Maryam Kheirabadi | Computer Science | Women Researcher Award

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Women Researcher Award

Maryam Kheirabadi
Affiliation Islamic Azad University
Country Iran
Scopus ID 57210153054
Documents 16
Citations 710
h-index 9
Subject Area Computer Science
Event International Academic Achievements & Awards
ORCID 0000-0001-8980-4299

Maryam Kheirabadi
Islamic Azad University, Iran

The Women Researcher Award recognizes distinguished scholarly achievement, research excellence, scientific innovation, and sustained academic contributions by women researchers. Maryam Kheirabadi has established a research profile in computer science, artificial intelligence, medical image analysis, and intelligent healthcare systems through peer-reviewed publications indexed in Scopus. Her work demonstrates interdisciplinary applications of deep learning, convolutional neural networks, recurrent neural networks, and medical decision-support systems, contributing to diagnostic technologies and intelligent recommendation systems.[1]

Abstract

Maryam Kheirabadi’s scholarly work focuses on applying artificial intelligence techniques to healthcare, medical image processing, pattern recognition, and educational recommendation systems. Her publications emphasize convolutional neural networks, deep learning architectures, image classification, recurrent neural networks with attention mechanisms, and computer-assisted diagnosis of gastric diseases. These contributions illustrate the integration of computer science methodologies into practical healthcare applications while advancing intelligent computational models for diagnostic accuracy and automated decision support.[2]

Keywords

Artificial Intelligence, Deep Learning, Computer Science, Medical Image Analysis, Convolutional Neural Networks, Healthcare Informatics, Machine Learning, Clinical Decision Support

Introduction

Artificial intelligence has become an increasingly influential discipline for solving complex healthcare challenges. The combination of machine learning, deep neural networks, and biomedical image analysis has improved diagnostic accuracy, automated disease detection, and clinical decision support. Within this evolving research landscape, Maryam Kheirabadi has contributed to studies addressing intelligent diagnostic systems, neural network optimization, and educational recommender systems, demonstrating interdisciplinary collaboration between computer science and healthcare research.[2]

Research Profile

According to the supplied Scopus metrics, Maryam Kheirabadi has authored sixteen indexed publications, received more than seven hundred citations, and achieved an h-index of nine. Her research portfolio centers on artificial intelligence, image processing, neural networks, biomedical signal analysis, and intelligent software systems. Her publications demonstrate continued engagement with computational approaches that support clinical diagnosis and intelligent educational technologies.[1]

Research Contributions

  • Development of deep convolutional neural network models for gastric cancer diagnosis using tongue image analysis.
  • Research on intelligent educational recommender systems employing recurrent neural networks and attention mechanisms.
  • Advancement of MRI brain tumor classification through convolutional neural network architectures.
  • Application of biomedical image processing techniques for healthcare diagnostics.
  • Contribution to explainable and data-driven medical decision-support technologies.

Publications

  • Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks (2021). Journal of Information Systems and Telecommunication.
  • Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique (2021). Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  • Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue (2021). Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  • Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation (2021). Biomedical Physics and Engineering Express.
    DOI: https://doi.org/10.1088/2057-1976/ac107b

Research Impact

The reported citation record reflects meaningful scholarly visibility within computer science and healthcare-related artificial intelligence research. Her studies contribute to the development of intelligent diagnostic systems, automated disease classification, medical image interpretation, and educational recommendation technologies. These interdisciplinary contributions support ongoing research into machine learning applications that improve analytical performance and healthcare decision-making.[3]

Award Suitability

Maryam Kheirabadi demonstrates qualifications consistent with the objectives of the Women Researcher Award through sustained scientific publication, interdisciplinary innovation, measurable citation performance, and contributions to artificial intelligence applications in healthcare. Her research profile reflects active engagement in internationally indexed scholarship and supports recognition for advancing computational methods with practical societal applications.[1]

Conclusion

Maryam Kheirabadi’s publication portfolio illustrates the integration of artificial intelligence with healthcare and intelligent information systems. Her contributions to deep learning, neural network design, biomedical imaging, and recommendation systems have strengthened interdisciplinary research in computer science. The documented publication record, citation performance, and research focus collectively support recognition within academic achievement and research excellence programs.

