Prof. Khaled Shaban | Data Science | Best Researcher Award

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Prof. Khaled Shaban | Data Science | Best Researcher Award

Prof. Khaled Shaban, Qatar University, Qatar

Prof. Khaled Shaban is a distinguished researcher and professor in Computer Science and Engineering at Qatar University. With expertise in Computational Intelligence, Machine Learning, and Data Science, he has significantly contributed to advancing pattern recognition, cloud computing, and cybersecurity. A senior member of IEEE and ACM, he has received multiple accolades for his groundbreaking research. He also holds an adjunct professorship at the University of Waterloo, reinforcing his global academic influence. His work focuses on AI-driven disease prediction, smart systems, and optimization techniques, making him a leader in intelligent computing innovations.

🌍 Professional Profile:

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🏆 Suitability for Best Researcher Award

Prof. Khaled Shaban’s research excellence, innovative contributions, and global recognition make him an ideal candidate for the Best Researcher Award. His pioneering work in Machine Learning, AI, and Computational Intelligence has led to influential publications and prestigious awards, such as the Best Paper Award at IRICT 2021. His ability to merge theory and application in AI, cloud computing, and cybersecurity has significantly impacted academia and industry. His leadership in top-tier conferences and IEEE/ACM communities underscores his commitment to advancing knowledge, making him a highly deserving candidate for this distinguished recognition.

🎓 Education

Prof. Khaled Shaban holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada (2006), specializing in Pattern Recognition and Machine Intelligence. His academic journey began with an M.Sc. in Engineering Systems and Computing (2002) from the University of Guelph, Canada, where he developed a strong foundation in computational intelligence and optimization. His interdisciplinary education has enabled him to integrate machine learning, data science, and engineering systems into cutting-edge research. His expertise in algorithms and computing theory has positioned him as a global leader in AI and intelligent systems research.

💼 Experience

Prof. Khaled Shaban has an extensive academic career, currently serving as a Professor at Qatar University’s College of Engineering (since April 2021). He previously held roles as Associate Professor (2016-2021) and Assistant Professor (2008-2016). Additionally, he is an Adjunct Professor at the University of Waterloo (2021-2027), collaborating on AI-driven computing innovations. His professional affiliations with IEEE, ACM, and international research communities enhance his impact on global technological advancements. Over the years, he has mentored numerous students and led transformative research in Artificial Intelligence, Data Science, and Optimization.

🏅 Awards & Honors

  • 🏆 Best Paper Award – IRICT 2021 for “C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques”
  • 🏅 Nomination for Best Paper Award – ICVS 2021 for “MARL: Multimodal Attentional Representation Learning for Disease Prediction”
  • 🎖 Promoted to Professor – Qatar University, 2021
  • 🔬 Senior Member, IEEE & ACM – Recognized for contributions to AI and Computational Intelligence
  • 🌍 International Collaborations – Adjunct Professor at the University of Waterloo, fostering global research partnerships

🔬 Research Focus

Prof. Khaled Shaban’s research lies at the intersection of Artificial Intelligence, Computational Intelligence, and Data Science. His work in Machine Learning-driven healthcare analytics, particularly in disease prediction and medical image analysis, is widely recognized. He has also made significant contributions to cybersecurity, cloud computing, and smart grid systems. His studies on optimization and knowledge discovery enhance IoT, AI-based automation, and intelligent computing solutions. Through numerous publications and projects, he has addressed real-world challenges in AI, energy-efficient computing, and adaptive learning systems, making his research impactful across academia and industry.

📖 Publication Top Notes

  • Urban Air Pollution Monitoring System with Forecasting Models

    • Year: 2016
    • Citations: 341

  • Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends

    • Year: 2016
    • Citations: 321

  • Delay-Aware Scheduling and Resource Optimization with Network Function Virtualization

    • Year: 2016
    • Citations: 266

  • A Reliability-Aware Network Service Chain Provisioning with Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

    • Year: 2017
    • Citations: 224

  • Deep Learning Models for Sentiment Analysis in Arabic

    • Year: 2015
    • Citations: 150

 

 

Dr. Siwei Guan | Deep Learning Award | Best Researcher Award

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Dr. Siwei Guan | Deep Learning Award | Best Researcher Award

Dr. Siwei Guan, Hangzhou Dianzi university, China

Dr. Siwei Guan, currently pursuing a Doctorate in Electronic Science and Technology at Hangzhou Dianzi University, China, stands at the forefront of groundbreaking research in anomaly detection. With a Master’s degree from the same university and a Bachelor’s from Jiangxi Normal University, his expertise shines in innovative approaches to multivariate time series data. Driven by a passion for advancement, his work, published in esteemed journals like Computer & Security and IEEE Sensors Journal, showcases pioneering techniques utilizing variational autoencoders and temporal neural networks. Supported by prestigious funding from the National Key Research and Development Program of China and the National Natural Science Foundation of China, he actively contributes to peer review activities, ensuring the quality of academic discourse. Dr. Guan’s dedication and achievements underscore his invaluable contributions to electronic science and technology, propelling the field forward with each innovative stride. 🌟

Professional Profile:

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🏫 Education:

Dr. Siwei Guan is currently pursuing a Doctorate in Electronic Science and Technology at Hangzhou Dianzi University, China, building upon his prior academic achievements. He holds a Master’s degree in Electronic Information from the same university and completed his Bachelor’s in Electronic Information Engineering at Jiangxi Normal University. His research focuses on innovative approaches to anomaly detection in multivariate time series data, as evidenced by his publications in reputable journals like Computer & Security and IEEE Sensors Journal.

