Dr. vincebt majanga | Artificial intelligence | Best Researcher Award

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Dr. vincebt majanga | Artificial intelligence | Best Researcher Award

Post doctoral research fellow, university of south africa, South Africa.

Dr. Vincent Idah Majanga  is a dynamic and passionate researcher in Artificial Intelligence (AI), with over a decade of impactful experience in developing cutting-edge algorithms to solve complex real-world problems. His primary expertise lies in machine learning, deep learning, neural network optimization, and computer vision—especially for medical imaging and diagnostic tasks. Dr. Majanga is proficient in Python and Java, and his interdisciplinary skills extend to computer-aided diagnostics, simulation and modeling, computer forensics, and networking. A devoted academician and mentor, he has served in teaching and research capacities across renowned institutions in Kenya and South Africa. His current role as a Postdoctoral Researcher at the University of South Africa (UNISA) underlines his continued contributions to AI-driven healthcare solutions and intelligent systems.

Publication Profile

ORCID

📘 Education Background

Dr. Majanga completed his Ph.D. in Computer Science from the University of KwaZulu-Natal  (2018–2022), focusing on dental image segmentation and AI-based diagnostic systems. He holds an MSc in Computer Science from the University of Nairobi (2012–2014), and a BSc in Computer Science (Upper Second Class) from Kabarak University  (2009–2011). He also studied Computer Engineering at Moi University (2005–2008, credit transferred), and attended Nairobi School for his secondary education (2001–2004). His academic foundation forms the bedrock of his AI-driven research innovations.

💼 Professional Experience

Dr. Majanga is currently a Postdoctoral Researcher at UNISA  (Dec 2023–Present), where he works on deep learning, neural networks, transfer learning, and model optimization in image processing. He is also a part-time lecturer at Masinde Muliro University of Science and Technology  since 2022. Previously, he served as an Assistant Lecturer at Laikipia University  (2015–2023), contributing to curriculum development and student supervision. He has also lectured part-time at JKUAT Nakuru Campus, Dedan Kimathi University, and Kabarak University. Across these roles, he has consistently contributed to high-impact teaching, curriculum development, and academic mentorship.

🏆 Awards and Honors

Dr. Majanga has earned recognition through certifications in Research Ethics from the Clinical Trials Centre at The University of Hong Kong 🏅, completing three modules between March and April 2024—Introduction to Research Ethics, Research Ethics Evaluation, and Informed Consent. These certifications affirm his commitment to ethical research standards and responsible conduct in AI healthcare studies.

🔬 Research Focus

Dr. Majanga’s research focuses on Artificial Intelligence applications in medical imaging and diagnostics, with a specialization in deep learning, computer vision, and unsupervised segmentation. His significant contributions include blob detection and component analysis techniques for identifying cancerous lesions and dental caries in radiographs. His Ph.D. research and publications highlight strong applications of active contour models, connected component analysis, and dropout regularization in healthcare AI systems.

📝 Conclusion

Dr. Vincent Idah Majanga is a dedicated AI researcher and academician with a rich educational and professional background that aligns with transformative applications of artificial intelligence in medical diagnostics. His teaching, ethical research approach, and cross-continental academic presence have made him a valuable contributor to the global AI and computer science communities.

📚 Top Publications Highlights

  1. Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images
    📅 2025 | 📰 Bioengineering, 12(4), p.364
    🔎 Cited by: 8 articles

  2. Active Contours Connected Component Analysis Segmentation Method of Cancerous Lesions in Unsupervised Breast Histology Images
    📅 2025 | 📰 Bioengineering, 12(6), p.642
    🔎 Cited by: 5 articles

  3. A Survey of Dental Caries Segmentation and Detection Techniques
    📅 2022 | 📰 The Scientific World Journal, 2022
    🔎 Cited by: 21 articles

  4. Automatic Blob Detection for Dental Caries
    📅 2021 | 📰 Applied Sciences, 11(19), p.9232
    🔎 Cited by: 17 articles

