Muhammad Tahir^1,2,*, Mingchu Li^1,2, Irfan Khan^1,2, Salman A. Al Qahtani³, Rubia Fatima⁴, Javed Ali Khan⁵, Muhammad Shahid Anwar⁶

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2529-2544, 2023, DOI:10.32604/cmc.2023.042403

Abstract Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics.… More >

S. Vidhya^1,*, V. Kalaivani²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2673-2692, 2023, DOI:10.32604/csse.2023.027724

Abstract In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored… More >

Yifei Xiao, Shijie Zhou^*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 169-185, 2023, DOI:10.32604/cmes.2022.021795

Abstract To achieve the high availability of health data in erasure-coded cloud storage systems, the data update performance in erasure coding should be continuously optimized. However, the data update performance is often bottlenecked by the constrained cross-rack bandwidth. Various techniques have been proposed in the literature to improve network bandwidth efficiency, including delta transmission, relay, and batch update. These techniques were largely proposed individually previously, and in this work, we seek to use them jointly. To mitigate the cross-rack update traffic, we propose DXR-DU which builds on four valuable techniques: (i) delta transmission, (ii) XOR-based data update, (iii) relay, and (iv)… More >

G. Neelamegam^*, P. Marikkannu

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1079-1093, 2023, DOI:10.32604/iasc.2023.031283

Abstract Due to the development of technology in medicine, millions of health-related data such as scanning the images are generated. It is a great challenge to store the data and handle a massive volume of data. Healthcare data is stored in the cloud-fog storage environments. This cloud-Fog based health model allows the users to get health-related data from different sources, and duplicated information is also available in the background. Therefore, it requires an additional storage area, increase in data acquisition time, and insecure data replication in the environment. This paper is proposed to eliminate the de-duplication data using a window size… More >

Eatedal Alabdulkreem¹, Jaber S. Alzahrani², Majdy M. Eltahir³, Abdullah Mohamed⁴, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵, Mohamed I. Eldesouki⁶, Mohammed Rizwanullah⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3039-3055, 2022, DOI:10.32604/cmc.2022.029835

Abstract Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents a method for analyzing healthcare… More >

Nithya Rekha Sivakumar^1,*, Ahmed Zohair Ibrahim²

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4467-4483, 2022, DOI:10.32604/cmc.2022.019041

Abstract

Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-and-forward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there… More >