Nael Radwan | Computer Science | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Nael Radwan
University of Idaho, United States

Nael Radwan
Affiliation University of Idaho
Country United States
Google Scholar ID 3SBNe6gAAAAJ
Documents 10
Citations 24
h-index 3
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0009-0007-3006-5509

Nael Radwan is recognised through the Innovative Research Award for research addressing the performance, reliability, and security of message-oriented communication systems in the Internet of Things (IoT). His listed 2026 publications examine MQTT broker behaviour under high-load conditions and the correct enforcement of authentication and access-control policies. These topics are relevant to dependable connected systems, where communication efficiency must be considered alongside security and operational stability. The research profile and publication details provide the basis for this academic recognition. [1] [2]

Abstract

This research profile focuses on experimental investigations into MQTT, a lightweight messaging protocol widely associated with IoT communication. The two supplied 2026 journal articles address complementary challenges: the effect of wildcard subscriptions on broker performance, stability, and security, and the correctness of authentication enforcement and the consequences of access-control list (ACL) misconfiguration. Together, these topics concern how messaging infrastructure behaves under demanding workloads and whether security policies are applied as intended. The publications appeared in Electronics and Applied Sciences, respectively. [1] [2]

Keywords

Internet of Things; MQTT; message brokers; wildcard subscriptions; authentication; access-control lists; cybersecurity; performance evaluation; system stability; secure communication.

Introduction

IoT environments depend on communication services that can distribute messages reliably while controlling access to sensitive information. MQTT supports publish–subscribe communication, but practical deployments require careful configuration and evaluation. Subscription patterns can influence message distribution and broker workload, while authentication and ACL rules determine which clients can connect and access particular topics. Experimental research into these mechanisms can help identify implementation weaknesses and inform more dependable deployment practices. [1] [2]

Research Profile

Radwan’s documented research interests, as represented by the supplied publications, encompass experimental evaluation of IoT messaging infrastructure, security enforcement, and broker behaviour under authenticated, high-load conditions. This work connects software performance assessment with practical cybersecurity concerns. Rather than treating efficiency and security as isolated objectives, the publication topics place them within the same operational environment, where configuration decisions may affect service behaviour and access restrictions. The supplied bibliographic information establishes the publication focus but does not independently verify every experimental result or establish additional institutional roles.

Research Contributions

The two studies address distinct but related evaluation priorities:

  • Performance and stability: Examining wildcard subscriptions under authenticated high-load IoT conditions provides a framework for investigating broker behaviour when message distribution demands increase. [1]
  • Security enforcement: Evaluating authentication correctness and ACL misconfiguration addresses whether access restrictions operate consistently with intended policies. [2]
  • Practical relevance: These research directions can inform testing, configuration reviews, and risk assessment for systems relying on MQTT-based communication.

Publications

  1. “Experimental Evaluation of Wildcard Subscription Impact on MQTT Broker Performance, Stability, and Security Under Authenticated High-Load IoT Conditions.” Electronics, August 2026. [1]
  2. “Experimental Quantification of Authentication Enforcement Correctness and ACL Misconfiguration Impact in Standards-Compliant MQTT Deployments.” Applied Sciences, April 2026. [2]

Research Impact

The supplied researcher metrics list 10 documents, 24 citations, and an h-index of 3. These figures offer a quantitative snapshot of the research record as provided; citation totals and author metrics can vary between databases and over time. The two 2026 publications address issues with practical relevance to IoT reliability and security. Their potential value lies in supporting evidence-based testing and safer configuration, although broader impact should be assessed using independently verified findings, subsequent citations, and adoption in operational settings.

Award Suitability

The research themes align with the Innovative Research Award’s emphasis on systematic investigation of technical problems. The publications focus on measurable system behaviour and security-policy enforcement rather than relying solely on conceptual discussion. Their complementary scope supports consideration of performance, stability, and access control as connected requirements for IoT infrastructure. This assessment is based on the supplied publication records and researcher metrics; it should not be interpreted as an independent audit of experimental methodology or a formal adjudication of award results.

Conclusion

Nael Radwan’s listed publications present a focused research direction in MQTT performance evaluation and deployment security. By addressing wildcard subscriptions, authentication enforcement, and ACL configuration, the work engages with practical challenges in dependable IoT communication. The publication record and supplied metrics provide a basis for academic recognition, subject to verification against the original articles and authoritative researcher profiles.

References

  1. MDPI. (2026). Experimental Evaluation of Wildcard Subscription Impact on MQTT Broker Performance, Stability, and Security Under Authenticated High-Load IoT Conditions. Electronics, 15(15), 3393.
    https://doi.org/10.3390/electronics15153393
  2. MDPI. (2026). Experimental Quantification of Authentication Enforcement Correctness and ACL Misconfiguration Impact in Standards-Compliant MQTT Deployments. Applied Sciences, 16(7), 3583.
    https://doi.org/10.3390/app16073583
  3. ORCID. (n.d.). Nael Radwan researcher identifier.
    https://orcid.org/0009-0007-3006-5509
  4. Google Scholar. (n.d.). Research profile supplied for Nael Radwan.
    https://scholar.google.com/citations?user=3SBNe6gAAAAJ&hl=en
  5. International Forensic Scientist Awards. (n.d.). Official awards website.
    forensicscientist.org

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

Published on by Forensic Scientist Awards

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

Fahimeh Ghasemian | Computer Science | Research Excellence Award

Published on by Forensic Scientist Awards

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

Ana Josselinne Alegre Mondragón | Data Science and Analytics | Editorial Board Member

