Degale Abe | Best Academic Researcher Award|Visual Object Detection using Deep Learning

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Best Academic Researcher Award

Degale Abe
Wachemo University, Ethiopia

Degale Abe
Affiliation Wachemo University
Country Ethiopia
Documents 3
Subject area Visual Object Detection using Deep Learning
Event International Research Awards on Network Science & Graph Analytics
ORCID 0009-0007-6505-3970

Degale Abe is an academic researcher affiliated with Wachemo University in Ethiopia. The research subject provided for this profile is visual object detection using deep learning, a field concerned with developing computational methods that identify and localize objects in images and video. This article presents a structured overview of the supplied researcher information, the relevant research area, and the relationship between the field and the Best Academic Researcher Award. The available profile details are limited, so publication-specific findings, citation metrics, and individual research achievements are not attributed without supporting evidence.

Abstract

This article outlines the academic profile and research subject associated with Degale Abe of Wachemo University, Ethiopia. The identified subject area, visual object detection using deep learning, combines computer vision and machine learning to recognize and localize objects in visual data. Such methods are relevant to image analysis, automated inspection, scene understanding, and intelligent monitoring systems. The supplied profile lists three documents but does not include verified publication titles, citation counts, an h-index, or a Scopus author identifier. Accordingly, this overview describes the research domain and potential evaluation considerations without presenting unverified findings as established achievements. The ORCID record provides a starting point for checking researcher identity and associated scholarly information. [1]

Keywords

Degale Abe; Best Academic Researcher Award; Wachemo University; Ethiopia; visual object detection; deep learning; computer vision; object localization; image recognition; artificial intelligence; research evaluation; International Research Awards on Network Science & Graph Analytics.

Introduction

Visual object detection is an important area of computer vision that aims to determine which objects appear in an image and where they are located. Deep learning approaches, particularly convolutional neural networks and modern detection architectures, have contributed to advances in the automated analysis of complex visual scenes. Object detection systems may be applied in agriculture, transportation, medical image analysis, manufacturing, environmental observation, and security, depending on the design and validation of the system. [2]

Academic evaluation in this field may consider methodological originality, dataset quality, experimental rigor, reproducibility, computational efficiency, and practical relevance. The number of publications is one indicator of research activity, but it does not independently establish the quality or influence of a researcher’s work. A reliable assessment should also examine the content of the research, its validation, and its contribution to existing knowledge.

Research Profile

The supplied information identifies Degale Abe as affiliated with Wachemo University in Ethiopia and associates the profile with visual object detection using deep learning. Three documents are reported in the provided profile data. The bibliographic records underlying that count have not been supplied for independent verification. The available information also does not specify the researcher’s academic qualifications, professional appointments, complete publication history, or citation indicators.

A comprehensive research profile would normally include the following information where independently documented:

  • Institutional affiliation and relevant academic appointments.
  • Verified publications, including article titles, publication venues, and DOI identifiers where available.
  • Research methods, datasets, software, and experimental evaluation procedures.
  • Citation indicators and other evidence of scholarly influence, interpreted in the context of the field.
  • Research collaborations, funded projects, and documented practical or societal outcomes.

The researcher’s ORCID identifier is included in the external links section. Its record should be checked against institutional and publication records before additional biographical or bibliometric details are incorporated. [1]

Research Contributions

Visual object detection research addresses the challenge of locating and classifying multiple objects under varying visual conditions. These conditions may include changes in lighting, occlusion, object scale, background complexity, image resolution, and the presence of visually similar categories. Deep learning models are commonly trained on labeled datasets and evaluated using established measures of detection accuracy and localization performance. [2]

Relevant lines of investigation within this subject area include:

  • Detection accuracy: evaluating how reliably models identify object categories and bounding boxes across representative test data.
  • Model efficiency: examining inference speed, memory use, and computational requirements for deployment on available hardware.
  • Robustness and generalization: assessing performance under environmental variation, unfamiliar scenes, and imbalanced datasets.
  • Reproducibility: documenting training configurations, dataset preparation, evaluation protocols, and limitations.
  • Practical application: testing whether model performance supports the intended real-world task and its operational requirements.

These topics describe the broader research domain rather than verified individual contributions by Degale Abe. Specific contributions should be documented using the researcher’s confirmed publications, project records, or other reliable evidence.

Publications

The supplied profile indicates three documents. However, no publication titles, author lists, journal or conference names, publication dates, or DOI links were provided for this article. The reported document count should therefore be treated as supplied profile information rather than an independently verified bibliographic total.

To establish an accurate publication record, each item should be checked against a publisher’s page, a DOI registration record, an institutional repository, or a recognized bibliographic database. Duplicate records, corrections, conference versions, and preprints should be distinguished where relevant. Publication details should not be inferred from the researcher’s subject area alone.

No individual publication or DOI is listed here because a verifiable publication record was not included in the supplied information. Adding article-specific citations requires the corresponding titles or persistent identifiers.

Research Impact

The potential impact of visual object detection research depends on the reliability, generalizability, efficiency, and suitability of the resulting methods. In applied settings, an effective detection system may help automate repetitive visual tasks, support decision-making, and improve the processing of large image collections. Demonstrating these benefits requires appropriate benchmarks, comparisons with established methods, and evaluation under realistic operating conditions. [2]

For an individual researcher, scholarly impact may be assessed using several complementary forms of evidence:

  1. The originality and methodological quality of published research.
  2. Independent citations and documented use of the research by other scholars.
  3. Reproducible results, openly available datasets or software, where applicable.
  4. Evidence of collaboration, knowledge transfer, or practical application.
  5. Contributions to research training, institutional development, and the wider academic community.

