Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

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Mr. Pingjie Ou | artificial intelligence | Best Researcher Award

Student, Guangxi University, China

Pingjie Ou is a passionate master’s student at Guangxi University, China, specializing in edge computing, cloud computing, and machine learning. With a strong academic foundation and growing research portfolio, he is actively contributing to next-generation computing paradigms. His early contributions in deep reinforcement learning applications for vehicular networks have already gained traction within the academic community. ๐Ÿง ๐Ÿ’ก

Professional Profile

Scopus

๐ŸŽ“ Education Background

Pingjie Ou is currently pursuing his master’s degree at Guangxi University, one of the prominent institutions in China. His academic focus lies in electrical and computer engineering, with emphasis on distributed computing and artificial intelligence. ๐Ÿ“˜๐Ÿซ

๐Ÿ’ผ Professional Experience

Although a student, Pingjie Ou has engaged in substantial research activities under funded projects including The National Natural Science Foundation of China (No. 62162003) and GuikeZY24212059 supported by the Guangxi Province. His active involvement in real-time research scenarios demonstrates promising professional potential. ๐Ÿ”ฌ๐Ÿ“Š

๐Ÿ… Awards and Honors

As an emerging scholar, Pingjie Ou has not yet accumulated major awards but has gained recognition through impactful publications and research citations. His growing citation record and h-index reflect the potential for future accolades. ๐Ÿ†๐Ÿ“ˆ

๐Ÿ” Research Focus

His core research interests include edge computing, cloud computing, vehicular networks, and machine learning. He is particularly focused on cooperative caching, resource management, and optimizing network efficiency using artificial intelligence approaches such as deep reinforcement learning. ๐Ÿš—โ˜๏ธ๐Ÿ“ถ

๐Ÿงพ Conclusion

Pingjie Ou is a driven young researcher dedicated to advancing intelligent computing technologies. With strong academic grounding, collaborative research exposure, and early citation impact, he stands as a promising candidate for recognition in the domain of computer science and engineering. His scholarly journey is on a clear upward trajectory. ๐Ÿš€๐Ÿ“š

๐Ÿ“š Publication Top Note

  1. PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning
    ๐Ÿ“… Published Year: 2025
    ๐Ÿ“– Journal: Ad Hoc Networks
    ๐Ÿ”— 10.1016/j.adhoc.2025.103888

 

Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Prof. Dr. Mohamed Maher Ben Ismail, King Saud University, Saudi Arabia

Dr. Mohamed Maher Ben Ismail is a distinguished full professor in the Computer Science Department at the College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia . With a prolific academic and research background spanning over two decades, Dr. Ben Ismail is recognized for his contributions in artificial intelligence, image processing, and data mining. His work bridges theory and practical applications in machine learning and statistical modeling, making him a leading voice in his field ๐ŸŒ๐Ÿ“š.

Professional Profile

Google Scholar

Scopus

๐ŸŽ“ Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) ๐Ÿ‡บ๐Ÿ‡ธ, where his dissertation focused on image annotation and retrieval using multi-modal feature clustering. He also earned a Masterโ€™s in Automatic and Signal Processing and a Bachelor’s in Electrical Engineering from the National School of Engineering of Tunis, Tunisia ๐Ÿ‡น๐Ÿ‡ณ. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams ๐Ÿง ๐Ÿ“˜.

๐Ÿง‘โ€๐Ÿซ Professional Experience

Dr. Ben Ismail currently serves as a Full Professor at King Saud University (2021โ€“present), following roles as Associate Professor (2017โ€“2021) and Assistant Professor (2011โ€“2017). Previously, he worked as a Design & Development Engineer at STMicroelectronics, Tunisia, and as a Graduate Research Assistant at the University of Louisvilleโ€™s Multimedia Research Lab, where he pioneered work on CBIR systems and integrated machine learning approaches. His academic role includes supervising thesis work, lecturing across AI, ML, algorithm design, and image processing ๐Ÿ’ผ๐Ÿ‘จโ€๐Ÿซ.

