John Bush Idoko | Cardiology | Research Excellence Award

Published on by Health Scientists

Health Scientists Awards

John Bush Idoko
Near East University, Cyprus

John Bush Idoko
Affiliation Near East University
Country Cyprus
Scopus ID 57200209705
Documents 55
Citations 481
h-index 10
Subject Area Cardiology
Event Health Scientists Awards

John Bush Idoko is a researcher affiliated with Near East University in Cyprus whose scholarly activities span medical and health sciences with notable engagement in cardiology-related research and interdisciplinary biomedical investigations. His publication portfolio demonstrates contributions to machine learning applications in health sciences, neurological signal processing, and clinical analytics. The researcher has established measurable academic visibility through indexed publications, citations, and collaborative scientific output documented in major international databases.[1]

Abstract

This academic article presents a structured overview of the scholarly profile and research contributions of John Bush Idoko of Near East University, Cyprus. The page evaluates the researcher’s publication activities, citation indicators, interdisciplinary collaborations, and contributions to health science and biomedical analytics. Particular attention is given to publications associated with neurological diagnostics, deep learning applications in medicine, and computational approaches to health-related signal analysis. The article further discusses the relevance of the researcher’s scientific portfolio within the context of the Health Scientists Awards and highlights the consistency of publication productivity and research visibility within indexed academic databases.[2]

Keywords

Cardiology, Biomedical Engineering, Machine Learning, EEG Analysis, Deep Learning, Health Sciences, Clinical Analytics, Medical Informatics, Computational Medicine, Scientific Impact

Introduction

Contemporary health sciences increasingly integrate computational technologies, biomedical analytics, and artificial intelligence methodologies to improve diagnostic accuracy and healthcare efficiency. Researchers working at the intersection of medical sciences and intelligent systems contribute significantly to the modernization of healthcare research infrastructure. John Bush Idoko’s scholarly activities align with these evolving academic trends through publications associated with neural network methodologies, biomedical signal processing, and clinical interpretation systems.[3]

The international research environment has increasingly recognized interdisciplinary scientific collaborations that combine computational modeling with clinical sciences. Academic researchers contributing to this area frequently engage in studies involving diagnostic systems, machine learning algorithms, and predictive analytics for medical applications. Publications attributed to John Bush Idoko indicate participation in these developing scientific domains with measurable scholarly visibility across indexed academic platforms.[1]

Research Profile

John Bush Idoko is affiliated with Near East University in Cyprus and has developed an academic profile characterized by interdisciplinary research contributions involving medical sciences, signal processing, and intelligent computational systems. According to indexed bibliographic records, the researcher has produced 55 documents with 481 citations and an h-index of 10, reflecting moderate and consistent scholarly engagement within internationally indexed literature.[1]

The researcher’s publication trajectory demonstrates collaborative engagement with scientists from multiple institutions and disciplinary backgrounds. Several studies involve neural networks, convolutional deep learning systems, biomedical image interpretation, and clinical decision-support methodologies. Such interdisciplinary research profiles are increasingly important in modern health science ecosystems where computational methods assist in healthcare diagnostics and patient management strategies.[4]

Research Contributions

The research contributions associated with John Bush Idoko include investigations into biomedical signal analysis, epileptic EEG identification systems, and deep learning approaches for medical pattern recognition. These contributions reflect ongoing scientific interest in leveraging computational intelligence to improve diagnostic precision and clinical interpretation methodologies.[2]

One area of scholarly contribution involves convolutional neural network applications for EEG signal classification. Research in this area supports the development of automated neurological analysis systems capable of assisting healthcare practitioners in diagnostic processes. Such studies contribute to broader efforts within biomedical engineering and clinical informatics aimed at improving healthcare efficiency and analytical reliability.[3]

Additional contributions include studies related to sign language translation using deep neural networks and interdisciplinary artificial intelligence frameworks applied to health communication technologies. These works collectively illustrate the researcher’s participation in computational approaches that support healthcare accessibility, diagnostic automation, and biomedical data interpretation.[4]

Publications

  • Abiyev, R., Arslan, M., Idoko, J. B., Sekeroglu, B., & Ilhan, A. “Identification of epileptic EEG signals using convolutional neural networks.” Applied Sciences, 2020.
    https://doi.org/10.3390/app10124089
  • Abiyev, R. H., Arslan, M., & Idoko, J. B. “Sign language translation using deep convolutional neural networks.” KSII Transactions on Internet and Information Systems, 2020.
    https://doi.org/10.3837/tiis.2020.02.018
  • Research publications involving biomedical data analytics, machine learning applications, and intelligent healthcare systems indexed within Scopus and Google Scholar databases.[1]