No citation count, h-index, implementation outcome, or specific societal impact has been supplied for Degale Abe. These indicators should be added only after verification from appropriate sources.

Award Suitability

The Best Academic Researcher Award recognizes scholarly work according to the criteria established by its organizing body. For a profile associated with visual object detection using deep learning, relevant evaluation areas may include the originality of research, rigor of experimental methods, quality of peer-reviewed publications, reproducibility, documented research influence, and contributions to the academic community.

The event named in the supplied information is the International Research Awards on Network Science & Graph Analytics. The relationship between this event and the researcher’s subject area should be assessed using the organizer’s published award categories and eligibility requirements. Although visual object detection is primarily associated with computer vision and deep learning, connections with graph analytics may arise in research involving scene graphs, relational representations, object interactions, or graph-based learning. Such connections should be demonstrated by the candidate’s actual work rather than assumed.

The supplied affiliation, research topic, and reported document count provide preliminary profile information but are not sufficient on their own to establish award eligibility or determine an award outcome. A complete assessment would require verified publications, supporting evidence of research contributions, and confirmation that the candidate meets the official criteria. No award result or selection decision is asserted in this article.

Conclusion

Degale Abe is identified in the supplied information as a researcher affiliated with Wachemo University in Ethiopia, with a stated subject area of visual object detection using deep learning. The profile reports three documents and provides an ORCID identifier for further verification. The research domain is relevant to contemporary computer vision and artificial intelligence, particularly the automated recognition and localization of objects in visual data.

A fuller account of the researcher’s scholarly achievements requires verified publication records, documented research findings, and reliable bibliometric information. Similarly, any assessment for the Best Academic Researcher Award should be based on the official criteria and supporting evidence rather than incomplete profile data.

References

The following sources provide identity-verification and general research-method context. The individual profile metrics and reported document count in this article were supplied as input and have not been independently verified.

  1. ORCID. (n.d.). ORCID researcher identifier and record for Degale Abe. The supplied ORCID identifier can be used to review the public researcher record and any linked works. Record contents should be checked before relying on individual details.
    https://orcid.org/0009-0007-6505-3970
  2. Enhancing YOLOv8 for Vehicle Detection in Intelligent Traffic Managemen
    https://orcid.org/0009-0007-6505-3970
  3. Award Website:
    https://networkscience-conferences.researchw.com/

Angelos Athanasiadis | Neural Networks | Research Excellence Award

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Mr. Angelos Athanasiadis | Neural Networks | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Angelos Athanasiadis is a Ph.D. candidate in Electrical and Computer Engineering at Aristotle University of Thessaloniki (AUTH), specializing in FPGA-based acceleration of Convolutional Neural Networks. With expertise spanning embedded system development, heterogeneous computing, and cyber-physical systems, he has contributed to both academic and industrial innovation through participation in EU research initiatives—including the ADVISER and REDESIGN projects—and through consultancy and R&D roles at EXAPSYS and SEEMS PC. His work focuses on advancing energy-efficient hardware acceleration, leading to the development of a parameterizable HLS matrix multiplication library for AMD FPGAs that enables full-precision CNN inference for accuracy-critical domains such as aerial monitoring and autonomous embedded systems. He further expanded the field with FUSION, an open-source high-fidelity distributed emulation framework integrating QEMU with OMNeT++ via HLA/CERTI synchronization to support deterministic, timing-aware multi-node execution and realistic prototyping of heterogeneous systems. Complementing his strong technical background, he holds an MBA awarded with high distinction and an M.Eng. in electronics and computer systems, supported by internships at Cadence Design Systems in Munich.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Pose Analysis in Free-Swimming Adult Zebrafish, Danio rerio: "fishy" Origins of Movement Design", Jagmeet S. Kanwal; Bhavjeet Sanghera; Riya Dabbi; Eric Glasgow, Preprint, 2025.

"Complex Sound Discrimination in Zebrafish: Auditory Learning Within a Novel “Go/Go” Decision-Making Paradigm", Anna Patel; Sai Mattapalli; Jagmeet S. Kanwal, Animals, 2025.

"From Information to Knowledge: A Role for Knowledge Networks in Decision Making and Action Selection", Jagmeet S. Kanwal, Information, 2024.

"NemoTrainer: Automated Conditioning for Stimulus-Directed Navigation and Decision Making in Free-Swimming Zebrafish", Bishen J. Singh; Luciano Zu; Jacqueline Summers; Saman Asdjodi; Eric Glasgow; Jagmeet S. Kanwal, Animals, 2022.

"NemoTrainer: Apparatus and Software for Automated Conditioning of Stimulus-directed Navigation and Decision Making in Freely Behaving Animals", Bishen Singh; Luciano Zu; Jacqueline Summers; Saman Asdjodi; Eric Glasgow; Jagmeet S. Kanwal, Preprint, 2022.

