Assoc. Prof. Dr. Maikel Leon | Artificial Intelligence | Research Excellence Award

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Assoc. Prof. Dr. Maikel Leon | Artificial Intelligence | Research Excellence Award

Associate Professor Practice | University of Miami | United States

Dr. Maikel Leon is a leading researcher in artificial intelligence, explainable AI, fuzzy cognitive maps, machine learning, and intelligent systems, with strong applications in business technology, cybersecurity, transportation, and sustainable computing. His scholarly work bridges theoretical AI models with real-world decision-making, emphasizing transparency, reasoning, and human-centered intelligence. He has authored influential contributions in top-tier journals and IEEE conferences, advancing cognitive mapping, AI safety, sentiment analysis, and large language model governance. His research impact is well established, with over 900 citations on Google Scholar (h-index 17, i10-index 23) and more than 350 Scopus citations across 38 indexed documents (Scopus h-index 11), reflecting sustained international influence and research excellence.

Citation Metrics (Google Scholar)

1000

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Citations
927

i10-index
23

h-index
17

                        🟦 Citations  🟥 i10-index  🟩 h-index


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Prof. Hedi Sakli | Artificial Intelligence | Research Excellence Award

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Prof. Hedi Sakli | Artificial Intelligence | Research Excellence Award

Professor | University of Tunis El Manar | Tunisia

Hedi Sakli is a senior researcher and academic expert in telecommunications, electromagnetics, antennas, wave propagation, and advanced radiofrequency systems, with strong contributions spanning 5G technologies, metamaterials, optical communications, signal processing, and applied artificial intelligence. His research integrates theoretical electromagnetism with practical engineering applications, including IoT, sensor networks, and AI-assisted health and communication systems. He has authored an extensive body of peer-reviewed scientific work with significant international visibility. According to indexed databases, his research impact includes more than 116 Scopus-indexed documents with over 1,197 citations and an h-index of 14, alongside more than 1,755 Google Scholar citations and an h-index of 17, reflecting sustained scholarly influence and research leadership.

Citation Metrics (Scopus)

1250

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0

 

 

Citations
1,197

Documents
116

h-index
14

           🟦 Citations   🟥 Documents   🟩 h-index


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Featured Publications

Nannan Zhang | Artificial Intelligence | Research Excellence Award

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Mrs. Nannan Zhang | Artificial Intelligence | Research Excellence Award

Senior Engineer | Research Institute of Petroleum Exploration & Development | China

Mrs. Nannan Zhang is a senior engineer and researcher specializing in petroleum remote sensing and environmental monitoring using advanced geospatial technologies. Her research focuses on applying high-resolution satellite imagery, deep learning, and data fusion techniques to support oil and gas infrastructure detection, environmental impact assessment, and ecological protection. She has led multiple applied research initiatives that bridge scientific innovation with industrial needs, contributing significantly to the practical deployment of remote sensing in energy and environmental fields. Her scholarly work is published in internationally recognized journals and conferences, complemented by patented technologies and a research monograph. She is also actively engaged in advancing professional collaboration within the remote sensing research community.

Citation Metrics (Scopus)

350

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10

0

Citations
304

Documents
12

h-index
5

                    🟦 Citations    🟥 i10-index    🟩 h-index


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Featured Publications

Mr. Abir Das | Artificial Intelligence | Research Excellence Award

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Mr. Abir Das | Artificial Intelligence | Research Excellence Award

Siliguri Government Polytechnic College | India

Abir Das is an emerging AI/ML researcher whose work spans deep learning, computer vision, medical imaging, and explainable AI. With a strong foundation in developing end-to-end AI systems, his research focuses on Vision Transformers, self-supervised learning, noisy-label correction, and interpretable models for high-stakes applications such as healthcare, EEG signal analysis, and industrial fault diagnosis. He has contributed as the first author to multiple international journals, working extensively on hybrid deep learning models, CLIP-based zero-shot learning, EEG motor imagery classification, and sensor-driven diagnostic pipelines. His research integrates expertise in PyTorch, TensorFlow, and modern transformer architectures, emphasizing human-centered, reliable, and transparent AI solutions. He has actively explored the intersection of computer vision and embedded systems, enhancing drone autonomy, depth estimation, and real-time object detection, while also contributing to speech technologies through accent-conversion and multimodal learning. His scientific output includes publications in reputable venues such as Scientific Reports, MDPI Sensors, and Computers, Materials & Continua. His growing scholarly impact is reflected in Scopus metrics: 11 citations from 11 documents with an h-index of 1, and Google Scholar metrics: 12 citations, h-index 1, i10-index 1. His work continues to advance practical and theoretically grounded AI methodologies, blending deep learning innovations with real-world applications across biomedical imaging, EEG analysis, and industrial AI systems.

