Essam Houssein | Computational Neuroscience | Innovative Research Award

Innovative Research Award

Essam Houssein
Minia University

Essam Houssein
Affiliation Minia University
Country Egypt
Scopus ID 43361385400
Documents 324
Citations 18,566
h-index 70
Subject area Computational Neuroscience
Event Computational Biologists Awards
ORCID 0000-0002-8127-7233

Essam Houssein is a researcher affiliated with Minia University in Egypt. His research profile is presented in the context of computational neuroscience, a field that applies computational methods and mathematical models to investigate neural systems and brain function. The Innovative Research Award recognizes research contributions that demonstrate originality, methodological development, and potential scientific value. This article summarizes the supplied researcher information, outlines the relevance of computational approaches to neuroscience, and describes the scholarly considerations associated with research recognition through the Computational Biologists Awards. Bibliometric figures should be checked against current source records.[1]

Abstract

This article presents the research profile of Essam Houssein, affiliated with Minia University, Egypt, and identifies computational neuroscience as the subject area associated with this recognition profile. The supplied Scopus metrics list 324 documents, 18,566 citations, and an h-index of 70. These indicators offer quantitative perspectives on publication activity and scholarly attention, although they do not independently establish research quality or originality. The Innovative Research Award provides a framework for recognizing contributions to scientific progress. Assessment of research innovation should consider methodological rigor, reproducibility, originality, practical relevance, and the significance of individual contributions within the broader research community..[3]

Keywords

Essam Houssein; Innovative Research Award; Computational Neuroscience; Minia University; Egypt; scientific innovation; bibliometrics; computational modeling; neural systems; research impact; scholarly communication; Computational Biologists Awards.[1]

Introduction

Computational neuroscience combines neuroscience with mathematical analysis, statistics, computer science, and computational modeling to investigate how nervous systems process information. Its methods can help researchers formulate hypotheses, analyze complex observations, and study mechanisms that are difficult to examine through experimental approaches alone. Computational research increasingly supports interdisciplinary collaboration across biological and quantitative sciences. Recognition in this area should reflect documented contributions, sound methodology, and scientific relevance. The supplied profile of Essam Houssein provides bibliometric context for an academic recognition article, while the researcher’s publications and source records remain the appropriate basis for evaluating specific scientific achievements.[1]

Research Profile

The information supplied for this profile identifies Essam Houssein with Minia University in Egypt and provides a Scopus Author ID of 43361385400. The associated bibliometric figures are 324 documents, 18,566 citations, and an h-index of 70. These numbers are reported as supplied and may change as indexing databases are updated. Institutional affiliation, author identity, publication attribution, and research subject classification should be confirmed against the corresponding authoritative records before this article is used for formal evaluation or institutional publication.[1]

Research Contributions

Research contributions in computational neuroscience may include the development of mathematical models, computational algorithms, statistical techniques, neural data-analysis pipelines, and reproducible methods for interpreting biological signals. Their scientific value depends on the clarity of the research question, the suitability of the methods, validation against appropriate evidence, and the extent to which results advance understanding. The supplied information does not identify individual publications or specific methods attributable to Essam Houssein. Accordingly, particular discoveries or technical innovations should not be assigned to the researcher without verification from relevant publications and institutional or bibliographic records.[2]

Publications

The supplied Scopus profile reports 324 documents associated with the researcher. A publication-level review is necessary to establish the titles, publication venues, authorship roles, research topics, and dates represented by this record. Publication counts can provide an overview of scholarly output, but they do not distinguish between article types, research significance, or individual contributions to collaborative work. A verified publication list should be retrieved from the author’s bibliographic profile and cross-checked against publisher records. No individual paper titles or DOI identifiers are asserted here because a verified publication list was not included in the supplied data. [1]

Research Impact

The supplied citation total of 18,566 and h-index of 70 describe aspects of the research profile’s indexed citation record. The h-index combines publication productivity with citation frequency by counting a set of papers that have each received at least the corresponding number of citations. Such indicators are influenced by disciplinary practices, publication age, collaboration patterns, database coverage, and citation behavior. They are best interpreted alongside qualitative assessment of originality, methodological reliability, research transparency, and contribution to scientific knowledge rather than used as standalone measures of research excellence.[3]

Award Suitability

The Innovative Research Award is intended, in this article’s recognition context, to highlight scientific work demonstrating originality, rigor, and potential value to a research field. The supplied publication and citation metrics offer quantitative background for reviewing Essam Houssein’s academic profile, but award suitability should be determined through evidence-based evaluation of actual research outputs. Relevant considerations include methodological innovation, documented research contributions, publication quality, and potential influence on future studies. The event’s formal eligibility requirements and judging criteria should be consulted before presenting this profile as confirmation of an award decision.[1]

