Ilya Levin | Artificial Intelligence | Research Excellence Award

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Prof. Ilya Levin | Artificial Intelligence | Research Excellence Award

Professor | Holon Institute of Technology | Israel

Prof. Ilya Levin is a researcher at Holon Institute of Technology, specializing in artificial intelligence, machine learning, and educational technology. His research focuses on computational thinking, neural networks, and AI-driven learning systems. He has strong expertise in algorithm design and interdisciplinary innovation. His contributions are recognized through impactful publications and academic work. According to Scopus, he has 552 citations, 88 documents, and an h-index of 13, reflecting his significant impact in AI and education research.

 

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View Scopus Profile View ORCID Profile View Google Scholar Profile

Featured Publications


Culture and Society in the Digital Age

– Information, 2021 (Citations: 275)


Massive open online courses – the modern concept in education and learning

– Vestnik Tomskogo State University, 2014 (Citations: 133)


Teaching machine learning in elementary school

– International Journal of Child-Computer Interaction, 2022 (Citations: 82)

 

Muhammad Furqan Zia | Artificial Intelligence | Young Scientist Award

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Mr. Muhammad Furqan Zia | Artificial Intelligence | Young Scientist Award

Researcher | Université du Québec à Trois-Rivières | Canada

Mr. Muhammad Furqan Zia is an emerging researcher at Université du Québec à Trois-Rivières, specializing in artificial intelligence and semantic communication. His research focuses on explainable AI and intelligent communication systems, addressing transparency and efficiency challenges. He has skills in AI modeling, data analysis, and system design, contributing to scholarly publications. His work is gaining recognition in the research community. According to Scopus, he has 5 citations, 4 documents, and an h-index of 2, reflecting his growing academic impact.

 

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Featured Publications


An Advanced Non-Orthogonal Multiple Access Security Technique for Future Wireless Communication Networks

– RS Open Journal on Innovative Communication Technologies, 2020 (Citations: 27)

 

Awele Okolie | Artificial Intelligence | Research Excellence Award

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Ms. Awele Okolie | Artificial Intelligence | Research Excellence Award

Data Analyst Intern | Wentworth Institute of Technology | United States

Ms. Awele Okolie is an emerging researcher in Machine Learning and Artificial Intelligence, with a strong focus on socially impactful, data-driven research. Her work spans food insecurity prediction, financial fraud detection, healthcare analytics, traffic safety modeling, and explainable AI, demonstrating applied innovation across public policy and safety-critical domains. Her research interests include predictive modeling, explainable machine learning, spatiotemporal analysis, and AI-driven decision systems, supported by skills in data analytics, statistical modeling, and real-world dataset integration. Her scholarly contributions show growing recognition, with Google Scholar metrics of 41 citations, 14 documents, and an h-index of 5, while Scopus citations, documents, and h-index are not provided. Overall, her work reflects a strong commitment to transparent, ethical, and high-impact artificial intelligence research.

 

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View Google Scholar Profile  View ResearchGate Profile

Featured Publications


Predicting food insecurity across US census tracts: A machine learning analysis using the USDA Food Access Research Atlas

– International Journal of Science and Research Archive, 17(2), 1156-1172, 2025 (Citations: 11)


An Explainable XGBoost Framework for Detecting Fraudulent Financial Transactions

– Journal of Scientific Research and Reports, 31(12), 244-255, 2025 (Citations: 6)


Spatiotemporal analysis and predictive modeling of traffic accidents in Boston: Insights for advancing Vision Zero initiatives

– International Journal of Science and Research Archive, 17(1), 528-543, 2025 (Citations: 6)


Machine learning approaches for predicting 30-day hospital readmissions: Evidence from Massachusetts healthcare data

– World Journal of Advanced Research and Reviews, 28(1), 1-12, 2025 (Citations: 6)

 

Andi Chen | Artificial Intelligence | Research Excellence Award

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Dr. Andi Chen | Artificial Intelligence | Research Excellence Award