Publication Profile

Scopus | Google Scholar

Featured Publications

Das, A., Singh, S., Kim, J., Ahanger, T. A., & Pisa, A. A. (2025). Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15(1), 27161.

Zereen, A. N., Das, A., & Uddin, J. (2024). Machine fault diagnosis using audio sensor data and explainable AI techniques: LIME and SHAP. Computers, Materials & Continua, 80(3).

Das, S. S. A. (2025). Few-shot and zero-shot learning for MRI brain tumor classification using CLIP and Vision Transformers. Sensors, 25(23), 7341.

Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Mehtab Alam |Assistant Professor | Delhi University | India

Dr. Mehtab Alam is an accomplished IT professional and academic specializing in Artificial Intelligence (AI), Internet of Things (IoT), Cyber Forensics, and Information Security. His research primarily focuses on developing AI-based smart IoT frameworks for intelligent healthcare systems, with a strong emphasis on predictive modeling, machine learning integration, and cloud-based data analytics. His scholarly contributions demonstrate a multidisciplinary approach combining computer science, data-driven healthcare innovation, and digital transformation. He has explored diverse research areas including smart city technologies, blockchain applications in e-governance, cybersecurity frameworks, and the application of swarm intelligence in network optimization. Dr. Alam has published extensively in reputed international journals and conferences, contributing to advancements in AI-driven sustainable systems and smart healthcare solutions. His works reflect technical depth and practical applicability, addressing modern challenges in digital infrastructure, public health informatics, and secure communication systems. He has authored 15 Scopus-indexed publications, with 30 Scopus citations and an h-index of 4. On Google Scholar, his research has received 256 citations with an h-index of 10 and an i10-index of 11, showcasing his growing academic influence.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Alam, M., Khan, E. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). The DIABACARE CLOUD: Predicting diabetes using machine learning. Acta Scientiarum Technology, 46(1).

Alam, M., Khan, I. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). Smart healthcare: Making medicine intelligent. Journal of Propulsion Technology, 44(3).

Alam, M., Khan, R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). AI for sustainable smart city healthcare. China Petroleum Processing and Petrochemical Technology Catalyst Research, 23(2), 2245–2258.

Ansari, A. A., Narain, L., Prasad, S. N., & Alam, M. (2022). Behaviour of motion of infinitesimal variable mass oblate body in the generalized perturbed circular restricted three-body problem. Italian Journal of Pure and Applied Mathematics, 47, 221–239.

Alam, M., Parveen, S. (2021). Shipment delivery and COVID-19: An Indian context. International Journal of Advanced Engineering Research and Science, 8(8), 145–154.

Dr. Ananthoju Vijay Kumar | Artificial Intelligence | Best Researcher Award

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Dr. Ananthoju Vijay Kumar | Artificial Intelligence | Best Researcher Award

Associate Professor | Jain Deemed to be University | India

Dr. Ananthoju Vijay Kumar is an accomplished academician and researcher currently serving as an Associate Professor in the Department of Computer Science and Engineering at Jain University, Bangalore. With nearly two decades of dedicated teaching and research experience, he has established himself as a recognized guide and mentor, supervising multiple doctoral candidates. His expertise spans across Cyber Security, Data Mining, Data Warehousing, Data Science, and Natural Language Processing. Dr. Kumar has made significant contributions to his field through impactful research collaborations, scholarly publications, and active participation in professional academic communities.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Dr. Ananthoju Vijay Kumar pursued his doctoral studies in Computer Science and Engineering at Acharya Nagarjuna University, where he developed a strong foundation in computational theories and advanced research methodologies. His education provided him with specialized knowledge in core computer science disciplines and helped him build a research-oriented outlook. This academic journey laid the groundwork for his professional career in both teaching and research, equipping him to mentor students and lead projects across multiple domains. His academic credentials reflect his deep engagement in the field and his commitment to advancing the boundaries of computer science education and innovation.

