Special issue on cognitive computing for Internet of Medical Things
• 大类 : 工程技术 - 2区
• 小类 : 计算机：理论方法 - 2区
The cognitive computing is computational technology that provides an artificial physical response, permitting a subject to test events and various activities comparable to those that can be established in reality. By employing sensors and intelligent algorithms, the machines or computers can sense similar to human behaviour (seeing, hearing and even feeling).
In recent years, the Internet of Medical Things (IoMT) support the out-of-hospital concept that modify and provide higher care standards. This is executed with individual data-driven treatment schemes and high performance optimised devices customised to act as individual requirements.
The IoMT are designed mainly to sense the individual health status data where it can be sent to the clinical for interpretation issue. This will be a crucial especially in the lake of physician’s number. With the aid of cognitive algorithms, a pre-learned intelligent system can be developed for improving the diagnosis process and automate it. Moreover, the valuable information from the clinical database is used for individual health prevention and protection through emergency situations.
However, the IoMT data analysis and managing still represent the main trend due to a huge number of devices that connect to the server environments which generate a significant medical data. Besides, the security of these data represents another challenge, where the medical data is highly classified and need to be guarded. Therefore, there is a necessity for providing stable, efficient and scalable intelligent algorithms that lead to additional sophisticated solutions and that can make operative decisions in developing IoMT.
This special issue is based on the developing of technical improvements considered in the process of intelligent systems by using cognitive techniques, IoMT deep learning and machine learning techniques for multimodal biomedical data processing, non-invasive interpretation and that has shown to be relatively efficient for improving patients interpretation quality. Furthermore, it objects to highlight the challenges of developing and proposing new ideas regarding the out-of-hospital dedicated systems directions.
Topics of Interest
Topics to be covered include, but are not limited to:
- Biomedical big data analysis
- IoMT data encryption and security
- Data storage scheme for IoMT
- The architecture of cognitive systems
- IoMT Cloud technique based and intelligent systems
- Deep learning interpretation of biomedical data
- Optimised wearable IoMT schemes
- Human-Machine Interactive Systems
- Health data mining in IoMT
- Machine learning for decision support systems in IoMT
- Data and information management in IoMT
- Performance assessment of IoMT architectures, services, and protocols