Vice President of the Student Union | Nanjing University | China

Dr. Andi Chen is an emerging researcher in computer science and artificial intelligence, with a strong focus on machine learning, deep learning architectures, and pattern recognition. His research interests center on hybrid quantum-inspired neural networks, particularly the integration of ResNet and DenseNet models to improve feature representation, classification performance, and computational efficiency in complex data environments. He demonstrates solid research skills in AI algorithm design, deep neural network modeling, pattern recognition, data analysis, and experimental evaluation, with applications relevant to intelligent systems and next-generation computing. Dr. Chen’s scholarly contributions include publications in reputable venues such as Neurocomputing, reflecting growing visibility in the AI research community. While no major awards or funded projects are currently reported, his work shows strong potential for future recognition. According to Scopus, his research profile records 3 documents, 1 citation, and an h-index of 1. In conclusion, Dr. Chen’s research trajectory highlights promising contributions to advanced AI methodologies and quantum-inspired intelligent computing.

 

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Featured Publications


Image Compression and Reconstruction Based on Quantum Network


– IEEE International Parallel and Distributed Processing Symposium, 2024 (Citations: 5)


Quantum Sparse Coding and Decoding Based on Quantum Network


– Applied Physics Letters, 2024 (Citations: 1)

 

Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

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Dr. Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

Biomedical Engineer | Semnan University | Iran

Dr. Maedeh Azadi Moghadam is an emerging researcher whose work advances the fields of biomedical engineering, neurotechnology, and human–machine interaction, with a particular focus on developing more reliable and human-centered brain–computer interface (BCI) systems. Her research interests span neural signal processing, SSVEP-based BCI optimization, cognitive fatigue detection, biomarker-based performance measurement, and the integration of physiological signals into more adaptive computational models. She is especially interested in understanding how fatigue and cognitive variability influence BCI accuracy, and her work aims to design intelligent systems capable of adjusting in real time to user states, ultimately improving usability for rehabilitation, assistive technologies, and next-generation neuroengineering applications. Dr. Moghadam’s research skills include biosignal analysis, EEG processing, feature extraction, algorithmic modeling, quantitative measurement techniques, and scientific writing, demonstrating her multidisciplinary strengths across engineering and neuroscience. According to Scopus, she has 3 indexed documents, 2 citations, and an h-index of 1, reflecting growing visibility and early academic impact in her domain. Although no formal awards or honors are listed for her in the available Scopus record, her contributions to innovative metrics—such as a continuous fatigue index for SSVEP-based BCI performance—highlight her potential for future recognition in neurotechnology and biomedical measurement science. Her publications demonstrate a commitment to improving the efficiency, accuracy, and adaptability of neuroengineering systems, particularly those intended for people with motor impairments or communication limitations. In conclusion, Dr. Maedeh Azadi Moghadam represents a promising researcher whose interdisciplinary work is helping shape the future of intelligent BCIs, cognitive state monitoring, and biomedical signal-driven technologies. Her expanding scientific contributions, combined with her advancing research skill set, position her for continued impact in the global scientific community and future leadership in neurotechnology innovation.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

Azadi Moghadam, M., & Maleki, A. (2023). Fatigue factors and fatigue indices in SSVEP-based brain–computer interfaces: A systematic review and meta-analysis. Frontiers in Human Neuroscience, 17, 1248474. Citations: 33

Maleki, A., & Azadimoghadam, M. (2022). Fatigue assessment using frequency features in SSVEP-based brain–computer interfaces. Iranian Journal of Biomedical Engineering, 16(3), 229–240.
Citations: 4

Moghadam, M. A., & Maleki, A. (2023). Fatigue detection in SSVEP-based BCIs using biomarkers: A comparative study. 2023 31st International Conference on Electrical Engineering (ICEE), 496–500. Citations: 2

Azadi Moghadam, M., & Maleki, A. (2024). Comparative study of frequency recognition techniques for steady-state visual evoked potentials according to the frequency harmonics and stimulus number. Journal of Biomedical Physics and Engineering. Citations: 1

Moghadam, M. A., & Maleki, A. (2025). A continuous fatigue index based on biomarkers for SSVEP-based brain–computer interfaces. Measurement, 118598.

