation, agriculture disease detection, predictive analytics in healthcare, and integration of AI/ML for societal applications like cybersecurity and smart farming.
    • Contributions are well-cited, with notable impact in the field of computational intelligence and applied machine learning.
  3. PROFESSIONAL EXPERIENCE:
    • Diverse professional background encompassing academia and industry, with over 6 years in teaching and 1 year in Android development.
    • Current role as an Assistant Professor at a reputed autonomous institution emphasizes leadership in research and teaching.
  4. MULTIDISCIPLINARY IMPACT:
    • Research spans critical areas such as healthcare, agriculture, and traffic prediction, reflecting versatility.
    • Innovative projects like IoT-based interactive clothing and AI-based health monitoring systems underscore practical applications of research.
  5. TEAM COLLABORATION:
    • Collaboration with various researchers and authors in multiple publications, indicating strong teamwork and networking abilities.

AREAS FOR IMPROVEMENT

  1. ONGOING RESEARCH FOCUS:
    • Completing the Ph.D. will further solidify expertise and enhance research credentials.
    • Exploring patent filings or practical implementations of research outcomes could amplify real-world impact.
  2. INCREASED GLOBAL PRESENCE:
    • Expanding participation in international conferences or collaborations could increase visibility on a global scale.
  3. FOCUSED SPECIALIZATION:
    • While multidisciplinary research is a strength, deeper specialization in one or two niches can establish leadership in specific domains.
  4. FUNDING AND GRANTS:
    • Securing funded projects or grants for research would demonstrate recognition and support for work.

EDUCATION

  • Ph.D. (Computer Engineering) – Pursuing at Vishwakarma Institute of Information Technology, Pune, affiliated with Savitribai Phule Pune University (2023–ongoing).
  • M.E. (Computer Engineering) – Completed at Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, with a CGPA of 8.25 (2016–2018).
  • B.E. (Computer Engineering) – Graduated from Sanjivani College of Engineering, Kopargaon, under Savitribai Phule Pune University, achieving 68.46% (2012–2016).

EXPERIENCE

  • Assistant Professor, Sanjivani College of Engineering (2021–present).
  • Assistant Professor, Shri Chatrapati Shivaji Maharaj College of Engineering, Ahmednagar (2021).
  • Assistant Professor, Adsul’s Technical Campus, Ahmednagar (2019–2021).
  • Lecturer, S.P.I.T. Polytechnic, Ahmednagar (2017–2019).
  • Android Developer, Advitiya IoT Solutions, Pune (2016–2017).

AWARDS AND HONORS

  • Recognized for impactful research contributions in Machine Learning and Artificial Intelligence.
  • Awarded for excellence in teaching and academic service.
  • Published influential papers in top-tier journals.
  • Honored as a mentor for guiding student innovations in computing and IoT.
  • Esteemed presenter at international conferences.

RESEARCH FOCUS

Prasad Mutkule focuses on developing intelligent systems using Machine Learning and Data Science. His areas of interest include healthcare diagnostics, agricultural optimization, and IoT applications. He aims to bridge the gap between academia and industry through innovative solutions in Artificial Intelligence and its transformative applications.

PUBLICATION TOP

📘 Development of machine learning and medical-enabled multimodal for brain tumor classification.
📗 Manipulation of flowering time to mitigate high temperature stress in rice.
📘 Identification of disease based on symptoms using ML.
📗 Efficient supervised learning algorithm for kidney stone prediction.
📘 One-stop solution for farmer-consumer interaction.
📗 Interactive clothing based on IoT with QR codes.
📘 A survey on interactive IoT-based clothing applications.
📗 ML algorithms for agricultural leaf disease detection.
📘 Predictive analytics for early brain tumor prevention using XAI.
📗 Applicability of AI in healthcare, banking, and education.

CONCLUSION

Prasad Mutkule has a strong academic and professional portfolio that demonstrates his expertise in machine learning, artificial intelligence, and their applications in healthcare and beyond. His research contributions are impactful, multidisciplinary, and address real-world challenges. With continued focus on specialization and global engagement, he is an excellent candidate for the Best Researcher Award.

Bin Yang – AI for Everything – Best Researcher Award

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Bin Yang - AI for Everything - Best Researcher Award

Chongqing University of Posts and Telecommunications - China

AUTHOR PROFILE

SCOPUS

ORCID

🧑‍🏫 ACADEMIC BACKGROUND AND RESEARCH PASSION

Dr. Bin Yang, also known as Sean Bin Yang, is an Assistant Professor at Chongqing University of Posts and Telecommunications. With a deep passion for leveraging big data and artificial intelligence (AI) to address urban challenges, he has been making significant contributions to the field. He is also a member of the Chongqing Key Laboratory of Image Cognition, working closely with Prof. Xinbo Gao.

