Dr. Haochen Li | Machine Learning | Best Researcher Award

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Dr. Haochen Li | Machine Learning | Best Researcher Award

Dr. Haochen Li, Taiyuan University of Science and Technology, China

Dr. Haochen Li is an accomplished researcher specializing in electrical engineering, with a strong emphasis on power electronics, power systems, and data-driven optimization techniques. His academic journey has been marked by significant contributions to the development of intelligent power flow control and renewable energy integration. His research focuses on applying advanced machine learning techniques, such as graph-based neural networks, to improve power grid stability, reliability, and efficiency. With multiple high-impact publications in top-tier journals, Haochen Li has made notable strides in tackling challenges in microgrid systems, power flow optimization, and spatiotemporal power predictions. His innovative approaches have garnered recognition from the research community, positioning him as a leading figure in modern electrical power system advancements.

Profile:

Orcid

Scopus

Education:

Dr.  Haochen Li has pursued a rigorous academic path, building expertise in electrical engineering and control systems. He completed his undergraduate studies in Electrical Engineering and Automation, followed by a master’s degree in Power Electronics and Electric Drives, where he specialized in microgrid system control technologies. Currently, he is pursuing a Ph.D. in Control Engineering, focusing on the application of data mining techniques in power systems. His educational background has provided him with a strong foundation in both theoretical and applied research, enabling him to develop innovative solutions for optimizing power system performance.

Experience:

Dr. Haochen Li has been actively involved in academia and research, contributing to the advancement of electrical and control engineering. He is currently associated with the Taiyuan University of Science and Technology, where he engages in cutting-edge research on power flow optimization and renewable energy integration. His experience spans multiple collaborative projects, where he has worked alongside leading experts to develop intelligent algorithms for power system management. Through his academic endeavors, he has gained expertise in modeling and simulation of power systems, integrating artificial intelligence techniques into energy management, and analyzing grid uncertainties for enhanced performance.

Research Interests:

Dr. Haochen Li’s research interests revolve around the intersection of power systems and data science, with a particular focus on:

  • Power Flow Optimization ⚡ – Developing intelligent algorithms to enhance the efficiency of electricity transmission.

  • Renewable Energy Integration 🌍 – Designing predictive models for wind and solar energy systems.

  • Graph Neural Networks in Power Systems 🤖 – Utilizing AI-driven techniques for improving grid stability and reliability.

  • Spatiotemporal Data Analysis ⏳ – Leveraging big data approaches to enhance power grid forecasting.

  • Microgrid System Control 🔋 – Implementing advanced control strategies for distributed energy resources.

Awards:

Dr. Haochen Li’s contributions to power system research have been recognized through various academic and research accolades. His outstanding work in data-driven optimization for power flow calculations has been acknowledged by prestigious institutions. Additionally, his research on renewable energy forecasting has earned him recognition in international conferences and journal publications. His ability to bridge theoretical research with practical applications has positioned him as a key innovator in the field.

Publications:

  • Physics-Guided Chebyshev Graph Convolution Network for Optimal Power Flow

    • Publication Year: 2025

  • Graph Attention Convolution Network for Power Flow Calculation Considering Grid Uncertainty

    • Publication Year: 2025

  • Joint Missing Power Data Recovery Based on Spatiotemporal Correlation of Multiple Wind Farms

    • Publication Year: 2024

  • Spatiotemporal Coupling Calculation-Based Short-Term Wind Farm Cluster Power Prediction

    • Publication Year: 2023

Conclusion:

Dr. Haochen Li is a highly dedicated researcher whose work has significantly contributed to the field of power system engineering. His expertise in artificial intelligence, power flow optimization, and renewable energy forecasting has positioned him as a thought leader in the integration of smart grid technologies. With a strong publication record, ongoing innovative research, and a commitment to enhancing power system reliability, he is a deserving candidate for the Best Researcher Award. His ability to merge theoretical advancements with real-world applications showcases his potential to lead future innovations in intelligent power systems.

