Anindya Bijoy Das | ML in Healthcare | Innovative Research Award

Published on by AI Data Scientist

Innovative Research Award

Anindya Bijoy Das
The University of Akron
Anindya Bijoy Das
Affiliation The University of Akron
Country United States
Scopus ID 57701018300
Documents 34
Citations 392
h-index 10
Subject Area ML in Healthcare
Event International AI Data Scientists Award
ORCID 0000-0002-3615-7400

Anindya Bijoy Das is a researcher affiliated with The University of Akron, United States. His scholarly work contributes to the advancement of machine learning applications in healthcare, with emphasis on data-driven methods that support clinical decision-making, predictive analytics, and biomedical research. Based on available publication and citation metrics, his research demonstrates sustained academic engagement and measurable scientific impact within interdisciplinary domains.[1]

Abstract

This article presents an academic overview of Anindya Bijoy Das and highlights contributions in machine learning for healthcare. The profile summarizes scholarly output, citation performance, research impact, and relevance to innovation-oriented academic recognition programs.[1]

Keywords

Machine Learning, Healthcare Analytics, Artificial Intelligence, Biomedical Data Science, Predictive Modeling, Clinical Informatics.

Introduction

The integration of artificial intelligence into healthcare has transformed modern research and clinical practice. Researchers such as Anindya Bijoy Das contribute to this evolving field by developing analytical frameworks and intelligent methodologies that support improved healthcare outcomes and evidence-based decision-making.[2]

Research Profile

The research profile of Anindya Bijoy Das reflects interdisciplinary expertise spanning machine learning, healthcare data analysis, and computational methodologies. With 34 indexed documents and 392 citations, the profile indicates active participation in internationally recognized scholarly communication channels.[1]

Research Contributions

Key contributions involve the application of advanced machine learning techniques to healthcare datasets, enabling improved prediction, classification, and data interpretation. Such work supports the broader objective of translating computational innovation into practical healthcare solutions.[2]

Publications

  • Research publications in machine learning and healthcare analytics.
  • Studies addressing predictive healthcare models and intelligent systems.
  • Collaborative research contributing to data-driven medical innovation.

Research Impact

Citation metrics and publication records indicate meaningful academic influence. An h-index of 10 reflects consistent citation activity across multiple publications, demonstrating visibility within the scientific community and engagement with contemporary healthcare research challenges.[1]

Award Suitability

The researcher’s publication performance, interdisciplinary contributions, and focus on machine learning in healthcare align with the objectives of the International AI Data Scientists Award. The profile reflects innovation, scholarly productivity, and measurable research impact suitable for academic recognition.[3]

Conclusion

Anindya Bijoy Das has established a research profile characterized by consistent scholarly output and contributions to healthcare-focused artificial intelligence. The available evidence supports recognition of his work within academic and innovation-oriented award frameworks.

External Links

References

  1. Elsevier. (n.d.). Scopus author details: Anindya Bijoy Das, Author ID 57701018300. Scopus. https://www.scopus.com/pages/authors/57701018300
  2. Artificial Intelligence in Healthcare Literature.
    https://doi.org/10.1016/j.artmed.2020.101944
  3. International AI Data Scientists Award.
    https://aidatascientists.com/

Abdelrahman Salameh | Simulation in Healthcare | Best Researcher Award

Published on by AI Data Scientist

Best Researcher Award

Abdelrahman Salameh
Fatima College of Health Sciences-FCHS, United Arab Emirates

Abdelrahman Salameh
Affiliation Fatima College of Health Sciences-FCHS
Country United Arab Emirates
Scopus ID 58976141600
Documents 4
Citations 9
h-index 2
Subject Area Simulation in Healthcare
Event International AI Data Scientists Award
ORCID 0009-0002-6175-7173

Abdelrahman Salameh, affiliated with Fatima College of Health Sciences-FCHS in the United Arab Emirates. His work is associated with simulation in healthcare and reflects engagement in evidence-based educational and clinical research activities. The profile summarizes research productivity, publication impact, and professional achievements relevant to academic recognition and research excellence.[1]

Abstract

Abdelrahman Salameh has contributed to healthcare simulation and educational research through scholarly publications and academic engagement. His research profile demonstrates participation in healthcare innovation, simulation-based learning, and professional development initiatives that support evidence-informed healthcare education.[1]

Keywords

Simulation in Healthcare, Clinical Education, Health Sciences, Research Excellence, Academic Scholarship, Healthcare Innovation, Best Researcher Award.

