Saeid Pashazadeh | Computer Science and Artificial Intelligence | Forensic Science Communication Award

Published on by Forensic Scientist Awards

Forensic Science Communication Award

Saeid Pashazadeh
University of Tabriz, Iran

             Saeid Pashazadeh
Affiliation University of Tabriz
Country Iran
Scopus ID 24825064000
Documents 74
Citations 769
h-index 16
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0000-0002-8949-9180

The Forensic Science Communication Award recognizes research and scholarly contributions that support the communication, interpretation, and responsible application of scientific knowledge. This academic recognition profile presents Saeid Pashazadeh, affiliated with the University of Tabriz, Iran, whose reported research record includes publications spanning artificial intelligence, distributed computing, Internet of Things technologies, and intelligent systems. These areas provide relevant technological foundations for developing reliable computational methods and communicating complex research findings across scientific disciplines. The profile summarizes the supplied bibliometric indicators and selected publications in the context of the International Forensic Scientist Awards.

Abstract

Saeid Pashazadeh’s supplied research profile records 74 documents, 769 citations, and an h-index of 16. The selected publications cover game-theoretic localization for Internet of Things healthcare applications, formal verification of crash-tolerant distributed systems, neural adaptation for unmanned aerial vehicle flight planning, and deep-learning-based voice analysis. Collectively, these works illustrate a research portfolio involving computational intelligence, algorithm design, and systems analysis. The award profile considers their relevance to interdisciplinary scientific communication without treating bibliometric indicators alone as proof of forensic specialization or award selection. [1]

Keywords

Forensic science communication; artificial intelligence; computer science; Internet of Things; game theory; distributed systems; formal verification; deep learning; neural adaptation; research impact.

Introduction

Scientific communication connects methodological advances with researchers, practitioners, and broader audiences. In computational research, this involves explaining algorithmic assumptions, evaluating evidence, and presenting technical outcomes in a reproducible and accessible form. Pashazadeh’s supplied publication records address several computational fields in which clarity, verification, and interpretation are important. His profile is presented for consideration in relation to the International Forensic Scientist Awards, with the distinction that the listed research topics do not independently establish direct forensic applications. [2]

Research Profile

The selected work reflects interests in intelligent algorithms, networked systems, computational modeling, and machine learning. These fields address challenges such as coordinating connected devices, checking distributed-system behavior, planning autonomous movement, and extracting patterns from voice data. Together, they demonstrate a cross-disciplinary technical orientation. The available information identifies the University of Tabriz as the researcher’s affiliation and Computer Science and Artificial Intelligence as the relevant subject area. [3]

Research Contributions

The supplied publication titles indicate several distinct contribution areas:

  • Game-theoretic localization methods for Internet of Things environments, including healthcare applications.
  • Cutoff theorems supporting model checking of crash-tolerant causal broadcast systems.
  • Intelligent three-dimensional flight-path planning for unmanned aerial vehicles using neural adaptation.
  • A comprehensive review of deep-learning approaches to voice-based gender detection.

These topics illustrate work across optimization, verification, autonomous systems, and machine learning. Their potential relevance to forensic practice would require assessment of specific applications, validation methods, and evidentiary requirements. [4]

Publications

  1. “Localization in the Internet of Things Based on Game Theory with Applications in the Healthcare.” Peer-to-Peer Networking and Applications, 2026.
  2. “Cutoff Theorems for the Model Checking of Crash-Tolerant Causal Broadcast.” Theory of Computing Systems, June 2026.
  3. “Intelligent 3-D Flight Path Planning for UAV Based on Epigenetic Learning Through Evolved Neural Adaptation (ELENA) Algorithm.” IEEE Open Journal of the Communications Society, 2026.
  4. “Voice-Based Gender Detection Using Deep Learning: A Comprehensive Review.” Book chapter, 2026.

Research Impact

The supplied Scopus indicators are 74 documents, 769 citations, and an h-index of 16. These measures offer a quantitative snapshot of publication and citation activity, but do not independently determine research quality, practical significance, or communication effectiveness. Citation totals can also vary over time as databases are updated. The figures should therefore be interpreted alongside article content, methodological rigor, reproducibility, and demonstrated application. [1]

Award Suitability

The research record offers material for evaluating technical scholarship and interdisciplinary communication. Relevant assessment criteria may include the clarity with which complex methods are explained, the accessibility of research findings, evidence of collaboration, and contributions to responsible scientific practice. Because the supplied publications primarily concern computational methods and intelligent systems, their specific relationship to forensic science communication should be established through documented applications or additional supporting evidence. This profile does not imply a confirmed award decision.