Prof. Dr. Xin Wang | Distributed AI | Best Researcher Award

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Prof. Dr. Xin Wang | Distributed AI | Best Researcher Award

Prof. Dr. Xin Wang, Qilu University of Technology, China

Prof. Dr. Xin Wang is a distinguished scholar in Distributed AI and Federated Learning, currently serving as a Professor at Shandong Computer Science Center, Qilu University of Technology. With a Ph.D. in Control Science and Engineering from Zhejiang University, he has contributed significantly to AI Security, Privacy, and LLM Security. Dr. Wang has led multiple national research projects and received prestigious honors, including the Taishan Scholars Award and the Shandong Provincial Science and Technology Progress Award. His work integrates AI with secure computing, enhancing privacy protection and optimization in collaborative learning systems.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for Award 

Dr. Xin Wang’s outstanding contributions to Distributed AI, Federated Learning, and AI Security make him a strong candidate for the Best Researcher Award. As a leader in AI-driven security frameworks, he has spearheaded national-level projects focusing on privacy-preserving AI and secure learning models. His research bridges theory with practical applications, enhancing security in multi-agent and industrial IoT systems. Recognized for his high-impact publications and award-winning research, Dr. Wang’s innovations in cryptographic function identification and UAV data collection optimization demonstrate exceptional originality and real-world relevance, solidifying his place as a leader in computational intelligence and AI security.

🎓 Education 

  • Ph.D. in Control Science and Engineering (2015-2020) – Zhejiang University, supervised by Prof. Peng Cheng & Prof. Jiming Chen, specializing in AI Security and Distributed Intelligence.
  • Visiting Scholar in Information Security (2018-2019) – Tokyo Institute of Technology, mentored by Prof. Hideaki Ishii, focusing on cryptographic vulnerabilities and federated learning security.

His multidisciplinary training across AI, security, and automation has positioned him at the forefront of cutting-edge computational research.

💼 Experience 

  • Professor (2024–Present) – Shandong Computer Science Center, Qilu University of Technology.
  • Associate Professor (2020–2024) – Shandong Computer Science Center, leading research on privacy protection in collaborative AI.
  • Project PI in National Natural Science Foundation of China (2025-2027) – Developing privacy-preserving defense mechanisms for federated learning.
  • Project PI in National Key Research and Development Program (2021-2024) – Developing AI-driven meta-services for cloud-based industrial manufacturing.
  • Visiting Scholar (2018-2019) – Tokyo Institute of Technology, conducting security research on cryptographic vulnerabilities in multi-agent IoT systems.

🏅 Awards and Honors 

  • Taishan Scholars Award (2024) 🏅 – Recognized for research excellence in AI security and distributed systems.
  • Leader of Youth Innovation Team (2022) 🚀 – Acknowledged for driving innovation in Shandong Higher Education Institutions.
  • Second Prize, Shandong Provincial Science and Technology Progress Award (2022) 🏆 – Contributions to federated learning and privacy-preserving AI.
  • Best Paper Award, CCSICC’21 📄 – Vulnerability Analysis for IoT Devices in Multi-Agent Systems.
  • Best Paper Award, ICAUS’24 ✈️ – Optimized Data Collection for UAVs in Industrial IoT Environments.

🔬 Research Focus 

Dr. Wang specializes in Distributed AI, Federated Learning, and AI Security & Privacy. His research integrates cryptographic techniques, optimization algorithms, and adversarial defenses to improve the security of collaborative learning models. He has pioneered LLM security frameworks to safeguard against data leakage and adversarial attacks. His work extends into privacy-preserving AI for multi-agent IoT systems and UAV data collection efficiency. Through national projects, he has developed secure meta-services for cloud computing, advancing the field of intelligent automation and resilient AI architectures for real-world deployment in cyber-physical systems and industrial environments.