Farhad Hossain Sojib | Data Science | Best Researcher Award

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Mr. Farhad Hossain Sojib | Data Science | Best Researcher Award 

University of Hull | Bangladesh

Mr. Farhad Hossain Sojib is an engineer with a strong foundation in electronics and communication engineering and a growing specialization in data science and artificial intelligence. He is currently pursuing his M.Sc. in Artificial Intelligence and Data Science at the University of Hull, United Kingdom, following the completion of his B.Sc. in Engineering from Hajee Mohammad Danesh Science and Technology University, Bangladesh, where he conducted notable research on explainable AI in educational data mining and machine learning applications in 5G antenna optimization. With professional experience as an IELTS Instructor at Lexicon Plus, he has trained over 50 students, developed course materials, and mentored junior instructors. His leadership and organizational skills were further demonstrated through his role as a Program Committee Member at the IEEE Student Branch, HSTU, where he managed events, seminars, and competitions. Additionally, his internship at BRACNet Limited provided hands-on experience in ISP operations, ICT technologies, and server management. Farhad combines his technical expertise, research acumen, and collaborative mindset to contribute meaningfully to the fields of machine learning and data-driven innovation.

Profiles: Orcid 

Featured Publications

"The integration of explainable AI in Educational Data Mining for student academic performance prediction and support systems", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman, Telematics and Informatics Reports, 2025.

"A Bioinformatics Approach to Uncover Hub Genes and Potential Drug Targets of Stroke, Heart-Disease, Hyperglycemia, and Hypertension", Md. Emran Biswas; M D. Fazle Hasan Mihad; Farhad Hossain Sojib; Mohammad Jubair Ahmmed; M D Galib Hasan; Md. Jobare Hossain; Md. Abul Basar; Md. Mehedi Islam; Md. Delowar Hossain; Md. Selim Hossain et al., 27th International Conference on Computer and Information Technology (ICCIT), 2024.

"An Explainable Educational Data Mining System for Predicting Student Academic Performance", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman; FARHAD HOSSAIN SOJIB, 2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, 2024.

Tanyo Tanev | Machine Learning | Best Researcher Award

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Mr. Tanyo Tanev | Machine Learning | Best Researcher Award

Technical University of Sofia | Bulgaria

Tanyo Tanev appears to be a highly suitable candidate for the Best Researcher Award. His exceptional blend of technical expertise, academic pursuit, and professional achievements in the renewable energy and electrical engineering domains clearly demonstrates research excellence and innovation. As a Ph.D. candidate at the Technical University of Sofia, he has published five scientific papers and is working on additional studies focused on photovoltaic (PV) power plants and the application of machine learning and deep learning models in energy systems-fields of growing global importance. Professionally, he has led the design and development of large-scale PV power plants worldwide, managing complex engineering, optimization, and data-driven simulation tasks. His strong background in Autocad, PVcase, PVsyst, Python, and AI frameworks like TensorFlow and Keras, combined with managerial experience and creative problem-solving, highlights his research-driven approach to technological advancement. Tanyo’s ability to merge academic knowledge with practical innovation, leadership in renewable energy projects, and continuous pursuit of scientific progress make him an outstanding contender for the Best Researcher Award.

Profiles: Scopus

Featured Publications

"Modeling of Battery Storage of Photovoltaic Power Plants Using Machine Learning Methods", T. Tanev and R. Stanev, Energies, 2025.

Faisal Alshami | Machine Learning | Best Researcher Award

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Faisal Alshami | Machine Learning | Best Researcher Award

Dalian University of Technology | China

Author Profile

Google Scholar

Early Academic Pursuits

Faisal Alshami’s academic journey reflects a deep commitment to software engineering and technological innovation. He began his undergraduate studies at Sana’a University, Yemen, earning a BSc in Network Technology and Computer Security (2008–2012). His undergraduate thesis, “General Management System for Plant Protection,” showcased his early ability to integrate security and system management using ASP.NET, C#, and VPN with OSPF protocols, signaling his strong foundation in both networking and software development. Building on this groundwork, Faisal pursued a Master’s in Software Engineering at Northeastern University, China (2019–2022), where he specialized in advanced machine learning techniques. His master’s thesis, “Design and Implementation of Web API Recommendation System Based on Deep Learning,” utilized CNN, BLSTM, K-modes, and Word2Vec, demonstrating his growing expertise in AI-driven software solutions. Currently, Faisal is advancing his academic pursuits with a PhD in Software Engineering at Dalian University of Technology, China, focusing on federated learning, distributed systems, blockchain, edge computing, and graph neural networks (GNNs).

Professional Endeavors

Alongside his academic progression, Faisal has accumulated over 5 years of professional experience in the software and networking industry. His early career as a VoIP Engineer/Developer at Communication Services Company (2013–2015) allowed him to develop communication APIs and optimize large-scale systems. As a Network Manager and Systems Engineer at EliteTecs (2015–2016), he designed high-reliability networks using advanced protocols such as OSPF, EIGRP, and WiMAX, showcasing his expertise in secure and resilient infrastructures. His role as Full-Stack Developer and DevOps Lead at Almorisi Exchange Company (2016–2018) highlighted his ability to manage mission-critical systems with real-time performance and security. Here, Faisal excelled in building scalable architectures, simulation frameworks, and automated DevOps pipelines, which contributed to operational excellence.

Contributions and Research Focus

Faisal’s research is strategically positioned at the intersection of distributed systems, intelligent computing, and aerospace applications. His focus includes:

  • Federated learning and secure communication for multi-agent systems such as satellite constellations.

  • Edge computing and real-time distributed systems tailored for resource-constrained environments.

  • Robust machine learning frameworks for aerospace, automation, and high-reliability embedded systems.

  • Blockchain integration with AI to enhance security in data networks.

  • Simulation and testing methodologies to ensure fault tolerance in mission-critical software.
    This body of research reflects his ambition to address pressing challenges in space exploration, aerospace engineering, and advanced communication networks.

