Lili Abdullah | Trafiic Analysis and Prediction | Best Researcher Award

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Assoc. Prof. Dr Lili Abdullah | Trafiic Analysis and Prediction | Best Researcher Award

Lecturer, Universiti Putra Malaysia, Malaysia

Prof. Dr. L.N. Abdullah is a distinguished academic and researcher at Universiti Putra Malaysia (UPM), specializing in Information Systems, Telematics, and Multimedia. He has extensive international experience, serving as a foreign professor and advisor at L.N. Gumilyov Eurasian National University, Kazakhstan. Dr. Abdullah is known for his contributions to academia, including leadership roles, notably as the Head of the Department of Multimedia at UPM. He also consults on various projects, including in the tech and media sectors, and collaborates with international institutions. His expertise extends to areas such as traffic safety, extended reality, and machine learning in multimedia.

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Strengths for the Award

Dr. L.N. Abdullah is a highly accomplished academic and researcher with extensive experience in Information Systems, Telematics, and Multimedia. His strengths include a robust academic background, with a PhD in Information Systems from Universiti Kebangsaan Malaysia, and a Master’s degree from the University of Sheffield. His diverse international experience as a foreign professor and advisor at institutions like L.N. Gumilyov Eurasian National University, Kazakhstan, and teaching attachments at the University of Taibah, Saudi Arabia, reflects his strong global academic influence.

Dr. Abdullah’s research focuses on crucial areas such as traffic safety, machine learning, extended reality, and multimedia applications. His work on traffic accident risk prediction, deep learning for image recognition, and innovative systems for engineering education demonstrates his ability to apply cutting-edge technologies to solve real-world problems. His interdisciplinary approach, combining technology with education, health, and public safety, makes him a standout candidate.

Areas for Improvements

Although Dr. Abdullah’s research has had a notable impact, there are areas for improvement that could enhance his contributions even further. These include:

  1. Collaboration with Industry: While his research in academia is robust, increasing collaborations with industry leaders and companies can foster the real-world implementation of his work, particularly in areas like traffic safety technology and extended reality applications.
  2. Increased Focus on Sustainable Technologies: Integrating sustainability into his research, especially with the growing need for eco-friendly technological solutions, could open up new interdisciplinary research opportunities.
  3. Broader Dissemination: Expanding his research publications to a more diverse set of journals and presenting his work in more global conferences will increase his research’s visibility and impact.

Education

Dr. L.N. Abdullah holds a PhD in Information Systems from Universiti Kebangsaan Malaysia (2007), a Master of Engineering (Telematics) from the University of Sheffield (1996), and a Bachelor’s degree in Computer Science from Universiti Pertanian Malaysia (1992). His academic journey has provided a strong foundation for his career in both teaching and research, influencing developments in the field of information technology and multimedia systems. His education spans across diverse disciplines, which allow him to contribute to several interdisciplinary research areas.

Experience

Dr. Abdullah’s career spans over three decades, beginning as a tutor at Universiti Pertanian Malaysia (UPM) from 1993-1996, followed by a faculty position as a lecturer until 2009. He became an Associate Professor in 2009, and later served as Head of the Department of Multimedia from 2009 to 2012. His leadership roles have been complemented by international teaching attachments at the University of Taibah in Saudi Arabia and consultancy at Catapult Studios. Since 2022, he has been a foreign professor and advisor at L.N. Gumilyov Eurasian National University, contributing significantly to global academia.

Awards and Honors

Dr. Abdullah’s contributions to academia have been recognized through numerous accolades. He has made significant contributions to research and teaching, particularly in the areas of Information Systems, Telematics, and Multimedia. His career is highlighted by his international collaborations, leadership in academic departments, and recognition in various scholarly circles. Additionally, he has received multiple awards for his role in advancing technology education, including recognition in the fields of extended reality, machine learning, and traffic safety.

Research Focus

Dr. Abdullah’s research primarily focuses on Information Systems, Telematics, and Multimedia. His work involves traffic safety and accident prediction models, augmented reality, and machine learning for multimedia applications. He has also contributed to advancements in deep learning for image recognition and multimedia data analysis. His interdisciplinary approach connects technology with practical applications in education, health, and engineering. Dr. Abdullah’s recent projects focus on developing predictive models and innovative systems for enhancing public safety and improving education through immersive technologies.

Publication Top Notes

  1. Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence 🚗
  2. Pioneering Driver Safety: Evaluating Weather Impacts with the Multi-Class-Weather Algorithm 🌧️
  3. Classification of Traffic Accidents’ Factors Using TrafficRiskClassifier 🚓
  4. Students’ Satisfaction Levels of an Immersive Extended Reality for Engine Assembly Tasks in Engineering Empirical Education 🔧
  5. Deep Learning Mango Fruit Recognition Based on TensorFlow Lite 🥭
  6. Playing the Malay Gamelan Bonang in the Air: A Pilot Study 🎶
  7. GoMap: Combining Step Counting Technique with Augmented Reality for a Mobile-Based Indoor Map Locator 🗺️
  8. Copy-Move Forgery Detection Using Canny Edge Detector and SIFT-Based Blob Analysis 🔍
  9. Effectiveness on Training Method Using Virtual Reality and Augmented Reality Applications in Automobile Engine Assembly 🚗
  10. Detection of Compressed DeepFake Video Drawbacks and Technical Developments 🎥

Conclusion

Dr. L.N. Abdullah is a worthy candidate for the Best Researcher Award due to his exemplary contributions to academia and research, particularly in the fields of Information Systems, Telematics, and Multimedia. His research not only addresses relevant technological issues but also improves societal safety and education. By fostering more industry ties and focusing on sustainable tech, Dr. Abdullah can further enhance his global impact and leadership in his field. His current standing in research, demonstrated by his numerous publications, citations, and international roles, solidifies his reputation as a prominent researcher deserving of this award.

 

 

Jingpan Bai | Smart Transport | Best Researcher Award

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Mr Jingpan Bai | Smart Transport | Best Researcher Award

Associate Professor, Yangtze University, China

Dr. Jingpan Bai is a Lecturer at the School of Computer Science, Yangtze University, China. He earned his Ph.D. in Engineering from Wuhan University of Technology in 2022, where he focused on mobile edge computing. Prior to this, he completed his M.S. in 2016 at Northwest Minzu University and his B.S. in 2013 at Tangshan Normal University. His academic career is marked by a strong interest in edge computing, artificial intelligence, and distributed computing. With a proven track record of publishing in prestigious journals and conferences, Dr. Bai is a rising scholar in his field, particularly in the integration of edge computing with emerging technologies such as UAVs, blockchain, and IoT. His research aims to optimize network efficiency, resource management, and security in distributed systems, and he is actively contributing to innovative solutions for modern computing challenges.

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Strengths for the Award

  1. Research Contributions: Dr. Jingpan Bai has made significant contributions to the fields of edge computing, artificial intelligence, and distributed computing. His work, including publications in high-impact journals like the IEEE Internet of Things Journal and IEEE Transactions on Industrial Informatics, showcases a focus on cutting-edge technologies such as UAV-assisted edge computing, blockchain, and digital twins. These topics are highly relevant and address current technological challenges.
  2. Diverse Research Topics: His research spans across multiple key areas, including caching strategies in edge computing, resource provisioning, task migration, and power allocation in distributed systems. This breadth of expertise suggests a versatile and comprehensive approach to solving complex problems in modern computing environments.
  3. Publication Impact: Dr. Bai’s publications are well-cited, reflecting their influence and relevance in the academic community. With a solid h-index and significant citations for his work, his research is being recognized by peers in the field, demonstrating the quality and impact of his contributions.
  4. Academic and Research Mentorship: As a lecturer at Yangtze University, Dr. Bai is also actively involved in educating and mentoring future generations of researchers. His role as an academic supervisor shows a commitment to advancing knowledge and fostering the growth of emerging scholars.
  5. Interdisciplinary Approach: Dr. Bai’s involvement in various interdisciplinary projects, such as integrating machine learning with edge computing and leveraging blockchain for decentralized systems, highlights his ability to bridge multiple domains, enhancing the innovation and practical applications of his research.

Areas for Improvement

  1. Research Collaboration Expansion: While Dr. Bai has already co-authored with many researchers, further expanding his international and interdisciplinary collaborations could lead to even more diverse perspectives and greater global impact. Collaborative projects with leading researchers from different areas (e.g., smart cities, autonomous systems) could yield new insights and breakthroughs.
  2. Industry Engagement: While his academic achievements are commendable, there could be further emphasis on translating his research into real-world applications or commercial solutions. Collaborating with industry or technology companies on practical implementations would enhance the relevance and applicability of his research.
  3. Broader Recognition in AI and Edge Computing: Dr. Bai’s work in AI, UAVs, and edge computing is strong, but given the rapid advancements in these fields, positioning himself as a thought leader through keynote talks at conferences or leadership in large collaborative projects could further boost his visibility and influence in the academic and industry communities.
  4. Exploration of Emerging Topics: Though his work is cutting-edge, exploring newer emerging technologies, such as quantum computing in edge environments or AI-powered 6G networks, could place his research at the forefront of upcoming technological trends.

Education

Dr. Jingpan Bai holds a Doctor of Engineering from Wuhan University of Technology (2017-2022), where his thesis focused on “Hierarchical Cooperative Resource Provision Strategy in Mobile Edge Computing Environment.” He completed his Master of Science in Computer Science at Northwest Minzu University (2013-2016) and his Bachelor of Science in Mathematics and Computer Science at Tangshan Normal University (2009-2013). During his Ph.D. at Wuhan University of Technology, Dr. Bai worked under the supervision of Professor Chunlin Li, exploring advanced strategies for resource allocation and optimization in edge computing systems. His strong academic foundation in computer science has driven his commitment to pioneering research in edge computing, distributed systems, and artificial intelligence, allowing him to contribute significantly to solving complex computing challenges and advancing the field.

Experience

Dr. Jingpan Bai currently serves as a Lecturer at the School of Computer Science, Yangtze University (September 2022-Present), where he is involved in teaching and conducting research in edge computing, artificial intelligence, and distributed computing. Prior to this position, he completed his doctoral studies at Wuhan University of Technology (2017-2022), where he developed innovative resource management strategies for mobile edge computing. He has extensive experience in both academic and practical applications of cutting-edge technologies, particularly in the areas of Internet of Things (IoT), UAV-assisted systems, and blockchain-based solutions for edge environments. Throughout his career, Dr. Bai has collaborated with researchers across various domains, contributing to several influential publications in international journals and conferences. His expertise spans from optimizing network infrastructure to enhancing system efficiency, with a focus on improving performance and security in distributed and edge computing frameworks.

Awards and Honors

Dr. Jingpan Bai has received several accolades for his contributions to the field of computer science and edge computing. His research on optimizing mobile edge computing systems and exploring innovative strategies for resource provisioning has been recognized in top-tier journals such as the IEEE Internet of Things Journal and IEEE Transactions on Industrial Informatics. He has received multiple invitations to present his research at international conferences and has garnered attention for his collaborative work in UAV-assisted edge computing and IoT. While his most significant honor to date is his Ph.D. award from Wuhan University of Technology, he continues to earn respect and recognition from the academic community for his pioneering work. His excellence in both theoretical and applied research has led to his growing influence in the academic sphere, with numerous citations and the acknowledgment of his research in the global computing and technology landscape.

Research Focus

Dr. Jingpan Bai’s research focuses on cutting-edge advancements in edge computing, artificial intelligence, and distributed computing systems. His work primarily addresses the optimization of resource allocation, task migration, and power management in mobile edge computing environments. He is particularly interested in the integration of artificial intelligence and machine learning techniques to enhance the performance and efficiency of edge networks. Another key area of his research is UAV-assisted edge computing, where he explores strategies for improving task offloading, data management, and system synchronization in aerial networks. Dr. Bai’s work also delves into blockchain-based decentralized systems, aiming to improve data security, resource management, and caching strategies. His interdisciplinary research approach bridges several advanced fields, including IoT, digital twins, and edge-cloud computing, with the goal of developing efficient, secure, and scalable solutions to address modern computing challenges.

Publication Top Notes

  1. The Joint Optimization of Caching and Content Delivery in Air-Ground Cooperation Environment 📡
  2. Joint Optimization Strategy of Task Migration and Power Allocation Based on Soft Actor-Critic in Unmanned Aerial Vehicle-Assisted Internet of Vehicles Environment 🚁
  3. The Node Selection Strategy for Federated Learning in UAV-Assisted Edge Computing Environment 🤖
  4. Blockchain-Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment 🔗
  5. Improved LSTM-Based Abnormal Stream Data Detection and Correction System for Internet of Things 📊
  6. Heterogeneity-Aware Elastic Provisioning in Cloud-Assisted Edge Computing Systems ☁️
  7. Resource and Replica Management Strategy for Optimizing Financial Cost and User Experience in Edge Cloud Computing Systems 💸
  8. Opinion Community Detection and Opinion Leader Detection Based on Text Information and Network Topology in Cloud Environment 💬
  9. Joint Optimization of Data Placement and Scheduling for Improving User Experience in Edge Computing 📅
  10. Community Detection Using Hierarchical Clustering Based on Edge-Weighted Similarity in Cloud Environment 🌐
  11. Clustering Routing Based on Mixed Integer Programming for Heterogeneous Wireless Sensor Networks 📡

Conclusion

Dr. Jingpan Bai’s qualifications, research expertise, and contribution to the fields of edge computing, artificial intelligence, and distributed systems make him a strong contender for the Best Researcher Award. His body of work is highly impactful, with significant academic contributions that address critical challenges in modern computing. While there are opportunities to further broaden his research collaborations and engagement with industry, Dr. Bai’s ongoing commitment to advancing knowledge and pushing the boundaries of technology positions him as a leading figure in his field. His research has the potential to influence both academia and industry, reinforcing his candidacy for this award.