Conclusion

Saeid Pashazadeh’s reported research record combines publication activity with work across machine learning, distributed computing, and intelligent systems. The four selected publications provide concrete examples of this technical range. Consideration for the Forensic Science Communication Award should additionally examine evidence of communication quality and direct forensic relevance, ensuring that recognition reflects documented contributions as well as quantitative indicators.

References

  1. Elsevier. (n.d.). Scopus author details: Saeid Pashazadeh, Author ID 24825064000. Scopus. Bibliometric figures in this profile are based on the supplied researcher data and should be checked against the live record.
    https://www.scopus.com/authid/detail.uri?authorId=24825064000
  2. International Forensic Scientist Awards. (n.d.). Official awards website.
    https://forensicscientist.org/
  3. ORCID. (n.d.). Saeid Pashazadeh researcher identifier.
    https://orcid.org/0000-0002-8949-9180
  4. “Localization in the Internet of Things Based on Game Theory with Applications in the Healthcare.” (2026). Peer-to-Peer Networking and Applications.
    https://doi.org/10.1007/s12083-026-02300-z
  5. “Cutoff Theorems for the Model Checking of Crash-Tolerant Causal Broadcast.” (2026). Theory of Computing Systems.
    https://doi.org/10.1007/s00224-026-10283-w
  6. “Intelligent 3-D Flight Path Planning for UAV Based on Epigenetic Learning Through Evolved Neural Adaptation (ELENA) Algorithm.” (2026). IEEE Open Journal of the Communications Society.
    https://doi.org/10.1109/OJCOMS.2026.3710732
  7. “Voice-Based Gender Detection Using Deep Learning: A Comprehensive Review.” (2026). Book chapter.
    https://doi.org/10.1007/978-3-032-27860-9_13

Fahimeh Ghasemian | Computer Science | Research Excellence Award

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Research Excellence Award

Fahimeh Ghasemian
Shahid Bahonar University of Kerman, Iran

Fahimeh Ghasemian, affiliated with Shahid Bahonar University of Kerman, Iran, is a researcher in the field of Computer Science with recognized scholarly contributions in artificial intelligence, machine learning, natural language processing, medical informatics, and computational optimization. Her academic record reflects interdisciplinary research activity across healthcare analytics, deep learning, and intelligent systems, supported by indexed publications, citation impact, and international research visibility.[1]

Fahimeh Ghasemian
Affiliation Shahid Bahonar University of Kerman
Country Iran
Scopus ID 57190766313
Documents 26
Citations 235
h-index 9
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0000-0002-2176-7089

Abstract

Fahimeh Ghasemian has established a research portfolio centered on artificial intelligence and data-driven computational methodologies, with particular emphasis on healthcare applications, medical imaging, and predictive analytics. Her work integrates deep learning architectures, natural language processing systems, machine learning models, and optimization algorithms to address challenges in disease diagnosis, prognosis, and intelligent healthcare management. Published research associated with her academic profile demonstrates interdisciplinary collaboration and methodological innovation across computer science and medical informatics domains.[2]

Keywords

  • Artificial Intelligence
  • Deep Learning
  • Natural Language Processing
  • Machine Learning
  • Medical Informatics
  • Social Network Analysis

Introduction

The increasing role of artificial intelligence in biomedical and healthcare research has created opportunities for interdisciplinary investigations integrating computational intelligence with clinical decision-making. Researchers in computer science are contributing to this transformation through the development of predictive algorithms, intelligent diagnostic systems, and advanced analytical models. Within this context, Fahimeh Ghasemian has contributed to research exploring machine learning applications in healthcare environments, including COVID-19 prognosis, CT image classification, and natural language processing systems for medical analysis.[3]

Her scholarly activities further extend into optimization algorithms and computational modeling, reflecting broader interests in intelligent systems and data-centric problem solving. The combination of applied healthcare analytics and theoretical algorithmic research demonstrates an interdisciplinary framework aligned with contemporary trends in artificial intelligence and computer engineering.[4]

Research Profile

Fahimeh Ghasemian is affiliated with Shahid Bahonar University of Kerman in Iran. Her academic background includes studies in Computer Engineering, including graduate and postgraduate qualifications from Amirkabir University of Technology and doctoral research at the University of Isfahan. Her research output indexed in Scopus includes journal articles, reviews, and interdisciplinary computational studies with citation activity across medical and engineering domains.[1]