Research Impact

The measurable research impact associated with John Bush Idoko is reflected through citation activity, interdisciplinary collaborations, and publication visibility across recognized indexing platforms. Citation metrics indicate that the researcher’s publications have contributed to ongoing academic discussions within biomedical engineering, computational medicine, and health informatics.[1]

The integration of deep learning methodologies into biomedical diagnostics represents an active area of international scientific development. Contributions related to EEG analysis and medical classification systems have relevance to modern healthcare technologies and emerging intelligent diagnostic infrastructures. Such research may contribute to future clinical applications, data interpretation systems, and computational healthcare solutions.[2]

Award Suitability

The scholarly profile of John Bush Idoko demonstrates characteristics commonly associated with academic recognition programs in health sciences, including interdisciplinary collaboration, indexed publication output, citation visibility, and engagement with emerging biomedical technologies. The integration of artificial intelligence methodologies into health science investigations aligns with evolving priorities within international healthcare research communities.[4]

The Health Scientists Awards program recognizes researchers whose academic contributions support innovation, scientific advancement, and interdisciplinary development in healthcare-related fields. Based on available bibliographic indicators and publication themes, the researcher’s profile demonstrates relevance to these academic evaluation criteria.[5]

Conclusion

John Bush Idoko’s academic profile reflects ongoing contributions to interdisciplinary health science research involving machine learning, biomedical analytics, and intelligent diagnostic systems. Indexed publications, citation metrics, and collaborative research activities collectively indicate sustained engagement with contemporary computational healthcare methodologies. The researcher’s portfolio demonstrates relevance within emerging scientific areas that integrate biomedical sciences and artificial intelligence technologies. Such contributions support broader international efforts aimed at improving healthcare diagnostics, analytical efficiency, and digital health innovation.[1]

References

  1. Elsevier. (n.d.). Scopus author details: John Bush Idoko, Author ID 57200209705. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57200209705
  2. Abiyev, R., Arslan, M., Idoko, J. B., Sekeroglu, B., & Ilhan, A. (2020). Identification of epileptic EEG signals using convolutional neural networks. Applied Sciences.
    https://doi.org/10.3390/app10124089
  3. Google Scholar. (n.d.). John Bush Idoko citation profile and indexed publications.
    https://scholar.google.com/citations?user=eVqc6HkAAAAJ&hl=en&oi=ao
  4. Sekeroglu, B., Abiyev, R., Ilhan, A., Arslan, M., & Idoko, J. B. “Systematic literature review on machine learning and student performance prediction: Critical gaps and possible remedies.” Applied Sciences, 11(22), 10907. https://doi.org/10.3390/app112210907
  5. Health Scientists Awards. (n.d.). Official award information and evaluation categories.
    https://healthscientists.org/

Saeed Aghebat Bekheir | Translational Research | Research Excellence Award

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Dr. Saeed Aghebat Bekheir | Mental Health | Research Excellence Award

PhD Candidate in Toxicology | Tehran University of Medical Sciences | Iran

Dr. Saeed Aghebat Bekheir is a dedicated researcher and PhD candidate in Toxicology at Tehran University of Medical Sciences, working under the mentorship of Prof. Abdollahi. His research focuses on mitochondrial toxicology, cardiotoxicity, and mechanistic modeling of toxin-induced bioenergetic failure, with a particular emphasis on developing therapeutic strategies for aluminum phosphide–induced mitochondrial dysfunction. Dr. Aghebat Bekheir has demonstrated expertise in a wide range of experimental approaches, including in vivo animal studies, cellular and molecular assays, and in vitro toxicological models, integrating mechanistic toxicology with translational research to address acute mitochondrial injury and cardiotoxicity. His scholarly contributions are reflected in several peer-reviewed publications in high-impact journals, and he actively serves as a reviewer for multiple journals in toxicology and biomedical sciences, highlighting his commitment to advancing scientific rigor and dissemination. Collaboratively, he engages in research partnerships within the toxicology and mitochondrial research community, fostering interdisciplinary approaches to environmental and translational toxicology challenges. His work contributes to a deeper understanding of oxidative stress, bioenergetic disturbances, and cardiotoxic mechanisms, with broader implications for public health and therapeutic innovation. Beyond his laboratory and scholarly pursuits, Dr. Aghebat Bekheir’s research emphasizes societal relevance by addressing environmental toxicants and their impacts on human health, providing foundational knowledge for developing interventions to mitigate toxicity-related diseases. Through his professional memberships, editorial activities, and collaborative initiatives, he actively contributes to shaping the global toxicology landscape, promoting evidence-based strategies, and inspiring emerging researchers in the field. His ongoing research underscores a commitment to integrating scientific excellence with practical solutions for human health, reflecting both scholarly distinction and societal impact. He has 23 citations from 5 documents with an h-index of 3.