References

  1. Elsevier. (n.d.). Scopus Author Details: Maryam Kheirabadi, Author ID 57210153054. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57210153054
  2. Kheirabadi, M., et al. (2021). Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks. Journal of Information Systems and Telecommunication.
    http://www.scopus.com/inward/record.url?eid=2-s2.0-85111735812&partnerID=MN8TOARS
  3. Kheirabadi, M., et al. (2021). Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique. Applied Artificial Intelligence. DOI: https://doi.org/10.1080/08839514.2021.2005298
  4. Kheirabadi, M., et al. (2021). Increasing the Accuracy in the Diagnosis of Stomach Cancer Based on Color and Lint Features of Tongue. Biomedical Signal Processing and Control. DOI: https://doi.org/10.1016/j.bspc.2021.102782
  5. Kheirabadi, M., et al. (2021). Y-net: A Reducing Gaussian Noise Convolutional Neural Network for MRI Brain Tumor Classification with NADE Concatenation. Biomedical Physics and Engineering Express. DOI: https://doi.org/10.1088/2057-1976/ac107b

Quang Minh Tran | Computer Science | Innovative Research Award

Published on by Academic Achievements

Innovative Research Award

Quang Minh Tran
Affiliation University of Wollongong
Country Australia
ORCID 0009-0007-9413-2600
Documents 2
Subject Area Computer Science
Event International Academic Achievements & Awards

Quang Minh Tran
Institution: University of Wollongong,

Quang Minh Tran is a researcher in the field of Computer Science whose recent work focuses on trustworthy artificial intelligence, deepfake audio detection, adversarial machine learning, and multimedia security. His research investigates the robustness of deep learning systems against sophisticated adversarial attacks while contributing to the development of reliable forensic methods for synthetic audio detection. These studies address important challenges in AI security, digital trust, and the protection of multimedia systems against manipulation.[1]

Abstract

This article presents an academic profile of Quang Minh Tran in recognition of research contributions to Computer Science, particularly in adversarial machine learning and deepfake audio detection. His work examines the resilience of artificial intelligence systems under universal adversarial perturbations while advancing forensic methods capable of identifying manipulated synthetic speech. The research contributes to improving the security, reliability, and robustness of AI-enabled multimedia technologies.[2]

Keywords

Computer Science, Artificial Intelligence, Deepfake Audio Detection, Adversarial Machine Learning, Multimedia Security, Universal Adversarial Perturbations, AI Robustness, Audio Forensics, Digital Trust, Machine Learning Security.

Introduction

The increasing adoption of artificial intelligence has intensified concerns regarding the misuse of generative technologies, including deepfake audio. Detecting synthetic speech while maintaining robustness against adversarial attacks represents a significant challenge in AI security. Quang Minh Tran’s research explores these issues through systematic evaluation of deepfake detectors and vocoder fingerprint detectors, supporting the development of trustworthy AI systems suitable for practical deployment.[2]

Research Profile

  • Research field: Computer Science.
  • Primary interests include AI security and multimedia forensics.
  • Research emphasizes adversarial robustness of deep learning systems.
  • Investigates deepfake audio detection and vocoder fingerprint analysis.
  • Contributes to trustworthy artificial intelligence and secure multimedia applications.

Research Contributions

Quang Minh Tran has contributed to the evaluation of adversarial robustness in deepfake audio detection systems through comprehensive analysis of universal adversarial perturbations. His work investigates vulnerabilities in deep learning-based forensic models while identifying approaches that improve detector resilience. These contributions are relevant to cybersecurity, digital media authentication, trustworthy AI, and the broader development of reliable machine learning systems capable of operating under adversarial conditions.[2]

Publications

  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Future Internet, 2026. DOI:10.3390/fi18070344
  • Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations. Preprint, 2026. DOI:10.20944/preprints202606.0272.v1

Research Impact

The research addresses an increasingly important area of artificial intelligence by strengthening the understanding of adversarial vulnerabilities affecting deepfake detection technologies. The findings provide valuable insights for researchers, cybersecurity practitioners, and developers seeking to improve the resilience of AI-based forensic systems. This work contributes to ongoing efforts aimed at enhancing digital trust, secure communication, and responsible deployment of artificial intelligence.[2]

Award Suitability

Based on the available scholarly publications, Quang Minh Tran demonstrates emerging research contributions in artificial intelligence security, adversarial machine learning, and multimedia forensics. His work addresses contemporary challenges involving deepfake detection and AI robustness using rigorous scientific methodology. These contributions provide a sound academic basis for consideration within the Innovative Research Award category of the International Academic Achievements & Awards program.[1]

Conclusion

Quang Minh Tran’s research advances the field of Computer Science by addressing the robustness and security of artificial intelligence systems against adversarial manipulation. His investigations into deepfake audio detection and multimedia forensics contribute to the growing body of knowledge supporting trustworthy AI technologies. The combination of technical innovation, practical relevance, and scientific rigor reflects meaningful scholarly progress within the rapidly evolving domain of AI security.