The Scopus profile associated with Author ID 57190766313 indicates documented contributions across healthcare-oriented machine learning systems, predictive modeling, image analysis, and metaheuristic optimization algorithms. Citation metrics and h-index indicators demonstrate measurable scholarly engagement and international visibility within the scientific literature.[5]

Research Contributions

Among the notable areas of contribution is the application of machine learning for healthcare diagnostics and prognostic systems. Research publications involving COVID-19 mortality prediction and hospitalization duration employed data mining and machine learning approaches to support clinical analytics and decision-making processes.[6]

Additional contributions include deep learning frameworks for computerized tomography image classification. The AFEX-Net model introduced adaptive feature extraction strategies within convolutional neural network architectures, demonstrating the integration of artificial intelligence methods into medical image interpretation workflows.[7]

Her research also includes systematic reviews of machine learning models for image-based diagnosis and prognosis in COVID-19 contexts, contributing to evidence synthesis within medical informatics literature. These studies provide comparative perspectives on computational methodologies applied in healthcare analytics and intelligent diagnostics.[8]

Beyond healthcare applications, Fahimeh Ghasemian participated in the development of optimization approaches such as the Human Urbanization Algorithm, a metaheuristic framework designed for solving optimization problems through population-based search mechanisms.[9]

Publications

  • Possibilistic–Probabilistic Consumer Participation Modelling and Cybersecure Demand Response Enabled by Convolutional–Bidirectional Long Short‐Term Memory Forecasting, IET Smart Grid (2026).
  • AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images, DIGITAL HEALTH (2024).
  • Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms, DIGITAL HEALTH (2023).
  • Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review, JMIR Medical Informatics (2021).
  • Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review, Frontiers in Health Informatics (2021).

Research Impact

The research contributions associated with Fahimeh Ghasemian reflect the growing integration of computational intelligence into healthcare and biomedical sciences. Citation records indicate scholarly engagement with her work, particularly in studies involving machine learning-based diagnostics, COVID-19 predictive systems, and medical image analysis.[5]

Her publications in peer-reviewed journals and interdisciplinary outlets contribute to discussions on artificial intelligence methodologies applicable to clinical support systems, predictive healthcare analytics, and intelligent data processing. The combination of systematic reviews and methodological studies enhances the visibility and applicability of her research within computer science and health informatics communities.[8]

Award Suitability

Fahimeh Ghasemian demonstrates qualifications aligned with recognition in international scientific and research award contexts through her interdisciplinary scholarship, publication record, and contributions to intelligent healthcare systems. Her research activity integrates advanced computational methodologies with real-world healthcare applications, reflecting the objectives commonly associated with research excellence and scientific innovation awards.[2]

The diversity of her research topics, including artificial intelligence in medicine, machine learning, natural language processing, and optimization algorithms, further supports the relevance of her profile for academic recognition programs emphasizing scientific impact, interdisciplinary collaboration, and technological advancement.[9]

Conclusion

Fahimeh Ghasemian has contributed to the advancement of computer science and medical informatics through interdisciplinary investigations in artificial intelligence, machine learning, and intelligent healthcare systems. Her scholarly record includes research on predictive analytics, medical image classification, natural language processing, and optimization methodologies. Indexed publications, citation activity, and participation in computational healthcare research collectively demonstrate an established academic profile suitable for scholarly recognition within international research communities.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Fahimeh Ghasemian, Author ID 57190766313. Scopus.
    www.scopus.com/authid/detail.uri?authorId=57190766313
  2. ORCID. (n.d.). Fahimeh Ghasemian ORCID profile.
    orcid.org/0000-0002-2176-7089
  3. Ghasemian, F., et al. (2021). Natural Language Processing Systems for Diagnosing and Determining Level of Lung Cancer: A Systematic Review. Frontiers in Health Informatics.
  4. Ghasemian, F., et al. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
    https://doi.org/10.1016/j.matcom.2020.05.023
  5. Scopus Preview. (n.d.). Citation metrics and indexed documents associated with Fahimeh Ghasemian.
    https://www.scopus.com/authid/detail.uri?authorId=57190766313
  6. Ghasemian, F., et al. (2023). Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. DIGITAL HEALTH.
    https://doi.org/10.1177/20552076231170493
  7. Ghasemian, F., et al. (2024). AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images. DIGITAL HEALTH.
    https://doi.org/10.1177/20552076241232882
  8. Ghasemian, F., et al. (2021). Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review. JMIR Medical Informatics.
    https://doi.org/10.2196/25181
  9. Elsevier. (2020). Human urbanization algorithm: A novel metaheuristic approach. Mathematics and Computers in Simulation.
    https://doi.org/10.1016/j.matcom.2020.05.023

