Yassine Benachour | Biomedical Engineering | Best Researcher Award

Dr. Yassine Benachour | Biomedical Engineering
| Best Researcher Award

Lecturer at Higher Colleges of Technology, United Arab Emirates.

Dr. Yassine BENACHOUR, Ph.D., FHEA, PMP®, is a seasoned academic and researcher with over 15 years of interdisciplinary teaching and research experience. Currently based in Dubai and holding a French nationality with Golden Visa status, Dr. Benachour excels in data science, artificial intelligence, and applied physics. He has mentored numerous master’s students, contributed to industry-aligned research, and developed cloud-based AI models using AWS SageMaker. His deep statistical expertise, programming knowledge (Python, R, MATLAB, Java, SQL), and practical teaching approach make him a leader in academic innovation. As a Fellow of the Higher Education Academy and PMP-certified project manager, he integrates robust project management and digital pedagogy with scientific inquiry to solve real-world problems across healthcare, photonics, and industrial systems.

Scopus

ORCID 

🏆 Suitability for the Best Researcher Award :

Dr. Yassine BENACHOUR is an exemplary candidate for the Best Researcher Award due to his impactful contributions at the convergence of AI, applied physics, and healthcare analytics. His work addresses real-world challenges, such as disease detection and human motion analysis, using cutting-edge technologies like deep learning, IoT, and cloud-based machine learning. With significant achievements in both academic research and industrial applications, he brings a rare blend of theoretical expertise and practical innovation. His leadership in projects involving nonlinear optics, photonic crystal optimization, and data-driven predictive modeling underscores his versatility. Coupled with his international educational credentials, recognized certifications (PMP®, FHEA), and a proven track record of research excellence, Dr. Benachour exemplifies the qualities deserving of this prestigious honor.

🎓 Education :

Dr. Yassine BENACHOUR holds a Ph.D. and has pursued elite training in data science from globally renowned institutions. In 2023, he completed a Professional Education program in Data Science on Cloud with Great Learning, mastering AWS SageMaker and the end-to-end machine learning lifecycle. He previously earned an Applied Data Science Program certificate from MIT, covering deep learning, CNNs, and real-world applications. Additionally, he completed the Google Data Analytics Professional Certificate in 2022, acquiring practical skills in data cleaning, visualization, and project management. His education is marked by an interdisciplinary focus blending computing, statistics, AI, and physics, equipping him with the knowledge to tackle complex problems through both theoretical frameworks and hands-on solutions.

🏢 Work Experience :

Dr. Benachour brings 15 years of rich experience in academia, covering teaching, mentorship, and practical training in mathematics, data analytics, physics, project management, and computer science. He has supervised numerous master’s students, guided their internships and theses, and contributed to curriculum development. He is well-versed in digital education tools such as Blackboard and has certifications in digital teaching and learning. His industry-aligned expertise spans Python, R, SQL, MATLAB, and cloud-based AI solutions using AWS SageMaker. Additionally, his programming experience in Java and C++, and his PMP® certification, reflect his capability to bridge technical, educational, and project management roles effectively. His career blends research, instruction, and technical innovation to prepare students for real-world challenges.

🏅Awards and Honors

Dr. Yassine BENACHOUR has received multiple accolades reflecting his academic leadership and research excellence. He is a Fellow of the Higher Education Academy (FHEA), a recognition of his commitment to teaching excellence in higher education. He is PMP® certified by the Project Management Institute, validating his expertise in managing large-scale, multidisciplinary projects. His Blackboard Certified Specialist status recognizes his digital teaching proficiency. His academic achievements include certifications from MIT and Google, highlighting his mastery of modern data science and analytics. Additionally, his Golden Visa in the UAE signifies national recognition of his high-level expertise and long-term contributions. These honors underscore his standing as a globally respected educator and innovative researcher.

