Prof. Changyi XU | Fault Diagnosis | Best Researcher Award

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Prof. Changyi XU | Fault Diagnosis | Best Researcher Award

Prof. Changyi XU , Associate professor , Dalian University of Technology, China.

Dr. Changyi Xu is a dedicated Associate Researcher at the School of Control Science and Engineering, Dalian University of Technology, China . With a strong foundation in automation and physics, Dr. Xu brings extensive expertise in digital twins, fault diagnosis, and model-based system engineering (MBSE). His interdisciplinary approach integrates control science, embedded intelligence, and system modeling to address real-world engineering problems in aerospace and automation. He actively contributes to high-impact journals and international conferences while leading innovative research funded by national and provincial agencies.

Professional Profile

Scopus

ORCID

🎓 Education Background

Dr. Xu obtained his Ph.D. in Automation from the Institut National des Sciences Appliquées de Lyon, France in December 2020. Earlier, he earned his Master’s degree in Condensed Matter Physics from the University of Chinese Academy of Sciences in January 2016. His academic journey began with a Bachelor’s degree in Electronic Information Science and Technology from Jilin University in September 2012.

🧑‍🏫 Professional Experience

Since July 2021, Dr. Xu has been serving as an Associate Researcher at Dalian University of Technology, School of Control Science and Engineering. He has led and contributed to multiple government and industry-sponsored projects, including collaborations with the Ministry of Science and Technology, Liaoning Province, Beijing Institute of Control, and private sector firms. His work spans engine control systems, aerospace modeling, and smart platform development.

🏆 Awards and Honors

Dr. Xu has earned several prestigious accolades, including a national first prize and provincial special prize in the 18th “Challenge Cup” Innovation Competition, and a Gold and National Bronze Award at the Internet Innovation Competition for graduate students. He also received the Third Prize in the “Course Ideology and Politics” Teaching Competition at Dalian University of Technology, showcasing his commitment to both research and teaching excellence.

🔬 Research Focus

His core research interests include fault diagnosis of complex control systems, digital twin technologies, and Model-Based System Engineering (MBSE). Dr. Xu actively publishes in top-tier journals and contributes to major conferences, working on topics such as adaptive control, perovskite-based devices, drag-free satellite thrust control, and reinforcement learning in speed control systems. He also serves as a committee member in the Embodied Intelligence Committee of the Chinese Association of Automation.

🧩 Conclusion

Dr. Changyi Xu exemplifies interdisciplinary innovation in the field of control engineering. His advanced research in automation and digital modeling bridges theoretical advancements with real-world applications. Through international education, impactful publications, and consistent academic recognition, he continues to lead groundbreaking efforts in smart system control and intelligent engineering design.

📚 Publication Top Notes

  1. Adaptive Anti-Disturbance Bumpless Transfer Control for Switched Neural Network Systems – IEEE Transactions on Circuits and Systems I, 2024
    Cited by: 5 articles

  2. Micro-Newton scale variable thrust control technique and its noise problem for drag-free satellite platforms: a review – Journal of Zhejiang University – Science A, 2024
    Cited by: 7 articles

  3. A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction – IEEE Transactions on Industrial Informatics, 2024
    Cited by: 3 articles

  4. Domain Adversarial Enhanced Multi-Channel Graph Networks for Aeroengine Gas Path Fault Diagnosis – IECON Proceedings, 2024
    Cited by: 2 articles

  5. Set-Membership State Estimation for 2-D Roesser Systems Based on Zonotope Radius and Its Application – IEEE Transactions on Circuits and Systems II, 2024
    Cited by: 4 articles

  6. Vacuum-Deposited Perovskite LED by Interface Defect Passivation With Better Color Stability – IEEE Photonics Technology Letters, 2024
    Cited by: 3 articles

  7. Control of perovskites crystallization via polymer scaffold towards pure blue Light-emitting diodes – Materials Letters, 2022
    Cited by: 9 articles

 

 

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 🌐📚.

Professional Profile

Google Scholar

Scopus

🎓 Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) 🇺🇸, where his dissertation focused on image annotation and retrieval using multi-modal feature clustering. He also earned a Master’s in Automatic and Signal Processing and a Bachelor’s in Electrical Engineering from the National School of Engineering of Tunis, Tunisia 🇹🇳. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams 🧠📘.

🧑‍🏫 Professional Experience

Dr. Ben Ismail currently serves as a Full Professor at King Saud University (2021–present), following roles as Associate Professor (2017–2021) and Assistant Professor (2011–2017). Previously, he worked as a Design & Development Engineer at STMicroelectronics, Tunisia, and as a Graduate Research Assistant at the University of Louisville’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 💼👨‍🏫.

🏆 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 Dean’s Citation Award (2011), and the IEEE Outstanding CECS Student Award (2011) 🥇. 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 🎖️.

🔬 Research Focus

Dr. Ben Ismail’s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion 🤖🧠. 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 📊📈.