  5. Dental Images’ Segmentation Using Threshold Connected Component Analysis
    📅 2021 | 📰 Computational Intelligence and Neuroscience, 2021
    🔎 Cited by: 12 articles

  6. Dropout Regularization for Automatic Segmented Dental Images
    📅 2021 | 📰 Asian Conference on Intelligent Information and Database Systems, Springer
    🔎 Cited by: 6 articles

  7. A Deep Learning Approach for Automatic Segmentation of Dental Images
    📅 2019 | 📰 MIKE 2019, Springer
    🔎 Cited by: 18 articles

  8. Component Analysis
    📅 2025 | 📰 WIDECOM 2024, Vol. 237, p.139, Springer Nature
    🔎 Cited by: 2 articles

 

Mr. Abdullah Al Mamun | Deep Learning | Young Scientist Award

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Mr. Abdullah Al Mamun | Deep Learning | Young Scientist Award

Lecturer, Dhaka University of Engineering & Technology (DUET), Gazipur, Bangladesh

Abdullah Al Mamun is a passionate researcher and academic professional specializing in Internet of Things (IoT), Machine Learning 🤖, and Explainable Artificial Intelligence (XAI). Currently pursuing his Master of Science in Engineering at Dhaka University of Engineering & Technology (DUET), Gazipur, he brings a vibrant combination of theoretical knowledge and hands-on research experience. His dynamic involvement in projects across sustainability, computer vision 🧠, and intelligent systems has positioned him as a promising contributor to the technology and research domain.

Publication Profile

ORCID

🎓 Education Background

Abdullah Al Mamun is presently pursuing his M.Sc. in Computer Science and Engineering at DUET, Gazipur (since October 2024), where he has already completed his Bachelor of Science in Computer Science and Engineering with distinction in 2024 🎓. His consistent academic journey showcases his dedication to computing, innovation, and advanced research.

💼 Professional Experience

Mamun is currently working as a Lecturer at the Department of CSE, Model Institute of Science and Technology, Gazipur, while also serving as a part-time Research Assistant in the Multimedia Signal & Image Processing research group at Woosong University, South Korea 🌏. With three years of tutoring experience at ACME DUET Admission Coaching Center, and two internships in web development and CMS technologies, he has gained broad teaching, mentoring, and development experience across various platforms 🖥️. His administrative roles in DUET Career & Research Club and DUET Computer Society also underscore his leadership and community contributions.

🏆 Awards and Honors

Abdullah has been recognized for his academic and problem-solving excellence. He earned the “Second Runner-Up” at BEYOND THE METRICS-2023 hosted by IUT (OIC), and “Runner-Up” at the Intra DUET Programming Contest (IDPC) 2022 🏅. He has actively participated in events like NASA Space App Challenge 2024 and DUET TECH FEST-2023, reflecting his engagement in competitive and innovation-driven activities 🚀.

🔬 Research Focus

Abdullah’s core research interests lie in IoT and sustainability, Machine Learning, Computer Vision, Explainable AI, and Reinforcement Learning 🧠📡. He has been instrumental in implementing real-world projects such as IoT-based energy monitoring systems and child safety monitoring, defect detection via XAI, and skin cancer classification using optimized deep learning models. His collaborative projects with global research teams exhibit his strong contribution to the evolving field of intelligent systems and digital transformation.

✅ Conclusion

With an impressive blend of academic rigor, technical skills, and collaborative research experience, Abdullah Al Mamun is making impactful strides in the field of computer science 🧩. His work exemplifies innovation, sustainability, and intelligence in engineering systems. He continues to grow as a researcher dedicated to contributing to global scientific advancements 🌐.