Wael Badawy | Computer Science | Best Researcher Award

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Prof. Dr. Wael Badawy | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Prof. Wael Badawy, is a distinguished engineer, researcher, and academic leader with over 28 years of experience in higher education, research, technology commercialization, and innovation management. He earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette, USA, and an equivalent Ph.D. in Electrical Engineering recognized by the Egyptian Higher Council of Universities, complemented by M.Sc. and B.Sc. degrees in Computer Science and Automatic Control Engineering from Alexandria University, Egypt. Prof. Wael Badawy has held senior academic and leadership positions, including Executive Director of ABM College, Canada, Program Head of Data Science and Cybersecurity at the Egyptian Russian University, and professorships at Nile University, Badr University, and the American University in Cairo, where he has taught and supervised students in Artificial Intelligence, Deep Learning, Multimedia Engineering, Cybersecurity, and Information Technology Management. His research contributions encompass over 400 publications in high-impact journals and conferences, 56 books and proceedings, and 34 co-invented patents, with highly cited work including the IEEE Transactions on Circuits and Systems for Video Technology (2018). Prof. Badawy has received more than 90 prestigious awards and honors, including the QS Reimagine Education Awards (2023, shortlisted), Silicon Review “30 Innovative Brands of the Year” (2022), and multiple distinctions in STEM, business innovation, and leadership. He actively serves on international standardization committees, professional organizations such as IEEE and ACM, and national research councils, contributing to curriculum development, program design, and strategic planning in higher education. Prof. Wael Badawy’s extensive contributions to research, innovation, and education demonstrate his sustained impact on technology, society, and the global academic community, making him an exemplary candidate for the Best Researcher Award.

Profile: Google Scholar | ORCID | LinkedIn | Staff Page

Featured Publications

  • Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2012). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits and Systems for Video Technology, 23(2), 311–325.

  • Rahman, C. A., Badawy, W., & Radmanesh, A. (2003). A real-time vehicle’s license plate recognition system. In Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance.

  • Shehata, M. S., Cai, J., Badawy, W. M., Burr, T. W., Pervez, M. S., Johannesson, R. J., … (2008). Video-based automatic incident detection for smart roads: The outdoor environmental challenges regarding false alarms. IEEE Transactions on Intelligent Transportation Systems, 9(2), 349–360.

  • Ghallab, Y. H., Badawy, W., Kaler, K. V. I. S., & Maundy, B. J. (2005). A novel current-mode instrumentation amplifier based on operational floating current conveyor. IEEE Transactions on Instrumentation and Measurement, 54(5), 1941–1949.

  • Du, S., Shehata, M., & Badawy, W. (2011). Hard hat detection in video sequences based on face features, motion and color information. In 2011 3rd International Conference on Computer Research and Development, 4, 25–29.

  • Ghallab, Y., & Badawy, W. (2004). Sensing methods for dielectrophoresis phenomenon: From bulky instruments to lab-on-a-chip. IEEE Circuits and Systems Magazine, 4(3), 5–15.

  • Badawy, W., & Gomaa, H. (2015). Analyzing a segment of video. U.S. Patent No. 9,014,429.

  • Ghallab, Y. H., & Badawy, W. (2010). Lab-on-a-chip: Techniques, circuits, and biomedical applications. Artech House.

  • Badawy, W. (2009). Mesh based frame processing and applications. U.S. Patent No. 7,616,782.

  • Badawy, W. (2009). Video based monitoring system. U.S. Patent No. 7,612,666.

Sam Clarke | Computer Science | Best Researcher Award

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Mr. Sam Clarke | Computer Science | Best Researcher Award

Canterbury Christ Church University | United Kingdom

Sam Clarke is a dynamic and forward-thinking educator with over eight years of experience as a class teacher across Key Stages 1 and 2, and a key contributor to senior leadership teams. With a career rooted in both classroom practice and strategic educational leadership, Sam has transitioned seamlessly into higher education as a Lecturer of Primary Education at Canterbury Christ Church University. His expertise spans teaching, mentoring, research, and academic innovation, particularly in the realm of artificial intelligence (AI) in education. A passionate advocate for equity and ethical innovation, Sam combines classroom experience with pioneering research, curriculum design, and community outreach. His professional philosophy echoes Nelson Mandela’s powerful belief that “Education is the most powerful weapon to change the world.”

Professional profile👤

ORCID

Strengths for the Awards✨

  1. Innovative Research in AI and Education
    Sam Clarke’s work sits at the intersection of two rapidly evolving fields: education and artificial intelligence. His research addresses critical questions about GenAI’s impact on pedagogy, curriculum design, and interdisciplinary learning. His publication “Education in the Age of GenAI” and his co-edited journal BQIL demonstrate a commitment to pioneering research.

  2. Leadership in Academic Initiatives
    Sam is not just a participant but a leader in several academic and professional settings. As Founding Co-Editor of BQIL, Peer Reviewer for research funding, and Lecturer at CCCU, he has shown initiative and scholarly leadership.

  3. Policy-Relevant Contributions
    His guest lectures at UCL, Cambridge, and the Association of Citizenship Teaching suggest his work resonates beyond academia. He connects research to practice, influencing national discourse on AI in education.

  4. Community Engagement and Equity
    Through outreach in underprivileged schools and staff CPD on AI literacy, Sam applies his research to reduce educational inequities, making a tangible social impact.

  5. Collaborative and Interdisciplinary Work
    Clarke’s collaboration with prestigious institutions like the University of Oxford and his emphasis on interdisciplinary knowledge building demonstrate a wide-ranging and cooperative research ethos.

  6. Strong Publication Record with Open Access & Accessibility
    His commitment to knowledge dissemination is evident in his open-access publications and engagement in practitioner journals, ensuring that research reaches a diverse audience.

🎓 Education

Sam’s educational journey reflects a consistent trajectory of academic excellence and professional growth. He holds a Bachelor of Arts in Education Studies and Geography (First Class, 2014), a Postgraduate Certificate in Education with QTS (Distinction, 2015), and a Master of Arts in Education (Merit, 2024)—all from Canterbury Christ Church University. Most recently, he achieved a University Certificate of Advanced Practice (Distinction, 2025), further cementing his commitment to lifelong learning and pedagogical excellence.

👨‍🏫 Experience

Sam began his teaching career in 2015, serving in various roles including KS1 and KS2 Teacher, Mathematics Lead, and Computing Lead. His leadership capabilities emerged early, culminating in a place on the Senior Leadership Team at Sussex Road Primary School, where he managed appraisals, led curriculum initiatives, and coached newly qualified teachers. From 2017 to 2019, Sam also contributed as a School Improvement Facilitator for the Education Development Trust. Transitioning into higher education in 2024, he now lectures at Canterbury Christ Church University, shaping future educators with research-informed teaching and innovation-driven curriculum design.

🔬 Research Interests On Computer Science

Sam’s primary research interests lie in the ethical and educational implications of Generative AI within primary and higher education settings. He is especially passionate about exploring how AI can foster interdisciplinary learning, democratize access to education, and promote epistemic insight. His involvement with the Professoriate Group on AI and as a board member of the AI Ethics Committee at UCL underscores his commitment to ethical innovation. Sam also co-founded the academic journal Big Questions and Interdisciplinary Learning, promoting complex thinking and boundary-crossing knowledge creation.

🏆 Awards

Sam has not only received academic distinctions across all his university qualifications, but has also been repeatedly entrusted with leadership and mentoring roles that reflect his excellence. His invitation to prestigious conferences and involvement in ethics committees, editorial boards, and university strategic roles stands as a testament to the recognition of his thought leadership within the educational community.

📄 Publications

Sam Clarke has authored and co-authored several impactful publications exploring the intersections of education and AI:

  • Clarke, S. and Billingsley, B. (2024). Education in the Age of GenAI. Cambridge Generative AI in Education Conference Booklet of Abstracts.
    Published in 2024 | Cited by 3 articles

  • Clarke, S. (2024). A General Election and My Classroom in the Age of AI. Teaching Citizenship, Issue 59, pp. 8–9.
    Published in 2024 | Cited by 1 article

  • Clarke, S., Billingsley, B., & Heath, L. (Eds.) (2024). Big Questions and Interdisciplinary Learning. Zenodo.
    Published in 2024 | Cited by 2 articles

  • Clarke, S., Billingsley, B. and O’Leary, S. (2024). Using Generative Artificial Intelligence to Catalyse Further Interdisciplinarity Across Higher Education. Graduate College Working Papers, CCCU. Read paper
    Published in 2024 | Cited by 4 articles

✅ Conclusion

Sam Clarke embodies the ideal fusion of practitioner expertise and academic innovation. His contributions to AI in education—through teaching, research, publication, and community outreach—are both timely and transformative. With a consistent record of academic achievement, leadership, and ethical vision, Sam is poised to continue shaping the future of education at the intersection of pedagogy and technology. His work reflects a profound dedication to empowering educators and learners alike in a rapidly evolving digital world. 🌟