Prof. Khaled Shaban | Data Science | Best Researcher Award

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:

Google Scholar

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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 AwardIRICT 2021 for “C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques”
  • 🏅 Nomination for Best Paper AwardICVS 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. Tanushree Bhattacharjee | Emerging Technologies | Best Researcher Award

Dr. Tanushree Bhattacharjee | Emerging Technologies | Best Researcher Award

Dr. Tanushree Bhattacharjee, GRIDsentry Private Limited, India

Dr. Tanushree Bhattacharjee is a distinguished cybersecurity expert specializing in substation automation, OT security, and intrusion detection systems (IDS). With a Ph.D. in Electrical Engineering from Jamia Millia Islamia, she has over seven years of experience securing critical infrastructure. As Sr. R&D Manager at GRIDsentry Pvt. Ltd., Bengaluru, she leads cutting-edge research in forensic analysis, deep packet inspection, and AI-powered threat modeling. Dr. Bhattacharjee has played a vital role in national and international cybersecurity testbeds, contributing to the advancement of IEC 61850, power grid security, and microgrid protection. Her expertise in AI/ML-based anomaly detection ensures the resilience of modern power systems. 🔐⚡

🌍 Professional Profile:

Google Scholar

Orcid

Scopus

🏆 Suitability for the Best Researcher Award 

Dr. Tanushree Bhattacharjee is an outstanding candidate for the Best Researcher Award, given her pioneering work in substation automation security and digital transformation. She has made significant contributions to intrusion detection, vulnerability assessment, and OT security in power grids. Her leadership in developing IDS/IPS solutions, coupled with her expertise in AI-powered anomaly detection, positions her as a key innovator in cyber-physical security. With a strong background in threat modeling, forensic analysis, and protocol security, her research directly impacts critical infrastructure protection. Her proven ability to bridge AI with cybersecurity makes her a deserving nominee for this prestigious recognition. 🏆🔍

🎓 Education

Dr. Tanushree Bhattacharjee holds a Ph.D. in Electrical Engineering from Jamia Millia Islamia, New Delhi (2017-2022), where she focused on substation automation and microgrid protection. She completed her Master’s in Power Systems at the Indian Institute of Engineering Science & Technology, Shibpur (2012-2014). Her academic work involved IEC 61850 protocols, cybersecurity in digital substations, and AI-driven security frameworks. Through hands-on research in power system modeling, microgrid security, and forensic analysis, she has contributed to cybersecurity innovations in critical infrastructure. Her education has provided a robust foundation for her advancements in intrusion detection and digital protection strategies. 🎓⚡🔬

💼 Experience 

As Sr. R&D Manager at GRIDsentry Pvt. Ltd., Bengaluru, Dr. Bhattacharjee leads research on intrusion detection systems (IDS), AI-driven threat modeling, and forensic analysis. Previously, as a Product Manager, she specialized in deep packet inspection and anomaly detection. She also worked as a Power System Security Engineer, focusing on IPS/IDS development and OT cybersecurity. Her tenure at Jamia Millia Islamia involved substation automation, protocol security, and microgrid testing. With expertise in vulnerability assessments, access control, and live cybersecurity testing, she has significantly contributed to the security of modern power infrastructures. 🔒💡🚀

🏅 Awards & Honors 

Dr. Bhattacharjee has received multiple accolades for her contributions to power system cybersecurity. She has been recognized for her outstanding research in IDS and AI-driven security mechanisms. Her work on IEC 61850-based intrusion detection won Best Paper Awards at leading cybersecurity conferences. She has been acknowledged by cybersecurity organizations for her role in developing AI-based threat detection tools. Additionally, she has contributed to national security projects, earning commendation from government agencies and industry leaders. Her expertise in forensic analysis, digital substation security, and OT cybersecurity has positioned her as a trailblazer in the field. 🏆🔍⚡

🔬 Research Focus

Dr. Bhattacharjee’s research integrates emerging technologies with cybersecurity, focusing on power system protection, IEC 61850 protocols, and digital substation automation. Her expertise includes intrusion detection, AI-based anomaly detection, and forensic security analysis. She explores cyber-physical system security, ensuring resilience against DDoS, MITM, and replay attacks. Her work in deep learning for security event detection enhances smart grid protection. She also specializes in protocol security, AI-driven attack mitigation, and operational technology (OT) cybersecurity. Through machine learning, threat modeling, and real-time testing, her research aims to fortify modern power infrastructures against evolving cyber threats. 🛰️🔐⚙️