๐Ÿ† Awards and Honors

Throughout his career, Dr. Ben Ismail has received numerous accolades, including the Best Faculty Member Award (2017) at King Saud University, the Graduate Deanโ€™s Citation Award (2011), and the IEEE Outstanding CECS Student Award (2011) ๐Ÿฅ‡. He is also a member of the Golden Key International Honor Society and received early recognition through his promotion at STMicroelectronics and various graduate assistantships and scholarships ๐ŸŽ–๏ธ.

๐Ÿ”ฌ Research Focus

Dr. Ben Ismailโ€™s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion ๐Ÿค–๐Ÿง . His work emphasizes robust statistical modeling and intelligent systems design, often applied to domains like IoT security, brain tumor detection, real estate prediction, and hyperspectral imaging. His prolific publication record in top-tier journals and conferences highlights his continuous contributions to advanced computational techniques and interdisciplinary innovation ๐Ÿ“Š๐Ÿ“ˆ.

๐Ÿ“Œ Conclusion

With a solid educational foundation, impactful research contributions, and extensive teaching experience, Dr. Mohamed Maher Ben Ismail stands as a key figure in advancing AI-driven solutions in academia and industry. His dedication to excellence and innovation marks him as a thought leader and an inspirational academic voice in the global computer science community ๐ŸŒŸ๐Ÿง‘โ€๐Ÿ”ฌ.

๐Ÿ“š Top Publications Notes

  1. YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
    ๐Ÿ“… Published in: Sensors, 2025
    ๐Ÿ” Cited by: 12 articles (as of mid-2025)
    ๐Ÿง  Highlights: Proposes a YOLO-based system for recognizing actions across video frames.

  2. MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
    ๐Ÿ“… Published in: Sensors, 2025
    ๐Ÿ” Cited by: 9 articles
    ๐Ÿง  Highlights: Enhances brain tumor classification using deep adversarial networks.

  3. RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic
    ๐Ÿ“… Published in: Sensors, 2024
    ๐Ÿ” Cited by: 18 articles
    ๐Ÿ” Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    ๐Ÿ“… Published in: Cancers, 2024
    ๐Ÿ” Cited by: 25 articles
    ๐Ÿงฌ Highlights: Applies CNNs to early skin cancer detection using medical images.

  5. A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five YOLO Versions
    ๐Ÿ“… Published in: Computation, 2024
    ๐Ÿ” Cited by: 14 articles
    ๐Ÿ’ก Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

  6. Toward an Improved Machine Learning-based Intrusion Detection for IoT Traffic
    ๐Ÿ“… Published in: Computers, 2023
    ๐Ÿ” Cited by: 20 articles
    ๐Ÿ”’ Highlights: Develops a secure ML framework to prevent intrusions in smart devices.

  7. Simultaneous Deep Learning-based Classification and Regression for Company Bankruptcy Prediction
    ๐Ÿ“… Published in: Journal of Business & Economic Management, 2023
    ๐Ÿ” Cited by: 8 articles
    ๐Ÿ’ผ Highlights: Innovative DL model integrating financial classification with regression.

  8. Novel Dual-Constraints Based Semi-Supervised Deep Clustering Approach
    ๐Ÿ“… Published in: Sensors, 2025
    ๐Ÿ” Cited by: 6 articles
    ๐Ÿ“Š Highlights: Enhances clustering accuracy using semi-supervised constraints in DL.

  9. Better Safe than Never: A Survey on Adversarial Machine Learning Applications towards IoT Environment
    ๐Ÿ“… Published in: Applied Sciences, 2023
    ๐Ÿ” Cited by: 22 articles
    ๐Ÿ” Highlights: Comprehensive survey exploring adversarial ML attacks and defense for IoT.

  10. Detecting Insults on Social Network Platforms Using a Deep Learning Transformer-Based Model
    ๐Ÿ“… Published in: IGI Global Book Chapter, 2025
    ๐Ÿ” Cited by: 11 articles
    ๐ŸŒ Highlights: Uses transformer models to detect hate speech and insults online.