📌 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 🌟🧑‍🔬.

📚 Top Publications Notes

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

  2. MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 9 articles
    🧠 Highlights: Enhances brain tumor classification using deep adversarial networks.

  3. RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic
    📅 Published in: Sensors, 2024
    🔍 Cited by: 18 articles
    🔐 Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    📅 Published in: Cancers, 2024
    🔍 Cited by: 25 articles
    🧬 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
    📅 Published in: Computation, 2024
    🔍 Cited by: 14 articles
    💡 Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

  6. Toward an Improved Machine Learning-based Intrusion Detection for IoT Traffic
    📅 Published in: Computers, 2023
    🔍 Cited by: 20 articles
    🔒 Highlights: Develops a secure ML framework to prevent intrusions in smart devices.

  7. Simultaneous Deep Learning-based Classification and Regression for Company Bankruptcy Prediction
    📅 Published in: Journal of Business & Economic Management, 2023
    🔍 Cited by: 8 articles
    💼 Highlights: Innovative DL model integrating financial classification with regression.

  8. Novel Dual-Constraints Based Semi-Supervised Deep Clustering Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 6 articles
    📊 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
    📅 Published in: Applied Sciences, 2023
    🔍 Cited by: 22 articles
    🔍 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
    📅 Published in: IGI Global Book Chapter, 2025
    🔍 Cited by: 11 articles
    🌐 Highlights: Uses transformer models to detect hate speech and insults online.

 

Assist. Prof. Dr. Yuchae Jung | Generative AI | Distinguished Scientist Award

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Assist. Prof. Dr. Yuchae Jung | Generative AI | Distinguished Scientist Award

Assist. Prof. Dr. Yuchae Jung, Open Cyber University of Korea , South Korea.

Dr. Yuchae Jung is an accomplished Affiliated Professor at KAIST School of Computing, Seoul, South Korea. With an interdisciplinary background spanning computer science, medical sciences, and artificial intelligence, she brings a unique integration of biomedical knowledge and computational innovation to her research. Over the years, Dr. Jung has held key academic and research roles in prestigious institutions, including Harvard Medical School and State University of New York. Her professional journey reflects a strong commitment to advancing digital healthcare, AI-driven diagnostics, and computational biology. 🧠💻🧬

Professional Profile

Google Scholar

🎓 Education Background

Dr. Jung earned her Ph.D. and M.S. in Medical Science from The Catholic University of Korea (2008, 2002), following her undergraduate degree in Computer Science from Sookmyung Women’s University in 2000. This solid academic foundation has enabled her to contribute innovatively to both computer science and medical informatics. 🎓📚

🧪 Professional Experience

Dr. Jung is currently affiliated with KAIST’s School of Computing as a professor. She has previously held significant roles at The Catholic University of Korea, Boin IT, Seoul National University, and Sookmyung Women’s University. She has also conducted postdoctoral research at Brigham & Women’s Hospital (Harvard Medical School) and State University of New York. Her professional engagements include lectures, research leadership, and AI-based system development across medical and computing fields. 🏥🖥️📊

🏅 Awards and Honors

Dr. Jung has been the Principal Investigator of several prestigious grants from organizations such as the Ministry of SMEs and Startups, National Library of Korea, Ministry of Science, and Ministry of Education. Her projects span from NLP-based clinical dialogue systems to cancer therapy algorithms and bioinformatics applications in glioblastoma research. She was also honored as a keynote speaker by The Korean Society of Pathologists. 🏆📜🇰🇷

🔬 Research Focus

Her core research interests lie in Medical AI, including deep transfer learning for digital pathology image analysis, clinical Natural Language Processing (Bio-NLP), and cancer genomics (TFs, repeat sequences, miRNAs). She also explores gene expression network analysis in cancer and functional informatics for precision diagnostics. Her work bridges cutting-edge AI with real-world healthcare applications. 🧬🤖📈

✅ Conclusion

Dr. Yuchae Jung is a pioneering figure in interdisciplinary AI and bioinformatics, contributing impactful research to cancer genomics and healthcare AI. With a dynamic academic trajectory and a clear focus on translational science, she continues to be a driving force in computational medicine and smart health systems. Her extensive contributions position her as a deserving candidate for recognition in digital healthcare innovation. 🌐💡👩‍⚕️

📝 Top Publications Highlights

  1. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning
    📅 Published: 2021 in MDPI Sensors
    📊 Cited by: 39 articles (Google Scholar)
    🔍 A groundbreaking study applying deep transfer learning for pathology image classification.

  2. Impact of tumor purity on immune gene expression and clustering analyses across multiple cancer types
    📅 Published: 2018 in Cancer Immunology Research
    📊 Cited by: 107 articles
    🔬 Investigates how tumor purity affects gene expression in cancer immunology.

  3. Hybrid-Aware Model for Senior Wellness Service in Smart Home
    📅 Published: 2017 in MDPI Sensors
    📊 Cited by: 25 articles
    🏡 Explores smart health monitoring using a hybrid AI model in smart homes.

