Impact Score 5.45
Dr. Mian Ahmad Jan (Lead Guest Editor), Abdul Wali Khan University Mardan, Pakistan, [email protected] Houbing Song, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA, [email protected] Fazlullah Khan, Rozetta Institute (formerly CMCRC Ltd), Sydney Australia, [email protected] Ateeq Ur Rehman, Abdul Wali Khan University Mardan, Pakistan, [email protected] Lie-Liang Yang, Southampton University, UK, [email protected]
The advancements in communication and hardware technologies have enabled the wearable devices of smart healthcare systems (CSHS) to generate an enormous amount of data. However, it is not clear what information can be obtained from the collected data that can be used by practitioners in CSHS. Recently, various machine learning algorithms and big data analytic techniques have been used to investigate the effectiveness of CSHS. In CSHS, efficient machine learning and big data analytics are mandatory because the gathered data is in an unorganized form and in different volumes, velocities, and varieties. CSHS are persuasive global needs due to the growth of the world’s population and a rapid increase in urbanization. As the urban population continuously increases, the need for improved quality of life, efficient delivery of health services to citizens becomes paramount. As CSHS continues to mature, specialized machine learning algorithms and big data analytic techniques are required to maintain the voluminous amount of medical data used in CSHS.
The expansion of CSHS is resulting in the production of gigantic data at an exceptional pace. Unfortunately, mostly produced data are washed away without pulling out useful knowledge and information due to the inadequacy of recognized standards, algorithms, and mechanisms. Besides, the dynamic environments of CSHS demand new machine learning algorithms and big data analytic techniques that are flexible to deal with the dynamic nature of medical data to achieve analytics and learn in real-time.
In this topical collection, the challenge of underutilizing the big data generated by CSHS is highlighted from machine learning and data analytics perspectives. It is argued that semi-supervision is mandatory for CSHS to deal with this challenge. A learning framework is required to match the nature of data produced in the CSHS and need to be scalable enough to fulfill the requirements of the services of CSHS. As the data have been collected by the wearable devices, and the data analytics has to become mature, it is possible to conquer this challenge with novel machine learning algorithms to analyze the data in real-time.
This proposal aims to bring together the experience of using machine learning for big data manipulation in CSHS to highlight its importance in real-world applications. This proposal would cover core content and extended content, both on the theoretical and technical aspects of exploring CSHS. This proposal aims to explore the potentials of machine learning and big data analytics in CSHS by going beyond the existing conventional approaches and presents more advanced practices for knowledge-based systems with well authentic implementations and results. An incredible wealth of data provided to healthcare practitioners need to be used to promote individual safety and health security in a fast and economic way. A thorough analysis of such large-scale data extracted from CSHS will pave the way for optimizing healthcare systems resources. We also solicit contributions coming from the industrial community to present concrete applications of these novel means using machine earning for big data analytics and knowledge extraction. The topics of interest for this topical collection include but not limited to the following:
Submission Deadline: 15th February 2021 Notification Due: 15th April 2021Revision Due: 31st May 2021Second Review Notification: 30th June 2021Final Manuscript: 31stJuly 2021 Publication Date: 2021
Submission Format and Review Guidelines
Peer Review Process
All the papers will be reviewed by at least two reviewers. A thorough check will be done and the guest editors will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended. At least 30% of new content is expected.
Paper submissions for the special issue should follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).
Manuscripts should be submitted online at https://www.editorialmanager.com/ncaa/default.aspx and follow the "Submit A Manuscript" link on that page. Authors should select ‘TC: ML4BD_SHS' during the submission step 'Additional Information'.