🎓 EDUCATION AND GLOBAL COLLABORATIONS

Dr. Yang obtained his Ph.D. in Computer Science from Aalborg University in 2022, under the guidance of Prof. Bin Yang and Associate Prof. Jilin Hu. During his doctoral studies, he collaborated with renowned researchers at the Center for Data-Intensive Systems (Daisy) and the Machine Learning Group. He also spent time at the Mila-Quebec AI Institute in Canada, working with Associate Prof. Jian Tang.

📚 PROLIFIC PUBLICATION RECORD

Dr. Yang has authored more than 20 peer-reviewed publications in prestigious international journals and conferences, including KDD, ICML, and TKDE. His work, such as the development of lightweight path representation models, has gathered over 452 citations, with an h-index of 13. His innovative research in data mining, machine learning, and AI continues to push the boundaries of knowledge in these fields.

💡 INNOVATIVE PATENTS AND TECHNOLOGY APPLICATIONS

Dr. Yang's commitment to practical applications of his research is demonstrated by his filing of over 10 patents in China. These patents reflect his dedication to advancing technology through innovation, particularly in the fields of AI-driven solutions for urban and transportation challenges.

🎓 SUPERVISION AND MENTORSHIP

As a dedicated mentor, Dr. Yang has supervised numerous student research projects, including those on construction waste management through AI techniques. His guidance has led to the publication of impactful research articles, helping his students make meaningful contributions to the field of artificial intelligence and urban problem-solving.

🔬 RESEARCH IN AI AND URBAN CHALLENGES

Dr. Yang's research focuses on using AI to tackle complex urban issues, such as waste management, transportation optimization, and infrastructure development. His work in path representation learning, unsupervised learning, and predictive autoscaling has significantly contributed to the advancement of smart city technologies.

🏅 CONFERENCE AND JOURNAL INVOLVEMENT

Dr. Yang is an active member of the research community, serving as a Program Committee member for top conferences like ICML, KDD, and IJCAI. His expertise is frequently sought as a reviewer for leading journals such as IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Intelligent Transportation Systems, highlighting his influence in the AI and big data research domains.

NOTABLE PUBLICATION

Title:Extended-state-observer-based double-loop integral sliding-mode control of electronic throttle valve
Authors: Y. Li, B. Yang, T. Zheng, Y. Li, M. Cui, S. Peeta
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2015

Title: Unsupervised path representation learning with curriculum negative sampling
Authors: S.B. Yang, C. Guo, J. Hu, J. Tang, B. Yang
Journal: arXiv preprint arXiv:2106.09373

Title: Context-aware path ranking in road networks
Authors: S.B. Yang, C. Guo, B. Yang
Journal: IEEE Transactions on Knowledge and Data Engineering
Year: 2020

Title: Luenberger-sliding mode observer based fuzzy double loop integral sliding mode controller for electronic throttle valve
Authors: B. Yang, M. Liu, H. Kim, X. Cui
Journal: Journal of Process Control
Year: 2018

Title: An extended continuum model incorporating the electronic throttle dynamics for traffic flow
Authors: Y. Li, H. Yang, B. Yang, T. Zheng, C. Zhang
Journal: Nonlinear Dynamics
Year: 2018

Jiaming Zhong – Artificial intelligence – Best Researcher Award

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Jiaming Zhong - Artificial intelligence - Best Researcher Award

Wuyi university - China

AUTHOR PROFILE

SCOPUS

📚 SCIENTIFIC RESEARCH ACHIEVEMENTS

Jiaming Zhong has made significant contributions to the fields of video classification and tactile sensing. His groundbreaking papers include "Exploring Cross-video Matching for Few-shot Video Classification via Dual-Hierarchy Graph Neural Network Learning," published in Image and Vision Computing, and "Text-guided Graph Temporal Modeling for Few-Shot Video Classification," featured in Engineering Applications of Artificial Intelligence. These studies, published in top-tier journals, highlight Zhong's innovative approaches in utilizing graph neural networks and multimodal models for advanced video analysis and classification.

🛠️ PATENTS AND TECHNOLOGICAL INNOVATIONS

Zhong holds several patents that showcase his expertise in developing practical solutions for various technological challenges. His patents include methods for video anomaly classification, chip defect detection, and mobile robot obstacle avoidance. These patents reflect his commitment to translating theoretical research into tangible technological advancements that address real-world problems.

