Topical Collection on Neural Computing for IOT based Intelligent Healthcare Systems
Internet of things (IoT) is a dynamic network of sensors, cloud storage and multiple embedded electronic devices connected with each other through network connectivity for exchange of data. IoT is bringing paradigm shift in field of intelligent systems as multiple connected device makes the system more robust.
The real time health monitoring by sophisticated sensors will not only improve life style of patients, but can also emerge as life saver in critical situations. The data from IoT sensors will be stored in clouds which will be then analysed and shared with healthcare professionals. The healthcare professional can diagnose the condition and provide online consultation. In this manner telemedicine is able to provide healthcare services to vast population in a cost effective manner. Therefore, there is a huge potential in research in field of IoT in healthcare domain.
Neural Computing has come a long a way since its conception. Deep learning is quite a popular variant of Neural Computing that is in high demand these days. Deep learning is a large architecture comprising of a multilayer artificial neural network (ANN). The structure is inspired by data processing capability of human brain and imitates the way a human being would learn new things. Deep learning finds its application in various fields like image/speech recognition, natural language processing, etc. Deep learning is emerging as a best contender for healthcare data analysis; it has immense opportunity in research.
With the advent of Internet of Things (IoT) pioneering work is done in patient health monitoring. IoT is also behind emergence of wearable medical devices. The data from wearable devices or other devices could help in conquering ailments at very early stage. Enormous amount of data is generated from different medical instruments including wearable medical devices. The amount of data generated is beyond the capability of humans to monitor. In this manner medical field is looking towards technologies for data analysis.
Medical science and technology are coming together to provide better healthcare services. Deep learning has emerged as number one contender for medical data analysis. The generated medical data includes various modalities in form of single dimension signals like ECG, EEG, and multidimensional signals in form of MRI, CT, angiography, X-ray images, etc. Deep learning has been proved a phenomenal tool in the field of image processing and data analysis. Application of deep learning in healthcare will help in fast track solution to chronic ailments.
The intermingling disciplines of healthcare data analysis, deep learning and IoT are pathway towards better and smart healthcare services, it is the high time for research in this area, this field have attracted researchers in past and will continue to do so. The research covers numerous multidisciplinary topics catering towards academia and industries.
Recommended topics include (but are not limited to) the following:
- IoT sensors for smart health devices
- Data security in IoT based healthcare
- Telemedicine and medical informatics
- Medical image classification with deep learning
- Medical image segmentation with deep learning
- Medical image fusion with deep learning
- ECG and EEG classification using learning
Dr. Deepak Kumar Jain (Lead Guest Editor), Chongqing University of Posts and Telecommunications, China, email@example.com
Prof. Thierry Bouwmans, University La Rochelle, France, firstname.lastname@example.org
Prof. Marco Leo, National Research council DHITECH - University Campus of Lecce, via Monteroni , 73100 Lecce, Italy, email@example.com
Deadline for first submission: 30th April 2021
Completion first review round: 15th June 2021
Deadline revised manuscripts: 15th September 2021
Completion of the review and revision process: 30th October 2021
Peer Review Process
All the papers will go through a double blind review process and 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).
Authors should select ‘SI: Neural Computing for IOT based Intelligent Healthcare Systems' during the submission step 'Additional Information'.