Marwan | AI Advancements | Excellence in Research

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Dr. Marwan | AI Advancements | Excellence in Research

Dr. Marwan | King Faisal University | Saudi Arabia

The applicant is an accomplished scholar in Computer Information Systems with specialized expertise in Artificial Intelligence and Data Science, supported by extensive experience in academia, research, and innovation. Over more than a decade of university-level teaching and research, the applicant has contributed significantly to advancing intelligent systems, machine learning applications, IoT security, biomedical imaging, and pattern recognition. Their doctoral work introduced a novel model for Arabic handwritten text recognition, forming the foundation for a strong research career in AI-driven language and image processing technologies. The applicant has authored and co-authored numerous impactful, refereed journal publications in well-recognized international outlets such as Sustainability, IJACSA, Traitement du Signal, and the Journal of Ayub Medical College. Research contributions span cancer therapy enhancement, anomaly detection, cephalometric landmark detection, multiple sclerosis classification, industrial IoT security, biometric iris recognition, palm disease classification, and Arabic word recognition. Several works have been indexed in Scopus and other reputable databases, with active collaborations involving interdisciplinary and multinational research teams. Beyond publications, the applicant has secured competitive research funding, including a grant supporting the development of a weighted-voting IoT security model targeting BASHLITE and Mirai cyberattacks. Ongoing research activities include hybrid deep learning systems for intrusion detection, medical image analysis, human nail disease diagnosis, music-brain interactions, and predicate-logic-based machine translation. The applicant has demonstrated strong academic service through extensive peer-reviewing, participation in scientific councils, and membership in research committees. Their professional development includes participation in conferences, training programs, and workshops focused on e-learning, scientific publishing, and advanced teaching strategies. Recognized for excellence, the applicant has received awards such as the Distinguished Scientific Research Award and the Outstanding International Publishing Award, reflecting sustained research quality and global scholarly impact. Their overall portfolio showcases a balanced blend of innovative research, academic leadership, and contributions to the AI and data science community.

Profiles: Scopus 

Featured Publications

Siddiqi, M. H., Alhwaiti, Y., Elaiwat, S., & Abu-Zanona, M. (2024). Dynamic healing process analysis: Image morphing with warping technique for nose and esophagus studies. The International Arab Journal of Information Technology, 21(3). (Accepted December 26, 2023).

 

Marco Zanotti | Data Science | Best Researcher Award

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Mr. Marco Zanotti | Data Science | Best Researcher Award

Mr. Marco Zanotti | University of Milan-Bicocca | Italy

Marco Zanotti is an accomplished Machine Learning Engineer and Data Scientist specializing in time series analysis, forecasting, anomaly detection, and econometrics. With extensive experience across sectors such as e-commerce fashion, tourism, aviation, and digital services, he has played a key role in designing and improving advanced forecasting systems that drive data-informed business decisions. He currently serves as a Data Scientist and Forecasting Specialist at Wanan Luxury (Rome, Italy), following previous positions at Blogmeter, T-Voice, and Uvet Amex GBT, where he contributed to predictive modeling, machine learning optimization, and process automation. In academia, Marco is an Adjunct Professor at the University of Milan and other leading Italian universities, where he teaches modern time series forecasting, machine learning, programming, and statistics at both Bachelor’s and Master’s levels. He holds a Ph.D. in Statistics from the University of Milano-Bicocca, a Post-graduate Diploma in Data Science for Economics, Business and Finance, and an M.Sc. in Economics and Finance from the University of Milan. Fluent in Italian, English, and French, Marco is proficient in R, Python, SQL, Git, Shiny, and Google Cloud Platform. A member of the International Institute of Forecasters, he is passionate about bridging the gap between academic research and industrial applications, mentoring young data scientists, and advancing the science of predictive analytics.

Profile: Orcid

Featured Publications

Zanotti, M. (2025, December). On the retraining frequency of global models in retail demand forecasting. Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2025.100769

Zanotti, M., & Mazzucchelli, L. (2021). dispositionEffect [Research protocol or software].

Samia ZAOUI | AI Advancements | Women Researcher Award

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Mrs. Samia ZAOUI | AI Advancements | Women Researcher Award

Mrs. Samia ZAOUI | Mohammed VI Foundation of Health and Sciences | Morocco

Dr. Samia Zaoui, based in Rabat, Morocco, is a multidisciplinary researcher and project leader bridging artificial intelligence, aeronautics, and healthcare systems innovation. She is currently pursuing her Ph.D. in Computer Science Engineering (AI & Aeronautics) at the Higher Institute of Aeronautics and Space (ISAE-SUPAERO) and INP Toulouse, France. Her research focuses on the application of AI technologies for supply chain resilience, predictive modeling, and sustainable industrial systems, with a strong emphasis on pharmaceutical and healthcare logistics. Dr. Zaoui has an extensive background in strategic project development, digital transformation, and industrial management, having held leadership roles at the Mohammed VI Foundation of Health and Sciences and the Cheikh Zaid Foundation. She has spearheaded projects in sports medicine innovation, pharmaceutical manufacturing, and medical technology transfer, fostering collaborations with international organizations such as WHO, LCIF, and Smile Train. Her scientific contributions include several peer-reviewed publications in international journals, such as the Global Journal of Flexible Systems Management and Production Planning & Control, covering topics like AI-driven supply chain viability, sustainability in Industry 5.0, and pharmaceutical risk prediction using machine learning. Dr. Zaoui’s research integrates AI-based decision systems with aeronautical and industrial engineering principles, contributing to global efforts in intelligent, resilient, and sustainable supply networks. She also actively participates in international technology exhibitions and collaborative industrial initiatives across Europe, Asia, and Africa.