Profiles: Google Scholar | Scopus

Featured Publications

1. Ghanati, K., Jahanbakhsh, M., Shakoori, A., Aghebat-Bekheir, S., … (2024). The association between polycystic ovary syndrome and environmental pollutants based on animal and human study; a systematic review.

2. Ghanati, K., Eghbaljoo, H., Akbari, N., Mazaheri, Y., Aghebat-Bekheir, S., … (2023). Determination of melamine contamination in milk with various packaging: A risk assessment study.

3. Aghebat-Bekheir, S., Abdollahi, M. (2024). Discovering the most impactful treatments for aluminum phosphide cardiotoxicity gleaned from systematic review of animal studies.

4. Reihani, A., Marboutian, F., Aghebat-Bekheir, S., Reyhani, A., Akhgari, M. (2024). Diagnostic aspects of paraquat in the forensic toxicology: A systematic review.

5. Aghebat-Bekheir, S., Hekmatirad, S., Asar, N., Abdollahi, M. (n.d.). Aluminum phosphide as a model for exploring acute mitochondrial disorders.

Premalatha S | Neuroscience | Best Researcher Award

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Dr. Premalatha S | Neuroscience | Best Researcher Award 

Assistant Professor | Karpagam College of Engineering | India

Dr. S. Premalatha is currently serving as an Assistant Professor in the Science and Humanities Department at Karpagam College of Engineering, Coimbatore, Tamil Nadu, India. She completed her M.Phil. in Mathematics in 2016 at Bharathiar University, Coimbatore, and earned her Ph.D. in 2025 from the same institution, demonstrating a sustained commitment to advancing mathematical research. Her key research areas include Neural Networks, Differential Equations, and the theoretical and applied aspects of memristive inertial neural networks (MINNs), with a particular focus on stability, synchronization, dissipativity analyses, and reduced-order modeling of complex dynamical systems. Over the years, Dr. Premalatha has published six high-impact research articles in reputed SCI and Scopus-indexed journals, significantly contributing to the understanding of nonlinear neural network behaviors and secure communication frameworks. Her work has been cited widely, reflecting her growing influence in the field, with a citation index of 143. She is an active member of professional bodies such as ISTE, and her dedication to academic excellence has earned her nominations for awards including the Best Researcher Award, Women Research Award, Young Scientist Award, and Best Paper Award. While she has not yet held formal editorial roles, her publications have contributed to shaping contemporary research directions in neural network theory and applied mathematics, and she continues to mentor and inspire the next generation of engineers and mathematicians through her teaching and scholarly endeavors.

Profile: Google Scholar

Featured Publications

1. Rakkiyappan, R., Premalatha, S., Chandrasekar, A., & Cao, J. (2016). Stability and synchronization analysis of inertial memristive neural networks with time delays.

2. Rajan, R., Gandhi, V., Soundharajan, P., & Joo, Y. H. (2020). Almost periodic dynamics of memristive inertial neural networks with mixed delays.

3. Premalatha, S., Santhosh Kumar, S., & Jayanthi, N. (2022). Results on periodicity of memristive inertial neural networks with mixed delays. International Conference on Data Analytics and Computing.

4. Premalatha, S. S. K., Shanmugapriya, M. M., & Jayanthi, N. (2025). Dissipativity analysis of memristive inertial neural networks with time-varying and distributed delay via reduced order strategy.

5. Premalatha, S. S. K., Indumathi, P., & Aarthi, D. (2025). LMI approach to dissipative of memristive inertial neural networks with time-varying and distributed delay. Advances in Nonlinear Variational Inequalities.