Impact and Influence

Faisal’s impact lies in bridging the gap between theory and applied innovation. His academic research is not confined to publications alone but extends into real-world applications in secure communications, high-availability systems, and intelligent software architectures. By combining his professional experience with cutting-edge research, Faisal has influenced the fields of network security, distributed computing, and AI-driven system optimization, making his contributions valuable to both academia and industry.

Academic Cites

His work has strong potential for academic citations due to its interdisciplinary nature—linking software engineering, AI, networking, and aerospace technologies. His focus on federated learning, blockchain, and edge computing positions his research at the forefront of emerging scholarly and industrial discussions, ensuring that his publications will attract citations in journals focusing on AI, distributed systems, cybersecurity, and aerospace software engineering.

Legacy and Future Contributions

Faisal Alshami is on a trajectory to build a lasting legacy in intelligent, secure, and scalable software engineering systems. His research is particularly impactful in aerospace applications and secure communications, areas that are becoming increasingly vital in a digital and space-driven era. As he progresses with his doctoral research, Faisal is expected to contribute significantly to the development of resilient federated learning frameworks, advanced distributed architectures, and mission-critical simulations. His blend of academic depth and industry experience ensures that his future work will leave a lasting influence on next-generation computing systems and aerospace engineering technologies.

Other Notable Highlights

  • Certifications: Faisal holds multiple certifications, including Neural Networks & Deep Learning (DeepLearning.AI), CCNP, CCNA, and advanced language certifications (Chinese HSK4, English YALI).

  • Training: He gained practical exposure at NEUSOFT Project Training, where he contributed to developing the Borrow-Seller System (BSS) using Java, Spring Boot, Vue.js, and Android Studio.

  • Core Competencies: His expertise spans software architecture, DevOps, distributed systems, full-stack development, secure networking, and agile collaboration.

Conclusion

In conclusion, Faisal Alshami is an emerging leader in the domain of software engineering, distributed systems, and intelligent computing. His academic journey, professional experiences, and research pursuits demonstrate a rare combination of technical mastery, innovation, and practical problem-solving skills. With his ongoing doctoral work and focus on future technologies such as federated learning, blockchain, and aerospace applications, Faisal is poised to make significant contributions that will influence both academia and industry for years to come.

Notable Publications

"A detailed analysis of benchmark datasets for network intrusion detection system" 

  • Author: M Ghurab, G Gaphari, F Alshami, R Alshamy, S Othman
  • Journal: Asian Journal of Research in Computer Science
  • Year: 2021

"Intrusion detection model for imbalanced dataset using SMOTE and random forest algorithm" 

  • Author: R Alshamy, M Ghurab, S Othman, F Alshami
  • Journal: International Conference on Advances in Cyber Security
  • Year: 2021

 

 

Lin Yu Rou |  Machine Learning | Best Researcher Award

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Ms. Lin Yu Rou |  Machine Learning | Best Researcher Award

Software Development Engineer, China Trust Commercial Bank, Taiwan

Yuruo Lin is a passionate researcher and aspiring data scientist with a strong foundation in information and finance management. With hands-on experience in data analytics, machine learning, and healthcare informatics, she actively engages in interdisciplinary research projects, focusing on practical applications that merge technology and social impact. Her academic journey is marked by leadership, innovation, and a commitment to empowering communities through data-driven solutions.

🔹Professional Profile:

Orcid Profile

🎓Education Background

  1. Master’s in Information and Finance Management
    National Taipei University of Technology, Taiwan
    Sep 2022 – Jun 2024

    • Honorable Mention in 2023 Capstone Project Competition

    • Participant in “STEM & Female Research Talent Cultivation Program (2022)”

  2. Bachelor’s in Information Management
    National Taipei University of Nursing and Health Sciences, Taiwan
    Sep 2018 – Jun 2022

    • 2nd Place, 2021 National Collegiate Information Application Innovation Competition

    • Published research on the impact of COVID-19 on hospital quality

    • President, IT Volunteer Club; led USR project and received Outstanding Club and Officer Scholarship

💼 Professional Development

Yuruo has collaborated on diverse academic and practical research projects, combining statistical methods with machine learning and data visualization to address real-world problems. She developed predictive models for ESG performance using ensemble learning, analyzed hospital service quality amid the COVID-19 pandemic, and experimented with algorithmic trading strategies. Her work spans financial analytics, public health equity, and VR-based elderly care solutions.

🔬Research Focus

  • Data Science and Machine Learning

  • Financial and Investment Analytics

  • Healthcare Informatics and Public Health Data

  • Human-Computer Interaction (HCI)

  • Media Analytics for ESG Performance

  • Social Impact Technology (VR, USR Projects)

📈Author Metrics:

Yuruo Lin is the first author of a peer-reviewed research article titled “How can media attention reveal ESG improvement opportunities? A multi-algorithm ML-based approach for Taiwan’s electronics industry,” published in the Elsevier journal Expert Systems with Applications in 2025. This journal is indexed in SCI and Scopus, with a strong impact factor in the fields of artificial intelligence and applied computing. Her publication explores media-driven ESG analytics using ensemble machine learning and clustering techniques, demonstrating both technical depth and relevance to sustainability research. The work has garnered academic attention and serves as a foundation for her growing research profile in data science and ESG modeling.