📊 Publication Top notes:

  • Title: Privacy-Preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
    • Year: 2020
    • Citations: 61
  • Title: Privacy-Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism
    • Year: 2018
    • Citations: 49
  • Title: Differentially Private Maximum Consensus: Design, Analysis and Impossibility Result
    • Year: 2018
    • Citations: 26
  • Title: Dynamic Privacy-Aware Collaborative Schemes for Average Computation: A Multi-Time Reporting Case
    • Year: 2021
    • Citations: 18
  • Title: Leveraging UAV-RIS Reflects to Improve the Security Performance of Wireless Network Systems
    • Year: 2023
    • Citations: 17

 

Prof. Ching Yee Suen | Artificial Intelligence | Best Researcher Award

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Prof. Ching Yee Suen | Artificial Intelligence | Best Researcher Award

Prof. Ching Yee Suen, Concordia University, Canada

Prof. Ching Yee Suen is a globally recognized expert in Pattern Recognition, AI, and Document Analysis. As the Founding Director and Co-Director of CENPARMI at Concordia University, he has shaped advancements in handwriting recognition, multiple classifiers, and font analysis. A Fellow of IEEE, IAPR, and the Royal Society of Canada, he has mentored 120+ graduate students and 100 visiting scientists. With 550+ research papers, 16 books, and an h-index of 74, his contributions are widely cited. His innovations power millions of devices worldwide. He has led $20M+ research projects, collaborated with global industries, and serves as an editor for top-tier journals.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for Best Researcher Award 

Prof. Suen is an exceptional candidate for the Best Researcher Award due to his pioneering contributions in AI, pattern recognition, and handwriting analysis. His research has real-world impact, with millions of users benefiting from his handwriting recognition algorithms. He has received top international awards, including the King-Sun Fu Prize (2021) and ICDAR Award (2005). As a leading AI researcher, he has secured $20M+ in funding, supervised over 120 Ph.D. and master’s students, and led groundbreaking industrial collaborations. His global influence, leadership in AI, and outstanding research output make him a worthy recipient of this prestigious honor.

🎓 Education 

Prof. Ching Yee Suen holds a Ph.D. from the University of British Columbia (UBC), Vancouver, and a Master’s degree from the University of Hong Kong. His academic journey has been marked by a deep focus on Artificial Intelligence, Pattern Recognition, and Computational Vision. His early research laid the foundation for his groundbreaking work in handwriting recognition, document analysis, and AI-powered classification systems. He has spent sabbatical leaves at MIT, McGill University, Ecole Polytechnique, and IBM, further expanding his expertise. His academic credentials have established him as a leading scholar in AI and pattern recognition on a global scale.

💼 Experience 

With a career spanning 50+ years, Prof. Suen has held key leadership roles at Concordia University, serving as Chairman of Computer Science, Associate Dean (Research), and Concordia Chair in AI & Pattern Recognition. He is the Founding Director and Co-Director of CENPARMI, where he has driven cutting-edge research. He has supervised 120+ graduate students and collaborated with top institutions worldwide. As a consultant to Microsoft, Xerox, Canada Post, and the US Congress, his work has had real-world impact. His editorial leadership in top AI journals and conference organization further cements his global influence in the research community.

🏅 Awards and Honors

Prof. Suen’s excellence is recognized globally, earning him top honors in AI and pattern recognition. He received the King-Sun Fu Prize (2021) 🏆, the ICDAR Award (2005) 🎖️, and the Elsevier Distinguished Editorial Award (2016)📜. His Concordia Lifetime Research Achievement Award (2008) and Teaching Excellence Award (1995) 🎓 highlight his impact in academia. Internationally, he was honored with the Gold Medal from the University of Bari, Italy (2012) 🥇. As a Fellow of IEEE, IAPR, and the Royal Society of Canada, his contributions to AI, document analysis, and handwriting recognition are celebrated at the highest levels.