🔬 PROJECT EXPERIENCE: PEEL RECOGNITION

In a project focused on the precise identification of Chenpi years using a multimodal model, Zhong's work involved designing lightweight modules and fine-tuning models to achieve high recognition accuracy. His use of the CLIP multimodal model for feature extraction led to a remarkable 99% accuracy in recognizing Chenpi years with limited sample data. This project, detailed on GitHub, demonstrates his proficiency in applying advanced machine learning techniques to practical problems.

🎥 PROJECT EXPERIENCE: FEW-SHOT VIDEO CLASSIFICATION

Zhong's research in video behavior classification involved addressing challenges related to data scarcity and model capabilities. Collaborating with Macau University of Science and Technology and Wuyi University, he developed a dual-hierarchy graph neural network that significantly improved classification performance through cross-video frame matching. This innovative approach was published in Image and Vision Computing and showcased Zhong's ability to enhance model performance through sophisticated temporal modeling.

🔍 PROJECT EXPERIENCE: MULTIMODAL REPRESENTATION LEARNING

In a project focused on multimodal video behavior analysis, Zhong led efforts to develop a novel framework for self-supervised learning using multimodal data. This project, supported by a 500,000 RMB research grant, involved developing a text-guided feature optimization module and a query text token learning mechanism. His research aimed to leverage multimodal knowledge to improve the classification performance of few-shot video behaviors, with results published in top journals.

📈 IMPACTFUL RESEARCH AND PUBLICATIONS

Zhong's work has significantly impacted the fields of video classification and sensor technology. His papers in renowned journals and his patents contribute to advancing the understanding and application of these technologies. His research not only addresses current challenges but also paves the way for future innovations in these areas.

🏆 ACKNOWLEDGEMENTS AND RECOGNITION

Zhong's contributions to scientific research and technology have earned him recognition within the academic and professional communities. His innovative work in video classification and sensor technology continues to influence the field and inspire further research and development.

NOTABLE PUBLICATION

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment
Authors: K. Ma, D. Su, B. Qin, Y. Xin, X. He
Year: 2024
Journal: Chemical Engineering Journal

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7
Authors: F. Deng, J. Chen, L. Fu, J. Li, N. Li
Year: 2024
Journal: Frontiers in Plant Science

Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning
Authors: F. Deng, J. Zhong, N. Li, D. Wang, T.L. Lam
Year: 2023
Journal: Image and Vision Computing

Senbagavalli – Artificial Intelligence – Best Researcher Award

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Senbagavalli - Artificial Intelligence - Best Researcher Award

Alliance University - India

AUTHOR PROFILE

SCOPUS

EXPERT IN OPINION MINING AND FEATURE SELECTION

Senbagavalli's groundbreaking research in opinion mining of health data for cardiovascular disease diagnosis using an unsupervised feature selection algorithm spans five years. Her Ph.D. work is a testament to her dedication to leveraging data for medical advancements.

FACIAL RECOGNITION INNOVATOR

With a master's degree in engineering, Senbagavalli developed a face recognition system using Laplacian faces, showcasing her expertise in computer vision and pattern recognition. This project exemplified her ability to apply complex algorithms to practical applications within six months.

PIONEER IN UNICODE FILE SYSTEMS

During her undergraduate studies, Senbagavalli created a file system using the Unicode character set, a project completed in just six months. Her work in this area highlights her proficiency in software development and system design.

CREATOR OF GRAPHIC GAMING SYSTEMS

In her mini-project as an undergraduate, she developed a gaming system using graphics within three months. This early project laid the foundation for her interest in interactive and visual computing systems.

SEASONED ACADEMIC AND PROFESSOR

With 18 years and 7 months of teaching experience, Senbagavalli has held positions at prestigious institutions, including Alliance University and Kuppam Engineering College. Her extensive experience has made her a respected figure in the academic community.

VERSATILE SUBJECT EXPERT

Senbagavalli has taught a wide range of subjects to undergraduate, postgraduate, and Ph.D. students, including Data Modeling and Optimization, Object-Oriented Programming, and Software Engineering. Her comprehensive knowledge spans multiple domains of computer science.

ACTIVE RESEARCHER AND REVIEWER

An active member of various academic councils and editorial boards, Senbagavalli reviews for renowned publishers like Bentham Science and Elsevier. Her involvement in curriculum development, project evaluation, and seminar organization reflects her commitment to academic excellence and continuous learning.