📚 Top Publications 

  1. Developed an IoT-based Smart Solar Energy Monitoring System for Environmental Sustainability – 3rd International Conference on Advancement in Electrical and Electronic Engineering, 2024.
    Cited by: 7 articles 📑

  2. Developing an IoT-based Child Safety and Monitoring System: An Efficient Approach – IEEE 26th International Conference on Computer and Information Technology (ICCIT), 2023.
    Cited by: 13 articles 🔐

  3. Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques – IEEE Journal, 2024.
    Cited by: 15 articles ⚙️

  4. IoT-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8 – MDPI Sensors Journal, 2023.
    Cited by: 10 articles 🚗

  5. Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN – 2nd NCIM Conference, Bangladesh, 2024.
    Cited by: 5 articles 🧬

  6. Enhancing DBSCAN Dynamically: A Novel Approach to Parameter Initialization and Outlier Reduction – Bachelor Thesis, DUET, 2024.
    Cited by: 3 articles 🔍

 

Prof. Dr. Jörg Schäfer | Machine Learning | Best Researcher Award

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Prof. Dr. Jörg Schäfer | Machine Learning | Best Researcher Award

Professor, Frankfurt University of Applied Sciences, Germany

Professor Dr. Jörg Schäfer is a renowned academic and researcher in the field of Computer Science, currently serving at the Frankfurt University of Applied Sciences in Germany. With a distinguished background in mathematics and a dynamic career bridging academia and industry, Dr. Schäfer is celebrated for his expertise in object-oriented programming, distributed systems, databases, and machine learning. His innovative research in artificial intelligence and human activity recognition, paired with decades of experience in technology strategy and complex system architecture, have made him a leading figure in both academic and professional circles.

Publication Profile

🎓 Education Background:

Dr. Schäfer completed his Ph.D. in Mathematics with summa cum laude at Ruhr-Universität Bochum (1991–1993) under the supervision of Prof. Dr. Sergio Albeverio. His doctoral work was part of the elite DFG graduate program “Geometrie und Mathematische Physik” and included an academic travel scholarship to Japan. Before his Ph.D., he earned a diploma in Mathematical Physics with distinction from Ruhr-Universität Bochum (1987–1991), laying the groundwork for his future interdisciplinary research.

💼 Professional Experience:

Dr. Schäfer’s professional career blends deep academic involvement with high-impact industry roles. Since 2009, he has been a professor at Frankfurt University of Applied Sciences, teaching subjects such as object-oriented programming, distributed systems, and machine learning. He is the founding member of the Industrial Data Science (INDAS) research group and serves as Chairman of the B.Sc. Computer Science program. Prior to his academic tenure, Dr. Schäfer held senior positions at Accenture (2005–2009) and Cambridge Technology Partners (2000–2005), where he was responsible for large-scale architecture design, pre-sales, delivery, and enterprise integration strategies. His early career includes project management roles at Westdeutsche Landesbank and a trainee program at Salomon Brothers, as well as scientific assistant roles focused on stochastic analysis.

🏅 Awards and Honors:

Professor Schäfer has received several prestigious accolades throughout his career. Most notably, he was awarded the Hessischer Hochschulpreis in 2022 for excellence in teaching. During his academic formation, he was also a scholar of the Studienstiftung des deutschen Volkes (1987–1991), reflecting his outstanding academic promise from an early stage.

🔬 Research Focus:

Dr. Schäfer’s research is focused on artificial intelligence, machine learning, mobile and distributed systems, and human activity recognition. His work leverages WiFi channel state information (CSI) for device-free activity detection, contributing significantly to the field of pervasive computing. He also has a foundational background in mathematical physics, particularly in Chern–Simons theory and stochastic analysis, which informs his unique approach to computer science problems.

🧩 Conclusion:

With a remarkable blend of academic rigor and real-world application, Professor Dr. Jörg Schäfer stands out as a multifaceted scholar and technology leader. His research continues to shape the future of data science and AI-driven systems, while his dedication to teaching and mentorship inspires the next generation of computer scientists.