Conclusion

The supplied profile identifies Essam Houssein as a researcher affiliated with Minia University in Egypt and reports a Scopus record of 324 documents, 18,566 citations, and an h-index of 70. Computational neuroscience offers an interdisciplinary setting in which computational methods contribute to the investigation of neural systems and biological information processing. These bibliometric indicators provide context rather than a complete assessment of scientific achievement. A robust academic recognition profile should combine verified researcher identity, publication-level evidence, documented contributions, and transparent evaluation criteria. Current source records should be consulted before the information is used in formal award materials..[3]

References

  1. Elsevier. (n.d.). Scopus author details: Essam Houssein, Author ID 43361385400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=43361385400
  2. Journal article. (2025). Integrated deep learning-based IRACE and convolutional neural networks for chest X-ray image classification.
    https://doi.org/10.1016/j.knosys.2025.114293
  3. Journal article. (2025). Computer-aided diagnosis system for predicting liver cancer disease using modified Genghis Khan Shark Optimizer algorithm.
    https://doi.org/10.1016/j.eswa.2025.128017
  4. Computational Biologists Awards. (2026. )  Computational Biologists Awards.
    https://computationalbiologists.com/
  5. ORCID. (n.d.). Essam Houssein: ORCID researcher identifier.
    https://orcid.org/0000-0002-8127-7233

Shariq Bashir | Computational Neuroscience | Research Excellence Award

Dr. Shariq Bashir | Computational Neuroscience | Research Excellence Award

Imam Mohammad bin Saud Islamic University | Saudi Arabia

Dr. Shariq Bashir is an accomplished academic and researcher in data science, information retrieval, and machine learning, currently serving as an Associate Professor at Imam Mohammad Ibn Saud Islamic University, Riyadh. With over two decades of teaching and research experience, he has contributed extensively to areas such as data mining, big data analytics, and artificial intelligence. He completed his PhD with distinction from Vienna University of Technology and pursued postdoctoral research at New York University Abu Dhabi, focusing on learning-to-rank approaches. Dr. Bashir has taught a wide range of undergraduate and graduate courses across international institutions and has an impressive publication record in high-impact journals. He actively serves as an editor, reviewer, and program committee member for leading conferences and journals, reflecting his strong influence in the global research community.

Citation Metrics (Scopus)

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Citations
583
Documents
48
h-index
13

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Documents

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View Scopus Profile            View Orcid Profile

Featured Publications

Reference Recommendation for Large Language Models-Generated Text Using Deep Textual Representations

– Romanian Journal of Information Science and Technology

Zero-Shot Pre-Retrieval Prompt Performance Prediction for Large Language Models

– IEEE Access

A Machine Learning Framework for Inferring Properties of Embedded Devices

– Ad Hoc Networks

A Weakly Supervised MIL Approach to Fake News Detection via Propagation Tree Analysis

– International Journal of Advanced Computer Science and Applications

Asymmetric Watermarking for Large Language Models With Public and Private Verification

– IEEE Access

maryam | Computational Neuroscience | Best Researcher Award

Dr. maryam | Computational Neuroscience | Best Researcher Award 

faculty at Shahrekord University, Iran.

Dr. Maryam Taghizadeh is an Assistant Professor of Communication Engineering at Shahrekord University, Iran. With a Ph.D. from Isfahan University of Technology, her research focuses on signal and image processing, particularly in medical applications such as brain tumor segmentation and autism diagnosis using EEG and deep learning. She has published extensively in reputable journals and has actively contributed to academic conferences as a chair and committee member. Dr. Taghizadeh is also a dedicated mentor, guiding graduate research in biomedical signal processing and AI-driven diagnostics.

🎓Academic Background:

Dr. Maryam Taghizadeh holds a distinguished academic background in Electrical and Communication Engineering. She earned her Ph.D. in Communication Engineering from Isfahan University of Technology in 2014, following her M.Sc. (2003) and B.Sc. (2001) degrees from the same institution. Her educational journey laid a strong foundation in signal processing, communications, and applied research, setting the stage for her impactful contributions in both academia and biomedical research.

Profile:

💼 Professional Experience:

Dr. Maryam Taghizadeh has been serving as an Assistant Professor at the Faculty of Technology and Engineering, Shahrekord University, Iran, since 2014. In this role, she has actively engaged in teaching, research, and student supervision in the fields of communication engineering and biomedical signal processing. Her expertise lies in image analysis, EEG signal interpretation, and the application of artificial intelligence in medical diagnostics. She has led numerous research projects related to brain disorders such as autism and brain tumors, using advanced techniques like deep learning, wavelet transforms, and active contour models. In addition to her academic responsibilities, Dr. Taghizadeh has contributed significantly to the scientific community by participating in the organization of major conferences. She served as the Chairperson of the 3rd International Conference on Pattern Recognition and Image Analysis and has been a Scientific and Executive Committee Member in multiple editions of national and international conferences on machine vision and image processing. Through her research, mentorship, and leadership, she continues to play a vital role in advancing interdisciplinary engineering and healthcare innovation in Iran and beyond.