  4. Aneuploidy meets network analysis: leveraging copy number alterations
    📅 Published: 2017 in Translational Cancer Research
    📊 Cited by: 15 articles
    🧬 Integrates systems biology with cancer genomics.

  5. Cancer stem cell targeting: Are we there yet?
    📅 Published: 2015 in Archives of Pharmacal Research
    📊 Cited by: 55 articles
    💡 Reviews strategies to target elusive cancer stem cells.

  6. Systemic approaches identify Z-ajoene as a GBM stem cell-specific targeting agent
    📅 Published: 2014 in Molecules and Cells
    📊 Cited by: 40+ articles
    🧪 Identifies garlic-derived compound with anti-glioblastoma activity.

  7. Numb regulates glioma stem cell fate and growth
    📅 Published: 2012 in Stem Cells
    📊 Cited by: 100+ articles
    📈 A critical study in stem cell regulation in glioma.

  8. GEAR: Genomic Enrichment Analysis of Regional DNA Copy Number Changes
    📅 Published: 2008 in Bioinformatics
    📊 Cited by: 80+ articles
    🧬 Proposes a novel method for regional DNA copy number analysis.

  9. DNA methylation patterns of ulcer-healing genes in gastric cancers
    📅 Published: 2010 in Journal of Korean Medical Science
    📊 Cited by: 35 articles
    🔬 Connects epigenetics with cancer pathology.

  10. PathCluster: a framework for gene set-based hierarchical clustering
    📅 Published: 2008 in Bioinformatics
    📊 Cited by: 90+ articles
    📂 Presents a tool widely adopted in gene expression analysis.

 

Dr. Panagiotis Marhavilas | Management Systems | Distinguished Scientist Award

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Dr. Panagiotis Marhavilas | Management Systems | Distinguished Scientist Award

Dr. Panagiotis Marhavilas, Electrical & Computer Engineer (Dipl. Eng., MSc, PhD x2), Laboratory Teaching & Research Staff Member, Department of Production & Management Engineering , Democritus University of Thrace, Greece

Dr. Panagiotis Marhavilas is a distinguished Electrical and Computer Engineer with dual PhDs—one in Health & Safety Management Systems and another in Space Science & Technology. Currently serving as a Laboratory Teaching and Research Staff Member at the Department of Production & Management Engineering, Democritus University of Thrace, Greece, he brings over a decade of academic excellence and research leadership. His work spans EU-funded projects, curriculum development, and editorial roles in prestigious journals. Recognized for risk analysis and occupational safety, Dr. Marhavilas has published widely and actively contributes to both scientific innovation and public policy development.

Professional Profile

Google Scholar

ORCID

Scopus

🎓 Education Background

Dr. Marhavilas holds a Diploma in Electrical and Computer Engineering, an MSc, and two PhDs—one focused on Health & Safety Management Systems and the other in Space Science and Technology. His academic journey is marked by interdisciplinary rigor, combining engineering with complex analytical systems. His education is rooted in research excellence and aligns with high-impact projects in collaboration with ESA, NASA, and EU-funded platforms. These qualifications form the backbone of his technical proficiency and innovative capacity in fields such as electrodynamics, occupational safety, and space data analysis.

💼 Professional Experience

With over 10 years in academia and research, Dr. Marhavilas is currently a faculty member at Democritus University of Thrace. He has played critical roles in policy analysis, EU-funded projects, and university administration. His consultancy work includes two industry-sponsored projects, and his earlier technical contributions to ESA/NASA missions (ULYSSES and VOYAGER) underscore his expertise in data analysis and space science. Beyond research, he mentors students, develops curricula, and chairs scientific committees at international conferences like RETASTE, reinforcing his commitment to education and innovation.

🏅 Awards and Honors

Dr. Marhavilas’ academic recognition includes editorial appointments in Sustainability (MDPI) and Discover Analytics (Springer Nature). He was selected as a Scientific Committee member for the RETASTE Conferences and has long been a committee member of Hellenic Society of Occupational and Environmental Medicine. While no formal individual awards are mentioned, his accolades include leadership roles in international collaborations and publication milestones in high-impact journals, signifying recognition by global academic and scientific communities.

🔬 Research Focus

His primary research areas include occupational health and safety, risk analysis, reliability in construction and industry, electrodynamics, and time series data analysis. His notable project, “System and Workplace Safety: Risk Analysis and Assessment – Probabilistic Approaches and Modelling”, funded by ETAA/TSMEDE, showcases his expertise in applied safety engineering. Additionally, his contributions to ESA and NASA missions and EU projects like SEPServer highlight his interdisciplinary research scope, merging engineering with space and computational science for robust data-driven solutions.

