The Application of a Computer Monitoring System Using IoT Technology

Zhu, Zhen; Hu, Qian

doi:10.1155/2022/4033886

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…Tere are some shortcomings in this study. (1) Due to the updating of technology, the convergence and quasi-combination of the algorithm do not reach the optimal solution. At the same time, the degree of matching of diferent data types to the algorithm is not nearly the same, which needs more detailed research in the future.…”

Section: Discussionmentioning

confidence: 99%

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Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Gao

Qing

et al. 2023

Computational Intelligence and Neuroscience

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Objective. This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization. Methods. Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data. This will be an efficient and highly scalable data analysis platform through embedding embedded Sparkmlib in the Hadoop ecosystem. Results. With the concept of “Internet + medical integration,” the overall architecture design of the big data platform for disease burden management was proposed based on the Spark engine and Python language. The main system composition and application scenarios are given at four levels: multisource data collection, data processing, data analysis, and the application layer, according to application scenarios and use requirements. Conclusion. The big data platform of disease burden management helps to promote the multisource convergence of disease burden data and provides a new path for the standardized paradigm of disease burden measurement. Provide methods and ideas for the deep integration of medical big data and the formation of a broader standard paradigm.

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Section: Discussionmentioning

confidence: 99%

“…The explosive prosperity of Internet of things technology and artificial intelligence has promoted profound changes in the industry worldwide [ 1 ]. Information resources are gradually becoming one of the most essential strategic resources.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Gao

Qing

et al. 2023

Computational Intelligence and Neuroscience

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“…The computer-based monitoring systems can be effectively enhanced by utilizing the application of Internet of Things (IoT) technology [14]. Jozsef Katona et al developed a technique in 2019 in the field of BCI.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development

Lasefr,

Elleithy,

Reddy

et al. 2023

Applied Sciences

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Epileptic seizure detection classification distinguishes between epileptic and non-epileptic signals and is an important step that can aid doctors in diagnosing and treating epileptic seizures. In this paper, we studied the existing epileptic seizure detection methods in terms of challenges and processes developed based on electroencephalograph (EEG) signals. To identify the research deficiencies and provide a feasible solution, we surveyed the existing techniques at each phase, including signal acquisition, pre-processing, feature extraction, and classification. Most previous and current research efforts have used traditional features and decomposing techniques. Therefore, in this paper, we introduced an enhanced and efficient epileptic seizure technique using EEG signals, for which we also developed a mobile application for monitoring the classification of EEG signals. The application triggers notifications to all associated users and sends a visual notification should an EEG signal be classified as epileptic. In this research, we have used publicly available EEG data from the University of Bonn. Our proposed method achieved an average accuracy of 98% by utilizing different machine-learning algorithms for classification, and it has outperformed recently published studies. Though there have been other mobile applications for epileptic seizure detection, they have been based on motion and falling detection, as opposed to ours, which was developed based on EEG classification. Our proposed method will have an impact in the medical field, particularly for epilepsy seizure monitoring as well as in the Human–Computer Interaction fields, majorly in the Brain–Computer Interaction (BCI) applications.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

mentioning

confidence: 99%

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%

Retracted: The Application of a Computer Monitoring System Using IoT Technology

Intelligence and Neuroscience

2023

Computational Intelligence and Neuroscience

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The Application of a Computer Monitoring System Using IoT Technology

Cited by 4 publications

References 29 publications

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development

Retracted: The Application of a Computer Monitoring System Using IoT Technology

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