🧪 Scientific and Research Excellence:

Dr. Maryam Taghizadeh has demonstrated outstanding scientific and research excellence in the interdisciplinary fields of signal processing, image analysis, and biomedical engineering. Her pioneering work in developing automated methods for brain tumor segmentation, EEG-based autism diagnosis, and functional brain network analysis reflects a deep integration of engineering principles with healthcare innovation. She has published high-impact research in internationally recognized journals, such as IET Computer Vision and the International Journal of Neural Systems, showcasing both originality and practical relevance. Dr. Taghizadeh’s ability to apply machine learning, deep networks, and wavelet transforms to real-world medical problems marks her as a leader in AI-driven healthcare solutions. Furthermore, her active roles in organizing international conferences and mentoring graduate students further amplify her contribution to advancing science and fostering the next generation of researchers.

🧠 Areas of Expertise:

Dr. Maryam Taghizadeh’s research bridges the fields of signal and image processing, biomedical engineering, and artificial intelligence, with a special focus on medical applications. Her work includes developing algorithms for brain tumor segmentation, autism detection using EEG signals, and analyzing bioacoustic patterns in insects. She has authored and co-authored several impactful papers in reputable journals such as IET Computer Vision and International Journal of Neural Systems. Her research is widely recognized for its innovation in applying deep learning, wavelet transforms, and active contour models to real-world medical challenges.

👩‍🏫 Student Mentorship:

As a dedicated mentor, Dr. Maryam Taghizadeh has supervised numerous master’s theses in areas such as EEG signal processing, autism detection, cognitive brain analysis, and deep learning models for healthcare diagnostics. Her students have explored cutting-edge topics, contributing to the advancement of neuroscience and computational medicine under her guidance.

🌐 Academic Leadership:

Beyond her research, Dr. Maryam Taghizadeh has played a vital role in academic leadership. She has served on the Scientific Committee of the Iranian Conference on Machine Vision and Image Processing and the International Conference on Pattern Recognition and Image Analysis. Notably, she chaired the 3rd edition of the latter and was a member of the executive committee for its 6th edition in 2023, showing her ongoing engagement in shaping research dialogue and collaboration.

Publications:

  • A Review of Coronary Vessel Segmentation Algorithms
    M.T. Dehkordi, S. Sadri, A. Doosthoseini
    Journal of Medical Signals & Sensors, 1(1), pp. 49–54, 2011.
    Cited by: 81

  • Local Feature Fitting Active Contour for Segmenting Vessels in Angiograms
    M. Taghizadeh Dehkordi, A.M. Doost Hoseini, S. Sadri, H. Soltanianzadeh
    IET Computer Vision, 8(3), pp. 161–170, 2014.
    Cited by: 37

  • Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals
    H.A. Ardakani, M. Taghizadeh, F. Shayegh
    International Journal of Neural Systems, 32(11), Article 2250046, 2022.
    Cited by: 17

  • Segmentation of Coronary Vessels by Combining the Detection of Centerlines and Active Contour Model
    M. Taghizadeh, S. Sadri, A. Doosthoseini
    Proceedings of the 7th Iranian Conference on Machine Vision and Image Processing, 2011.
    Cited by: 8

  • A New Active Contour Model for Tumor Segmentation
    M.T. Dehkordi
    3rd International Conference on Pattern Recognition and Image Analysis, 2017.
    Cited by: 6

  • An Effective System to Detect Face Drowsiness Status Using Local Features in a Hierarchical Decision-Making Structure
    M.T. Dehkordi, F.S. Hafshejani, H. Pourghasem
    International Research Journal of Engineering and Technology, 5(4), pp. 646–654, 2018.
    Cited by: 3

  • Bioacoustics of Trogoderma granarium Everts (Coleoptera: Dermestidae)
    P.A. Farsani, N.S. Dehkordi, R. Ebrahimi, A. Nemati, M.T. Dehkordi
    Journal of Asia-Pacific Entomology, 27(1), Article 102189, 2024.
    Cited by: 1

  • An Automated Method for Brain Tumor Segmentation Based on Level Set
    Maryam Taghizadeh Dehkordi
    International Journal of Computer, 30(1), pp. 59–69, 2018.
    Cited by: 1

  • Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network
    F. Bahrami, M. Taghizadeh, F. Shayegh
    Journal of Medical Signals & Sensors, 14(8), Article 24, 2024.
    Cited by: 0 (as of now)