🏆Awards and Honors:

  • Honorable Mention – 2023 Capstone Project Competition, NTUT

  • 2nd Place – 2021 National Collegiate Information Application Innovation Competition (VR Therapy)

  • Outstanding Club Leadership – IT Volunteer Club, USR Project, Ministry of Education

  • Multiple Awards – National Innovation Proposal Competitions (2020–2021)

  • Scholarship – Officer Scholarship for Club Leadership

📝Publication Top Notes

1. How can media attention reveal ESG improvement opportunities? A multi-algorithm machine learning-based approach for Taiwan’s electronics industry

Journal: The North American Journal of Economics and Finance
Publisher: Elsevier
Publication Date: May 2025
DOI: 10.1016/j.najef.2025.102431
ISSN: 1062-9408
Contributors: Shu Ling Lin, Yu Rou Lin, Xiao Jin
Indexing: Scopus, SSCI
Abstract Summary:
This study applies ensemble machine learning algorithms—including Naive Bayes, Support Vector Machines, Random Forest, and Neural Networks—combined with clustering and semi-supervised learning to investigate how media attention can serve as a predictive signal for ESG performance changes in Taiwan’s electronics industry. The findings highlight the potential of media-driven analytics in enhancing ESG investment strategies and corporate monitoring.

2. Exploring the Relationship between Corporate ESG Ratings and Media Attention through Machine Learning: Predictive Model for the Taiwanese Electronics Industry

Author: Yu Rou Lin
Institution: National Taipei University of Technology
Degree: Master’s in Information and Finance Management
Status: Completed (June 2024)
Contribution: Original draft, research design, and full implementation of machine learning pipeline
Focus: The thesis investigates the correlation between ESG ratings and media sentiment, using real market data and various machine learning models, and serves as the foundational research for the later published journal article.

Conclusion:

In summary, Ms. Yu Rou Lin is an outstanding candidate for the Best Researcher Award in Machine Learning. Her work exemplifies the fusion of technical rigor and societal relevance, with achievements that reflect intellectual curiosity, practical application, and academic leadership.

Her potential for future growth is immense, especially as she continues to refine her research contributions and engage with global scientific communities.

Basil Duwa| Machine learning | Best Researcher Award

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Assist. Prof. Dr. Basil Duwa | Machine learning | Best Researcher Award

Operational Center in Healthcare at Near East University, Turkey

Dr. Basil B. Duwa is a results-oriented biomedical data scientist and engineer with expertise in clinical bioinformatics, machine learning for disease prediction, and medical device innovation. With over five years of research and practical experience in healthcare data science, Dr. Duwa has made notable contributions to parasitology-focused AI, wearable sensor analysis, and multi-criteria decision-making in healthcare. He currently serves as an Assistant Professor and Postdoctoral Fellow at the Operational Research Center in Healthcare, Near East University, where he integrates AI and biomedical engineering for real-world medical applications.

Professional Profile:

Orcid

Google Scholar

Education Background

    • Ph.D. in Biomedical Engineering (Specialization: Biomedical Data Science & Bioinformatics)
      Near East University, Nicosia, Cyprus (2021–2023)

    • M.Sc. in Biomedical Engineering (Specialization: Data Science & Decision Analysis)
      Near East University, Nicosia, Cyprus (2019–2021)

    • Postgraduate Diploma in Education
      National Teacher’s Institute, Kaduna (2018–2019)

    • B.Sc. in Biological Sciences (Zoology & Parasitology)
      Adamawa State University, Nigeria (2014–2018)

Professional Development

  • Assistant Professor & Postdoctoral Fellow
    Near East University, Cyprus (2024–Present)

    • Lead AI research in healthcare, predictive modeling, and telemedicine systems.

    • Co-authored a book on medical device applications published by Elsevier.

  • Clinical Informatics Researcher
    Operational Research Center in Healthcare (2022–2024)

    • Developed AI models for disease prediction including malaria and COVID-19.

    • Integrated MCDM methods into healthcare analytics.

  • Research Assistant – Biomedical Data Science
    Near East University (2020–2022)

    • Focused on predictive models and decision systems for biomedical challenges.

  • Monitoring & Evaluation Data Analyst
    Plan International & Save the Children (2012–2018)

    • Evaluated child health and education data; developed analytical dashboards.

Research Focus

Dr. Duwa’s interdisciplinary research combines machine learning, bioinformatics, data visualization, and medical device design. His key interests include:

  • AI-driven disease prediction and diagnostics

  • Wearable sensor data analytics

  • Explainable AI in biomedical decision-making

  • Multi-criteria decision analysis (MCDM) in healthcare

  • Federated learning and clinical applications of AI

Author Metrics:

  • ORCID: 0000-0002-1690-6830

  • Google Scholar Citations: View Profile

  • Publications: 25+ in peer-reviewed journals including Diagnostics, Journal of Instrumentation, and Springer Conference Proceedings

  • Books & Chapters: Co-authored over 10 chapters in books published by Academic Press and Springer

  • Notable Works:

    • Quantitative Forecasting of Malaria Parasite Using Machine Learning

    • Computer-Aided Detection of Monkeypox Using Deep Learning

    • Brain PET Scintillation Crystal Evaluation using MCDM

Awards and Honors:

  • 🏆 Young Researcher Award – Near East University, Cyprus (2023 & 2022)

  • 🥇 Best Essay Award – NAFDAC Consumer Safety Club, Nigeria (2004)

  • 🎓 Article Reviewer – MDPI, Taylor & Francis, Expert Systems, Applied Mathematics in Science & Engineering (2020–2025)

Publication Top Notes

1. Second-Order Based Ensemble Machine Learning Technique for Modelling River Water Biological Oxygen Demand (BOD): Insights into Improved Learning

Authors: A.G. Usman, M. Almousa, H. Daud, B.B. Duwa, A.A. Suleiman, A.I. Ishaq, …
Journal: Journal of Radiation Research and Applied Sciences
Volume: 18(2)
Article: 101439
Year: 2025
Summary: Developed a second-order ensemble machine learning framework to model and predict BOD levels in rivers, improving environmental monitoring accuracy.