References

  1. ORCID. (n.d.). Quang Minh Tran ORCID Record.
    https://orcid.org/0009-0007-9413-2600
  2. Future Internet. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.3390/fi18070344
  3. Preprints.org. (2026). Evaluating Adversarial Robustness of Deepfake Audio Detectors and Vocoder Fingerprint Detectors Against Universal Adversarial Perturbations.
    https://doi.org/10.20944/preprints202606.0272.v1

Md Minhajul Amin | Computer Science | Best Innovator Award

Published on by Academic Achievements

Best Innovator Award

Md Minhajul Amin
Affiliation Intento Analytics
Country United States
Google Scholar 8Cc4X70AAAAJ
Documents 11
Citations 86
h-index 4
Subject Area Computer Science
Event International Academic Achievements & Awards

Md Minhajul Amin
Intento Analytics, United States

Md Minhajul Amin is a data analyst and researcher specializing in business analytics, artificial intelligence, healthcare informatics, project management, and big data applications. His multidisciplinary research integrates predictive analytics, machine learning, digital healthcare, and operational decision-making. Through academic publications, IEEE conference papers, industrial projects, and editorial activities, he has contributed to advancing evidence-based management and intelligent data-driven systems.[1]

Abstract

Md Minhajul Amin has established an interdisciplinary research portfolio connecting artificial intelligence, predictive analytics, healthcare systems, fraud detection, digital communication, and project management. His work emphasizes practical analytical solutions that improve organizational efficiency and support evidence-based decision-making across healthcare and business environments.[2]

Keywords

Business Analytics, Artificial Intelligence, Machine Learning, Healthcare Analytics, Telemedicine, Big Data, Project Management, Data Visualization, Predictive Analytics, Digital Transformation.

Introduction

Holding an M.S. in Information Systems from Central Michigan University, Md Minhajul Amin combines academic research with industry experience in data analytics. His research focuses on solving real-world organizational challenges through statistical analysis, visualization, artificial intelligence, and business intelligence technologies.[3]

Research Profile

  • IEEE conference contributor.
  • Associate Editor at IFR Discovery.
  • Editorial Board Member of The Science Post Journal.

Research Contributions

His publications cover ethical business analytics, AI-powered project management, fraud detection, cancer classification using machine learning, telemedicine implementation, customer segmentation, healthcare dashboards, RF communication systems, and network management technologies. His research demonstrates the practical integration of advanced analytics into healthcare, engineering, and organizational management.[4]

Publications

  • Ethical Challenges in Business Analytics.
  • Developing a Project Management Dashboard for Telehealth.
  • Business Analytics in the Era of Big Data.
  • AI-Powered Personalized Marketing.
  • Classification of Cancer Stages Using Machine Learning.

Research Impact

His scholarly output has attracted citations across business analytics, healthcare informatics, artificial intelligence, and project management disciplines. His professional activities further include patented AI-driven analytical devices and collaborative research addressing practical industry challenges.

Award Suitability

Considering his multidisciplinary research portfolio, peer-reviewed publications, IEEE conference participation, editorial responsibilities, industrial analytics experience, and measurable research impact, Md Minhajul Amin demonstrates qualifications aligned with recognition under an Innovative Research Award category.

Conclusion

Md Minhajul Amin continues contributing to applied research that bridges artificial intelligence, business analytics, healthcare systems, and project management. His academic achievements and industry experience illustrate the growing role of data-driven methodologies in addressing modern organizational and societal challenges.

External Links

References

    1. Amin, M. M., Munmun, Z. S., Atayeva, J., Ahmed, S. W., Shamim, I., & Akter, M. H. (2025).
      Developing a Project Management Dashboard for Telehealth Implementation.
      https://www.researchgate.net/publication/392591167_Developing_a_Project_Management_Dashboard_for_Telehealth_Implementation
    2. Google Scholar. (2026).
      Md Minhajul Amin – Google Scholar Profile.
      https://scholar.google.com/citations?user=8Cc4X70AAAAJ&hl=en
    3. ResearchGate. (2026).
      Md Minhajul Amin – Research Profile.
      https://www.researchgate.net/profile/Md-Minhajul-Amin
    4. LinkedIn. (2026).
      Md Minhajul Amin – Professional Profile.
      https://www.linkedin.com/in/md-minhajul-amin-cmu

Irene Govender | Programming | Best Researcher Award

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Prof. Dr. Irene Govender | Programming | Best Researcher Award

Prof. Dr. Irene Govender, Professor at University of KwaZulu-Natal, South Africa

Professor Irene Govender is a full professor in Information Systems and Technology (IS&T) with a distinguished career marked by her contributions to computing and technology in academia. As an NRF-rated researcher, she has explored critical intersections between education, technology, and interdisciplinary research, with a focus on empowering students through innovative teaching methods and leveraging technology to improve educational outcomes. With over 90 publications in peer-reviewed journals and numerous contributions to international conferences, Professor Govender is recognized globally for her scholarly excellence and dedication to advancing research in IS&T. Her experience as an academic leader and extensive network of international collaborations highlight her commitment to promoting academic excellence and fostering partnerships that drive meaningful change in the field of Information Systems and beyond.

