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 🔍

 

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.

 

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