Professional Experience

Dr. Ananthoju Vijay Kumar has held several important academic positions during his career, shaping his path as a teacher, researcher, and guide. Prior to joining Jain University as an Associate Professor, he served in Sree Chaitanya College of Engineering in Telangana, where he contributed to academic growth and program development in computer applications. At Jain University, he continues to lead both undergraduate and postgraduate courses, while simultaneously mentoring doctoral candidates. His ongoing research includes international collaborations, such as a major project with Melbourne University, Australia, further reflecting his active contribution to the global research community.

Awards and Honors

Throughout his career, Dr. Ananthoju Vijay Kumar has been recognized for his excellence in teaching, research, and academic leadership. Notably, he was honored with the APJ Abdul Kalam Lifetime Achievement National Award, presented by the International Institute of Socio Economic Reforms in Bangalore. This recognition underscores his significant contributions to the academic and research ecosystem. His role as a recognized doctoral guide at Jain University further highlights his influence and dedication to nurturing future researchers. His academic and professional achievements stand as a testament to his dedication to advancing knowledge and societal progress through impactful research and mentorship.

Research Focus

Dr. Ananthoju Vijay Kumar’s primary research interests encompass a wide range of areas within computer science. His focus extends across Cyber Security, Data Mining, Data Warehousing, Data Science, and Natural Language Processing. He has successfully guided research scholars in emerging domains such as agricultural data mining and advanced applications of security systems. His collaboration with international institutions has allowed him to address interdisciplinary challenges and deliver innovative solutions. With more than forty publications in reputed national and international journals, he continues to explore cutting-edge topics while contributing to both academic literature and practical applications of technology.

Publication Notes

  • Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation
    Published Year: 2025
    Citation: 5

  • Investigating the Determinants of Indian Rupee Exchange Rate: An Empirical Analysis of Influential Factors and Their Impact Level: Part 1
    Published Year: 2024
    Citation: 1

  • A Personalized System to Recommend a Healthy Diet Based on an Individual’s Unique Dietary Needs and Goals
    Published Year: 2023
    Citation: 2

  • Penetration Testing to Investigate Security Vulnerabilities, Bugs and Potential Threats in Flip Kart, JioMart, and Amazon Mobile Application
    Published Year: 2023
    Citation: 1

  • Hybrid Algorithm for Real-Time Sign Language Detection System
    Published Year: 2023
    Citation: 5

Conclusion

In summary, Dr. Ananthoju Vijay Kumar stands out as a distinguished academician with a strong record of teaching, mentoring, and impactful research. His academic background, professional experience, and recognized contributions to the field of computer science demonstrate his commitment to innovation and academic growth. His awards and ongoing projects highlight his active role in both national and international research communities. Through his expertise and dedication, Dr. Kumar continues to inspire students and researchers while making meaningful contributions to technology and society.

Prof. Erping Zhao | Neural Networks | Best Researcher Award

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Prof. Erping Zhao | Neural Networks | Best Researcher Award

Master, Xizang Minzu University, China.

Erping Zhao, originally from Binxian County in Shaanxi Province, is a highly respected professor at Xizang Minzu University, where he also mentors master’s students. With a career rooted in academia and research, Dr. Zhao has emerged as a distinguished figure in the field of computer science. After earning his master’s degree in software engineering from Xidian University in 2006, he embarked on a teaching journey that has spanned nearly two decades. His academic pursuits have taken him to China Renmin University as a visiting scholar in Big Data and Knowledge Graphs. Currently, he holds several influential positions, including outstanding member of the China Computer Association, executive member of the Information System Committee, and expert evaluator in the national graduate education monitoring database.

Publication Profile

ORCID

🎓 Education Background

Dr. Zhao completed his B.S. in Computer Application from Xidian University in 1999. He later returned to the same university and earned his M.S. in Software Engineering in April 2006. His passion for continuous learning led him to further expand his research capabilities as a visiting scholar in 2016 at China Renmin University, focusing on Big Data and Knowledge Graphs.

💼 Professional Experience

Beginning his career in the industry, Erping Zhao worked at Dang Tang Telecom Co., Ltd. from 1999 to 2003. Transitioning into academia, he joined the College of Information Engineering at Xizang Minzu University in June 2006. Over the years, he has risen through the ranks to become a professor, having led multiple research projects funded by both provincial and national bodies. His leadership has been instrumental in the successful completion of Natural Science Foundation projects and key technology initiatives for Tibet.