Profile

ORCID

Education

Professor Govender’s academic journey is rooted in a strong foundation of scientific and technological study, including a Ph.D. in Computing. Her education reflects a deep and ongoing commitment to understanding and advancing the fields of computer science and technology. She earned her MSc in Computer Science with a specialization in Artificial Intelligence, building on her BSc in Computer Science and Mathematics. Additionally, her early training included a Junior Secondary Education Diploma (JSED) in Mathematics and Physics, coupled with a teaching diploma that paved the way for her role in academia. Her education and professional qualifications are further complemented by her involvement with esteemed professional bodies, including SACLA, SAICSIT, ACM, IEEE, and ISI, where she has remained an active member, continually engaging with the latest research and development in her field.

Experience

Professor Govender has served in academia for several decades, most notably as an academic leader of IS&T from 2014 to 2020. During this tenure, she led numerous initiatives aimed at enhancing educational practices and research output within the department. She has taught a wide range of modules, including research methodology, systems design, networking and security, programming, and mobile application development, imparting critical knowledge and skills to students at both undergraduate and postgraduate levels. She has also contributed to global academic discussions by serving as a reviewer for various NRF committees, international journals, and conference proceedings. Her role as an external examiner and part of the institutional audit teams across different universities showcases her expertise and authority in IS&T. In her career, she has supervised and guided over 20 master’s and doctoral students, supporting them through research and professional development, thus contributing to the growth of future researchers in the field.

Research Interest

Professor Govender’s research interests span a wide range of interdisciplinary areas that bring together technology, education, and societal impact. Her primary areas of focus include Object-Oriented Programming (OOP), Big Data Analytics, and ICT4D, with more recent explorations into robotics and coding for teachers. Her interest in technology for teaching and learning has driven her to develop innovative ways to use mobile and digital platforms for education, making her research both timely and highly impactful. Additionally, her work in computational thinking has involved developing resources and strategies for youth, helping them cultivate critical skills through mobile and digital platforms. This unique combination of educational technology and interdisciplinary research has positioned her as a thought leader in the IS&T community, with significant contributions to discussions on the role of technology in transforming education and supporting development in underserved communities.

Awards

Professor Govender’s contributions to IS&T have been recognized through several accolades, which underscore her dedication to research and academic service. She is an NRF-rated researcher, which highlights her standing as a scholar of high caliber within the South African research community. Her service as the Chief Examiner for the national senior certificate for IT, along with her role in academic audits and external examination panels, reflects her commitment to upholding standards of excellence in education. Additionally, her reputation as an invited speaker at international conferences and symposiums has earned her widespread respect among her peers and in the broader academic community. These recognitions serve as a testament to her contributions to academia and her role as an inspiring figure in the field of Information Systems and Technology.

Publications

Fostering Youth Wellbeing Through mHealth Apps: Embracing Physical Activity for a Healthier Lifestyle (2024) – Book chapter in Crossref, DOI: 10.1007/978-3-031-50192-0_35.
Enhancing Learning Engagement: A Study on Gamification’s Influence on Motivation and Cognitive Load (2024) – Education Sciences, DOI: 10.3390/educsci14101115.
UX/UI Design of Online Learning Platforms and Their Impact on Learning (2022) – International Journal of Research in Business and Social Science, DOI: 10.20525/ijrbs.v11i10.2236.
Digital Game-Based Learning for Information Technology (2022) – E-Journal of Humanities, Arts and Social Sciences, DOI: 10.38159/ehass.20223137.
THE ROLE OF E-BOOKS IN ICT INTEGRATION IN SECONDARY SCHOOLS IN SWAZILAND (2019) – Conference paper at University of Kwazulu Natal.
Students’ Views on the Use of Facebook as an Online Learning Management System (2019) – Conference paper at University of Kwazulu Natal.
Information Technology Policy Formulation for Socio-economic Growth (2019) – Conference paper at University of Kwazulu Natal.

Conclusion

Professor Irene Govender’s scholarly achievements and her contributions to IS&T reflect a deep commitment to advancing technology in education and interdisciplinary research. Her work spans teaching, research, and service to the academic community, demonstrating a broad and impactful career. Her dedication to mentoring students and fostering an environment of innovation in education is evident through her collaborations and wide-ranging research interests, which include OOP, Big Data, and ICT4D. Professor Govender’s reputation as a researcher and educator makes her an outstanding candidate for the Best Researcher Award, as she continues to set high standards in her field while inspiring others to explore the transformative potential of technology in education and beyond.