 

Dr. Zeynep Ilkilic Aytac | Artificial Intelligence | Best Researcher Award

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Dr. Zeynep Ilkilic Aytac | Artificial Intelligence | Best Researcher Award

Dr Lecturer, Ondokuzmayฤฑs University, Turkey

Dr. Zeynep Ilkilic Aytac is a dynamic and innovative academician serving as a Lecturer at Ondokuz Mayฤฑs University, YeลŸilyurt Demir ร‡elik Vocational School, Department of Mechatronics ๐Ÿซ. With over eight years of teaching experience, she has contributed significantly to interdisciplinary research that merges mechatronics, artificial intelligence ๐Ÿค–, and sustainable technologies ๐ŸŒฑ. Her strong academic foundation and passion for practical innovation enable her to mentor engineering students while advancing the frontiers of medical diagnostics and control systems. She is widely recognized for her work in MEMS gyroscope control, CNN-based cancer detection, and emission modeling using AI.

Publication Profile

๐ŸŽ“ Education Background

Dr. Aytac earned her BSc, MSc, and PhD degrees in Mechatronics Engineering from Fฤฑrat University, Turkey . Her academic journey showcases a strong foundation in mechanical-electrical integration, AI-driven design, and intelligent control systems. Her doctoral research focused on developing robust control strategies for MEMS gyroscopes, laying the groundwork for her multifaceted research career.

๐Ÿ’ผ Professional Experience

Currently a Lecturer at Ondokuz Mayฤฑs University, Dr. Aytac brings over eight years of higher education teaching and project supervision experience. She has led various academic initiatives and research projects that combine engineering principles with AI and sustainability ๐ŸŒ. Her interdisciplinary projects have strengthened both academic and industry collaborations, reflecting her commitment to applied research and impactful innovation.

๐Ÿ… Awards and Honors

Dr. Aytac has gained recognition for her research through publication in reputable international journals and conference proceedings ๐Ÿ†. Although specific awards are not listed, her extensive interdisciplinary contributions and active role in innovation-driven education suggest an academic career marked by peer respect and institutional acknowledgment.

๐Ÿ”ฌ Research Focus

Her research interests lie in the robust control of MEMS gyroscopes, artificial intelligence in medical imaging ๐Ÿง , and emission prediction from internal combustion systems using neural networks. She has also focused on CNN-based thyroid cancer detection, leveraging hybrid metaheuristic optimization algorithms like COOT, GWO, PSO, and CMA-ES. Her contributions uniquely combine mechatronics, control theory, deep learning, and sustainability for real-world applications across engineering and healthcare.

๐Ÿงฉ Conclusion

Dr. Zeynep Ilkilic Aytac exemplifies the spirit of modern engineering innovationโ€”bridging theoretical knowledge with hands-on impact. Her work continues to shape the convergence of control systems, AI, and biomedical diagnostics, enriching both academic fields and practical industries ๐Ÿ”ง๐Ÿงฌ. Through dedicated teaching, collaborative research, and a commitment to sustainable technology, she inspires the next generation of engineers and scientists.

๐Ÿ“š Top Publicationsย 

AI-Based Emission Prediction Using Artificial Neural Networks Optimized by CMA-ES Algorithm.
Journal: Energy Reports, Year: 2022
Cited by: 24 articles

Robust Control of MEMS Gyroscopes Using Adaptive Sliding Mode Techniques.
Journal: Microsystem Technologies, Year: 2021
Cited by: 17 articles

Deep CNN Optimization for Thyroid Cancer Detection Using GWO and PSO.
Journal: Sensors, Year: 2023
Cited by: 12 articles

Hybrid AI Approaches in Digital Pathology: A CNN-Based Study.
Journal: IEEE Access, Year: 2022
Cited by: 9 articles

ย Metaheuristic Optimization in CNNs for Histopathological Image Classification.
Journal: Expert Systems with Applications, Year: 2023
Cited by: 7 articles

QIANG QU | Artificial Intelligence Award | Best Researcher Award

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Prof. QIANG QU | Artificial Intelligence Award | Best Researcher Award

PROFESSOR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Dr. Qiang Qu is a distinguished professor and a leading researcher in blockchain, data intelligence, and decentralized systems. He serves as the Director of the Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Additionally, he holds a professorship at Shenzhen University of Advanced Technology and has previously served as a guest professor at The Chinese University of Hong Kong (Shenzhen). Dr. Qu has also contributed as the Director and Chief Scientist of Huawei Blockchain Lab. With a strong international academic presence, he has held research positions at renowned institutions such as ETH Zurich, Carnegie Mellon University, and Nanyang Technological University. His pioneering work focuses on scalable algorithm design, data sense-making, and blockchain technologies, making significant contributions to AI, data systems, and interdisciplinary studies.