The Dr. Maedeh Azadi moghadam’s research advances global innovation in neurotechnology by improving the accuracy, stability, and human-centered design of brain–computer interface systems through biomarker-driven fatigue detection and advanced signal analysis. By enhancing the reliability of assistive technologies and cognitive monitoring tools, the nominee’s work contributes meaningful benefits to science, healthcare, and industry, ultimately supporting more accessible, intelligent, and high-performing human–machine interaction solutions for society.

 

Maliki Moustapha | Computer Science | Best Researcher Award

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Dr. Maliki Moustapha | Computer Science | Best Researcher Award

PhD | Erciyes University | Turkey

Dr. Maliki Moustapha, an accomplished researcher from Erciyes University, is recognized for his expertise in Artificial Intelligence (AI), Deep Transfer Learning, and Data Engineering, with a strong focus on the integration of intelligent algorithms and data-driven models to address real-world computational challenges. His academic background is rooted in computer science and engineering, where he developed advanced skills in machine learning, neural networks, data mining, and smart systems design. Professionally, Dr. Moustapha has been actively engaged in both research and academic mentorship, contributing to the development of innovative solutions in AI-powered automation, pattern recognition, and intelligent monitoring systems. His major research interests encompass computer vision, deep learning model optimization, spatiotemporal data analysis, and Internet of Things (IoT)-based smart healthcare systems. Among his most cited contributions is the publication titled “A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition” in the International Journal on Artificial Intelligence Tools (2023), which demonstrates superior accuracy and efficiency in image recognition. He has also published influential works on spatial and spatiotemporal clustering algorithms and IoT-based patient monitoring, bridging the gap between data intelligence and applied computing. His research skills span across Python programming, neural network modeling, big data analytics, data preprocessing, and model training for intelligent systems. Though early in his academic journey, Dr. Moustapha has earned recognition for his impactful work, showing promising potential in advancing AI technologies. According to Scopus and Google Scholar, he has achieved 9 citations, an h-index of 1, and several published documents reflecting growing international recognition. Dr. Moustapha’s research continues to contribute meaningfully to the fields of artificial intelligence and computational intelligence. In conclusion, his innovative approach, interdisciplinary mindset, and technological vision position him as a forward-thinking researcher committed to shaping the next generation of intelligent data systems and AI-driven innovations.

Profiles: ORCID | Google Scholar

Featured Publications

1. Moustapha, M., Taşyürek, M., & Öztürk, C. (2023). A novel YOLOv5 deep learning model for handwriting detection and recognition. International Journal on Artificial Intelligence Tools, 32(04), 2350016.

2. Moustapha, M. (2024). Spatial and spatiotemporal clustering algorithms in data mining. In Proceedings of the 3rd International Conference on Data and Electronics and Computing (ICDEC).

3. Moustapha, M. (2019). Alternative approach of patient monitoring system based on Internet of Things. In Proceedings of the II. International Science and Academic Congress (INSAC).

Francisco Javier Álvaro Afonso | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Francisco Javier Álvaro Afonso | Artificial Intelligence | Best Researcher Award

Universidad Complutense De Madrid, Spain

Prof. Dr. Francisco Javier Álvaro-Afonso is a visionary clinical researcher specializing in diabetic foot osteomyelitis, blending podiatry, pharmacy, and cutting-edge AI diagnostics. Holding a PhD in Podiatry, he excels in pioneering non-invasive strategies for bone infection detection, leveraging radiographic interpretation and deep learning models to reshape clinical decision-making. With a robust h-index and over 1,500 citations, his scholarly footprint spans high-impact journals and international collaborations. He balances academic rigor with real-world impact, guiding best practices through his clinical experience at Complutense University and the Diabetic Foot Unit. His work empowers both patients and practitioners with smarter, faster, and more accurate diagnostic tools, leading to better outcomes and improved quality of life. Innovative, interdisciplinary, and deeply committed to transforming diabetic foot care, Prof. Álvaro-Afonso consistently sets a high bar for research excellence and patient-centered innovation.