📚 Top Publications

  1. Computer-implemented method for ensuring the privacy of a user, computer program product, device
    J Schäfer, D Toma
    US Patent 8,406,988, 2013
    Cited by: 237 articles

  2. Device free human activity and fall recognition using WiFi channel state information (CSI)
    N Damodaran, E Haruni, M Kokhkharova, J Schäfer
    CCF Transactions on Pervasive Computing and Interaction, 2020
    Cited by: 109 articles

  3. Human activity recognition using CSI information with nexmon
    J Schäfer, BR Barrsiwal, M Kokhkharova, H Adil, J Liebehenschel
    Applied Sciences, 2021
    Cited by: 75 articles

  4. Abelian Chern–Simons theory and linking numbers via oscillatory integrals
    S Albeverio, J Schäfer
    Journal of Mathematical Physics, 1995
    Cited by: 53 articles

  5. A rigorous construction of Abelian Chern-Simons path integrals using white noise analysis
    P Leukert, J Schäfer
    Reviews in Mathematical Physics, 1996
    Cited by: 43 articles

  6. Fall detection from electrocardiogram (ECG) signals and classification by deep transfer learning
    FS Butt, L La Blunda, MF Wagner, J Schäfer, I Medina-Bulo, et al.
    Information, 2021
    Cited by: 40 articles

  7. Device free human activity recognition using WiFi channel state information
    N Damodaran, J Schäfer
    2019 IEEE SmartWorld Conference
    Cited by: 37 articles

  8. Cloud computing – Evolution in der Technik, Revolution im Business
    G Münzl, B Przywara, M Reti, J Schäfer, et al.
    Berlin: BITKOM, 2009
    Cited by: 37 articles

 

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. 🧠💡

Professional Profile

Scopus

🎓 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. 📘🏫

💼 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. 🔬📊

🏅 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. 🏆📈

🔍 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. 🚗☁️📶

🧾 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. 🚀📚

📚 Publication Top Note

  1. PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning
    📅 Published Year: 2025
    📖 Journal: Ad Hoc Networks
    🔗 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 🌐📚.

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. Keyong Hu | artificial intelligence | Best Researcher Award

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Dr. Keyong Hu | artificial intelligence | Best Researcher Award

Teacher, Hangzhou Normal University, China

Dr. KeYong Hu is an accomplished academic and researcher specializing in artificial intelligence and new energy technology. He earned his Ph.D. from the Zhejiang University of Technology in 2016 and is currently serving as an Associate Professor at Hangzhou Normal University, within the School of Information Science and Technology. Dr. Hu has contributed significantly to the intersection of AI and energy systems, with numerous publications in international journals, showcasing his expertise in predictive modeling and intelligent optimization.

Publication Profile

ORCID

🎓 Education Background

Dr. KeYong Hu completed his doctoral studies at the Zhejiang University of Technology, Hangzhou, China, where he received his Ph.D. in 2016. His academic training laid a strong foundation in computational intelligence and energy-related engineering applications.

💼 Professional Experience

Dr. Hu holds the position of Associate Professor at Hangzhou Normal University, Hangzhou, Zhejiang, China, affiliated with the School of Information Science and Technology. He has been actively involved in teaching, mentoring, and high-impact research since earning his doctorate.

🏆 Awards and Honors

While specific awards are not listed, Dr. Hu’s prolific publishing record in top-tier peer-reviewed journals like Mathematics, Heliyon, Sustainability, and Computers and Electrical Engineering underscores his recognition and influence in the fields of AI and energy optimization.

🔬 Research Focus

Dr. Hu’s research centers on the integration of artificial intelligence with new energy technologies, particularly photovoltaic power forecasting, energy system optimization, and cross-modal data analysis. His innovative use of algorithms such as Copula functions, Transformers, and Dung Beetle Optimization showcases his depth in AI-driven energy analytics.

