Concurrent Healthcare Data Processing and Storage Framework using Deep Learning in Distributed Cloud Computing Environment

Abstract : Distributed cloud computing environments rely on sophisticated communication and sharing paradigms for ease of access, information processing, and analysis. The challenging characteristic of such cloud computing environments is the concurrency and access as both the service provider and end-user rely on the common sharing platform. In this manuscript, retrieval and storage-based indexing framework (RSIF) is designed to improve the concurrency of user and service provider access to the cloud-stored healthcare data. Concurrency is achieved through replication-free and continuous indexing and time-constrained retrieval of stored information. The process of classifying the constraints for data augmentation and update is performed using deep learning for all the storage instances. Through conditional assessment, the learning process determines the approximation of indexing and ordering for storing and retrieval respectively. This helps to reduce the time for access and retrieval concurrently, provided the process is not dependent. The simulation analysis using the metrics discontinuous indexing, replicated data, retrieval time, and cost proves the reliability of the proposed framework.
 It is entering an era of big data, which facilitated great improvement in various sectors. Particularly, assisted by wireless communications and mobile computing, mobile devices have emerged with a great potential to renovate the healthcare industry. Although the advanced techniques will make it possible to understand what is happening in our body more deeply, it is extremely difficult to handle and process the big health data anytime and anywhere. Therefore, data analytics and mobile computing are significant for the healthcare systems to meet many technical challenges and problems that need to be addressed to realize this potential.
 The challenge facing mobile computing is the issue of energy consumption, especially in the medical field, which often requires long monitoring durations and lengthy testing of patient-related physiological indicators. With the improvement of electro medical and wearable devices, the data volume of healthcare systems has been extensively increasing.
 The advanced healthcare systems have to be upgraded with new capabilities such as machine learning, data analytics, and cognitive power for providing human with more intelligent and professional healthcare services. To explore recent advances and disseminate state-of-the-art techniques related to data analytics and mobile computing on designing, building, and deploying novel technologies, to enable intelligent healthcare services and applications, this paper presents the detailed design for developing intelligent healthcare systems assisted by data analytics and mobile computing. Moreover, some representative intelligent healthcare applications are discussed to show that data analytics and mobile computing are available to enhance the performance of the healthcare services.
 ? There are various healthcare data generated and stored in healthcare systems, such as medical record, hospitalization records, medical imaging, and surgery data. These multisource data include text, image, audio, and video . More importantly, the same category of healthcare data collected through different devices may follow the different data standard defined by providers. ? The value of mining single source healthcare data is very limited. Thus, more research attempted to develop data fusion based approach to discover more knowledge from various data to provide more valuable services, such as personalized health guidance and public health warnings. ? With the increasingly in-depth research being performed, mobile computing will have a better future in the medical field.
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