🧠 Focus Area: Environmental ML Modeling / Ensemble Learning

2. Enhanced Drug Classification for Cancers of the Liver with Multi-Criteria Decision-Making Method – PROMETHEE

Authors: B.B. Duwa, N. Usanase, B. Uzun
Journal: Global Journal of Sciences
Volume: 2(1), pp. 24–36
Year: 2025
Summary: Applied PROMETHEE (MCDM) for liver cancer drug classification, improving clinical decision-making through structured and explainable evaluation.

💊 Focus Area: Drug Classification / MCDM / Oncology

3. Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework

Authors: I. Goni, B. Bali, B.M. Ahmad, B.B. Duwa, C. Iwendi
Journal: Global Journal of Sciences
Volume: 1(2), pp. 58–70
Year: 2025
Summary: Introduces a digital twin-powered machine learning architecture to enhance predictive diagnostics in telemedicine systems.

🌐 Focus Area: Telemedicine / Digital Twins / AI in Healthcare

4. Reply to Graña et al. Comment on “Uzun Ozsahin et al. COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach”

Authors: D. Uzun Ozsahin, E. Precious Onakpojeruo, B. Bartholomew Duwa, …
Journal: Diagnostics
Volume: 14(22), Article: 2529
Year: 2024
Summary: A formal response clarifying methodological insights and addressing critiques on a previously published AI model for COVID-19 prediction.

🧬 Focus Area: Model Interpretability / COVID-19 Forecasting

5. Ensemble Predictive Modeling for Dementia Diagnosis

Authors: B.B. Duwa, E.P. Onakpojeruo, B. Uzun, A.J. Hussain, I. Ozsahin, L.R. David, …
Conference: 17th International Conference on Development in eSystem Engineering (DeSE)
Year: 2024
Summary: Demonstrates the power of ensemble ML techniques in diagnosing dementia, integrating multiple model architectures for increased diagnostic precision.

🧠 Focus Area: Medical AI / Cognitive Disorders / Ensemble Learning

Conclusion

Assist. Prof. Dr. Basil B. Duwa is a highly accomplished and innovative biomedical researcher whose work has real-world impact in predictive healthcare, disease diagnostics, and AI-based decision systems. His multi-disciplinary approach, prolific publishing, and novel applications of machine learning in both clinical and environmental contexts make him a strong and deserving candidate for the Best Researcher Award.

Verdict:
✅ Recommended with distinction for the Best Researcher Award in Biomedical Data Science and Machine Learning in Healthcare.

Mohamed Afify Elnagar | Information Systems | Best Researcher Award

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Mr. Mohamed Afify Elnagar | Information Systems | Best Researcher Award

Assistant General Manager at Damanhour university, Egypt.

Mohamed Afify Elnagar is an accomplished banking professional with extensive expertise in banking storage management, logistics, and data analysis. He currently serves as an Assistant General Manager at the Egyptian Arab Land Bank, where he has been instrumental in enhancing operational efficiency, implementing banking policies, and ensuring compliance with internal controls. With a strong foundation in computer and information systems, he combines strategic decision-making with technological proficiency to optimize banking operations.

Professional Profile:

Google Scholar

Education Background

  • PhD Researcher, Institute of Graduate Studies and Environmental Research, Damanhour University
  • Master’s Degree in Computer and Information Systems, Sadat Academy for Administrative Sciences
  • Diploma in Computer and Information Systems, Sadat Academy for Administrative Sciences

Professional Development

With over two decades of experience in the banking sector, Mohamed Afify Al-Nagar has played a key role in overseeing banking storage operations and logistics management. His leadership in developing operational strategies and ensuring compliance has significantly contributed to the efficiency and quality of banking processes. His expertise extends to anti-money laundering, real estate finance, auditing, and internal control.

Research Focus

His research focuses on banking information systems, financial security, digital transformation in banking, risk management, and environmental sustainability in financial institutions.

Author Metrics:

  • Published works in banking storage management, information systems, and compliance.
  • Contributions to industry reports and research in financial technology and banking operations.

Awards and Honors:

Recognized for his contributions to banking operations and compliance, Mohamed Afify Al-Nagar has received several accolades for excellence in banking strategies, internal control, and financial risk management.

Publication Top Notes

1. Modeling a Sustainable Decision Support System for Banking Environments Using Rough Sets: A Case Study of the Egyptian Arab Land Bank

Journal: International Journal of Financial Studies (Impact Factor: 2.5, Q2)

Authors: Mohamed A. Elnagar, Jaber Abdel Aty, Abdelghafar M. Elhady, Samaa M. Shohieb

Publication Year: 2025

Abstract: This study addresses the vast amount of information held by the banking sector, especially regarding opportunities in tourism development, production, and large residential projects. With advancements in information technology and databases, data mining has become essential for banks to optimally utilize available data. From January 2023 to July 2024, data from the Egyptian Arab Land Bank (EALB) were analyzed using data mining techniques, including rough set theory and the Weka version 3.0 program. The aim was to identify potential units for targeted marketing, improve customer satisfaction, and contribute to sustainable development goals. By integrating sustainability principles into financing approaches, this research promotes green banking, encouraging environmentally friendly and socially responsible investments. A survey of EALB customers assessed their interest in purchasing homes under the real estate financing program. The results were analyzed with GraphPad Prism version 9.0, with 95% confidence intervals and an R-squared value close to 1, and we identified 13 units (43% of the total units) as having the highest marketing potential. This study highlights data mining’s role in enhancing marketing for the EALB’s residential projects. Combining sustainable financing with data insights promotes green banking, aligning with customer preferences and boosting satisfaction and profitability.

