Ibrahim El Didi | Machine Learning | Innovative Research Award

Published on by International Popular Scientist Awards

Innovative Research Award

Ibrahim El Didi
Prince Mohammad Bin Fahd University, Saudi Arabia

Ibrahim El Didi
Affiliation Prince Mohammad Bin Fahd University
Country Saudi Arabia
Scopus ID 59699937500
Documents 3
Citations 1
h-index 1
Subject Area Machine Learning
Event International Popular Scientist Awards
ORCID 0000-0001-8812-788X

Ibrahim El Didi is a researcher affiliated with Prince Mohammad Bin Fahd University in Saudi Arabia whose documented scholarly work spans machine learning, artificial intelligence, cybersecurity, healthcare Internet of Medical Things (IoMT), renewable-energy systems, and computational methods. His recent publication record includes journal and conference contributions addressing intelligent intrusion detection, deep-learning-based cybersecurity, photovoltaic-system resilience, and machine-learning-supported healthcare applications.[1][2]

Abstract

Ibrahim El Didi is associated with research at the intersection of machine learning and applied intelligent systems. His documented publications include work on Parkinson’s disease classification using gait-related information, AI-based resilience for photovoltaic systems, intelligent intrusion detection for healthcare IoMT environments, layered security frameworks for cloud-connected healthcare systems, and deep-learning approaches to cybersecurity. The publication record also includes a contribution in computational mathematics, illustrating a broader engagement with applied computational research.[1][2][3]

Keywords

Machine learning; artificial intelligence; deep learning; cybersecurity; intrusion detection; healthcare IoMT; photovoltaic systems; network security; Parkinson’s disease classification; intelligent systems; computational methods; applied research.

Introduction

Machine learning has become an important methodological component of modern research in engineering, healthcare, cybersecurity, and energy systems. Its application to complex datasets allows researchers to develop classification, prediction, anomaly-detection, and decision-support approaches for problems that may be difficult to address using conventional analytical techniques alone. El Didi’s listed publications reflect this broader development by applying artificial intelligence and machine learning to multiple technological contexts.[1][4]

Research Profile

The available bibliographic information identifies Ibrahim El Didi with Prince Mohammad Bin Fahd University and associates his research subject area with machine learning. The supplied Scopus profile information records Scopus Author ID 59699937500, three indexed documents, one citation, and an h-index of 1. These metrics describe the indexed record available at the time represented by the supplied profile data and should be interpreted in the context of publication timing, indexing coverage, and the normal evolution of an academic research profile.[6]

Research Contributions

The documented contributions can be grouped into several applied research themes. In healthcare, the Parkinson’s disease study applies a physiology-anchored multiple-instance framework with confidence-stratified training to gait-based classification. Such a research direction connects machine-learning methodology with physiological information and disease-oriented classification problems.[1][2]

Publications

  1. A Physiology-Anchored Multiple-Instance Framework with Confidence-Stratified Training for Parkinson’s Disease Classification Based on Gait. Applied Sciences, 2026-08-22.
  2. SPARK: Shutdown Prevention and AI-Based Resilience Framework for PV Systems. 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT), 2026-04-07.
  3. CloudMed-IDS: Intelligent Multi-class and Zero-day Intrusion Detection System for HealthCare IoMT. 10th International Conference on Engineering, Technologies & Applied Sciences (ICETAS), Bahrain, 2026-04-01. Contributors.
  4. MedGuard-IoMT: A Machine Learning-Enabled Layered Security Framework for Intrusion Detection in Cloud-Connected Healthcare Systems. 2025 IEEE 6th International Conference on Computer, Big Data, Artificial Intelligence (ICCBD+AI), 2025-11-21.

