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

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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

Dinh-Thai Kim | Computer Science and Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Dinh-Thai Kim
Vietnam National University, Vietnam

Dinh-Thai Kim
Affiliation Vietnam National University
Country Vietnam
Scopus ID 57265279400
Documents 49
Citations 194
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0000-0002-9060-4769

Dinh-Thai Kim is a researcher affiliated with Vietnam National University whose documented research profile is situated within computer science and artificial intelligence. The available bibliometric record lists 49 documents, 194 citations, and an h-index of 8. [1] Recent publications demonstrate work spanning computer vision, deep learning, federated learning, speech emotion recognition, agricultural imaging, and Vietnamese sign-language classification.

Abstract

Dinh-Thai Kim’s research profile reflects applied artificial intelligence research addressing visual recognition, natural-language and speech-related intelligence, and machine-learning applications. Recent work includes lightweight object detection for cashew kernel assessment, growth-stage detection in lychee orchards, privacy-preserving speech emotion recognition, and graph-based Vietnamese sign-language classification. [2]–[5]

Keywords

Artificial intelligence; computer vision; deep learning; object detection; federated learning; speech emotion recognition; sign-language classification; agricultural AI.

Introduction

The research record associated with Kim illustrates the application of contemporary machine-learning methods to practical recognition problems. The publications supplied for this profile indicate an emphasis on deployable computer-vision systems and specialized intelligent models, including applications involving agricultural products, orchard monitoring, human speech, and gesture-based language recognition.

Research Profile

Kim’s documented research spans several complementary areas of artificial intelligence. The work on YOLOv8s-based lychee detection applies object-detection techniques under natural orchard conditions, while CSTAR-Det addresses lightweight vision-based assessment of cashew kernels. [2] [3] Other research extends machine learning toward Vietnamese speech emotion recognition and sign-language classification. [4] [5]

Research Contributions

  • Development of lightweight vision-based approaches for agricultural quality assessment.
  • Application of object detection to crop growth-stage recognition under natural conditions.
  • Investigation of prototype-based federated learning for privacy-preserving speech emotion recognition.
  • Use of graph convolutional networks and skeletal points for Vietnamese sign-language classification.

Publications

CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array, listed publication date December 2026. [2]

YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics, 27 July 2026. [3]

EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics, 23 April 2026. [4]

Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering, March 2026. [5]

Research Impact

The supplied Scopus metrics record 194 citations across 49 documents, with an h-index of 8. [1] These indicators provide a quantitative view of scholarly visibility, while the publication record shows application-oriented research across multiple AI domains. Citation indicators should be interpreted in relation to publication age, field practices, and database coverage.

Award Suitability

The documented record provides evidence relevant to recognition in a research category focused on computer science and artificial intelligence. The combination of bibliometric activity and recent research involving computer vision, federated learning, and deep-learning classification offers a factual basis for consideration under the Best Researcher Award category. [1]–[5]

Conclusion

Dinh-Thai Kim’s documented research profile combines measurable scholarly activity with recent applications of artificial intelligence to agricultural vision, speech processing, privacy-preserving learning, and sign-language recognition. The available record supports a multidisciplinary description within computer science and artificial intelligence and provides the principal bibliographic and impact indicators presented on this page.

References

  1. Elsevier. (n.d.). Scopus author details: Dinh-Thai Kim, Author ID 57265279400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57265279400
  2. Kim, Dinh-Thai. (2026). CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array.
    https://doi.org/10.1016/j.array.2026.101282
  3. Kim, Dinh-Thai. (2026). YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.12140
  4. Kim, Dinh-Thai. (2026). EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.11595
  5. Kim, Dinh-Thai. (2026). Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering.
    https://doi.org/10.22068/IJEEE.22.1.3717
  6. ORCID. (n.d.). Dinh-Thai Kim, ORCID record.
    https://orcid.org/0000-0002-9060-4769
  7. International Forensic Scientist Awards. (n.d.). Official award website.
    forensicscientist.org

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.

Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

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Prof. Dr. Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

Henan University of Science and Technology | China

Prof. Dr. Yongyi Yan, Professor of Control Science at Henan University of Science and Technology, has devoted over 15 years to advancing robust control and intelligent optimization for industrial and autonomous systems, demonstrating a rare blend of theoretical excellence and practical impact. He has authored 65 publications, including influential articles in IEEE Transactions on Automation Science and Engineering and SCIENCE CHINA Information Sciences, accumulating 506 citations with an h-index of 10, reflecting his work’s high recognition and application in both academia and industry. Professor Yan has led multiple National Natural Science Foundation of China projects (U1804150, 62073124, 12571584), supervised three PhD graduates, and served as associate editor for Control Engineering Practice. His pioneering contributions include an adaptive robust control algorithm that reduces oscillation by 40% in high-precision machining and an AI-driven optimization framework (patents ZL2021 1 0419210.4 and ZL202110418903.1) that decreases energy consumption by 18% in automotive assembly lines, now adopted by leading manufacturers. His collaborations with Luoyang Sanwu Cable Group have resulted in significant breakthroughs in high-conductivity aluminum stranded wire cable production, integrating multi-machine coordination, dynamic compensation, and laser-guided visual positioning to enhance efficiency, product quality, and equipment control. By bridging fundamental research with industrial applications, Professor Yan has advanced smart manufacturing, autonomous navigation, and process optimization. His sustained innovation, leadership, and scholarly achievements exemplify research excellence, making him highly deserving of the Best Researcher Award for transformative contributions to control science and engineering.

Profile: Scopus

Featured Publications

  • Li, X., Yan, Y., Yue, J., & Zhang, S. (2025, September). Algebraic insight into universal logic functions and implications for logical system modeling.