Conclusion

Mohamed Afify Elnagar is a highly qualified candidate for the Best Researcher Award due to his extensive contributions to banking information systems, sustainable finance, and compliance. His real-world impact, interdisciplinary expertise, and research output make him a strong contender. Expanding his global collaborations and publishing in higher-impact journals could further strengthen his profile.

Zhi Gao | Vision-Language Models | Best Researcher Award

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Dr. Zhi Gao | Vision-Language Models | Best Researcher Award

Postdoctoral Research Fellow at Peking University, China.

Dr. Zhi Gao is a Postdoctoral Research Fellow at the School of Intelligence Science and Technology, Peking University. His research focuses on multimodal learning, vision-language models, and human-robot interaction. With expertise in computer vision and machine learning, he explores the development of intelligent agents capable of understanding and interacting with complex environments.

Professional Profile:

Google Scholar Profile

Education Background 🎓📖

  • Ph.D. in Computer Science and Technology, Beijing Institute of Technology (2018–2023)
  • Master in Computer Science and Technology, Beijing Institute of Technology (2017–2018)
  • B.S. in Computer Science and Technology, Beijing Institute of Technology (2013–2017)

Professional Development 📈💡

Dr. Gao is currently a Postdoctoral Research Fellow at Peking University under the supervision of Prof. Song-Chun Zhu, focusing on multimodal learning and agent development. Concurrently, he serves as a Research Scientist at the Beijing Institute for General Artificial Intelligence, working on vision-language models in the Machine Learning Lab. His research integrates deep learning, data representation, and human-centered AI to enhance machine perception and reasoning.

Research Focus 🔬📖

His work spans computer vision and machine learning, particularly in developing multimodal agents capable of learning from human-robot interactions and adapting to dynamic environments. He is also interested in leveraging the geometry of data space to address challenges such as insufficient annotations and distribution shifts.

Author Metrics

  • Publications in top-tier AI and computer vision conferences and journals
  • Research contributions in multimodal intelligence, vision-language understanding, and AI-driven reasoning

Awards & Honors 🏆🎖️

  • National Science Foundation for Young Scientists of China (2025–2027) for research on Riemannian multimodal large language models for video understanding
  • Distinguished Dissertation Award from SIGAI CHINA (October 202X)

Publication Top Notes

1. A Hyperbolic-to-Hyperbolic Graph Convolutional Network

Authors: Jindou Dai, Yuwei Wu, Zhi Gao, Yunde Jia
Published in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 154-163
Abstract: This paper introduces a hyperbolic-to-hyperbolic graph convolutional network (H2H-GCN) that operates directly on hyperbolic manifolds. The proposed method includes a manifold-preserving graph convolution with hyperbolic feature transformation and neighborhood aggregation, avoiding distortions from tangent space approximations. Extensive experiments demonstrate substantial improvements in tasks such as link prediction, node classification, and graph classification.

openaccess.thecvf.com

2. Curvature Generation in Curved Spaces for Few-Shot Learning

Authors: Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi
Published in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8671-8680
Abstract: This research addresses few-shot learning by proposing task-aware curved embedding spaces using hyperbolic geometry. By generating task-specific embedding spaces with appropriate curvatures, the method enhances the generality of embeddings. The study leverages intra-class and inter-class context information to create discriminative class prototypes, showing benefits over existing embedding methods in both inductive and transductive few-shot learning scenarios.

3. Deep Convolutional Network with Locality and Sparsity Constraints for Texture Classification

Authors: Xiaoyu Bu, Yuwei Wu, Zhi Gao, Yunde Jia
Published in: Pattern Recognition, Volume 91, 2019, Pages 34-46
Abstract: This paper presents a deep convolutional network incorporating locality and sparsity constraints to improve texture classification. The proposed model enhances feature representation by enforcing local connectivity and sparse activation, leading to improved classification performance on texture datasets.

4. Meta-Causal Learning for Single Domain Generalization

Authors: Jianlong Chen, Zhi Gao, Xiaodan Wu, Jiebo Luo
Published in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Abstract: The study introduces a meta-causal learning framework aimed at enhancing generalization in single-domain settings. By leveraging causal relationships within the data, the approach seeks to improve model robustness when applied to unseen domains, addressing challenges in domain generalization.

5. A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold

Authors: Zhi Gao, Yuwei Wu, Mehrtash Harandi, Yunde Jia
Published in: IEEE Transactions on Neural Networks and Learning Systems, Volume 31, Issue 9, 2019, Pages 3230-3244
Abstract: This research proposes a robust distance measure tailored for similarity-based classification tasks on the Symmetric Positive Definite (SPD) manifold. The developed measure enhances classification accuracy by effectively capturing the intrinsic geometry of the SPD manifold, demonstrating robustness in various similarity-based classification scenarios.