Profile: Google Scholar

Featured Publications

Zaoui, S., Foguem, C., Tchuente, D., Fosso-Wamba, S., & Kamsu-Foguem, B. (2023). The viability of supply chains with interpretable learning systems: The case of COVID-19 vaccine deliveries. Global Journal of Flexible Systems Management, 24(4), 633–657. https://doi.org/10.1007/s40171-023-00357-w

Zaoui, S., Foguem, C., Tchuente, D., & Kamsu-Foguem, B. (2025). The application of artificial intelligence technologies in the resilience and the viability of supply chains: A systematic literature review. Production Planning & Control, 1–18.

Zaoui, H., Zaoui, S., Kamsu-Foguem, B., & Tchuente, D. (2024). Sustainability: The main pillar of Industry 5.0. Oklahoma International Publishing (OkIP) Books. https://doi.org/10.55432/978-1-6692-0007-9_13

StEER – Structural Engineering Extreme Event Reconnaissance. (2024). Hualien City, Taiwan Earthquake: Preliminary Virtual Reconnaissance Report (PVRR). https://doi.org/10.17603/ds2-0d2z-9682

Chaitanya Kumar Mankala | Computational Science | Best Researcher Award

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Mr. Chaitanya Kumar Mankala | Computational Science | Best Researcher Award

Mr. Chaitanya Kumar Mankala | Villanova University | United States

Dr. Chaitanya K. Mankala is a distinguished technologist and researcher specializing in artificial intelligence, cloud architecture, and large-scale enterprise systems. He has led transformative initiatives at global organizations including Deloitte, Capgemini, and Unisys, driving innovation through the integration of Microsoft Dynamics 365, SAP, and Salesforce platforms. As a top-performing scholar at Villanova University with advanced degrees in Software Engineering and Health Informatics, he is currently pursuing a Ph.D. in Artificial Intelligence. His research explores sustainable real-time natural language processing using serverless architectures and next-generation neuroidal networks, with notable publications and conference presentations at leading international forums such as MDPI, IEEE ISBI, and ICEHTMC. Dr. Mankala’s work bridges AI, cloud technologies, and enterprise intelligence, focusing on developing scalable, ethical, and high-performance digital solutions that shape the future of intelligent systems.

 

Tsomene Doungmo Stany Lionel | Computational Science | Best Researcher Award

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Mr. Tsomene Doungmo Stany Lionel | Computational Science | Best Researcher Award

Mr. Tsomene Doungmo Stany Lionel | University of Yaoundé 1 | Cameroon

Tsomene Doungmo Stany Lionel is a passionate and dedicated researcher in organic chemistry with a strong academic foundation from the University of Yaoundé I. Guided by Dr. Angélique Nicolas Messi, he has developed a deep interest in chemical synthesis, molecular modeling, and sustainable material development. His academic journey has been complemented by active participation in international workshops, scientific conferences, and professional training programs focused on structural biology, drug discovery, and research funding. He possesses excellent digital and analytical skills, proficient in a range of scientific software used for chemical analysis and molecular visualization. Fluent in French and English, Lionel communicates scientific ideas effectively and is committed to contributing to innovative and sustainable advancements in chemistry.

Profiles:  Orcid

Featured Publications

Mbeket, S. B. N., Doungmo, S. L. T., & Nicolas, M. A. (2025). Computational insights into C–O–C-type biflavonoids as multi-target inhibitors of ERα, PR, EGFR, and mTOR in breast cancer therapy. Computational and Structural Biotechnology Reports, 100065. https://doi.org/10.1016/j.csbr.2025.100065

Saleem Ramadan | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Saleem Ramadan | Data Science | Best Researcher Award

Assoc. Prof. Dr. Saleem Ramadan | Al Hussein Technical University | Jordan

Dr. Saleem Z. Ramadan is an accomplished Data Analyst and Business Analyst with a strong interdisciplinary background in industrial engineering, systems optimization, and data science. With academic and consulting experience across the U.S. and Jordan, he has applied data-driven decision-making, predictive analytics, and optimization modeling to complex problems in healthcare, manufacturing, and finance. Dr. Ramadan holds a Ph.D. in Systems Engineering from Ohio University and has served as Acting Chair and Associate Professor at Al Hussein Technical University, leading research and teaching initiatives integrating machine learning and operations analytics. He has developed impactful analytics solutions—ranging from hydroponic resource optimization and radiology workflow improvement to financial risk dashboards—using tools such as Python, SQL, Power BI, Tableau, and Minitab. His consulting work with Healthcare Operations & Performance Excellence (HOPE) led to measurable improvements in hospital performance through Six Sigma and process control techniques. A Certified Analytics Professional (CAP) and Microsoft Power BI Data Analyst Associate, Dr. Ramadan has authored 20 peer-reviewed publications, accumulating 236 citations from 230 documents with an h-index of 6. His recent works focus on machine learning–driven optimization, surgical scheduling prediction, and additive manufacturing parameter tuning. Dr. Ramadan’s combination of technical proficiency, academic leadership, and applied research impact uniquely positions him at the intersection of analytics innovation and business performance excellence.

Profiles:  Scopus | Google Scholar | LinkedIn

Featured Publications

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2025). A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency. Journal of Industrial Engineering and Management.

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2024). Optimizing tensile strength and energy consumption for FDM through mixed-integer nonlinear multi-objective optimization and design of experiments. Heliyon.