  • An, Z., Yan, Y., Yue, J., & Li, X. (2025, May). Construction of automaton observer based on matrix semi-tensor product. In Conference proceedings.

  • Wang, X., Yan, Y., Yue, J., & An, Z. (2025, May). Construction and synchronization analysis of state power set automata based on algebraic methods. In Conference proceedings.

  • Dong, C., Yan, Y., Li, H., & Yue, J. (2024, November). Semi-tensor product approach to controllability, reachability, and stabilizability of extended finite state machines.

  • Zhang, S., Yan, Y., Hao, P., & Yue, J. (2024, October). Structural simplification of finite state machines using pruning operators based on semi-tensor product of matrices. In Conference proceedings.

  • Zhang, S., Yan, Y., Wang, C., & Yue, J. (2024, October). Implementation of automaton product networks: From formal language to algebraic models. In Conference proceedings.

  • Yan, Y., Hao, P., Yue, J., & Feng, J. (2024, October). An STP look at logical blocking of finite state machines: Formulation, detection, and search.

  • Yan, Y., Dong, C., Li, H., & Yue, J. (2024, July). Algebraic implementation of extended finite state machine networks.

Gokalp Çınarer | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Gokalp Çınarer | Artificial İntelligence | Best Researcher Award

Yozgat Bozok University Computer Engineering Department | Turkey

Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. His expertise lies in artificial intelligence, machine learning, image processing, and deep neural networks. Over the years, he has contributed significantly to the academic community with applications of AI in diverse fields, including medicine, agriculture, food technologies, and environmental analysis.

Professional profile👤

Google Scholar

ORCID

Scopus

Strengths for the Awards✨

  • Diverse Research Portfolio: Dr. Çınarer has made significant contributions across multiple domains — medicine, agriculture, food technologies, and environmental analysis — leveraging artificial intelligence and machine learning, showcasing versatility and cross-disciplinary impact.
  • High Research Output: With over 50 publications indexed in SCI-Expanded, SSCI, and Scopus, his research output is impressive, indicating a consistent contribution to advancing knowledge in computer engineering and AI applications.
  • Innovative Work in Medical Imaging: His PhD thesis on brain tumor detection through image processing and classification algorithms reflects a critical application of AI in medical diagnostics, directly contributing to healthcare advancements.
  • Academic Leadership: As Head of the Department of Information Processing and Computer Engineering Software at Yozgat Bozok University, Dr. Çınarer plays a pivotal role in shaping academic programs, guiding research initiatives, and mentoring students.
  • Teaching Excellence: He teaches a broad range of courses, including Machine Learning, Deep Learning, and Artificial Intelligence Applications, fostering the next generation of AI researchers and practitioners.

🎓 Education

Dr. Çınarer earned his PhD in Computer Engineering from Kırıkkale University between 2017 and 2021. His doctoral research focused on “Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms.”

💼 Experience

Since 2021, Dr. Çınarer has been serving as the Head of the Department of Information Processing and the Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses, including Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, and Artificial Intelligence Applications.

🔬 Research Interests On Artificial Intelligence

Dr. Çınarer’s research interests encompass artificial intelligence, machine learning, image processing, and deep neural networks. His work delves into AI applications across various fields such as medicine, agriculture, food technologies, and environmental analysis.

🏆 Awards

Dr. Çınarer has been recognized for his pioneering work in artificial intelligence and its applications across multiple disciplines, earning accolades for his contributions to AI-driven medical analysis and agricultural technologies.

📚 Publications

  • Classification of brain tumors by machine learning algorithms
    Authors: G Çınarer, BG Emiroğlu
    Year: 2019
    Citations: 74

  • Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
    Authors: G Çınarer, BG Emiroğlu, AH Yurttakal
    Year: 2020
    Citations: 57

  • Öğretmenlerin Teknolojik Araçlarla Eğitime Yönelik Tutumlarının Çeşitli Değişkenlere Göre İncelenmesi Yozgat İli Örneği
    Author: G Çınarer
    Year: 2016
    Citations: 27

  • Classification of hazelnuts according to their quality using deep learning algorithms
    Authors: N Erbaş, G Çınarer, K Kiliç
    Year: 2022
    Citations: 21

  • A comparative study on segmentation and classification in breast mri imaging
    Authors: AH Yurttakal, H Erbay, T İkizceli, S Karacavus, G Çinarer
    Year: 2018
    Citations: 21

  • Brain Tumor Classification Using Deep Neural Network
    Authors: G Çınarer, BG Emiroğlu, RS Arslan, AH Yurttakal
    Year: 2020
    Citations: 16

  • Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector
    Authors: G Çınarer, MK Yeşilyurt, Ü Ağbulut, Z Yılbaşı, K Kılıç
    Year: 2024
    Citations: 11

  • Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference
    Authors: G Çınarer, C Kahraman, S Cebi, SC Onar, B Oztaysi, AC Tolga, IU Sari
    Year: 2021
    Citations: 9

  • Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks
    Authors: AH Yurttakal, H Erbay, G Çınarer, H Baş
    Year: 2021
    Citations: 8

  • A Deep Transfer Learning Framework For The Staging Of Diabetic Retinopathy
    Authors: G Çınarer, K Kılıç, T Parlar
    Year: 2022
    Citations: 6

📄 Conclusion

Dr. Gökalp Çınarer stands at the forefront of AI research, leveraging machine learning and image processing to solve complex problems in medicine, agriculture, and environmental science. His work continues to inspire advancements in interdisciplinary fields and pave the way for future innovations.