NOTABLE PUBLICATION

Identification of Biomarker for Autism Spectrum Disorder Using EEG: A Review.
Authors: K. Lalli, M. Senbagavalli
Year: 2023
Conference: Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, pp. 45–50

Facemask Detection System Using CNN Model.
Authors: M. Senbagavalli, S. Debnath, R. Rajagopal, K. Ghildial
Year: 2023
Conference: International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

An Evaluation of Machine Learning Techniques for Detecting Banking Frauds.
Authors: R. Rajagopal, M. Senbagavalli, S. Debnath, K. Darshan, K.S. Varun Tejas
Year: 2023
Conference: International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 359–365

Deep Learning Model for Flood Estimate and Relief Management System Using Hybrid Algorithm.
Authors: M. Senbagavalli, V. Sathiyamoorthi, S.K. Manju Bargavi, S. Shekarappa G., T. Jesudas
Year: 2023
Book: Artificial Intelligence and Machine Learning in Smart City Planning, pp. 29–44

An Effective Model for Predicting Agricultural Crop Yield on Remote Sensing Hyper-Spectral Images Using Adaptive Logistic Regression Classifier.
Authors: V. Sathiyamoorthi, P. Harshavardhanan, H. Azath, A.M. Viswa Bharathy, B.S. Chokkalingam
Year: 2022
Journal: Concurrency and Computation: Practice and Experience, 34(25), e7242

Fangyu Wu – Artificial Intelligence – Best Researcher Award

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Fangyu Wu - Artificial Intelligence - Best Researcher Award

AUTHOR PROFILE

SCOPUS

ACADEMIC AND PROFESSIONAL BACKGROUND

Fangyu Wu is a distinguished researcher and academic in the field of computer science, specializing in deep learning, multi-modal learning, and intelligent data analysis. He is currently an Associate Professor at Xi’an Jiaotong-Liverpool University (XJTLU) in China, where he supervises PhD and Master's students focusing on innovative research topics such as multi-modal learning and deep learning for computer vision. His previous role included co-supervising PhD students at Zhejiang University, contributing to advancements in facial recognition and image-text retrieval.

HONORS AND AWARDS

Dr. Wu's achievements have been recognized through several prestigious awards. He was named a Suzhou Youth Innovation Leading Talent in 2023 and won first prize at the 7th China Innovation Challenge for his project on intelligent tracking systems using infrared thermal imaging. Additionally, he received the Lotfi Zadeh Best Paper Award at ICMLC&ICWAPR 2017 and has been honored with the Outstanding Graduates award from Xi’an Jiaotong-Liverpool University and National Encouragement Scholarships from China.

RESEARCH PROJECTS

Fangyu Wu leads several high-impact research projects. These include “Intelligent Multimodal Data Analysis for Digital Twin Cities” under the Gusu Innovation and Entrepreneurship Leading Talents Programme, and “Relational Modeling and Reasoning for Reliable Cross-Modal Retrieval” funded by the Zhejiang Natural Science Foundation. His projects also cover advanced topics such as distributed AI platforms for Metaverse scenarios and optimization software for injection molding processes.

PUBLICATIONS

Dr. Wu has an extensive list of publications in top-tier conferences and journals. Notable works include papers on fine-grained image-text matching, relation-aware prototype networks, and pose-robust face recognition. His research has been featured at prestigious conferences such as CVPR, ECCV, and ICPR, showcasing his contributions to advancements in deep learning and computer vision.

CONFERENCE ORGANIZATION

In addition to his research, Fangyu Wu plays a vital role in organizing academic conferences. He served as the Publication Chair for the IEEE 17th International Conference on Computer Science & Education (ICCSE 2022) and as General Co-Chair for the 5th International Symposium on Emerging Technologies for Education (SETE 2020). His involvement ensures the smooth execution of these events and contributes to the dissemination of cutting-edge research.

STUDENT SUPERVISION

Fangyu Wu is actively engaged in supervising students at both the PhD and Master’s levels. He currently supervises a PhD student at XJTLU focusing on multi-modal learning and has previously co-supervised a PhD student at Zhejiang University on deep learning for computer vision. His mentorship extends to six Master’s students at XJTLU and three at Zhejiang University, covering areas such as facial recognition and image-text retrieval.

COMPETITIONS AND RECOGNITION

Dr. Wu has achieved notable success in various competitions. His project on human motion recognition based on deep neural networks won third prize at the China First Smart Manufacturing and Big Data Innovation Competition. Additionally, his participation in competitions has been marked by significant awards, including the first prize in the China Innovation Challenge for his intelligent tracking system.