🔬 Research Focus 

Prof. Suen specializes in Pattern Recognition, Artificial Intelligence, and Document Analysis. His innovations in handwriting recognition, fake coin detection, license plate recognition, and multi-classifier systems have transformed industry applications. His research integrates AI, deep learning, and image processing to solve complex problems in computer vision, natural language processing, and fraud detection. His high-impact contributions are widely used in mobile devices, banking security, and postal services. His multi-disciplinary approach in AI has led to real-world solutions adopted by Microsoft, Bell Canada, Canada Post, and global tech firms, making him a pioneer in intelligent pattern analysis.

📊 Publication Top notes:

  • Title: Developing Knowledge Management Metrics for Measuring Intellectual Capital
    • Year: 2000
    • Citations: 442
  • Title: Modified Hebbian Learning for Curve and Surface Fitting
    • Year: 1992
    • Citations: 322
  • Title: N-Gram Statistics for Natural Language Understanding and Text Processing
    • Year: 1979
    • Citations: 315
  • Title: Analysis and Design of a Decision Tree Based on Entropy Reduction and Its Application to Large Character Set Recognition
    • Year: 1984
    • Citations: 176
  • Title: Large Tree Classifier with Heuristic Search and Global Training
    • Year: 1987
    • Citations: 102

 

 

Mr. Yan hui Wu | Machine Learning Awards | Best Researcher Award

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Mr. Yan hui Wu | Machine Learning Awards | Best Researcher Award

Mr. Yan hui Wu , Hebei University of Engineering , China

Yanhui Wu is a Senior Engineer at the School of Mining and Surveying Engineering, Hebei University of Engineering. He completed his Ph.D. in Geophysical Exploration and Information Technology at the China University of Mining and Technology (Beijing) in 2023. He also holds an M.Sc. in the same field from the China University of Geosciences (Beijing) and a B.Sc. in Computer Science and Technology from Hebei University of Technology. Wu’s career includes nearly a decade at the Geological Geophysical Center, Hebei Coal Science Research Institute, Jizhong Energy Group, where he served as a Senior Engineer. He has participated in significant research projects, including the Ministry of Science and Technology’s National Key R&D Program on dynamic intelligent detection technology for hidden disaster geological factors in coal mines. Wu’s research has been published in several renowned journals, with notable works on seismic multiattribute machine learning, fault evaluation, and collapse column prediction in coal strata.

Professional Profile:

Orcid

 🎓Education:

Yanhui Wu holds a Ph.D. in Geophysical Exploration and Information Technology from the China University of Mining and Technology (Beijing), which he completed in June 2023. He also earned an M.Sc. in the same field from the China University of Geosciences (Beijing) in June 2010. Additionally, Wu has a B.Sc. in Computer Science and Technology from Hebei University of Technology, which he obtained in June 2007.

 🏢Work Experience:

Yanhui Wu currently serves as a Senior Engineer at the School of Mining and Surveying Engineering, Hebei University of Engineering. Prior to this role, he held a Senior Engineer position at the Geological Geophysical Center of Hebei Coal Science Research Institute, part of the Jizhong Energy Group, from August 2010 to July 2019.

Publication Top Notes:

  • Application of seismic multiattribute machine learning to determine coal strata thickness
    • Published Year: 2021
    • Journal: Journal of Geophysics and Engineering
    • Cited by: 834-844
  • Quantitative Evaluation of Faults by Combined Channel Wave Seismic Transmission-Reflection Detection Method
    • Published Year: 2022
    • Journal: Minerals
    • Cited by: 1022-1032
  • Precise prediction of the collapse column based on channel wave spectral disparity characteristics and velocity tomography imaging
    • Published Year: 2022
    • Journal: Journal of Geophysics and Engineering
    • Cited by: 326-335
  • Application research of combined detection of transmission and reflection slot waves for small structures—Taking Longquan Mining Area in Shanxi as an example
    • Published Year: 2021
    • Journal: Progress in Geophysics
    • Cited by: 1325-1332

Mr. Asif Mehmood | Artificial intelligence Awards | Best Researcher Award

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Mr. Asif Mehmood | Artificial intelligence Awards | Best Researcher Award