✅ Conclusion

Dr. KeYong Hu stands out as a forward-thinking researcher contributing impactful work at the intersection of artificial intelligence and sustainable energy. Through his academic leadership and research contributions, he continues to shape the future of intelligent energy systems in China and beyond. 🌍📈

📚 Top Publications 

🔗 Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads
Journal: Mathematics | Year: 2025
Cited by: Check on Google Scholar

🔗 Short-term Photovoltaic Forecasting Model with Parallel Multi-Channel Optimization Based on Improved Dung Beetle Algorithm
Journal: Heliyon | Year: 2024
Cited by: Check on Google Scholar

🔗 Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm
Journal: Mathematics | Year: 2024
Cited by: Check on Google Scholar

🔗 Automatic Depression Prediction via Cross-Modal Attention-Based Multi-Modal Fusion in Social Networks
Journal: Computers and Electrical Engineering | Year: 2024
Cited by: Check on Google Scholar

🔗 Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer
Journal: Sustainability | Year: 2024
Cited by: Check on Google Scholar

Dr. Aiai Wang | Machine Learning | Best Researcher Award

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Dr. Aiai Wang | Machine Learning | Best Researcher Award

Doctoral student, University of Science and Technology Beijing, China

Ai-Ai Wang is a passionate and dedicated young researcher born in March 1998 in Langfang, Hebei Province, China. A proud member of the Communist Party of China (CPC), she is currently based at the University of Science and Technology Beijing (USTB), where she serves as the Secretary of the 16th Party Branch, 4 Zhaizhai. With a solid academic foundation in mining and civil engineering, Ai-Ai has excelled in both academic and research spheres, contributing significantly to digital and intelligent mining technologies. Her work emphasizes physical dynamics in tailings sand cementation and filling, showing strong potential for innovation in sustainable mining practices.

Publication Profile

Scopus

🎓Education Background:

Ai-Ai Wang completed her Bachelor of Science in Mining Engineering from North China University of Science and Technology in 2021. She further pursued her Master’s degree in Civil Engineering at the University of Science and Technology Beijing (2021.09–2024.06), affiliated with the School of Civil and Resource Engineering.

🛠️Professional Experience:

Alongside her academic journey, Ai-Ai has undertaken significant responsibilities, currently serving as Secretary of the Party Branch at USTB. Her leadership extends beyond administration into collaborative research projects, software development, and patent contributions under renowned mentors such as Prof. Cao Shuai. She has played vital roles in developing intelligent systems for mining operations, reinforcing her multidisciplinary strengths.

🏅Awards and Honors:

Ai-Ai Wang has been recognized extensively for her academic and research excellence. Notable accolades include the “Top Ten Academic Stars” at USTB (2023), a National Scholarship for Master’s Degree Students (2022), the prestigious Taishan Iron and Steel Scholarship (2023), and multiple First-Class Academic Scholarships from USTB. She was twice named an Outstanding Three-Good Graduate Student and honored by her school as an outstanding individual. Moreover, she has received scientific awards such as the First Prize from the China Gold Association and the Second Prize from the China Nonferrous Metals Industry for her impactful contributions to green and safe mining.

🔬Research Focus:

Ai-Ai Wang’s research is rooted in advanced techniques of tailings sand cementation, intelligent filling systems, and digital mining. She explores the structural stability of backfills, application of nanomaterials, and CT-based 3D modeling of internal structures. Her work blends civil engineering, environmental safety, and digital innovation, aiming to enhance sustainability and efficiency in modern mining. She also contributes to cutting-edge software systems and patented technologies for mining design and operation support.

📝Conclusion:

Ai-Ai Wang stands out as a promising engineer and researcher whose academic achievements, professional dedication, and innovative research in intelligent mining set a high standard for future civil and mining engineers. Her trajectory reflects not just technical mastery but a deep commitment to sustainable and smart engineering solutions in the mining industry.

