Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system

Fouad, H.; Mahmoud, Nourelhoda M.; Issawi, Mohammed Sayed El; Al-Feel, Haytham

doi:10.1016/j.comcom.2020.01.020

Cited by 37 publications

(16 citation statements)

References 37 publications

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“…Also, another critical issue is to minimize communication errors. In this context, [161] stated that it would be possible to minimize communication errors using the incorporated recurrent learning process.…”

Section: Remote Services Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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Section: Remote Services Layermentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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“…By allowing self-care by patients and to be monitored remotely by regularising follow-ups [17], telemedicine helped obstetrics administrations to ladies with entanglements in pregnancy which diminishes neonatal mortality, decrease in various days in neonatal concentrated consideration and decreased expense of consideration [18], access to psychological well-being administrations, which incorporates crisis psychiatry administrations using two-way audio-video intelligent consulting sessions and counselling, Regular ophthalmologic screening of patients diagnosed with diabetes for retinopathy in their essential social insurance focus, with information translated by an expert or an ophthalmologist at an alternate area [15], use of portable computerized mammography for cooperative analysis of tumour and remote access to clinical preliminaries to improve access to disease screening device, Remote monitoring and care for patients with heart failure. Several treatment measures such as crisis prevention, treatment, stabilization and critical support for patients are highlighted in [19,20], It is inevitable that the growth of Internet technology and web access across the world has led healthcare professionals to interact to entire work to maximize their performance. Telemedicine seems to be a promising service and will helps to grow gradually.…”

Section: Figure 1 Typical Telemedicine Frameworkmentioning

confidence: 99%

Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

2020

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The healthcare sector is one of the most rapidly growing sectors globally. With the evergrowing technology, patient care, regulatory compliance, and digital transformation, there is an increased need for healthcare sectors to collaborate with all stakeholders-both within the healthcare ecosystem and in concurring industries. In recent times, telemedicine has proven to provide high quality, affordable, and predominantly adapted healthcare services. However, telemedicine suffers from several risks in implementation, such as data breach, restricted access across medical fraternity, incorrect diagnosis and prescription, fraud, and abuse. In this work, introduce blockchain-based framework that would unlock the future of the healthcare sector and improved services. Our proposed solution utilizing Ethereum smart contracts to develop a transparent, tamper-proof telemedicine healthcare framework, and ensure the integrity of sensitive patient data eliminating a central administrator. Moreover, the smart contract regulates the interaction between all the parties involved in the network and keeps the patient meticulously informed about the transactions in the network.

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“…Human-machine interactions (HMIs) involve communicating and cooperating with the machine through a user interface. These interactions are carried out between the user and the machine to control the machine's intuitive behaviors [6]. In recent years, specific sensors have been used to capture the normal, abnormal, and neural postures necessary for controlling the machine [7].…”

Section: Introductionmentioning

confidence: 99%

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

Alfarraj

Tolba

2020

Complex Intell. Syst.

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The computer vision (CV) paradigm is introduced to improve the computational and processing system efficiencies through visual inputs. These visual inputs are processed using sophisticated techniques for improving the reliability of human–machine interactions (HMIs). The processing of visual inputs requires multi-level data computations for achieving application-specific reliability. Therefore, in this paper, a two-level visual information processing (2LVIP) method is introduced to meet the reliability requirements of HMI applications. The 2LVIP method is used for handling both structured and unstructured data through classification learning to extract the maximum gain from the inputs. The introduced method identifies the gain-related features on its first level and optimizes the features to improve information gain. In the second level, the error is reduced through a regression process to stabilize the precision to meet the HMI application demands. The two levels are interoperable and fully connected to achieve better gain and precision through the reduction in information processing errors. The analysis results show that the proposed method achieves 9.42% higher information gain and a 6.51% smaller error under different classification instances compared with conventional methods.

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Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system

Cited by 37 publications

References 37 publications

Analysis of factors affecting IoT-based smart hospital design

Analysis of factors affecting IoT-based smart hospital design

Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

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