📖 Publication Top Notes

  1. Hardware Development and Interoperability Testing of a Multivendor-IEC-61850-Based Digital Substation
    • Citations: 11
    • Year: 2022
  2. Planning of Renewable DGs for Distribution Network Considering Load Model: A Multi-Objective Approach
    • Citations: 9
    • Year: 2014
  1. Designing a Controller Circuit for Three-Phase Inverter in PV Application
    • Citations: 6
    • Year: 2018
  2. Digital Substations with the IEC 61850 Standard
    • Citations: 3
    • Year: 2021
  3. Power Quality Improvement of Grid Integrated Distributed Energy Resource Inverter
    • Citations: 2
    • Year: 2021

 

Prof. Jiantao Shi | Information Technology | Best Researcher Award

Prof. Jiantao Shi | Information Technology | Best Researcher Award

Prof. Jiantao Shi, Njing Tech University, China

Prof. Jiantao Shi is a distinguished researcher in control science and information technology, currently serving as a Professor at Nanjing Tech University. He holds a Ph.D. in Control Science and Engineering from Tsinghua University and has extensive experience in multi-robot cooperative control, fault diagnosis, and UAV learning control. His research has been published in leading IEEE journals, and he has significantly contributed to distributed system reliability. With a strong academic background and practical research experience, he has advanced intelligent control methodologies for autonomous systems. His contributions have positioned him as a leader in modern automation and robotics.

🌍 Professional Profile:

ORCID

🏆 Suitability for Best Researcher Award 

Prof. Jiantao Shi is an outstanding candidate for the Best Researcher Award due to his pioneering contributions to intelligent control systems, multi-robot cooperation, and UAV learning control. His work integrates cutting-edge AI techniques with control science, enabling the development of robust and fault-tolerant autonomous systems. With over 60 high-impact journal and conference papers in prestigious IEEE and SCI-indexed publications, he has made fundamental advances in the field. His leadership in both academic and applied research underscores his influence on the next generation of intelligent automation technologies. His innovative solutions make him highly deserving of this recognition.

🎓 Education

Prof. Jiantao Shi obtained his Bachelor’s degree in Electrical Engineering and Automation from Beijing Institute of Technology in 2011. He then pursued a Ph.D. in Control Science and Engineering at Tsinghua University, earning his doctorate in 2016. His academic journey at these top institutions equipped him with expertise in control systems, automation, and intelligent sensing technologies. His doctoral research focused on advanced fault diagnosis and cooperative control of multi-agent systems. This solid educational foundation has propelled him to the forefront of intelligent control and automation, enabling him to address complex challenges in distributed autonomous systems.

💼 Work Experience

Prof. Jiantao Shi has an extensive research career spanning academia and industry. From 2016 to 2018, he worked as an Associate Research Fellow at the Nanjing Research Institute of Electronic Technology, specializing in intelligent sensing. He was promoted to Research Fellow in 2019, leading projects in autonomous systems and fault-tolerant control. Since 2021, he has been a Professor at Nanjing Tech University, where he mentors students and advances research in AI-driven control methodologies. His experience in both applied research and academia allows him to bridge theoretical advancements with real-world applications in robotics, UAVs, and industrial automation.

🏅 Awards & Honors

Prof. Jiantao Shi has received several prestigious awards recognizing his contributions to control science and automation. His research has been featured in top-tier IEEE Transactions journals, demonstrating its high impact. He has been honored with multiple best paper awards at international conferences. Additionally, his work on UAV control and multi-robot systems has been acknowledged with research grants and government funding for innovation in automation. As a key contributor to cutting-edge intelligent control systems, he continues to earn accolades for his groundbreaking contributions, positioning himself as a leading researcher in distributed autonomous system control.

🔬 Research Focus

Prof. Jiantao Shi’s research centers on advanced control methodologies for intelligent automation. His key areas of expertise include cooperative control of multi-robot systems, fault diagnosis and fault-tolerant control of distributed systems, and learning-based control of UAVs. His work integrates AI and machine learning with traditional control science to enhance system resilience and autonomy. By developing robust, intelligent algorithms, he aims to improve automation reliability in real-world applications. His research has profound implications for robotics, autonomous vehicles, and industrial automation, paving the way for next-generation intelligent systems with enhanced adaptability, efficiency, and fault resilience.

📖 Publication Top Notes 

  1. A Parallel Weighted ADTC-Transformer Framework with FUnet Fusion and KAN for Improved Lithium-Ion Battery SOH Prediction
    • Publication Year: 2025
  2. Bipartite Fault-Tolerant Consensus Control for Multi-Agent Systems with a Leader of Unknown Input Under a Signed Digraph
    • Publication Year: 2025
  3. Iterative Learning-Based Fault Estimation for Stochastic Systems with Variable Pass Lengths and Data Dropouts
    • Publication Year: 2025
  1. A Two-Stage Fault Diagnosis Method with Rough and Fine Classifiers for Phased Array Radar Transceivers
    • Publication Year: 2024
  2. An Intuitively-Derived Decoupling and Calibration Model to the Multi-Axis Force Sensor Using Polynomials Basis
    • Publication Year: 2024
  3. Event-Based Adaptive Fault Tolerant Control and Collision Avoidance of Wheel Mobile Robots with Communication Limits
    • Publication Year: 2024

 

Prof. Dr. Chen-Tung Chen | Performance Analysis | Best Researcher Award

Prof. Dr. Chen-Tung Chen | Performance Analysis | Best Researcher Award

Prof. Dr. Chen-Tung Chen, National United University, Taiwan

Chen-Tung Chen, a renowned academic in Industrial Engineering, holds a distinguished career as a professor at the Department of Information Management, National United University, Taiwan, since 2005. He earned his Bachelor’s and Master’s degrees in Industrial Engineering from National Tsing-Hua University, Taiwan, in 1987. In 1995, he completed his Ph.D. in Industrial Engineering and Management at National Chiao-Tung University, Taiwan. With an extensive academic background, Professor Chen has made significant contributions to various fields, including decision support systems, knowledge management, project management, data mining, and supply chain management. His research integrates fuzzy set theory and multiple criteria decision-making to address complex issues in the aforementioned domains. He has published numerous influential articles in prominent academic journals, shaping the evolution of Industrial Engineering and Information Management in Taiwan and beyond. His work continues to inspire students and professionals alike, making him a key figure in his field.

Professional Profile:

Scopus

Google Scholar

Suitability for Best Researcher Award:

Professor Chen-Tung Chen is highly suitable for the Best Researcher Award due to his remarkable contributions to the fields of Industrial Engineering and Information Management. His work in fuzzy set theory and multiple criteria decision-making has shaped the way complex problems in areas like project management, supply chain management, and knowledge management are approached. Professor Chen’s interdisciplinary expertise, particularly in decision support systems and data mining, has not only advanced academic knowledge but has also driven innovation in practical applications, benefiting both businesses and research communities. His pioneering research and continuous focus on bridging theory with practice demonstrate his leadership and impact in his field.

🎓Education 

Chen-Tung Chen’s academic journey began with a Bachelor’s and Master’s degree in Industrial Engineering from the Department of Industrial Engineering at National Tsing-Hua University, Taiwan, in 1987. Driven by a passion for research and academia, he pursued his Ph.D. in Industrial Engineering and Management at National Chiao-Tung University, Taiwan, which he completed in 1995. His doctoral research further deepened his understanding of decision-making processes and their applications in management systems. This solid educational foundation provided the groundwork for his academic career, allowing him to delve into cutting-edge research areas such as fuzzy set theory, decision support systems, and knowledge management. Throughout his career, Professor Chen has remained committed to the continuous pursuit of knowledge, contributing to advancements in his field. His education, along with his rigorous research and teaching experience, has positioned him as a respected leader in the fields of Industrial Engineering and Information Management.

🏢Experience 

Professor Chen-Tung Chen has had an esteemed career in academia, holding the position of Professor at the Department of Information Management, National United University, Taiwan, since 2005. Prior to this, he earned a solid foundation in Industrial Engineering and Management, where he gained extensive experience in both teaching and research. His early academic journey included earning his Bachelor’s and Master’s degrees in Industrial Engineering from National Tsing-Hua University and his Ph.D. from National Chiao-Tung University, Taiwan. Over the years, Professor Chen has contributed to the growth of his department by offering innovative courses and mentoring numerous graduate students. His interdisciplinary expertise spans several areas, including fuzzy set theory, project management, data mining, and supply chain management. His influence extends beyond the classroom as he has become a key figure in advancing research in decision support systems, knowledge management, and e-business. His vast experience in academia has made him an invaluable resource for students and the research community.

🏅Awards and Honors 

Professor Chen-Tung Chen has been widely recognized for his outstanding contributions to the fields of Industrial Engineering and Information Management. Over the years, he has received several academic accolades for his excellence in research and teaching. As a respected figure in the academic community, he has earned recognition from numerous prestigious journals, where his work has been published. His research, particularly in the application of fuzzy set theory and multiple criteria decision-making, has garnered attention and praise from his peers. Professor Chen’s ability to bridge the gap between theory and practice has led to his involvement in significant projects in the fields of supply chain management, knowledge management, and e-business. His work has had a profound impact on both the academic and professional realms, earning him awards for his contributions to decision support systems and project management. His dedication to advancing his field continues to inspire colleagues, students, and researchers globally.

🔬Research Focus 

Professor Chen-Tung Chen’s research interests are centered on several critical areas in Industrial Engineering and Information Management. His primary focus lies in the application of fuzzy set theory and multiple criteria decision-making techniques to complex decision-making problems in various fields, including project management, supply chain management, and knowledge management. His work in developing decision support systems has led to innovative solutions for optimizing business processes and improving decision-making efficiency. Professor Chen also explores the role of data mining in extracting actionable insights from large datasets, enhancing organizational performance. His research in e-business focuses on improving digital transformation strategies and decision-making processes in modern enterprises. Additionally, his contributions to the design of supply chain management systems aim to create more efficient and responsive global supply networks. Overall, Professor Chen’s research aims to bridge theoretical knowledge with practical applications, providing valuable tools for both academic and professional communities.

Publication Top Notes:

  1. “Extensions of the TOPSIS for group decision-making under fuzzy environment”
    • Citations: 5275
  2. “A fuzzy approach for supplier evaluation and selection in supply chain management”
    • Citations: 2532
  3. “Acute toxicity and biodistribution of different sized titanium dioxide particles in mice after oral administration”
    • Citations: 1526
  4. “Acute toxicological effects of copper nanoparticles in vivo”
    • Citations: 1358
  5. “Diverse applications of nanomedicine”
    • Citations: 1315

 

 

 

 

 

Tayfun Abut | Methods and Algorithms | Best Researcher Award

Assist Prof. Tayfun Abut | Methods and Algorithms | Best Researcher Award

Assist Prof Dr. Tayfun Abut, Mus Alparslan University, Turkey

Dr. Tayfun Abut is an Assistant Professor of Mechanical Engineering at Mus Alparslan University. He earned his Doctoral and Master’s degrees from Firat University, where he also completed his Bachelor’s degree. Dr. Abut has held various academic and leadership positions, including Vice Dean and Head of Major Department, contributing significantly to his institution’s academic and administrative functions. His research focuses on control systems, haptic teleoperation, and dynamic analysis of mechanical systems, with numerous publications in reputable journals. Dr. Abut’s work has earned him several honors, including the Highly Commended Paper Award from Emerald Publishing and TÜBİTAK’s Publication Incentive Awards. He is dedicated to continuous learning, actively participating in workshops and training to further his expertise.

🌍 Professional Profile:

ORCID 
Scopus

🎓 Educational Background:

Dr. Tayfun Abut earned his Doctoral degree from Firat University’s Institute of Science on March 10, 2022. He previously obtained a Master’s degree (Thesis) from the same institution on August 27, 2015, and completed his Bachelor’s degree on June 15, 2012. His academic journey has been rooted in Firat University, where he has built a strong foundation in Mechanical Engineering.

💼 Experience:

Dr. Abut has a diverse range of academic positions. He started as a Research Assistant at Firat University’s Faculty of Engineering, specializing in Mechanical Theory and Dynamics. He continued his career at Mus Alparslan University, serving as a Research Assistant in the Department of Mechanical Engineering, focusing on System Dynamics and Control. Since August 5, 2022, Dr. Abut has been an Assistant Professor at Mus Alparslan University in the Department of Mechanical Engineering.

📚 Workshops and Training:

Dr. Abut’s work has been recognized with several honors and awards. Notably, he received the Highly Commended Paper Award from Emerald Publishing in 2020. He has also been awarded Publication Incentive Awards from TÜBİTAK in 2016 and 2017, highlighting his contributions to research and academic excellence.

🏅 Honors and Awards:

She has received several accolades, including the Outstanding Graduate Award from the School of International Education, Dalian University of Technology (2024), the DUT International Students Presidential Scholarship (full scholarship), and the Youth Star Award (2022). She also earned the Best Teacher Award for the 2018-2019 session from Sir Syed School, Wah Cantt, Pakistan, and a merit scholarship for her top performance in her MS and BS programs.

Publication Top Notes:

  • Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum
    • Year: 2019
    • Citations: 31
  • Haptic industrial robot control and bilateral teleoperation by using a virtual visual interface | Sanal bir görsel arayüz kullanarak haptik endüstriyel robot kontrolü ve iki yönlü teleoperasyon
    • Year: 2018
    • Citations: 6
  • Real-time control of bilateral teleoperation system with adaptive computed torque method
    • Year: 2017
    • Citations: 10
  • Haptic industrial robot control with variable time delayed bilateral teleoperation
    • Year: 2016
    • Citations: 18
  • Motion control in virtual reality based teleoperation system | Sanal Gerçeklik Tabanlı Teleoperasyon Sisteminde Hareket Kontrolü
    • Year: 2015
    • Citations: 2