💼 Work & Research:

As a Doctoral candidate, Dr. Siwei Guan is actively engaged in groundbreaking research, including the development of novel anomaly detection techniques using variational autoencoders and temporal neural networks. His work has received significant funding from prestigious institutions, including the National Key Research and Development Program of China and the National Natural Science Foundation of China. Additionally, he contributes to the academic community through peer review activities for esteemed journals such as Exper System with Application and ISA Transactions.

📊 Funding & Peer Review:

Dr. Siwei Guan has successfully secured funding to support his research endeavors, demonstrating the recognition and significance of his work in the field. Furthermore, his involvement in peer review activities reflects his commitment to advancing the scientific knowledge and contributing to the quality of research publications.

🌟 Achievements:

Dr. Siwei Guan’s contributions to the field of electronic science and technology have earned him recognition and support from prestigious funding programs and academic journals. With his dedication to innovative research and scholarly pursuits, he continues to make valuable contributions to the advancement of anomaly detection methodologies in multivariate time series data.

Publication Top Notes:

  1. Multivariate time series anomaly detection with variational autoencoder and spatial–temporal graph network
    • Published in Computers & Security, April 2024.
  2. Conditional normalizing flow for multivariate time series anomaly detection
    • Published in ISA Transactions, December 2023.
  3. TPAD: Temporal-Pattern-Based Neural Network Model for Anomaly Detection in Multivariate Time Series
    • Published in IEEE Sensors Journal, December 15, 2023.
  4. GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection
    • Published in Entropy, May 2022.

 

 

 

 

 

Dr. Ali Rohan | Artificial Intelligence Awards | Best Researcher Award

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Dr. Ali Rohan | Artificial Intelligence Awards | Best Researcher Award

Dr. Ali Rohan, National Subsea Centre, United Kingdom

👨‍🔬 Dr. Ali Rohan is a versatile researcher and educator in the fields of robotics, artificial intelligence (AI), and computer vision. With a strong academic background including a MSc – PhD in Electrical, Electronics & Control Engineering from Kunsan National University, South Korea, he has delved into various facets of cutting-edge technology. As a Lead Researcher at institutions like the National Subsea Centre in the UK and Dongguk University in South Korea, he spearheaded groundbreaking projects like SeaSense, focusing on underwater visual systems, and DAIRYVISION, revolutionizing livestock farming with AI and machine vision. His expertise spans from real-time implementation of AI for UAVs to structural damage monitoring using AI with UAVs. Dr. Rohan’s contributions extend beyond research, as he has also shared his knowledge as an educator, teaching courses on robotics, data science, and control systems engineering. With a passion for innovation and a dedication to advancing technology, Dr. Rohan continues to make significant strides in shaping the future of AI and robotics. 🤖✨

🌐 Professional Profile:

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🎓 Education

Ph.D. in Electrical, Electronics & Control Engineering
Department of Control & Robotics Engineering, Kunsan National University, Kunsan, South Korea
(Feb 2016 – Mar 2020)

B.Sc (Hons) in Electrical Engineering
School of Electrical Engineering, The University of Faisalabad, Pakistan
(Oct 2008 – Jul 2012)

🖥️ Technical Competence

  • Areas of Specialization: AI, Machine Learning, Deep Learning, Computer Vision, Robotics, Automation
  • Programming Languages: C, C++, C#, Matlab, Python
  • AI & Machine Learning Libraries: TensorFlow, PyTorch, Scikit-learn, Keras
  • Operating Systems: Windows, Linux, macOS, Robot Operating System (ROS)

🔍 Research Interests :

🤖 Dr. Ali Rohan, an accomplished researcher, specializes in Robotics, Artificial Intelligence (AI), Computer Vision, Automation and Control, Image Processing, Signal Processing, and Machine Learning. His expertise lies in leveraging these domains to innovate solutions for various real-world challenges, from enhancing industrial automation to advancing medical diagnostics. With a keen interest in interdisciplinary research, Ali consistently explores the intersection of these fields to develop cutting-edge technologies with profound societal impacts. 🚀

🔬 Research Experience & Projects

Dr. Rohan has led and contributed to various research projects in areas such as underwater robotics, agricultural monitoring using drones, AI for healthcare, and structural damage detection using UAVs. His work includes projects funded by prestigious bodies like the Net Zero Technology Centre, InnovateUK, and the Australian Research Council.

👨‍🏫 Teaching Experience

Dr. Rohan has taught a range of modules covering topics such as fundamentals of prognostics and health management, robotics, control systems engineering, data science, and power electronics. His teaching expertise spans both theoretical principles and practical applications in engineering and technology.

🏅 Certifications & Awards

Dr. Rohan holds certifications in areas such as Prognostics and Health Management and has received recognition for his contributions to research and academia.

📚 Publication Impact and Citations :

Scopus Metrics:

  • 📝 Publications: 19 documents indexed in Scopus.
  • 📊 Citations: A total of 437 citations for his publications, reflecting the widespread impact and recognition of Dr. Ali Rohan’s research within the academic community.

Google Scholar Metrics:

  • All Time:
    • Citations: 644 📖
    • h-index: 14  📊
    • i10-index: 15 🔍
  • Since 2018:
    • Citations: 629 📖
    • h-index: 14 📊
    • i10-index: 14 🔍

👨‍🏫 A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. 🌐🔬

Publication Top Notes: