Mobile Networks and Applications - Big Data Analytics and Machine Learning for IoT-enabled Smart Healthcare Systems
Overview:
The novel diseases are affecting the human lives across the globe on an unprecedented scale. These highly contagious and infectious diseases required early prediction of outbreak by formulating a dynamic system that need to predict the course of a disease and devise different management strategies. Big Data and Machine Learning are the two major technologies that are identified as the potential contenders to predict the outbreak of diseases. Big Data and Machine Learning techniques have been resolving many long-term complex problems. These techniques can provide an in-depth understanding of the diseases and its impact on the society. An extra vigilant investigation, based on existing data and expressive predictions could be highly valuable for decision-making and future policy setting.
The philosophy behind machine learning is to automate the formation of analytical models to allow algorithms to learn constantly with the help of accessible data. This can be extremely helpful for smart healthcare systems. New and emerging analytical models generate optimistic results and reduce the requirements of human intervention. These models can be utilized to automatically generate trustworthy decisions using Big Data produced by IoT-enabled smart health services. Machine learning algorithms are scrutinized through various datasets that human beings cannot possibly obtain, even in years. On the other hand, huge volume of scientific and epidemiological Big Data empowering epidemiologists, scientists, health workers, and strategy makers have smart decisions in healthcare. Big Data generated in the IoT-enabled healthcare lies at the heart of struggles to comprehend and predict the impact of diseases. There are immense determinations being arrayed to leverage Machine Learning on the Big Data sets, which are presently accessible globally. In addition, a research challenge is also hosted by Kaggle with Big Data sets to support outgrowth curiosity in the Machine Learning and Big Data knowledge community.
The goal of this special issue is to discover the promises of Big Data and Machine Learning in IoT-enabled healthcare systems and come across innovative solutions for useful insights with thriving results. In addition, the objectives include: to offer informative investigation and effective techniques that can support in tracking and predicting the diseases, preclude the epidemic from further spread, efficient resource utilization to improve the results, and control the consequences. This special issue aims to support the extended versions of papers submitted to EAI BigIoT-EDU 2023 conference. It is an open call for submission of high quality papers, however, the main audience of this special issue will be the extended version of papers submitted to EAI BigIoT-EDU 2023.
Topics
Topics of interest include, but are not limited to, the following scope:
|
|
Important Dates
- Manuscript submission deadline: 30 June 2024
- Notification of acceptance: 01 August 2024
- Submission of final revised paper: 31 August 2024
- Publication of special issue (tentative): Fourth Quarter, 2024
Submission Procedure
Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/ (this opens in a new tab).
A copy of the manuscript should also be emailed to the Guest Editors at the following email address: zhangyinjun@gxstnu.edu.cn (this opens in a new tab)
Guest Editors:
Prof. Zhang Yinjun (Lead Guest editor)
School of Computer Science and Engineering
Guangxi Science & Technology Normal University, China
Email: zhangyinjun@gxstnu.edu.cn (this opens in a new tab)
Prof. Mengji Chen
School of Electrical Engineering and IT,
Hechi University
China