Professional Profile

Google Scholar  | Scopus Profile |  ORCID Profile

Education

Prof. Dr. Francisco Javier Álvaro Afonso possesses a diverse and robust academic background that forms the foundation of his professional excellence. He earned his PhD in Podiatry from the Complutense University of Madrid, focusing his thesis on the interobserver variability of the probe-to-bone test and plain radiographs in diagnosing diabetic foot osteomyelitis. This was preceded by an Official Master’s Degree in Healthcare Research from the same institution, which strengthened his expertise in evidence-based medical practices. His academic journey also includes a Degree in Podiatry, completed, and a Degree in Pharmacy obtained, both from the Complutense University of Madrid. This multidisciplinary education enables him to merge clinical knowledge with pharmaceutical insights, allowing a more holistic approach to patient care. His formal education, characterized by both breadth and depth, has played a critical role in shaping his innovative research and teaching methodologies in healthcare sciences.

Experience

Prof. Dr. Francisco Javier Álvaro Afonso serves as a full-time professor in the Department of Nursing at the Faculty of Nursing, Physiotherapy, and Podiatry at the Complutense University of Madrid. In this role, he contributes extensively to the academic development of students while advancing research in podiatric medicine. Beyond academia, he practices as a Deputy Podiatrist at the Diabetic Foot Unit of the University Podiatric Clinic at UCM, where he applies his clinical expertise to improve patient outcomes. He is also an active research member of the Interdisciplinary Diabetic Foot Study Group at the Health Research Institute of Hospital Clínico San Carlos (IdISSC) in Madrid. His professional experience reflects a seamless integration of teaching, research, and clinical service, allowing him to translate scientific findings into practical healthcare solutions. His leadership extends to coordinating innovative technology transfer projects, bridging the gap between medical research and its application in everyday clinical settings.

Research Interest

Prof. Dr. Francisco Javier Álvaro Afonso’s research interests lie at the intersection of clinical podiatry, diagnostic imaging, and artificial intelligence applications in healthcare. His work primarily focuses on the diagnosis and management of diabetic foot osteomyelitis, a serious complication that significantly impacts patient mobility and quality of life. He has developed advanced diagnostic strategies that enhance the accuracy of plain radiograph interpretation and has contributed to refining clinical diagnostic tools used in global practice. Additionally, he explores the use of artificial intelligence for automated detection of osteomyelitis, aiming to reduce diagnostic delays and improve treatment outcomes. His research has had a direct impact on international guidelines, ensuring that evidence-based practices are adopted worldwide. With a commitment to innovation and interdisciplinary collaboration, his work continues to bridge the gap between clinical expertise and emerging technologies, setting new standards for diabetic foot care and related healthcare challenges.

Award and Honor

Throughout his career, Prof. Dr. Francisco Javier Álvaro Afonso has received notable recognition for his contributions to podiatric medicine and healthcare research. He has been invited to speak at prestigious international conferences across Europe and Latin America, sharing his expertise with academic and clinical audiences. His reputation as a leading researcher is further evidenced by his role as a reviewer and invited editor for high-impact scientific journals in diabetic foot research and medical imaging. He has also served as principal investigator and coordinator for innovative teaching and healthcare technology projects, many of which have received institutional and academic commendations. These honors reflect his commitment to advancing both the science and practice of podiatric medicine, as well as his dedication to mentoring the next generation of researchers and clinicians. His awards and professional distinctions underscore his position as a respected and influential figure in his field.

Publication Top Notes

  • Title: Analysis of transfer lesions in patients who underwent surgery for diabetic foot ulcers located on the plantar aspect of the metatarsal heads
    Authors: RJ Molines‐Barroso, JL Lazaro‐Martinez, J Aragon‐Sanchez, FJ Álvaro-Afonso, et al.
    Year: 2013
    Citations: 101

  • Title: Clinical efficacy of therapeutic footwear with a rigid rocker sole in the prevention of recurrence in patients with diabetes mellitus and diabetic polineuropathy: A randomized trial
    Authors: M López-Moral, JL Lázaro-Martínez, E García-Morales, Y García-Álvarez, FJ Álvaro-Afonso, et al.
    Year: 2019
    Citations: 83

  • Title: Metalloproteinases in chronic and acute wounds: A systematic review and meta‐analysis
    Authors: A Tardáguila‐García, E García‐Morales, JM García‐Alamino, FJ Álvaro-Afonso, et al.
    Year: 2019
    Citations: 81

  • Title: The best way to reduce reulcerations: if you understand biomechanics of the diabetic foot, you can do it
    Authors: JL Lázaro-Martínez, J Aragón-Sánchez, FJ Álvaro-Afonso, et al.
    Year: 2014
    Citations: 71

  • Title: Topical treatment for plantar warts: A systematic review
    Authors: S García‐Oreja, FJ Álvaro‐Afonso, Y García‐Álvarez, E García‐Morales, et al.
    Year: 2021
    Citations: 67

  • Title: Clinical and Histological Outcomes of Negatively Charged Polystyrene Microspheres Applied Daily Versus Three Times per Week in Hard-to-Heal Diabetic Foot Ulcers: A Randomized Blinded Controlled Trial
    Authors: José Luis Lázaro-Martínez, Marta García-Madrid, Mateo López-Moral, Aroa Tardáguila-García, Francisco Javier Álvaro-Afonso, Yolanda García-Álvarez
    Year: 2025

  • Title: Comparative Clinical Outcomes of Patients with Diabetic Foot Infection Caused by MRSA or MSSA
    Authors: Francisco Javier Álvaro-Afonso, Esther García-Morales, Mateo López-Moral, Luis Alou-Cervera, Raúl Molines-Barroso, José Luis Lázaro-Martínez
    Year: 2025
    Citations: 6

  • Title: Effect of physical activity on tissue perfusion in patients with diabetes mellitus: Systematic review and meta-analysis
    Authors: Laura Palacios-Abril, Aroa Tardáguila-García, Francisco Javier Álvaro-Afonso, Sara García-Oreja, Sol Tejeda-Ramírez, José Luis Lázaro-Martínez
    Year: 2025

  • Title: Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation
    Authors: Francisco Javier Álvaro-Afonso, Aroa Tardáguila-García, Mateo López-Moral, Irene Sanz-Corbalán, Esther García-Morales, José Luis Lázaro-Martínez
    Year: 2025

  • Title: Clinical Effects of Weekly and Biweekly Low-Frequency Ultrasound Debridement Versus Standard of Wound Care in Patients with Diabetic Foot Ulcers: A Pilot Randomized Clinical Trial
    Authors: Sebastián Flores-Escobar, Yolanda García-Álvarez, Francisco Javier Álvaro-Afonso, Mateo López-Moral, Marta García-Madrid, José Luis Lázaro-Martínez
    Year: 2025

  • Title: Red-Laser Photodynamic Therapy with Toluidine Blue Gel as an Adjuvant to Topical Antifungal Treatments for Onychomycosis in Patients with Diabetes: A Prospective Case Series
    Authors: David Navarro-Pérez, Sara García-Oreja, Francisco Javier Álvaro-Afonso, Mateo López-Moral, José Luis Lázaro-Martínez, Aroa Tardáguila-García
    Year: 2025

  • Title: Diode Laser and Red-Laser Photodynamic Therapy with Toluidine Blue Gel for the Treatment of Onychomycosis: A Case Series
    Authors: Sara García-Oreja, Francisco Javier Álvaro-Afonso, Aroa Tardáguila-García, David Navarro-Pérez, Esther Alicia García-Morales, José Luis Lázaro-Martínez
    Year: 2025

Conclusion

Prof. Dr. Francisco Javier Álvaro-Afonso’s research corpus demonstrates a consistent and impactful focus on diabetic foot complications, wound healing, biomechanics, and innovative treatment approaches. His contributions span randomized clinical trials, systematic reviews, biomechanical studies, and the integration of artificial intelligence in diagnostic imaging. With multiple high-citation works, particularly in diabetic foot biomechanics and wound care, his publications have significantly influenced clinical practices and preventive strategies worldwide. His recent explorations into laser therapy, ultrasound debridement, and AI-powered diagnostics highlight his forward-looking approach to improving patient outcomes in podiatric medicine.