Ateke Goshvarpour | Biomedical and Healthcare Applications | Editorial Board Member

Published on by Computer Vision Awards

Assist. Prof. Dr. Ateke Goshvarpour | Biomedical and Healthcare Applications | Editorial Board Member

Assistant Professor | Imam Reza International University | Iran

Dr. Ateke Goshvarpour, affiliated with Imam Reza International University, Mashhad, Iran, is a distinguished researcher specializing in biomedical signal processing, cognitive neuroscience, and computational modeling of brain activity. With a prolific research portfolio comprising 70 publications and over 1,095 citations across 727 scholarly documents, Dr. Goshvarpour has established a strong global reputation for her contributions to the understanding and classification of cognitive and mental disorders using advanced signal analysis techniques.Her recent works focus on EEG-based diagnosis of schizophrenia, emotion recognition, and cognitive assessment, integrating concepts from quantum-inspired computation, chaotic dynamics, and neural connectivity analysis. Notable studies such as “Enhancing Schizophrenia Diagnosis through EEG Frequency Waves and Information-Based Neural Connectivity Feature Fusion” and “Quantum-Inspired Feature Extraction Model for Enhanced Schizophrenia Detection” highlight her innovative approach in bridging neuroscience with machine learning and chaos theory. Through the development of spectral–spatiotemporal models and graph-based signal representations, she provides novel pathways for noninvasive brain disorder diagnostics and affective computing.Collaborating with a network of 21 co-authors, Dr. Goshvarpour demonstrates an interdisciplinary outlook, integrating engineering, data science, and psychology to improve diagnostic precision and healthcare outcomes. Her h-index of 20 reflects both the impact and consistency of her research influence. Beyond academia, her work contributes significantly to societal well-being by enabling early and accurate detection of neurological conditions and enhancing emotional intelligence systems.Dr. Goshvarpour’s dedication to advancing the frontier of biomedical and cognitive signal processing underscores her role as a leading figure in computational neuroscience research, fostering a deeper understanding of human cognition through data-driven and bio-inspired intelligence frameworks.

Profiles: ORCID |  Scopus | Google Scholar

Featured Publications

1.Goshvarpour, A. (2025). Enhancing schizophrenia diagnosis through EEG frequency waves and information-based neural connectivity feature fusion. Biomedical Signal Processing and Control.

2.Goshvarpour, A. (2025). Quantum-inspired feature extraction model from EEG frequency waves for enhanced schizophrenia detection. Chaos, Solitons & Fractals. Cited By : 1

3.Goshvarpour, A. (2025). Cognitive-inspired spectral spatiotemporal analysis for emotion recognition utilizing electroencephalography signals. Cognitive Computation. Cited By : 4

4.Goshvarpour, A. (2025). Asymmetric measures of polar Chebyshev chaotic map for discrete/dimensional emotion recognition using PPG. Biomedical Signal Processing and Control. Cited By : 1

5.Goshvarpour, A. (2025). Diagnosis of cognitive and mental disorders: A new approach based on spectral–spatiotemporal analysis and local graph structures of electroencephalogram signals. Brain Sciences. Cited By : 3

Dr. Ateke Goshvarpour’s pioneering research in biomedical signal processing and neurocomputational modeling is transforming the early detection of mental and cognitive disorders. By integrating EEG analytics, chaos theory, and AI-driven methods, her work bridges neuroscience and technology—advancing precision diagnostics, enhancing emotional intelligence systems, and fostering global innovation in digital health and mental well-being.

Emmanuel Ukekwe | Data Analytics | Best Researcher Award

Published on by Computer Vision Awards

Dr. Emmanuel Ukekwe | Data Analytics | Best Researcher Award

Senior Lecturer | University of Nigeria | Nigeria

Dr. Emmanuel Ukekwe is a dedicated researcher and academic with expertise in artificial intelligence, expert systems, data science, computational programming, and software engineering, with a focus on applying intelligent technologies to solve societal problems. He obtained his Bachelor of Science in Computer/Statistics, Master of Science, and Ph.D. in Computer Science from the University of Nigeria, Nsukka, where he has grown into a respected lecturer and researcher. His professional journey includes roles as Senior Lecturer, Lecturer, and Instructor, as well as administrative positions such as Acting Head of Department and Acting Dean, demonstrating both academic and leadership excellence. His research interests span the application of machine learning and Python programming in data-driven problem solving, optimization models, recommender systems, and educational technologies. He has published extensively in recognized journals and conferences indexed in Scopus, covering healthcare systems, telecommunications, student performance, and COVID-19 analytics. He has been actively involved in university committees, curriculum development, and community-based research projects, and is a member of organizations such as the National Biotechnology Development Agency and the Technical Committee on UNESCO-HP projects. His skills include statistical analysis, software development, and advanced computational modeling, reflecting strong technical and analytical capabilities. His academic and research contributions have been recognized with professional memberships and community service engagements, marking him as an influential contributor to both academia and society. His research profile records 4 citations, 8 documents, and an h-index of 1.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Okereke, G. E., Bali, M. C., Okwueze, C. N., Ukekwe, E. C., Echezona, S. C., & Ugwu, C. I. (2023). K-means clustering of electricity consumers using time-domain features from smart meter data. Journal of Electrical Systems and Information Technology, 10(1), 2.

  2. Ukekwe, E. C., Obayi, A. A., Johnson, A., Musa, D. A., & Agbo, J. C. (2025). Optimizing data and voice service delivery for mobile phones based on clients’ demand and location using affinity propagation machine learning. Journal of the Nigerian Society of Physical Sciences, 7(2), 2109.

  3. Ukekwe, E. C., Ezeora, N. J., Obayi, A. A., Asogwa, C. N., Ezugwu, A. O., Adegoke, F. O., Raiyetumbi, J., & Tenuche, B. (2025). Examining the impact of mathematics ancillary courses on computational programming intelligence of computer science students using machine learning techniques. Computer Applications in Engineering Education, 33(4), e70054.

  4. Ukekwe, E. C., Ogbonna, G. U. G., Adegoke, F. O., Okereke, G. E., & Asogwa, C. N. (2023). Clustering Nigeria’s IDP camps for effective budgeting and re-settlement policies using an optimized K-means approach. African Conflict & Peacebuilding Review, 13(2), 60–85.

  5. Okereke, G. E., Azegba, O., Ukekwe, E. C., Echezona, S. C., & Eneh, A. (2023). An automated guide to COVID-19 and future pandemic prevention and management. Journal of Electrical Systems and Information Technology, 10(1), 16.