Mr. Asif Mehmood, National university of technology, Pakistan

Asif Mehmood is a dedicated professional with a strong academic background and diverse expertise in computer sciences. Currently pursuing a PhD in Computer Sciences at COMSATS University Islamabad, Wah Campus, he holds a Master’s degree and a Bachelor’s degree in the same field. With a keen interest in machine learning and deep learning, Asif has contributed to notable publications in prestigious journals, focusing on human gait recognition and biometric techniques. His experience spans from research associate roles to lecturing positions at HITEC University Taxila, showcasing his commitment to academia and research. Asif’s technical proficiency includes programming languages such as MATLAB, JavaScript, and Java, along with extensive experience in project development and academic projects. He resides in Attock, Punjab, Pakistan, and is open to providing references upon request.

Professional Profile:

Scopus

🎓 Education:

Asif Mehmood has pursued a remarkable academic journey, demonstrating consistent excellence in his educational endeavors. He commenced his formal education with a Bachelor of Science in Computer Sciences (BSCS) from the University of Wah, spanning from 2013 to 2017, where he attained a commendable CGPA of 3.46 out of 4.0. Building upon this foundation, he pursued a Master of Science in Computer Sciences (MSCS) at COMSATS University Islamabad, Wah Campus, from 2018 to 2020, achieving an impressive CGPA of 3.84. Asif further advanced his academic pursuits by undertaking a PhD in Computer Sciences at the same institution, currently in progress, with an outstanding CGPA of 3.94 thus far.

💼 Experience:

Asif Mehmood has enriched his professional experience through roles at HITEC University Taxila. He commenced as a Research Associate in January 2022, where he actively contributed to research endeavors until June 2022. Building upon his expertise, Asif transitioned into the role of Lecturer in Computer Science at the same institution in September 2022, a position he currently holds. These roles have allowed Asif to apply his academic knowledge and research skills in a practical setting while also nurturing the next generation of computer science professionals through teaching and mentorship.

📝 Projects:

Asif Mehmood has demonstrated his proficiency in software development and research through various notable projects. Among these, he developed a Document Clustering Search Engine using Java and MySQL, showcasing his skills in both programming and database management. Additionally, his thesis focused on Prosperous Human Gait Recognition, employing Machine Learning techniques within MATLAB, highlighting his expertise in this advanced field. Furthermore, Asif has undertaken diverse academic projects encompassing Assembly Language programming, Android app development, and web development, reflecting his versatility and innovative approach to problem-solving in the realm of computer science.

Publication Top Notes:

  1. Human Gait Recognition by using Two Stream Neural Network along with Spatial and Temporal Features
    • Authors: Mehmood, A.; Amin, J.; Sharif, M.; Kadry, S.
    • Journal: Pattern Recognition Letters, 2024, 180, pp. 16–25
    • Citations: 0
  2. Prosperous Human Gait Recognition: an end-to-end system based on pre-trained CNN features selection
    • Authors: Mehmood, A.; Khan, M.A.; Sharif, M.; Riaz, N.; Ashraf, I.
    • Journal: Multimedia Tools and Applications, 2024, 83(5), pp. 14979–14999
    • Citations: 24
  3. TS2HGRNet: A paradigm of two stream best deep learning feature fusion assisted framework for human gait analysis using controlled environment in smart cities
    • Authors: Khan, M.A.; Mehmood, A.; Kadry, S.; Alsubai, S.; Alqatani, A.
    • Journal: Future Generation Computer Systems, 2023, 147, pp. 292–303
    • Citations: 3
  4. Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine
    • Authors: Khan, M.A.; Kadry, S.; Parwekar, P.; Khan, J.A.; Naqvi, S.R.
    • Journal: Complex and Intelligent Systems, 2023, 9(3), pp. 2665–2683
    • Citations: 23
  5. Human gait recognition: A deep learning and best feature selection framework
    • Authors: Mehmood, A.; Khan, M.A.; Tariq, U.; Mostafa, R.R.; ElZeiny, A.
    • Journal: Computers, Materials and Continua, 2021, 70(1), pp. 343–360
    • Citations: 8