🔬 Research Focus :

Dr. Benachour’s research lies at the nexus of data science, AI, and applied physics, focusing on real-world solutions in healthcare, photonics, and smart systems. His work integrates deep learning, computer vision, and statistical modeling, with applications ranging from skin disease diagnosis and human motion tracking to malaria detection. He uses tools such as AWS SageMaker, Python, and MATLAB for scalable AI implementations. His early contributions in nonlinear optics and photonic crystal optimization evolved into a broader research agenda combining cloud-based ML, IoT, and healthcare analytics. His interdisciplinary approach fosters innovation in predictive modeling, sustainable systems, and industrial applications, making his research both scientifically significant and socially impactful.

📊 Publication Top Notes:

 Enhancing Malaria Detection Through Deep Learning: A Comparative Study of Convolutional Neural Networks
Year: 2025 | Cited by: IEEE Access

Towards Improved Human Arm Movement Analysis: Advanced Feature Engineering and Model Optimization
Year: 2025 |

Performance Evaluation of Deep Learning Models for Real-Time Trash Classification on Embedded Devices
Year: 2024 | Cited by: [Early citations likely after conference publication, ICSPIS 2024]

Optimization of Second-Order Nonlinear Optical Susceptibilities in 1D GaN Photonic Crystal
Year: 2023 | Cited by: 6 (as per Crossref/Google Scholar estimates)

Nonlinear Optics of Photonic Crystals
Year: 2020 | Cited by: 18 (IEEE Xplore)

Characterization of Planar Photonic Crystals Using Surface Coupling Techniques at Large Wavelengths
Year: 2007 | Cited by: 45 (Optica/Google Scholar)

Observation of Band Structure and Reduced Group Velocity Area in SOI 2D Planar Photonic Crystals
Year: 2007 | Cited by: 39 (Optica/Google Scholar)

Investigation of Planar Photonic Crystal Band Diagrams Under the Light Cone Using Surface Coupling Techniques
Year: 2007 | Cited by: 54 (Springer/Google Scholar)

🔚 Conclusion

Dr. Yassine Benachour exhibits an outstanding balance of research productivity, academic leadership, and technical versatility, with a deep commitment to addressing real-world health and engineering challenges through advanced computational methods. His ability to integrate AI with healthcare and physics-based systems makes him an exemplary researcher who not only generates new knowledge but also engineers solutions that benefit society. He is highly recommended for the Best Researcher Award, embodying the spirit of innovation, impact, and interdisciplinary excellence.

Saqib Qamar | Artificial Intelligence | Best Researcher Award

 Dr. Saqib Qamar |Artificial Intelligence
| Best Researcher Award

Assistant Professor at Sohar University, India.

Dr. Saqib Qamar is a dedicated Assistant Professor at Sohar University, Oman, specializing in computer science with expertise in medical image analysis and deep learning. With a Ph.D. from Huazhong University of Science and Technology, China, and postdoctoral research from Sweden’s top institutes—KTH and Umea University—he has demonstrated strong research capabilities through high-quality publications and international collaboration. His academic career spans teaching, curriculum development, and industry experience. Known for his work ethic, innovation, and research productivity, Dr. Qamar has a profound commitment to student success and scientific excellence. His scholarly contributions and interdisciplinary engagement make him a compelling candidate for the Best Researcher Award.

🌍 Professional Profile:

ORCID

Google Scholar

🏆 Suitability for the Best Researcher Award :

Dr. Saqib Qamar’s academic journey reflects consistent excellence and impactful contributions to computer science, particularly in medical image analysis using AI. With experience in global research environments and a record of peer-reviewed publications, he bridges theory with real-world applications. His Ph.D. work on 3D CNNs for brain MRI segmentation was both innovative and practically relevant. As a postdoctoral fellow at KTH and Umea University, he engaged in collaborative, cutting-edge research. He also actively mentors students and contributes to academic discourse. His awards, research leadership, and ongoing projects demonstrate a trajectory of influence, making him highly suitable for the Best Researcher Award.