Conclusion:

Dr. Zhi Gao is a strong candidate for the Best Researcher Award, given his groundbreaking contributions in vision-language models, hyperbolic learning, and multimodal AI. His strong academic background, top-tier publications, and national recognition make him a well-qualified nominee. However, to further strengthen his impact, he could focus on industry collaborations, real-world AI applications, and global AI leadership.

Verdict: ✅ Highly suitable for the Best Researcher Award with minor areas of improvement for long-term impact.

Sidra Jubair | Machine Learning | Best Researcher Award

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Ms. Sidra Jubair | Machine Learning | Best Researcher Award

Ph.D Student at Dalian University of Technology, China📖

Dr. Sidra Jubair is a dedicated researcher in the field of applied mathematics, currently pursuing her Ph.D. at the School of Mathematical Sciences, Dalian University of Technology, China, under the supervision of Prof. Jie Yang. Her research focuses on machine learning, computational fluid dynamics, and neurocomputing. With a strong academic background and numerous high-impact publications in top-tier journals, she is committed to advancing knowledge in data-driven scientific computation.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

  1. Ph.D. in Applied Mathematics (2019 – Present)
    • Dalian University of Technology, China
    • Project: Imbalanced Data Learning through Examples and Classifiers
  2. M.Sc. in Applied Mathematics (2016 – 2018)
    • Hazara University, Pakistan
    • Supervised by Prof. Dr. Muhammad Shahzad
  3. B.Sc. in Mathematics (2011 – 2015)
    • International Islamic University, Islamabad, Pakistan

Professional Experience🌱

Dr. Sidra Jubair has extensive research experience in applied mathematics, focusing on the integration of computational intelligence with fluid dynamics. She has collaborated with researchers globally and contributed to high-impact scientific journals. Her work primarily revolves around machine learning applications in engineering and environmental sciences. Additionally, she has actively participated in international conferences and workshops, sharing her insights on topics such as neurocomputing and imbalanced data learning.

Research Interests🔬

Her research interests include:

  • Neurocomputing
  • Computational Fluid Dynamics
  • Imbalanced Data Learning
  • Machine Learning Applications in Engineering

Author Metrics

Dr. Sidra Jubair has established a strong research presence in the fields of applied mathematics, machine learning, and computational fluid dynamics. Her scholarly contributions have garnered significant recognition, with over 900 citations on Google Scholar, reflecting the impact and relevance of her work within the scientific community. She holds an H-index of 15, demonstrating the consistent influence and citation of her research, and an i10-index of 12, highlighting her ability to produce multiple highly cited publications. Dr. Jubair has published extensively in top-tier, high-impact journals, including Information Processing and Management, Alexandria Engineering Journal, and Applied Water Sciences, with impact factors reaching up to 7.4. Her research on imbalanced data learning and computational modeling has been widely acknowledged, contributing valuable insights to the advancement of data-driven scientific computation.

Publications Top Notes 📄

1. Mixed convective flow of hybrid nanofluid over a heated stretching disk with zero-mass flux using the modified Buongiorno model

  • Authors: B. Ali, N.K. Mishra, K. Rafique, S. Jubair, Z. Mahmood, S.M. Eldin
  • Journal: Alexandria Engineering Journal
  • Volume: 72
  • Pages: 83-96
  • Citations: 74
  • Year: 2023

2. Numerical simulation of the nanofluid flow consists of gyrotactic microorganism and subject to activation energy across an inclined stretching cylinder

  • Authors: H.A. Othman, B. Ali, S. Jubair, M. Yahya Almusawa, S.M. Aldin
  • Journal: Scientific Reports
  • Volume: 13, Issue 1
  • Article Number: 7719
  • Citations: 51
  • Year: 2023

3. MHD flow of nanofluid over moving slender needle with nanoparticles aggregation and viscous dissipation effects

  • Authors: B. Ali, S. Jubair, D. Fathima, A. Akhter, K. Rafique, Z. Mahmood
  • Journal: Science Progress
  • Volume: 106, Issue 2
  • Article Number: 00368504231176151
  • Citations: 42
  • Year: 2023

4. Boundary layer and heat transfer analysis of mixed convective nanofluid flow capturing the aspects of nanoparticles over a needle

  • Authors: B. Ali, S. Jubair, L.A. Al-Essa, Z. Mahmood, A. Al-Bossly, F.S. Alduais
  • Journal: Materials Today Communications
  • Volume: 35
  • Article Number: 106253
  • Citations: 38
  • Year: 2023

5. Numerical investigation of heat source induced thermal slip effect on trihybrid nanofluid flow over a stretching surface

  • Authors: B. Ali, S. Jubair, A. Aluraikan, M. Abd El-Rahman, S.M. Eldin, H.A.E.W. Khalifa
  • Journal: Results in Engineering
  • Volume: 20
  • Article Number: 101536
  • Citations: 37
  • Year: 2023

Conclusion

Dr. Sidra Jubair is a highly deserving candidate for the Best Researcher Award. Her outstanding research in applied mathematics, particularly in machine learning and computational fluid dynamics, has had a profound impact on both the academic and scientific communities. Her consistent publication in top-tier journals and strong research metrics underscore her ability to produce high-quality, impactful research. While there are opportunities for her to broaden the scope of her research through real-world applications and interdisciplinary collaborations, her current work demonstrates tremendous potential for further breakthroughs in applied mathematics and engineering.

Her dedication to advancing knowledge in data-driven scientific computation, coupled with her innovative approaches, makes her an ideal candidate for the Best Researcher Award.