NOTABLE PUBLICATION

  • Fine-grained Image-text Matching by Cross-modal Hard Aligning Network
    • Authors: Pan, Z., Wu, F., Zhang, B.
    • Year: 2023
    • Conference: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
    • Pages: 19275–19284
  • Knowledge-embedded Prompt Learning for Zero-shot Social Media Text Classification
    • Authors: Li, J., Chen, Q., Wang, W., Wu, F.
    • Year: 2023
    • Conference: IEEE International Conference on Smart Computing (SMARTCOMP)
    • Pages: 222–224
  • Kernel Triplet Loss for Image-Text Retrieval
    • Authors: Pan, Z., Wu, F., Zhang, B.
    • Year: 2022
    • Conference: Computer Animation and Virtual Worlds
    • Article: e2093
  • FaceCaps for Facial Expression Recognition
    • Authors: Wu, F., Pang, C., Zhang, B.
    • Year: 2021
    • Conference: Computer Animation and Virtual Worlds
    • Article: e2021

Lili Nurliyana Abdullah – computer technology – Best Researcher Award

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Lili Nurliyana Abdullah - computer technology - Best Researcher Award

UPM - Malaysia

AUTHOR PROFILE

Scopus

EARLY ACADEMIC PURSUITS

Lili Nurliyana Abdullah's academic journey began with a Diploma in Computer Science from Universiti Pertanian Malaysia in 1990. She went on to earn a Bachelor's degree in Computer Science from Universiti Putra Malaysia in 1992, graduating with a 2nd Class Upper division. Her pursuit of higher education continued in the UK, where she obtained a Master in Engineering with a specialization in Telematics from the University of Sheffield in 1996. Lili's academic endeavors culminated in a Ph.D. in Information Science from the National University of Malaysia in 2007.

PROFESSIONAL ENDEAVORS

Lili's professional career spans various prestigious roles and institutions. She started as a tutor in Computer Science at UPM in 1993 and progressed to become a lecturer and then an Associate Professor in the Multimedia Department. She also served as the Head of the Multimedia Department at UPM from 2009 to 2012. Beyond academia, she worked as a Project Consultant at Catapult Studios and held teaching attachments and training roles at several universities, including Univ Taibah in Saudi Arabia and L.N. Gumilyov Eurasian National University in Kazakhstan.

CONTRIBUTIONS AND RESEARCH FOCUS

Lili Nurliyana Abdullah's research primarily focuses on computer technology and its applications. Her expertise in multimedia, digital content, and green computing has led to significant contributions in these fields. She has been actively involved in curriculum design, project consultancy, and professional development training, particularly in the areas of multimedia and STEM. Lili's dedication to green computing is evident through her certifications and her role in the Malaysian Communications and Multimedia Commission's Green IT initiatives.

IMPACT AND INFLUENCE

Lili's work has had a profound impact on both academic and professional communities. She has been recognized with numerous awards, including the Lifetime Achievement Award from the International Scientist Awards on Engineering, Science, and Medicine in 2022, and the Outstanding Educator Award from Lattice Science Publication in 2021. Her contributions to computer technology and green computing have influenced policy making, curriculum development, and environmental sustainability in IT practices.

ACADEMIC CITES

Lili's scholarly work is highly regarded, with numerous citations reflecting her influence in the academic community. Her research has been widely published and cited, contributing significantly to the fields of computer technology, green computing, and multimedia applications. She has also served as a peer reviewer and editorial board member for several esteemed journals and conferences, further establishing her reputation as a leading expert in her field.

LEGACY AND FUTURE CONTRIBUTIONS

Lili Nurliyana Abdullah's legacy lies in her pioneering contributions to computer technology and green computing. Her work has laid the foundation for future research and development in sustainable IT practices and multimedia technology. As she continues to innovate and collaborate, Lili's future contributions are expected to further advance these fields, fostering a more sustainable and technologically advanced society.

COMPUTER TECHNOLOGY

Throughout her career, Lili Nurliyana Abdullah has focused on advancing computer technology through her research, teaching, and professional endeavors. Her work on digital content and multimedia applications has enhanced the educational landscape, while her commitment to green computing has promoted sustainable IT practices. Lili's dedication to computer technology is reflected in her extensive professional engagements, certifications, and contributions to policy making and curriculum development in the field. As a recognized leader in computer technology, she continues to inspire and influence future generations of researchers and practitioners.

NOTABLE PUBLICATION

Students’ satisfaction levels of an immersive extended reality for engine assembly tasks in engineering empirical education 2023

Deep learning mango fruits recognition based on tensorflow lite 2023

Playing Gamelan Bonang in the Air: User Requirements for Designing a Digital Musical Instrument for the Malay Bonang 2022 (3)

Comparison of Edge Detection Algorithms for Texture Analysis on Copy-Move Forgery Detection Images 2022 (2)