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Prof. Ana Josselinne Alegre Mondragón | Data Science and Analytics | Editorial Board Member

Geospatial Information Sciences Research Center | Mexico

Prof. Ana Josselinne Alegre Mondragón is a distinguished geospatial information sciences researcher whose scholarly contributions advance the understanding of violence dynamics, clandestine graves detection, and geospatial applications for public security and forensic investigations. Her research integrates geographic profiling, spatial statistics, remote sensing, UAV-based photogrammetry, and geointelligence modeling to address critical national challenges, particularly the search for missing persons and the analysis of organized crime patterns. She has authored influential book chapters, peer-reviewed journal articles, and scientific reports published by Springer, Forensic Sciences International, and leading academic institutions in Mexico. Her work includes the development of spectral indices for detecting clandestine graves, multivariate regional analyses of burial sites, geospatial modeling of crime, assessment of soil alterations linked to criminal activities, and advanced methodologies for drone-based terrain morphology assessment. She has actively contributed to high-impact research projects such as Espacio Clandestino, geospatial infrastructures for the Ayotzinapa investigation, and scientific applications supporting national systems for the search of missing persons. Her publications and research tools are widely referenced, shaping new standards for evidence-based forensic search protocols. She has collaborated on cross-disciplinary initiatives involving forensic science, anthropology, criminology, and remote sensing, generating innovative methodologies and academic outputs recognized both nationally and internationally. Her scholarly trajectory demonstrates a sustained commitment to advancing geospatial science for human rights, security analysis, and public policy, positioning her as a leading figure in Mexico’s emerging field of forensic geospatial analytics.

Profile: Scopus | ORCID

Featured Publications

  • Alegre-Mondragón, A. J. (2024). Cambios organizacionales en la policía de la Ciudad de México. Del modelo CompStat a la evaluación con transparencia y apertura hacia la ciudadanía. Revista Digital de Estudios Organizacionales, Universidad de Xalapa, Veracruz, México, 53–92. DOI: https://doi.org/10.69509/wxj14d34

  • Silván-Cárdenas, J. L., Alegre-Mondragón, A. J., Ramírez Aceves E. D., Campos Cornejo, D. R. & Bautista Nadalón, M. (2024). Diseño y aplicación de índices espectrales para la detección de fosas clandestinas. En Interpretar la naturaleza para encontrar a quienes nos faltan. COBUPEJ & CentroGeo, 355–389.

  • Alegre-Mondragón, A. J. & Silván-Cárdenas, J. L. (2024). Morfología del terreno mediante fotogrametría con drones: oportunidades y limitaciones para la detección de fosas clandestinas. En Interpretar la naturaleza para encontrar a quienes nos faltan. COBUPEJ & CentroGeo, 323–350.

  • Silván-Cárdenas, J. L. & Alegre-Mondragón, A. J. (2024). Espacio Clandestino: A Nationwide Platform to Support Clandestine Graves Search in Mexico. In Recent Developments in Geospatial Information Sciences. Springer, 175–186.

  • Alegre-Mondragón, A. J., Vilalta-Perdomo, C. J., Silván-Cárdenas, J. L. & Silva-Arias, C. (2024). Characteristics of Clandestine Burial Sites in Mexico: A Regional Overview with Multivariate Analysis. In Recent Developments in Geospatial Information Sciences. Springer, 53–64.

Adimias Wendimagegn | Data Science | Best Researcher Award

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Mr. Adimias Wendimagegn | Data Science | Best Researcher Award

Kotebe University of Education | Ethiopia

Mr. Adimias Wendimagegn is a data scientist and biostatistician specializing in statistical modeling, distribution theory, and applications in public health. His research primarily focuses on advanced regression frameworks, including classical and Bayesian modeling approaches under GAMLSS and BAMLSS structures. He has introduced novel models such as the Alpha Power Transformed Beta (APTBeta) regression for analyzing antenatal care utilization and developed methodological frameworks for Gamma and Beta-type distributions. He has published multiple peer-reviewed journal articles indexed in Scopus and Web of Science, contributing significantly to statistical theory and biostatistical applications. His works include studies on antenatal care utilization, maternal age at first birth, and advanced distributional models for proportion data. He is also the author of two academic books published by LAP Lambert Academic Publishing, covering joint modeling of longitudinal and survival data and determinants of teenage fertility in Ethiopia. His scholarly contributions extend to the development of R packages for regression modeling and diagnostics, enhancing reproducibility and accessibility in statistical analysis. Mr. Adimias Wendimagegnserves as a reviewer for leading international journals such as PLOS ONE and Value in Health, and his publications have received multiple citations reflecting his growing impact in the field. Through his research, he continues to advance innovative statistical methodologies and their applications in health and demographic studies.

Profile: ResearchGate

Featured Publications

  • Wendimagegn, A., & Arero, B. G. (2025, October). Alpha power transformed beta regression with application on antenatal care visit proportions among Ethiopian women. Journal of Statistical Theory and Applications. https://doi.org/10.1007/s44199-025-00135-w

  • Wendimagegn, A., Goshu, A. T., & Arero, B. G. (2024, September). New alpha power transformed beta distribution with its properties and applications. Frontiers in Applied Mathematics and Statistics, 10. https://doi.org/10.3389/fams.2024.1433767

  • Wendimagegn, A. (2019, March). Determinant of solid-waste management in Debre Birhan Town. American Journal of Theoretical and Applied Statistics, 8(1), 26–33. https://doi.org/10.11648/j.ajtas.20190801.14

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.

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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.