Wireless sensors for medical applications: Current status and future challenges

Elayan, Hadeel; Shubair, Raed M.; Kiourti, Asimina

doi:10.23919/eucap.2017.7928405

Cited by 49 publications

(28 citation statements)

References 31 publications

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“…WSN can be defined as a group of special sensors, which are dispersed spatially for gathering data from the environment [69][70][71]. Because such sensors are cheap and easy to implement in terms of scalable network structures, there is a great interest in using WSN for different research problems [72][73][74]. Thanks to WSN models, it is possible to create wide networks where data are passed through nodes and the data is processes or directed by a central point (sink) for specific purposes [75].…”

Section: Wireless Sensor Network and Beaconsmentioning

confidence: 99%

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

2020

Turk J Elec Eng & Comp Sci

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Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor to gather data from visitors of the hotels and the analysis was supported with an optimization approach through the deep autoencoder network. According to the data density gathered from the structure developed with distributed computing and the API software in C# language, server group numbers were increased to list the most affordable hotel in the desired times. Thanks to the developed architecture and software, response times of the servers were significantly reduced. In detail, it was seen that the HAProxy responded 11 times faster than NetScaler as the new architecture responded 1160 times faster than the old one. Also, the HashSet system in the newly developed architecture responded 18 times faster than the List system and as general, the new architecture was found to be 9 times faster than the old architecture.

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Section: Wireless Sensor Network and Beaconsmentioning

confidence: 99%

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

2020

Turk J Elec Eng & Comp Sci

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“…It is responsible of taking information and intuition from the training dataset [2]. These applications have been utilized with expanding accomplishment to foresee persistent forecasts in numerous different zones of medication, for example, hospital re-admission prediction, breast cancer prediction, diabetic difficulties, cardiovascular mortality, and so on [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

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“…The future systems must contain many sensors in order to achieve a comprehensive assessment of the human condition as well as the environment. In addition, they need to transmit the information wirelessly to a base station, where data processing can take place [3][4][5][6][7][8][9]. Wrist-worn systems are bodyborne computational and sensory devices which can sense the person who wears them and/or their environment.…”

Section: Introductionmentioning

confidence: 99%

Embedded 3D multi‐band antenna with ETS process technology covering LTE/WCDMA/ISM band operations in a smart wrist wearable wireless mobile communication device design

Chung

2019

IET Microwaves, Antennas & Propagation

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An internal etching for three‐dimensional structures (ETS) antenna capable of operating in the 810–960, 1370–1450 and 1710–2630 MHz bands, respectively, for long‐term evolution (LTE)/wideband code division multiple access (WCDMA)/2.4 GHz industrial, scientific and medical (ISM) band operations suitable for being embedded within a smart wrist wearable wireless mobile communication device is proposed. The proposed antenna radiation efficiency can achieve 37.8–55.3% from 810–960 MHz, 53.1–92.63% from 1370–1450 MHz and 52.9–93.1% from 1710–1630 MHz for LTE/WCDMA bands and 87.4% for ISM band without the hand phantom in the free space condition. The antenna radiation efficiency of the LTE/WCDMA and ISM bands is reduced by half because of the hand phantom with lossy material properties. Furthermore, the health risk related to exposure to electromagnetic radiation, the specific absorption rates of the device are evaluated and the results were far below the limits required by the Federal Communications Commission (FCC) organisation. The embedded three‐dimensional (3D) multi‐band antenna can be adapted for the smart wrist wearable wireless mobile communication device. This study breaks the pattern that wearable devices must work with mobile phones. The wearable device proposed herein can directly communicate with the carrier base station independently.

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Wireless sensors for medical applications: Current status and future challenges

Cited by 49 publications

References 31 publications

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Embedded 3D multi‐band antenna with ETS process technology covering LTE/WCDMA/ISM band operations in a smart wrist wearable wireless mobile communication device design

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