Research Impact

The potential significance of El Didi’s research lies primarily in its application-oriented use of machine learning across domains where reliability, security, and intelligent analysis are important. Healthcare IoMT systems require mechanisms capable of detecting diverse cyber threats, while medical classification tasks require computational methods that can process physiological and behavioral information. The listed publications address both dimensions of intelligent healthcare systems.[1][3][4]

Award Suitability

The Innovative Research Award recognizes research activity characterized by the development or application of innovative approaches to significant academic or technological problems. Based on the supplied publication record, Ibrahim El Didi demonstrates several characteristics relevant to this recognition, including research activity in machine learning, application of artificial intelligence to healthcare and cybersecurity, and interdisciplinary work addressing renewable-energy systems.[1][2][4]

Conclusion

Ibrahim El Didi’s documented research profile reflects an interdisciplinary application of machine learning and artificial intelligence to healthcare, cybersecurity, renewable-energy systems, and computational research. His recent publications include work on Parkinson’s disease classification, photovoltaic-system resilience, intelligent intrusion detection for healthcare IoMT, cloud-connected healthcare security, deep-learning-based network protection, and computational differential equations.[1][2][3].

References

  1. Farfoura, M. E., Alkhatib, A. A. A., Elkhodr, M., El Didi, I., & Al-Sabbagh, A. (2026). A Physiology-Anchored Multiple-Instance Framework with Confidence-Stratified Training for Parkinson’s Disease Classification Based on Gait. Applied Sciences.
  2. Guler, N., Houda, T., El Didi, I., & Hindi, J. (2026). SPARK: Shutdown Prevention and AI-Based Resilience Framework for PV Systems. 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT).
    DOI: https://doi.org/10.1109/csnt69054.2026.11502347
  3. El Didi, I., Guler, N., & Ben Hazem, Z. (2026). CloudMed-IDS: Intelligent Multi-class and Zero-day Intrusion Detection System for HealthCare IoMT. 10th International Conference on Engineering, Technologies & Applied Sciences (ICETAS), Bahrain.
  4. El Didi, I., Elkhodr, M., Ullah, F., Zhao, Y., & Al-Sabbagh, A. (2025). MedGuard-IoMT: A Machine Learning-Enabled Layered Security Framework for Intrusion Detection in Cloud-Connected Healthcare Systems.
    DOI: https://doi.org/10.1109/iccbdai66607.2025.11388099
  5. El Didi, I., Trad, F., & Chehab, A. (2024). Enhancing Network Security Through Deep Learning: Mitigating Feature Engineering and Zero-Day Attacks. 2024 4th Intelligent Cybersecurity Conference (ICSC).
    DOI: https://doi.org/10.1109/icsc63108.2024.10895964
  6. Youssef, K., Moussa, M., Al-Husseini, M., El Misilmani, H. M., Kabalan, K. Y., & El Didi, I. (2024). Characteristic Mode Solution of Complex-Coefficient Complex-Solution Differential Equations.
    DOI: https://doi.org/10.61841/turcomat.v15i1.10082

Verónica Rodríguez-López | Machine Learning | Best Researcher Award

Published on by International Popular Scientist Awards

Best Researcher Award

Verónica Rodríguez-López
Technological University of the Mixteca, Mexico
    Verónica Rodríguez-López
Affiliation Technological University of the Mixteca
Country Mexico
Scopus ID 57222249124
Documents 24
Citations 340
h-index 7
Subject Area Machine Learning
Event International Popular Scientist Awards
ORCID 0000-0002-5976-9338

Verónica Rodríguez-López the Best Researcher Award recognition highlights notable scholarly contributions in the field of Machine Learning and related computational sciences. Verónica Rodríguez-López of the Technological University of the Mixteca has developed an academic profile characterized by research productivity, citation impact, and participation in advancing intelligent data-driven methodologies. Her documented scholarly output and measurable research indicators support consideration for international scientific recognition.[1]

Abstract

Verónica Rodríguez-López has established a scholarly record in Machine Learning through peer-reviewed publications, interdisciplinary research activities, and contributions to computational intelligence. Her academic achievements, reflected through publication output, citation performance, and sustained engagement with emerging analytical methodologies, demonstrate a commitment to advancing scientific knowledge within data-centric disciplines. The present article summarizes her research profile and examines the relevance of her accomplishments to the Best Researcher Award recognition framework.[1]

Keywords

Machine Learning, Artificial Intelligence, Data Analytics, Computational Intelligence, Pattern Recognition, Scientific Research, Academic Excellence, Research Impact, Knowledge Discovery, Best Researcher Award.