🧾 Conclusion

Dr. Panagiotis Marhavilas exemplifies academic excellence, interdisciplinary expertise, and commitment to public and workplace safety. His contributions—spanning from space science to health and risk management—are well-documented through over 100 Scopus-indexed publications and numerous conference presentations. His roles as guest editor, scientific committee member, and collaborator with international research agencies cement his position as a global thought leader in sustainable engineering. His nomination for the Distinguished Scientist Award reflects both his scholarly impact and societal contribution.

📚 Publication Top Notes

  1. Title: A new classification model of occupational risk for industries based on Bayesian networks
    Journal: Safety Science, 2022
    Cited by: 21 articles

  2. Title: A holistic safety analysis model using fuzzy cognitive maps
    Journal: Applied Sciences, 2020
    Cited by: 30 articles

  3. Title: Application of Probabilistic Safety Assessment in Occupational Systems
    Journal: International Journal of Occupational Safety and Ergonomics, 2019
    Cited by: 18 articles

  4. Title: A review of risk analysis and assessment methods in construction
    Journal: Journal of Construction Engineering and Management, 2021
    Cited by: 27 articles

  5. Title: Risk assessment and sustainability in industrial systems: A modeling approach
    Journal: Sustainability (MDPI), 2023
    Cited by: 9 articles

 

Assoc. Prof. Dr. Ying Zhao | fault diagnose | Best Researcher Award

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Assoc. Prof. Dr. Ying Zhao | fault diagnose | Best Researcher Award

IEEE, Dalian Maritime University, China

Dr. Ying Zhao is a distinguished Chinese academic and researcher in the field of intelligent control and automation, currently serving as an Associate Professor at the Department of Automation, Dalian Maritime University. With an active postdoctoral research role at Dalian University of Technology under the mentorship of Prof. Sun Ximing, he has significantly contributed to the domains of anti-disturbance and switching control systems. His research achievements are deeply rooted in cutting-edge technologies like cyber-physical systems, multi-agent navigation, and aero-engine systems.

Publication Profile

Scopus

🎓 Education Background

Dr. Zhao earned his Ph.D. in Control Theory and Control Engineering from Northeastern University in 2019 under the supervision of Prof. Zhao Jun, an IEEE Fellow. He previously completed his Master’s degree in Applied Mathematics from Liaoning Technical University in 2015 and his Bachelor’s degree in Mathematics and Applied Mathematics from the same university in 2012. His strong academic foundation in both mathematics and control engineering has guided his interdisciplinary research focus.

💼 Professional Experience

Since 2019, Dr. Zhao has been an Associate Professor at Dalian Maritime University, where he also mentors Master’s and PhD students. Concurrently, he has been pursuing postdoctoral research at the Dalian University of Technology under the guidance of Prof. Sun Ximing. His work has bridged both academia and industry through various collaborative and government-funded research projects in aerospace, robotics, and intelligent marine systems.

🏆 Awards and Honors

Dr. Zhao’s excellence in research and teaching has earned him numerous accolades, including the Liaoning Provincial Outstanding Thesis Advisor Award (2022), the Dalian Maritime University Teaching Excellence Award (2021 & 2023), and the prestigious China Postdoctoral International Exchange Fellowship (2020). These awards underscore his commitment to innovation and education in the field of control systems engineering.

🔬 Research Focus

Dr. Zhao’s research is centered on Intelligent Control for Autonomous Systems, focusing on switching and anti-disturbance control, networked cooperative control for unmanned vehicles, and robust cyber-physical applications. His work has led to the development of advanced control strategies that improve the performance and safety of multi-agent systems, unmanned surface vehicles (USVs), and aero-engine systems under complex dynamic environments.

✅ Conclusion

With a prolific research portfolio and a strong presence in both academic and industrial collaborations, Dr. Ying Zhao stands out as a leading figure in the field of control engineering. His work continues to advance intelligent systems, bringing innovation to real-world autonomous and aerospace applications.

📚 Top Research Publications with Highlights

  1. H∞ anti-disturbance event-triggered control for aero-engine systems via switched affine models
    Journal: IEEE Transactions on Aerospace and Electronic Systems, Year: 2024
    Cited by: 3 articles

  2. Time-event-memory triggered switching dynamic positioning for unmanned marine vehicles with mass-switched and dual-source disturbances
    Journal: IEEE Transactions on Intelligent Transportation Systems, Year: 2024, Vol. 25(12): 21210–21220
    Cited by: 5 articles

  3. H∞ anti-disturbance switching control for switched affine systems with its application to turbofan engine model
    Journal: IEEE Transactions on Circuits and Systems II: Express Briefs, Year: 2024
    Cited by: 2 articles

  4. Bumpless tracking switching control for interval type-2 switched positive T-S fuzzy systems
    Journal: IEEE Transactions on Fuzzy Systems, Year: 2024
    Cited by: 4 articles

  5. Dynamic memory event-triggered anti-disturbance control for switched cyber-physical systems and its application to switched RLC circuit
    Journal: IEEE Transactions on Circuits and Systems I, Year: 2024
    Cited by: 1 article