🎓 Education :

Dr. Saqib Qamar earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2015–2019), with a dissertation focused on 3D CNN models for brain MRI segmentation. His doctoral research integrated deep learning with medical imaging and received the HUST Excellence Award. He completed his Master’s in Computer Science at Aligarh Muslim University, India (2010–2013), where he studied AI, programming, and software engineering, and was awarded a Merit Cum Means scholarship. His academic foundation was laid through a B.Sc. (Hons.) in Statistics from the same university (2007–2010), where he focused on probability, statistics, and linear algebra, graduating with First Division.

🏢 Work Experience :

Dr. Saqib Qamar is currently an Assistant Professor at Sohar University, Oman (2025–present), where he teaches and conducts research in AI and medical image computing. He was previously a postdoctoral fellow at KTH Royal Institute of Technology (2024–2025) and Umea University (2022–2024), Sweden, focusing on machine learning and deep learning applications. At Umea, he also taught deep learning courses. Earlier, he served as Assistant Professor at Madanapalle Institute of Technology and Science, India (2019–2021), where he taught programming and ML subjects. Prior to academia, he worked as a Database Developer at nServices, Delhi (2013–2015), specializing in Oracle-based data systems and programming.

🏅Awards and Honors

Dr. Saqib Qamar has been recognized for his academic performance and research excellence throughout his career. He received the HUST Excellence Award during his Ph.D. at Huazhong University of Science and Technology, acknowledging his exceptional work in medical image segmentation using deep learning. He was also awarded the Merit Cum Means Scholarship by the Government of India during his Master’s studies. His postdoctoral research in Sweden was supported through prestigious international fellowships, and he has contributed to multiple international projects. Additionally, he has received appreciation for teaching excellence and academic service in both India and Oman. His record of honors reflects his dedication to advancing AI and medical informatics research globally.

🔬 Research Focus :

Dr. Saqib Qamar’s research focuses on medical image analysis, deep learning, and 3D convolutional neural networks. His doctoral work centered on brain MRI segmentation, proposing efficient and parallelized CNN architectures. He has expanded his expertise during postdoctoral stints in Sweden, exploring advanced AI techniques in healthcare imaging, neural cell recognition, and explainable AI. His work integrates datasets from MRI and CT scans with machine learning algorithms to enhance diagnostic capabilities. He is also interested in real-time data processing, parallel computing, and interpretable AI models. With an aim to bridge clinical needs with computational innovation, Dr. Qamar’s research contributes significantly to the domains of health informatics and intelligent medical systems.

📊 Publication Top Notes:

📘 Techniques of data mining in healthcare: a review
🗓️ Year: 2015 | ✍️ P Ahmad, S Qamar, SQA Rizvi | 📖 International Journal of Computer Applications | 🔢 Cited by: 174 📊

🧠 A variant form of 3D-UNet for infant brain segmentation
🗓️ Year: 2020 | ✍️ S Qamar, H Jin, R Zheng, P Ahmad, M Usama | 📰 Future Generation Computer Systems | 🔢 Cited by: 132 🧬

🦴 CT-based automatic spine segmentation using patch-based deep learning
🗓️ Year: 2023 | ✍️ SF Qadri, H Lin, L Shen, M Ahmad, S Qadri, S Khan, M Khan, SS Zareen, S Qamar | 🧾 International Journal of Intelligent Systems | 🔢 Cited by: 77 🧠

🧠 Context aware 3D UNet for brain tumor segmentation
🗓️ Year: 2020 | ✍️ P Ahmad, S Qamar, L Shen, A Saeed | 📘 MICCAI Brainlesion Workshop | 🔢 Cited by: 57 🧪

🧬 MH UNet: A multi-scale hierarchical based architecture for medical image segmentation
🗓️ Year: 2021 | ✍️ P Ahmad, H Jin, R Alroobaea, S Qamar, R Zheng, F Alnajjar, F Aboudi | 📰 IEEE Access | 🔢 Cited by: 51 🔬

🧴 Dense encoder-decoder–based architecture for skin lesion segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 🧠 Cognitive Computation | 🔢 Cited by: 50 🧪

🧠 HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 📘 Brainlesion: Glioma, MS, Stroke and TBI Workshop | 🔢 Cited by: 46 💡