🏆 Awards and Honors

Throughout his distinguished career, Dr. Zhao has received numerous accolades. These include the Third Prize in the Tibet Sub-Competition of the 2024 “Data Elements ×” Competition, the Xizang Autonomous Region Teaching Achievement Award, and the Xianyang Excellent Academic Paper Award. These honors reflect his commitment to both academic excellence and innovation in applied research.

🔬 Research Focus

Dr. Zhao’s research interests lie in the rapidly advancing domains of Natural Language Processing, Knowledge Graphs, Deep Learning, Intelligent Q&A and Recommendation Systems, and Large Language Models. He has made significant contributions in integrating big data analysis with knowledge representation, and his publications reflect a blend of theory and real-world applications.

🔚 Conclusion

In summary, Professor Erping Zhao stands out as a dedicated academician and accomplished researcher with profound contributions to artificial intelligence and computer science. His blend of academic insight, industrial experience, and scholarly recognition positions him as a thought leader in his field.

📚 Top Publications 

  1. A multi-head attention-based bidirectional gated recurrent unit and multilayer perceptron for relation extraction model
     2025 — Engineering Applications of Artificial Intelligence
     Cited by: 7 articles

  2. Aspect-Level Sentiment Analysis Based on Vector Projection and Adversarial Contrastive Learning
     2025 — Expert Systems with Applications
     Cited by: 4 articles

  3. A knowledge graph completion model based on weighted fusion description information and transform of the dimension and the scale
     2025 — Applied Intelligence
     Cited by: 3 articles

  4. Multi-Level Attention Based Coreference Resolution With Gated Recurrent Unit and Convolutional Neural Networks
     2023 — IEEE Access
     Cited by: 11 articles

  5. A Knowledge Graph Completion Method Based on Fusing Association Information
     2022 — IEEE Access
     Cited by: 18 articles

 

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. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

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Mr. Muhammad Tauqeer Iqbal | Machine Learning | Best Researcher Award

Mr. Muhammad Tauqeer Iqbal , Yangzhou University, China

Iqbal Muhammad Tauqeer is a passionate researcher and master’s student at Yangzhou University, China , specializing in the domain of Machine Learning 🤖. With a solid foundation in both industry and academia, he has combined practical management experience with cutting-edge AI research. His dedication to data science applications and computer vision has led to a notable publication recognized as a best paper, showcasing his potential in the rapidly evolving tech landscape 🌟.

Professional Profile

ORCID

🎓 Education Background

Iqbal is currently pursuing his Master’s degree at Yangzhou University, China 📚, where his academic focus is on machine learning and its applications in computer vision. His academic pursuits have been driven by a commitment to advancing AI-driven solutions in environmental monitoring and digital recognition systems.

💼 Professional Experience

Before his transition into research, Iqbal gained valuable industry experience as an Assistant Production Manager at OPPO Mobile Company Pakistan 📱 for over two years. This role provided him with deep insights into production workflows and industry standards, bridging the gap between theoretical learning and practical application.

🏆 Awards and Honors

Iqbal’s research has already earned accolades, with his paper titled “A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy” being recognized as a Best Paper 🥇. This early recognition is a testament to the impact and novelty of his contributions to AI-powered environmental diagnostics.

🔬 Research Focus

His research interests lie primarily in Machine Learning, Deep Learning, Transfer Learning, and Computer Vision 🧠📊. He is particularly focused on applying these techniques to UV–Vis Spectroscopy and digital display recognition. He is currently working on a second research project that extends his work in pattern recognition and visual AI.

🔚 Conclusion

With a unique blend of industrial management experience and academic rigor, Iqbal Muhammad Tauqeer is emerging as a promising contributor to the field of Artificial Intelligence. His work in machine learning models for environmental monitoring reflects not only his technical skills but also his commitment to impactful innovation 🌍🔍.

📚 Publication Top Note

  1. Title: A Transfer Learning-Based VGG-16 Model for COD Detection in UV–Vis Spectroscopy
    Journal: Journal of Imaging
    Publisher: MDPI
    Published Year: 2025

 

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.