Publication Profile

๐ŸŽ“ Education

Dr. Qiang Qu earned his Ph.D. in Computer Science from Aarhus University, Denmark, under the supervision of Prof. Christian S. Jensen. His doctoral research was supported by the prestigious GEOCrowd project under Marie Skล‚odowska-Curie Actions. He further enriched his academic journey as a Ph.D. exchange student at Carnegie Mellon University, USA. He holds an M.Sc. in Computer Science from Peking University, China, and a B.S. in Management Information Systems from Dalian University of Technology.

๐Ÿ’ผ Experience

Dr. Qu has a diverse professional background, reflecting his global expertise. Since 2016, he has been a professor at SIAT, leading groundbreaking research in blockchain and distributed IoT security. He also served as Vice Director of Hangzhou Institutes of Advanced Technology (SIATโ€™s Hangzhou branch). Prior to this, he was an Assistant Professor and the Director of Dainfos Lab at Innopolis University, Russia. His research journey includes being a visiting scientist at ETH Zurich, a visiting scholar at Nanyang Technological University, and a research fellow at Singapore Management University. He also gained industry experience as an engineer at IBM China Research Lab.

๐Ÿ… Awards and Honors

Dr. Qu has received several national and international research grants, recognizing his impactful contributions to blockchain and AI-driven data intelligence. He is a prominent editorial board member of the Future Internet Journal and serves as a guest editor for multiple high-impact journals. As an active contributor to the research community, he has been a TPC (Technical Program Committee) member for prestigious conferences and regularly reviews top-tier AI and data systems journals.

๐Ÿ”ฌ Research Focus

Dr. Quโ€™s research interests revolve around data intelligence and decentralized systems, with a strong focus on blockchain, scalable algorithm design, and data-driven decision-making. His work has been instrumental in developing efficient data parallel approaches, AI-driven network analysis, and cross-blockchain data migration techniques. His interdisciplinary contributions bridge AI, IoT security, and geospatial analytics, driving innovation in secure and intelligent computing.

๐Ÿ”š Conclusion

Dr. Qiang Qu stands as a thought leader in blockchain and data intelligence, combining academic excellence with real-world impact. His contributions to AI-driven decentralized systems and scalable data solutions continue to shape the fields of computer science and IoT security. His extensive research collaborations, editorial roles, and international experience make him a key figure in advancing secure and intelligent computing technologies. ๐Ÿš€

๐Ÿ“š Publications

SNCA: Semi-supervised Node Classification for Evolving Large Attributed Graphsย โ€“ IEEE Big Data Mining and Analytics (2024). Cited in IEEE ๐Ÿ“–

CIC-SIoT: Clean-Slate Information-Centric Software-Defined Content Discovery and Distribution for IoTย โ€“ IEEE Internet of Things Journal (2024). Cited in IEEE ๐Ÿ“–

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computingย โ€“ IEEE Journal on Selected Areas in Communications (2022). Cited in IEEE ๐Ÿ“–

On Time-Aware Cross-Blockchain Data Migrationโ€“ Tsinghua Science and Technology (2024). Cited in Tsinghua University ๐Ÿ“–

Few-Shot Relation Extraction With Automatically Generated Promptsย โ€“ IEEE Transactions on Neural Networks and Learning Systems (2024). Cited in IEEE ๐Ÿ“–

Opinion Leader Detection: A Methodological Reviewย โ€“ Expert Systems with Applications (2019). Cited in Elsevier ๐Ÿ“–

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classificationโ€“ IEEE Transactions on Affective Computing (2021). Cited in IEEE ๐Ÿ“–

Efficient Online Summarization of Large-Scale Dynamic Networks – ย IEEE Transactions on Knowledge and Data Engineering (2016). Cited in IEEE ๐Ÿ“–