📚Top Publications with Details

Effect of height to diameter ratio on dynamic characteristics of cemented tailings backfills with fiber reinforcement through impact loading – Construction and Building Materials, 2022
Cited by: 26 articles
Influence of types and contents of nano cellulose materials as reinforcement on stability performance of cementitious tailings backfill – Construction and Building Materials, 2022
Cited by: 20 articles
Quantitative analysis of pore characteristics of nanocellulose reinforced cementitious tailings fills using 3D reconstruction of CT images – Journal of Materials Research and Technology, 2023
Cited by: 12 articles

 

Prof. Dr. Otilia Manta | Artificial Intelligence | Innovative Research Award

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Prof. Dr. Otilia Manta | Artificial Intelligence | Innovative Research Award

Professor PhD, Romanian Academy, Romania

Prof. Dr. habil. Elena Otilia Manta  is a renowned economist, academic, and scientific researcher, currently serving as a Professor at the Romanian-American University and a Scientific Researcher at the Romanian Academy. With over two decades of experience in economic sciences, she is widely respected for her expertise in finance, FinTech, AI integration, and sustainable development. She also holds various leadership roles in international organizations, including Vice President of EUExperts in Brussels, reflecting her strong influence in policy and academic circles globally. 🌍💼

Publication Profile

🎓 Education Background

Prof. Manta holds a Ph.D. in Economics and the academic title of “habilitation,” enabling her to supervise doctoral research. Her education has been complemented by continuous development in areas like finance, international economic relations, and technological integration, laying a solid academic foundation for her contributions in both national and international academic communities. 🎓📖

💼 Professional Experience

Since 2014, Prof. Manta has been a Scientific Researcher at the Romanian Academy and since 2019, a full Professor at the Romanian-American University in Bucharest. She also serves as Vice President of the European Union Experts (EUExperts) and the International Research Institute for Economics and Management (IRIEM), among others. As founder and CEO of The Romanian Group for Investments and Consultancy (RGIC), she combines academic depth with real-world financial leadership. She has also served as an EU Expert and Rapporteur with the European Commission. 🌐🏛️📊

🏆 Awards and Honors

Prof. Manta has received numerous honors, including the “2006 Woman of the Year Commemorative Medal” by the American Biographical Institute. She is a respected honorary member of the Romanian-Italian Chamber of Commerce and holds various affiliations with prestigious academic, financial, and developmental organizations such as the UN HLPF Mechanism and the Financial Stability Oversight Council (NY). 🥇🎖️🌟

🔬 Research Focus

Her research focuses on FinTech, artificial intelligence integration in banking, sustainable economic development, academic transparency, and financial innovation. As an active participant in multidisciplinary fields, Prof. Manta is a frequent contributor to leading journals and conferences, continuously shaping discussions on the future of finance and digital transformation. 💡📈🤖

🔗 Publications – Top Notes

Banking Transformation Through FinTech and the Integration of Artificial Intelligence in Payments – FinTech, 2025 | DOI: 10.3390/fintech4020013 – A highly impactful paper exploring AI’s role in revolutionizing payment systems.

FinTech and AI as Opportunities for a Sustainable Economy – FinTech, 2025 | DOI: 10.3390/fintech4020010 – Widely cited for linking technology to green and inclusive finance.

The Transfer of Managerial Expertise in Romanian Companies through the Application of the DEMATEL Method – Journal for Future Society and Education, 2025 – Emphasizes decision science in corporate governance.

Ensuring Academic Integrity: Tools and Mechanisms for a Transparent Educational Environment – Preprints, 2025 – Focuses on digital tools fostering academic ethics.

🔚 Conclusion

Prof. Dr. habil. Otilia Manta is a distinguished leader, bridging the worlds of academia, finance, and international consultancy. Her career reflects a strong commitment to innovation, transparency, and global collaboration in economics. Through her scholarly research, institutional leadership, and consultancy, she continues to inspire future generations in both Romania and abroad. 🌟📘🌍

Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Posted on by Computer Scientists Awards

Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

🎓 Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

💼 Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

🏅 Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

🔬 Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

🔚 Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare. 🚀

📚 Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23 📖

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830 🏥

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018 🧠

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024 🖥️

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics 🌍

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision 🤖

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation  (2024) – Published in arXiv arXiv:2405.18383 🏥