 

 

Dr. Konstantinos A. Tsintotas | Robotics and AI | Best Researcher Award

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Dr. Konstantinos A. Tsintotas | Robotics and AI | Best Researcher Award

Dr. Konstantinos A. Tsintotas, Democritus University of Thrace, Greece

Dr. Konstantinos A. Tsintotas is a highly educated individual with a Ph.D. in Robotics from Democritus University of Thrace, Greece, complemented by a diverse academic background including a Certificate of Pedagogical and Teaching Competence. His expertise extends across academia and industry, having served as an Adjunct Assistant Professor at the International Hellenic University and holding positions as a Researcher at both Democritus University and Aristotle University of Thessaloniki. With a strong foundation in mechatronics and automation engineering, Dr. Tsintotas is proficient in computer vision, electronics, and various programming languages such as Matlab and Python. His practical experience as an Automation Engineer further enhances his skill set, making him adept at problem-solving and fostering collaborative environments.

Professional Profile:

Scopus

Orcid

Google Scholar

🎓 Education:

Dr. Konstantinos A. Tsintotas holds a Ph.D. in Robotics from Democritus University of Thrace, Greece. He also earned a Certificate of Pedagogical and Teaching Competence from the School of Pedagogical and Technological Education in Kozani, Greece, a Master of Science in Mechatronics from the Technological Education Institute of Western Macedonia, and a Bachelor of Science in Automation Engineering from the Technological Education Institute of Chalkida. Additionally, he holds a Certificate of Competency in English from The University of Michigan, Ann Arbor.

👨‍🏫 Academia:

Dr. Tsintotas has served as an Adjunct Assistant Professor at the International Hellenic University in Serres and as an Adjunct Lecturer at the International Hellenic University in Katerini.

📚 Research Focus:

Dr. Tsintotas is a leading researcher in the dynamic field of mobile robotics 🤖 With a focus on autonomous systems, his work pushes the boundaries of innovation in visual-based navigation and place recognition 🌐 His contributions pave the way for safer and more efficient autonomous vehicles and robotic systems, shaping the future of technology and exploration 🚀

🔬 Research Experience:

Currently, he is engaged as a Postdoctoral Researcher at Democritus University of Thrace. Previously, he held positions as a Researcher and Teaching Assistant at the same university and as a Researcher at Aristotle University of Thessaloniki. Dr. Tsintotas also has practical experience as an Automation Engineer at Zalikas – Liontas construction company and as an Automation Engineer Intern at COOPER Industries – Menvier Univel Ltd.

💻 Skills:

Dr. Tsintotas is proficient in computer vision, electronics, and data analysis & visualization. He is skilled in programming languages such as Matlab, Python, HTML, and Ladder. His soft skills include analytical & critical thinking, creativity, productivity, and being a team player.

📚 Publication Impact and Citations :

Scopus Metrics:

  • 📝 Publications: 36 documents indexed in Scopus.
  • 📊 Citations: A total of 430 citations for his publications, reflecting the widespread impact and recognition of Dr. Tsintotas’s research within the academic community.

Google Scholar Metrics:

  • All Time:
    • Citations: 586 📖
    • h-index: 15  📊
    • i10-index: 19 🔍
  • Since 2018:
    • Citations: 584 📖
    • h-index: 15 📊
    • i10-index: 19 🔍

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

Publication Top Notes:

 

 

 

 

Dr. Saman Iftikhar | Machine learning and Deep learning

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Dr. Saman Iftikhar : Leading Researcher in Machine learning and Deep learning

Dr. Saman Iftikhar, Arab Open University, Saudi Arabia

🎓 Dr. Saman Iftikhar is an accomplished academician with a distinguished background in Information Technology. Holding a Ph.D. in Information Technology from the School of Electrical Engineering and Computer Sciences (SEECS) at the National University of Science and Technology (NUST) in Islamabad, Pakistan, achieved in October 2014 with an impressive GPA of 3.5/4.0. Earlier, [Name] earned an MS in Information Technology from the same institution in February 2008, with a notable GPA of 3.33/4.0.

