Emerging Internet of Things for Biomedical Data and Applications

Submission Deadline: 31 March 2024 Submit to Special Issue

Guest Editors

Dr. Achyut Shankar, University of Warwick, UK.
Dr. Zahid Akhtar, State University of New York Polytechnic Institute, USA.


Biomedical and healthcare sciences now require more and more data, making it necessary to use sophisticated data mining algorithms to retrieve accessible data. For instance, data analysis methods are utilised to forecast treatment outcomes for children with acute lymphoblastic leukaemia using biomedical datasets, specifically DNA microarray or even next-generation sequence alignment data. In order to determine the function of corresponding genes involved in cellular and metabolic activities, clustering approaches are frequently used as well. The analysis of biomedical data is challenging due to the extremely high complexity, class imbalance, and limited sample numbers. Although the current study in this field has produced hopeful results, there is still a great deal of research ambiguity. Therefore, it is important to research features extraction techniques that allow for reliable genetic choices to improve prediction accuracy and interpretation. In addition, biological and medical big data analysis is unnecessary. The Internet of Things (IoT) is utilised in remote patient monitoring, remote health services, information transfer, monitoring, and diagnostics while integrating principles like collaboration on sensors, bio-sensors, e-healthcare, and virtual medical services, etc. It discusses the obstacles associated with heart monitoring in e-healthcare, how to use connected devices to solve particular patient problems, and how to leverage Microcontroller components to send data to the cloud for web-based applications. Also, the IoT's role in electroencephalogram (EEG) and magnetic resonance imaging (MRI), are both crucial in the realm of biomedical applications.


IoT enabled big data analytics for biomedical datasets and informatics
Edge computation for decentralized biomedical data understanding
Advancement Mobile Sensor Data for biomedical health informatics
Evolutionary computing enabled IoT for enhanced bioinformatics
IoT based Predictive analytics on biomedical enabled health informatics
Data-driven AI for Information Retrieval of biomedical Images

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