  6. Switching dynamic event-triggered prescribed performance control for underactuated ASVs
    Journal: IEEE Transactions on Vehicular Technology, Year: 2024
    Cited by: 2 articles

  7. Bumpless switching control for aircraft engine control systems with actuator fault based on switched LPV models
    Journal: IEEE Transactions on Industrial Electronics, Year: 2024
    Cited by: 3 articles

  8. Event-triggered adaptive anti-disturbance switching control for switched systems with dynamic neural network disturbance modeling
    Journal: IEEE Transactions on Neural Networks and Learning Systems, Year: 2023
    Cited by: 6 articles

  9. Event-triggered anti-disturbance control design for aeroengine systems via switched models
    Journal: AIAA Journal, Year: 2022, Vol. 60(9): 5448–5461
    Cited by: 9 articles

  10. Rate bumpless transfer control for switched linear systems and its application to aero-engine control design
    Journal: IEEE Transactions on Industrial Electronics, Year: 2020, Vol. 67(6): 4900–4910
    Cited by: 21 articles

 

Prof. Dr. Nicolaos Theodossiou | resource management | Best Researcher Award

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Prof. Dr. Nicolaos Theodossiou | resource management | Best Researcher Award

professor, Aristotle University of Thessaloniki, Greece

Professor Nicolaos Theodossiou is a highly esteemed academic and civil engineer at the Aristotle University of Thessaloniki, Greece. He serves as the Director of the Water Resources Engineering and Management Laboratory and Vice-President of the Department of Civil Engineering. Prof. Theodossiou is recognized internationally for his expertise in environmental engineering, water resources management, sustainable development, and renewable energy. He has led and participated in over 50 research projects and has delivered keynote speeches at more than 30 international conferences, solidifying his reputation as a leading voice in sustainability science.

Publication Profile

Google Scholar

🎓 Education Background

Prof. Theodossiou pursued his academic journey at the Aristotle University of Thessaloniki, where he obtained his degree and later earned a Ph.D. in Civil Engineering. His educational foundation laid the groundwork for his specialization in hydraulics and environmental engineering.

🏛️ Professional Experience

With decades of experience, Prof. Theodossiou has contributed extensively to academia, research, and engineering practice. As a professional civil engineer, he has been involved in numerous consulting and infrastructure studies. He holds leadership positions including Director of WREM Lab, Director of the postgraduate program in “Protection of the Environment and Sustainable Development,” and has served as President of the Hellenic Hydrotechnical Association and the Association of European Civil Engineering Faculties. He is an active member of multiple global sustainability-focused organizations, and founder of the Resilient and Innovation Sustainable Engineering hub (RISE).

🏅 Awards and Honors

Prof. Theodossiou is an elected Fellow of the World Academy of Art and Science (WAAS), a significant recognition of his global impact. He chairs the UN’s SDSN Black Sea and contributes to international bodies like CEET, AE4RIA, ESSSR, ENASRC, and GCIS, reflecting his leadership in climate action and sustainable development. His influence spans both academic and policy domains, bridging engineering expertise with global sustainability goals.

🔬 Research Focus

His research focuses on water resources engineering, environmental protection, hydropower, sustainable energy systems, groundwater optimization, flood risk assessment, and innovative engineering methods. He has published over 200 scientific papers and is widely cited, with over 2,000 citations recognizing his impactful contributions in environmental and civil engineering disciplines.

🔚 Conclusion

Prof. Nicolaos Theodossiou stands out as a visionary scholar and leader in environmental sustainability and civil engineering. Through academic excellence, global collaboration, and innovative research, he continues to shape the future of water management and sustainable development, serving as a role model for engineers and researchers worldwide.

📚 Top Publications Notes

  1. Evolution of water lifting devices (pumps) over the centuries worldwide
    Journal: Water (2015)
    Cited by: 401 articles

  2. Analysis of emerging technologies in the hydropower sector
    Journal: Renewable and Sustainable Energy Reviews (2019)
    Cited by: 352 articles

  3. Evaluation and optimisation of groundwater observation networks using the Kriging methodology
    Journal: Environmental Modelling & Software (2006)
    Cited by: 259 articles

  4. Sustainable energy modelling of non-interconnected Mediterranean islands
    Journal: Renewable Energy (2019)
    Cited by: 94 articles

  5. Assessing flood hazard at river basin scale with an index-based approach: The case of Mouriki, Greece
    Journal: Geosciences (2018)
    Cited by: 66 articles

  6. Combined use of BEM and genetic algorithms in groundwater flow and mass transport problems
    Journal: Engineering Analysis with Boundary Elements (1999)
    Cited by: 63 articles

  7. Multiobjective pump scheduling optimization using harmony search algorithm (HSA) and polyphonic HSA
    Journal: Water Resources Management (2013)
    Cited by: 62 articles

  8. Application of the harmony search optimization algorithm for the solution of the multiple dam system scheduling
    Journal: Optimization and Engineering (2013)
    Cited by: 58 articles

 

Dr. Congcong Wang | Wireless Communication | Best Researcher Award

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Dr. Congcong Wang | Wireless Communication | Best Researcher Award

Research Associate, Institute of Computing, Chinese Academy of Sciences, China

Congcong Wang is a dedicated Ph.D.-level Research Scientist currently serving as a Special Research Assistant at the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. She is recognized for her cutting-edge work in wireless communication systems, particularly in Visible Light Communication (VLC), Full-Spectrum Wireless Communication, and Body Area Networks. With over ten peer-reviewed publications in prestigious journals such as IEEE TCOM and Optical Express, and seven national and international patents, Dr. Wang combines theoretical insight with practical innovations in experimental platforms and system design.

Publication Profile

ORCID

🎓 Education Background

Congcong Wang has pursued advanced academic training leading to a doctoral-level research profile, focused intensively on next-generation wireless communication technologies. Her rigorous education laid a solid foundation in areas such as MIMO VLC systems, OFDM, and dimming control in communication systems.

💼 Professional Experience

Currently, Dr. Wang is affiliated with the Institute of Computing Technology, Chinese Academy of Sciences, where she works as a Special Research Assistant. In this role, she has contributed significantly to research and development efforts, combining theoretical modeling with real-world system implementations and contributing to international standards in wireless communication.

🏆 Awards and Honors

Congcong Wang has been recognized through numerous accolades, including accepted papers at prestigious IEEE conferences and journal publications. Her innovative work has led to multiple national and international patents, showcasing her as a leader in wireless communication R&D.

🔬 Research Focus

Her core research interests include MIMO-based Visible Light Communication, Full-Spectrum Wireless Communication systems, Robotic Body Area Wireless Networks, dynamic subcarrier activation schemes, and deep learning-based signal detection in OTFS systems. She is also exploring AI-enabled wireless sensing and channel prediction through advanced architectures like Sparse Graph Attention Networks and CanFormer.

📝 Conclusion

Congcong Wang stands out as a young, impactful researcher in the wireless communication domain. Her blend of theoretical rigor, experimental validation, and translational outcomes continues to influence next-generation communication technologies.

📚 Top Publications with Details

  1. Joint SIC-Based Precoding and Sub-connected Architecture Design for MIMO VLC Systems – IEEE Transactions on Communications, 2022

    • Cited by: 30+

    • DOI: 10.1109/TCOMM.2022.3142696

  2. Joint Ordered QR Precoding and SIC Detection for MIMO VLC Systems – Optics Express, 2023

    • Cited by: 10+

    • DOI: 10.1364/OE.471543

  3. A Dimmable OFDM Scheme With Dynamic Subcarrier Activation for VLC – IEEE Photonics Journal, 2020

    • Cited by: 40+

    • DOI: 10.1109/JPHOT.2020.2964744

  4. A Generalized Dimming Control Scheme for Visible Light Communications – IEEE Transactions on Communications, 2021

    • Cited by: 60+

    • DOI: 10.1109/TCOMM.2020.3038665

  5. SIC-based Precoding Scheme with Sub-connected Architecture for MIMO VLC Systems – IEEE GLOBECOM, 2022

    • Cited by: 15+

    • DOI: 10.1109/GLOBECOM48099.2022.10003233

  6. Generalized Dimming Control Scheme with Optimal Dimming Control Pattern for VLC – IEEE WCNC, 2020

    • Cited by: 25+

    • DOI: 10.1109/WCNC45663.2020.9118991

 

Mr. Md Saifur Rahman | Mathematical | Best Researcher Award

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Mr. Md Saifur Rahman | Mathematical | Best Researcher Award

Assistant Professor, RAJUK Uttara Model College, Bangladesh

Md Saifur Rahman is an accomplished Assistant Professor of Mathematics at RAJUK Uttara Model College, Dhaka, Bangladesh, with over 15 years of experience in academia. He is currently pursuing his Ph.D. at Bangladesh University of Engineering and Technology (BUET), focusing on the mathematical modeling and optimal control of brain encephalitis. He has made notable contributions in mathematical biology, computational modeling, and infectious disease epidemiology. His interdisciplinary work bridges mathematics with real-world biomedical problems, and he actively presents his research across national and international platforms.

Publication Profile

ORCID

🎓 Education Background

Md Saifur Rahman earned his MPhil in Mathematics from the Military Institute of Science and Technology (MIST), and his MS in Applied Mathematics from the University of Chittagong. He is currently pursuing a Ph.D. in Mathematical Modeling at BUET, Dhaka, Bangladesh, where his doctoral research explores the transmission dynamics and control strategies for brain encephalitis using mathematical and computational tools.

💼 Professional Experience

Currently serving as an Assistant Professor at RAJUK Uttara Model College, Dhaka, he has dedicated over 15 years to teaching and academic development. His expertise extends into scientific computing using COMSOL Multiphysics and MATLAB. He has delivered invited lectures on ICT-enabled education and continues to influence the educational landscape through both innovation and research.

🏆 Awards and Honors

Md Saifur Rahman received the Best Teacher Award in 2011 for his innovative use of multimedia content in teaching. He has also been recognized for delivering invited talks in ICT and education, and his academic influence spans several conferences across Bangladesh, Nepal, and Canada.

🔬 Research Focus

His research focuses on mathematical biology, fluid dynamics, and epidemiology, particularly modeling the transmission of infectious diseases like viral and bacterial encephalitis. He has developed mathematical models incorporating optimal control strategies to aid public health interventions. His work involves advanced computational tools such as COMSOL, Tecplot, and MATLAB to simulate disease spread and evaluate mitigation techniques.

🔚 Conclusion

Md Saifur Rahman is a dedicated scholar blending theoretical mathematics with applied health science, creating a meaningful impact in the fields of disease modeling and educational innovation. Through his interdisciplinary contributions and international presentations, he continues to build bridges between computation, biology, and societal well-being.

📚 Top Publications

  1. Mathematical Modeling and Optimal Control of Viral Encephalitis
    🔗 Published in MDPI Mathematics (2024)
    🗓 Year: 2024
    📊 Cited by: 4 articles on ResearchGate
    📌 Summary: This paper presents an optimal control model for viral encephalitis and its implications for intervention strategies.

  2. Numerical Study of Heat and Mass Transfer in Nanofluid Flow Through Lid-Driven Porous Cavity
    🔗 Published in Scopus-indexed conference proceedings (2022)
    🗓 Year: 2022
    📊 Cited by: 2 articles
    📌 Summary: Investigates nanofluid behavior using computational simulations with practical applications in energy systems.

  3. Computational Modeling of Japanese Encephalitis Transmission Dynamics
    🔗 Preprint on ResearchGate (2023)
    🗓 Year: 2023
    📊 Cited by: 1 article
    📌 Summary: Extends previous research to analyze vector-borne transmission and optimal interventions.

  4. Epidemiological Insights into Bacterial Encephalitis Using Mathematical Tools
    🔗 Under review, MDPI Mathematics
    🗓 Year: 2025 (expected)
    📌 Summary: Explores bacterial encephalitis modeling to enhance public health strategy development.

 

Dr. Yirga Yayeh Munaye | security | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Yirga Yayeh Munaye | secuirty | Best Researcher Award

PhD, Director of e-learning management unit, Injibara University, Ethiopia.

Dr. Yirga Yayeh Munaye is an Ethiopian academic and researcher with expertise in Electrical Engineering, Computer Science, and Information Technology. He currently serves as an Assistant Professor and Director of the E-learning Management Unit at Injibara University, Ethiopia. With a Ph.D. from National Taipei University of Technology, Taiwan, Dr. Munaye is known for his significant contributions in wireless communication, AI, and UAV-assisted resource management. His leadership in academia spans various universities, reflecting his passion for teaching, research, and community service.

Publication Profile

Google Scholar

🎓 Education Background

Dr. Munaye earned his Ph.D. in Electrical Engineering and Computer Science from National Taipei University of Technology (NTUT), Taiwan, in 2021 with a dissertation graded Excellent (91.4/100). Prior to that, he obtained an M.Sc. in Information Science from Addis Ababa University, Ethiopia, in 2014 and a B.Sc. in Information Technology from Bahir Dar University in 2009. His academic training reflects consistent excellence and specialization in advanced communication and AI applications.

👨‍🏫 Professional Experience

Dr. Munaye has served in various academic roles, including as Assistant Professor and Researcher at Injibara University since 2022, where he also coordinated postgraduate and community research services. Previously, he held teaching and research positions at Bahir Dar Institute of Technology and Assosa University. He has mentored Master’s and Ph.D. students, led network and internet chair units, and participated in proposal writing and journal editing, contributing significantly to Ethiopia’s higher education landscape.

🏆 Awards and Honors

Dr. Munaye has received numerous certificates and awards recognizing his academic contributions. These include participation in the Foundations for Excellence in Teaching Online masterclass (2023), the Science and Engineering Research training by AWB (2022), and international ICT training at XIDIAN University, China (2017). He has also earned honors for research writing, project proposal development, and higher diploma program achievements, underlining his commitment to continuous academic development.

🔬 Research Focus

Dr. Munaye’s research focuses on AI and wireless communication systems, UAV deployment strategies, mobile communications, and cybersecurity. He is especially passionate about the intersection of deep learning with resource management in next-generation networks. His work spans across emerging technologies including IoT security, biomedical sensors, and machine learning applications, reflecting a strong interdisciplinary and future-oriented research profile.

✅ Conclusion

With a career rooted in excellence, leadership, and innovation, Dr. Yirga Yayeh Munaye exemplifies the qualities of a modern researcher and educator. His contributions to teaching, mentoring, and groundbreaking research continue to make a lasting impact on Ethiopian academia and global knowledge systems.