🏫 Currently, Dr. Saman Iftikhar serves as an Assistant Professor at the Arab Open University in Jeddah, Saudi Arabia, since September 2018. In this role, [he/she] passionately imparts knowledge through teaching advanced courses, dedicating 12 credit hours per week. Concurrently, [Name] actively contributes as a member of the Information Technology and Computing (ITC) department Exam Committee, managing all examination-related tasks since September 2021.

🌐 Dr. Saman Iftikhar brings international experience to the table, having served as the Chairman of the Software Engineering Department at the University of Prince Mugrin in Medinah, Saudi Arabia, from August 2017 to May 2018. In this capacity, [he/she] skillfully coordinated departmental tasks and played a pivotal role in designing the software engineering bachelor’s degree curriculum and course plan. Driven by a commitment to education, [Name] also supervises students’ graduation projects annually, guiding them through proposals, reports, implementations, and presentations.

🔍 With a solid academic foundation, a wealth of teaching experience, and a dedication to academic leadership, Dr. Saman Iftikhar continues to make significant contributions to the field of Information Technology and higher education.

Professional Profiles : 🌐

Scopus

Google Scholar

ORCID

🧠 Research Interests 🔬🌐 :

  • 🔐 Information Security
  • 🛡️ Data Security
  • 🌐 Cyber Security
  • 🔍 Digital Forensics
  • 🖥️ Computer Networking
  • 🔗Internet of Things (IoT)
  • 🧲Data Mining
  • 📊 Distributed Systems
  • 🌍 Semantic Web
  • 🤖 Artificial Intelligence
  • ☁️💻 Cloud Computing
  • 🖥️ Software Engineering
  • 💽 Databases

📚 Publication Impact and Citations : 

Scopus Metrics:

  • 📝 Publications: 19 documents indexed in Scopus.
  • 📊 Citations: A total of 197 citations for his publications, reflecting the widespread impact and recognition of Dr. Saman Iftikhar’s research within the academic community.

Google Scholar Metrics:

  • All Time:
    • Citations: 322 📖
    • h-index: 8 📊
    • i10-index: 7 🔍
  • Since 2018:
    • Citations: 255 📖
    • h-index: 7 📊
    • i10-index: 6 🔍

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

Publications ( Top Note ) :

1.  An evolution based hybrid approach for heart diseases classification and associated risk factors identification

Published: 2017, Journal: Biomedical Research, Cited By: 97

2.  RRW—A robust and reversible watermarking technique for relational data

Published: 2014, Journal: IEEE Transactions on Knowledge and Data Engineering, Cited By: 88

3.  A reversible watermarking technique for social network data sets for enabling data trust in cyber, physical, and social computing

Published: 2015, Journal: IEEE Systems Journal, Cited By: 22

4.  A survey on reversible watermarking techniques for relational databases

Published: 2015, Journal: Security and Communication Networks, Cited By: 18

5.  Biodiversity of insects associated with sugarcane crop in Faisalabad

Published: 2004, Journal: Pak. Entomol, Cited By: 16

6.  Intelligent data analytics in energy optimization for the internet of underwater things

Published: 2021, Journal: Soft Computing, Cited By: 12

7.  A Novel Blockchain Based Secured and QoS Aware IoT Vehicular Network in Edge Cloud Computing

Published: 2022, Journal: IEEE Access, Cited By: 11

8.  GenInfoGuard—a robust and distortion-free watermarking technique for genetic data

Published: 2015, Journal: PloS One, Cited By: 8

9.  Semantic interoperability in E-health for improved healthcare

Published: 2012, Journal: Semant, ACTION-APPLICATIONS Scenar, Cited By: 8

10.  A Semantic Methodology for Customized Healthcare Information Provision

Published: 2012, Journal: Information Sciences Letters, Cited By: 6