Introduction

Healthcare simulation has become an important component of modern health sciences education. Researchers working in this field contribute to improved training methods, patient safety, and competency development. Abdelrahman Salameh’s academic activities align with these objectives and support ongoing advancements in healthcare education and simulation practices.[2]

Research Profile

According to publicly available scholarly records, the researcher has produced four indexed documents with a citation count of nine and an h-index of two. These metrics indicate emerging scholarly influence within the healthcare simulation domain and demonstrate active participation in academic publishing.[1]

Research Contributions

The researcher’s contributions emphasize simulation-based healthcare education, practical learning environments, and evidence-supported instructional methods. Such work supports the enhancement of healthcare training quality and contributes to improved educational outcomes for future healthcare professionals.[3]

Publications

  • Peer-reviewed publications related to healthcare simulation and educational practice.
  • Research articles indexed within recognized scholarly databases.
  • Studies supporting healthcare training and professional competency development.

Research Impact

The citation record demonstrates that the researcher’s work has been referenced by other scholars. Although the publication portfolio is developing, the existing impact indicators suggest relevance within specialized areas of healthcare education and simulation research.[1]

Award Suitability

Abdelrahman Salameh demonstrates qualities associated with the Best Researcher Award, including scholarly productivity, research dissemination, and contributions to healthcare simulation. His academic record reflects commitment to research-informed education and continuous professional advancement.[1]

Conclusion

This profile presents an overview of Abdelrahman Salameh’s research achievements and academic engagement within healthcare simulation. His publication activity, citation performance, and educational contributions provide a foundation for recognition through academic and professional award programs.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Abdelrahman Salameh, Author ID 58976141600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58976141600
  2. ORCID. (n.d.). Research activities and academic profile of Abdelrahman Salameh.
    https://orcid.org/0009-0002-6175-7173
  3. Google Scholar author details: Abdelrahman Salameh.
    https://scholar.google.com/citations?user=q1vzyzQAAAAJ&hl=en&oi=sra

Dr. Farnaz Farid | Healthcare industry | Best Researcher Award

Published on by AI Data Scientist

Dr. Farnaz Farid | Healthcare industry | Best Researcher Award

Multidisciplinary Researcher, Western Sydney University, Australia

Dr. Farnaz Farid is a distinguished and multidisciplinary researcher whose academic journey and professional experience span industry and academia, combining expertise in artificial intelligence, cybersecurity, human-centered systems, and applied computing. She holds a Doctor of Philosophy (PhD) degree from Western Sydney University, where her doctoral research focused on computational modeling, AI-driven predictive systems, and network quality of service frameworks; she also earned earlier degrees in engineering and computing from reputable institutions that shaped her foundation in IT, networks, and systems. Over the years, Dr. Farnaz Farid has served in both industry and academic roles: prior to joining academia, she worked at IBM as an IT Specialist, Application Developer, and Project Manager, contributing to enterprise integration, software development, and digital innovation; subsequently, she entered academia as an Associate Lecturer at the University of Sydney and then moved to Western Sydney University, where she is now a Senior Lecturer and Academic Program Advisor, co-leading global initiatives such as “Realising Digital Futures.” Her professional experience includes overseeing cross-disciplinary projects in AI, cybersecurity, IoT, and smart systems, mentoring postgraduate researchers, guiding curriculum development, and fostering partnerships with industry and community stakeholders. Her research interests encompass explainable AI, human-centred security, AI for healthcare, cyber‐physical systems, distributed networks, federated learning, and digital inclusion. Dr. Farid has received a number of awards and honors, such as the Google exploreCSR grant over multiple years to lead community‐based AI projects, the DVC Education Excellence in Teaching (Partnerships) award at her university, and the Teaching and Learning for Public Good Award in Social Sciences, all of which attest to her excellence in teaching, public engagement, and socially impactful research. Through her editorial service (for journals such as Symmetry and Sustainability), membership in the Asian Council of Science Editors (ACSE), and leadership of cross‐disciplinary grants, she has also contributed to the scientific community.

Profile: GOOGLE SCHOLAR  | SCOPUS | ORCID

Featured Publications

  • Farid, F. (2025). An explainable predictive model for the detection of mental health conditions in the workplace. (citation count: 13)

  • Farid, F. (2025). A threat analysis framework for cyberattacks in smart cities: ransomware in focus. (citation count: 24)

  • Dong, H., & Farid, F. (2024). A deep learning based patient care application for skin cancer detection.

  • Farid, F., & colleagues. (2024). AI technologies in reducing hospital readmission for chronic diseases: a recommended framework.

  • Lai, T., & Farid, F. (2024). Ensemble learning for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis.