Data science methodologies

Guest Editor

Imran Ashraf, PhD, Department of Information and Communication Engineering, Yeungnam University, Republic of Korea

BMC Medical Research Methodology welcomed submissions for our collection focused on Data science methodologies. Data science has transcended the boundaries of traditional disciplines, creating a rich ecosystem of methodologies that encompass statistical modeling, machine learning, artificial intelligence, and beyond. From the rise of explainable AI and interpretable machine learning models to the ethical considerations of data usage, this collection delved into the exciting frontiers of data science methodologies.

Meet the Guest Editor

Back to top

Imran Ashraf, PhD, Department of Information and Communication Engineering, Yeungnam University, Republic of Korea

Imran Ashraf received his PhD, in Information and Communication Engineering from Yeungnam University, South Korea. He was listed in Top 2% researcher list 2022. He is currently working as an Assistant Professor at the Information and Communication Engineering Department of Yeungnam University, Gyeongsan, South Korea. His research areas include bioinformatics, machine learning, decision support systems, positioning using next generation networks, communication in 5G and beyond, location-based services in wireless communication, and smart sensors (LIDAR) for smart cars.

About the Collection

BMC Medical Research Methodology is welcoming submissions for our collection focused on Data science methodologies. Data science has transcended the boundaries of traditional disciplines, creating a rich ecosystem of methodologies that encompass statistical modeling, machine learning, artificial intelligence, and beyond. Researchers and practitioners have been pushing the boundaries of data analysis, paving the way for innovative solutions in fields as diverse as healthcare, finance, environmental science, and social research. This collection seeks to capture the latest developments in data science, highlighting cutting-edge techniques, tools, and best practices.

From the rise of explainable AI and interpretable machine learning models to the ethical considerations of data usage, this collection will delve into the exciting frontiers of data science methodologies.

Image credit: Kentoh /stock.adobe.com

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Submission Guidelines

Back to top

This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp [hyperlink to journal's Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Data science methodologies" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.