Introduction

Machine Learning has become a foundational area of modern scientific inquiry, influencing fields ranging from engineering and healthcare to environmental monitoring and industrial automation. Researchers working in this domain contribute to the development of predictive models, intelligent systems, and analytical frameworks capable of extracting meaningful information from complex datasets. Recognition programs such as the International Popular Scientist Awards seek to acknowledge individuals whose scholarly efforts contribute to the advancement of these scientific objectives.[2]

Research Profile

Verónica Rodríguez-López is affiliated with the Technological University of the Mixteca in Mexico. Her scholarly profile includes 24 indexed publications, 340 citations, and an h-index of 7 according to available bibliometric records.[1] These metrics indicate consistent engagement with the scientific community and demonstrate the visibility of her published research.

Her research interests are situated within Machine Learning and associated computational methodologies. Through academic publication and collaboration, she has contributed to the dissemination of knowledge related to data-driven decision making, predictive modeling, and intelligent information systems.[3]

Research Contributions

The research activities associated with Verónica Rodríguez-López reflect contemporary developments in Machine Learning, emphasizing methodological rigor and practical applicability. Her work contributes to expanding understanding of computational models capable of processing large-scale information and generating predictive insights.[3]

Publications

Publication productivity remains an important indicator of scholarly engagement. The documented publication record of Verónica Rodríguez-López demonstrates continuous participation in research dissemination activities and reflects adherence to recognized academic standards.[1]

Research Impact

Research impact can be assessed through citation activity, publication quality, and influence on subsequent investigations. With 340 citations and an h-index of 7, the research profile of Verónica Rodríguez-López demonstrates measurable academic engagement and recognition within relevant scientific communities.[1]

Beyond quantitative indicators, research impact includes contributions to knowledge transfer, methodological innovation, and support for future studies. Machine Learning research often serves as a foundation for practical implementations across multiple sectors, thereby extending the relevance of scholarly outputs beyond academia.[4]

Award Suitability

Evaluation for the Best Researcher Award typically considers research productivity, citation influence, academic leadership, originality, and overall contribution to scientific advancement. The available bibliometric indicators, combined with scholarly activity in Machine Learning, suggest that Verónica Rodríguez-López meets several criteria commonly associated with international academic recognition programs.[1]

Conclusion

Verónica Rodríguez-López has developed a research profile characterized by scholarly productivity, measurable citation impact, and contributions to Machine Learning. Her academic accomplishments align with the objectives of international scientific recognition programs that seek to acknowledge excellence in research and innovation. Based on available bibliometric evidence and documented research activities, her profile represents a noteworthy example of sustained engagement in contemporary computational science.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Verónica Rodríguez-López, Author ID 57222249124. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57222249124
  2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  3. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  4. Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255–260
    DOI: https://doi.org/10.1126/science.aaa8415

Manjunath BR | Machine Learning | Best Researcher Award

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Prof. Dr. Manjunath BR | Machine Learning | Best Researcher Award

Professor | Tecnologico De Monterrey | Mexico

Prof. Dr. Manjunath BR is an accomplished academic leader and finance professional specializing in business analytics, financial modeling, econometrics, fintech, and artificial intelligence applications in finance. With extensive experience across academia and industry, he has contributed significantly to advancing data-driven financial education and research. His expertise spans financial analytics, investment management, corporate restructuring, and data visualization using advanced tools such as EViews, R, Python, Tableau, and Power BI. He has published extensively in ABDC, Scopus, UGC, and peer-reviewed journals, focusing on the intersection of finance, data science, and technology. As a researcher and educator, he integrates predictive analytics and machine learning into financial decision-making, contributing to the understanding of fintech adoption, banking innovations, and risk management. His academic leadership includes curriculum design, faculty development, and corporate collaborations to enhance experiential learning. He has served as a resource person for numerous international workshops and training programs on financial analytics, econometrics, and data visualization, empowering professionals and students with analytical and quantitative skills. Dr. Manjunath has authored and edited several books with leading global publishers, covering transformative areas such as AI in management education, blockchain economics, sustainable investment, and Quality 5.0 paradigms. He has also secured a patent for the application of AI in optimizing HR data management and authored a textbook on machine and deep learning. His professional journey embodies innovation, interdisciplinary scholarship, and a commitment to integrating technology with finance to foster global academic and industry excellence.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Raju, J. K., Manjunath, B. R., & Rehaman, M. (2018). An empirical study on the effect of gross domestic product on inflation: Evidence from Indian data. Academy of Accounting and Financial Studies Journal, 22(6), 1–11.

Raju, J. K., Manjunath, B. R., & Dhakal, M. H. (2015). Impact and challenges of merger and acquisition in Nepalese banking and financial institutions. Journal of Exclusive Management Science, 4(8), 25–33.

Raju, J. K., Manjunath, B. R., & G. M. M. N. (2015). Performance evaluation of Indian equity mutual fund schemes. Journal of Business Management & Social Sciences Research (JBM&SSR).

Manjunath, B. R., & Raju, J. K. (2020). Short-run performance evaluation of under-priced Indian IPOs. Law and Financial Markets Review.

Chaitra, R., Manjunath, B. R., & Rehaman, M. (2019). An analysis of pre and post-merger of Indian banks: An event analysis approach. International Journal for Research in Engineering Application & Management, 4.

Nooshin Nemati | Computer Science | Best Researcher Award

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Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7

 

Juhi Patel | Machine Learning | Best Researcher Award

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Ms. Juhi Patel | Machine Learning | Best Researcher Award

Assistant Professor at GLS University, India

Ms. Juhi Patel is a dedicated early-career researcher and assistant professor at the Faculty of Computer Applications and IT, GLS University, Ahmedabad, since June 2023. She is currently pursuing her Ph.D. in Computer Science at GLS University. Juhi has a strong background in IT, having worked as a Web Developer at ADELSEO (2020–2023) and as a WordPress Developer at Elsner Technologies Pvt. Ltd. (2016–2020).

Profile:

💼 Professional Experience:

  • Assistant Professor at Faculty of Computer Applications and IT, GLS University since June 2023.

  • Former Web Developer at ADELSEO (2020–2023).

  • WordPress Developer at Elsner Technologies Pvt. Ltd. (2016–2020).

🧠 Research & Innovation:

  • Holds a design patent for a “Safety Helmet Detection Device” (issued July 2024).

  • Published papers on Machine Learning, IoT, and Sustainable Agriculture in international conferences and journals.

  • Active participant and presenter at prestigious events such as ICTIS 2025 and ColCI 2024.

📚 Professional Development:

  • Completed numerous Faculty Development Programs (FDPs) on AI, Machine Learning, Blockchain, NEP 2020, and ICT tools.

  • Moderator of the ‘International Conference on Research and Innovations’.

🎓 Educational Qualifications:

  • Pursuing Ph.D. in Computer Science, GLS University

  • MCA (8.96 CGPA), Gujarat Technical University, 2017

  • BCA (7.65 CGPA), Gujarat University, 2015

Publication:

Navigating GMO Adoption in Agriculture: Balancing Controversies and Benefits
Current Agriculture Research Journal, 2025-01-15
DOI: 10.12944/CARJ.12.3.32
Contributors: Juhi Patel, Tejaskumar Bhatt, Aditi Joshi