📚 Top Publications Notes

  1. Cyber security: State of the art, challenges and future directions
    Cyber Security and Applications, 2024
    Cited by: 184

  2. UAV positioning for throughput maximization using deep learning approaches
    Sensors, 2019
    Cited by: 60

  3. An indoor and outdoor positioning using a hybrid of support vector machine and deep neural network algorithms
    Journal of Sensors, 2018
    Cited by: 58

  4. Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm
    IEEE ICASI Conference Proceedings, 2018
    Cited by: 38

  5. Big data: security issues, challenges and future scope
    International Journal of Computer Engineering & Technology, 2016
    Cited by: 37

  6. Deep-reinforcement-learning-based drone base station deployment for wireless communication services
    IEEE Internet of Things Journal, 2022
    Cited by: 33

  7. Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms
    WOCC Conference Proceedings, 2018
    Cited by: 33

  8. Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification
    International Journal of Information Technology, 2024
    Cited by: 32

 

Mr. Ayush Roy | Computer Vision | Young Researcher Award

Published on by Computer Scientists Awards

Mr. Ayush Roy | Computer Vision | Young Researcher Award

PhD, University at Buffalo, United States

Ayush Roy is an emerging researcher and innovator in the field of Electrical Engineering with a deep interest in AI, computer vision, and biomedical image analysis. Currently pursuing his B.E. at Jadavpur University, he has demonstrated exceptional potential through interdisciplinary research, AI-driven solutions, and impactful contributions to both academia and real-world applications. With multiple international publications and recognitions, Ayush is a dynamic force in the intersection of deep learning, signal processing, and intelligent systems.

Publication Profile

Google Scholar

🎓 Education Background

Ayush Roy is a final-year undergraduate student at Jadavpur University, West Bengal, India, enrolled in the Bachelor of Engineering (Electrical) program with an SGPA of 8.1/10 (2020–2024). He completed his schooling from Bhartiya Vidya Bhavan, West Bengal under the CBSE board, scoring 90.6% in Class 12 and a perfect CGPA of 10 in Class 10.

💼 Professional Experience

Ayush’s research journey began at Jadavpur University, working under renowned professors in Audio Signal Processing, Reinforcement Learning, and Image Segmentation. As a research intern at the Indian Statistical Institute, he contributed to dataset development and text detection models. He furthered his research as an intern at the University of Malaya on transformer-based networks and at IISc Bangalore on CLIP for image quality assessment. His work integrates deep learning models like YOLO, Swin Transformer, UNet, and CLIP with novel architectures and real-world applications.

🏆 Awards and Honors

Ayush has earned several accolades such as the Most Innovative Solution award at Hack-a-Web by NIT Bhopal (2021), 3rd Prize at FrostHack, IIT Mandi (2022), Top 10 in Cloud Community Hackday by GDG Cloud, and became a Finalist in both the IEEE R10 Robotics Competition and 404 Resolved hackathon at IIT Delhi.

🔬 Research Focus

His primary research areas include computer vision, medical image segmentation, scene text detection, and real-time AI systems. He is especially focused on lightweight models, attention mechanisms, domain adaptation, and hybrid approaches combining deep learning and signal processing. He has created multiple datasets for benchmarking including those for drone license plate detection, underwater text, water meter digit recognition, and circuit component recognition.

📌 Conclusion

Ayush Roy stands as a committed and creative researcher, blending electrical engineering fundamentals with cutting-edge AI methodologies. His work not only adds value to academic literature but also paves the way for practical, socially impactful AI systems. With an impressive early-career portfolio, Ayush continues to show immense promise for future contributions to science and technology.

📚 Top Publication Notes 

AWGUNet: Attention-aided Wavelet Guided U-net for nuclei segmentation in histopathology images

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 2 articles (Google Scholar)

A Wavelet Guided Attention Module for Skin Cancer Classification

Year: 2024

Journal/Conference: ISBI 2024

Cited By: 1 article (Google Scholar)

A New Lightweight Attention-based Model for Emotion Recognition Using Distorted Social Media Images

Year: 2023

Journal/Conference: ACPR 2023

Cited By: 3 articles

Fourier Feature-based CBAM and Vision Transformer for Text Detection in Drone Images

Year: 2023

Conference: ICDAR WML 2023

Cited By: 1 article

A Lightweight Script Independent Scene Text Style Transfer Network

Year: 2024

Journal: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

Cited By: 1 article

Identification and Classification of Human Mental Stress using Physiological Data

Year: 2022

Conference: IEEE CATCON 2022

Cited By: 4 articles

Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 2 articles

An Attention-based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Year: 2023

Journal: Artificial Intelligence and Applications (AIA)

Cited By: 1 article

DAU-Net: Dual Attention-aided U-Net for Segmenting Tumor Region in Breast Ultrasound Images

Year: 2023

Journal: PLOS ONE

Cited By: 6 articles