IoT-Inspired Smart Toilet System for Home-Based Urine Infection Prediction

Bhatia, Munish; Kaur, Simranpreet; Sood, Sandeep K.

doi:10.1145/3379506

Cited by 31 publications

(15 citation statements)

References 41 publications

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“…For execution, the creators utilized the UCI archive, different clinical sensors, and grouping calculations for anticipating the well-being weakness. Bhatia et al [ 3 ] proposed a system to investigate well-being status universally to anticipate probabilistic weakness in an effective manner. Additionally, to check the relevance of the proposed system, the authors performed implementations on four people with various demographics for multi-day information by utilizing IoT sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Nonetheless, the significant difficulties for medical care administrations are to gather exact information and give a better nature of administrations in an ongoing environment [ 2 ]. Recently, the episode of COVID-19 (coronavirus) has been perceived worldwide as a significant medical problem brought about by viral contamination that straightforwardly influences the different organs of the body [ 3 ]. Notwithstanding, strong well-being checking and control framework is required, particularly in those spots where the spread of COVID-19 is extensive [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

et al. 2021

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Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial–temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

et al. 2021

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“…Technology for the real-time detection, identification, and tracking of novel events could help delay the spread of disease [10,11]. These tools include in-depth analysis at the fog-cloud level, as well as the incorporation of captured remote tracking information, namely through mobile healthcare, and the continued monitoring of the health condition of patients [12,13].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…To capture heterogeneous data, physical structures are fitted with sensors [38]. There is restricted computing power and a limited lifespan for these sensors [11]. The more knowledge they gather, the more helpful the options provided [39].…”

Section: Proposed Approachmentioning

confidence: 99%

Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Aljumah

2021

Electronics

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From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

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“…By offering decision-making applications to users through a dense globally dispersed IoT paradigm, the cloud conceptualization must be used [6][7] [9]. According to cloud-based service delivery, the IoT paradigm must ensure the Everything-as-a-Service (XaaS) model, which enables applications and end-users to acquire useful data from every device in a time-sensitive manner [8] [12]. The fog-based computational platform has recently attracted a lot of attention because of its potential to meet needs that the present cloud computing idea has yet to solve.…”

mentioning

confidence: 99%

Fog-Assisted Healthcare Framework for Smart Hospital Environment

Ahanger

2021

Preprint

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The technological revolution brought by the Internet of Things (IoT) has mostly relied on cloud computing. However, to satisfy the demands of timesensitive services in the medical industry, Fog Computing, a novel computational platform based on the cloud computing paradigm, has shown to be a useful tool by extending cloud resources to the network’s edge. The current paper examines the role of the fog paradigm in the domain of healthcare decision-making, focusing on its primary advantages in terms of latency, network utilization, and power consumption. A fog-computing based health assessment framework is developed in the current paper. Moreover, based on effective performance parameters, the performance is evaluated and depicted. The results show that the presented strategy can reduce network congestion of the communication network by analyzing information at the local node. Moreover, increased security on health information can be maintained at local fog-node and enhanced data protection from unauthorized access can be acquired. Fog computing offers greater insights into the health condition of patients with enhanced accuracy, precision, reliability and stability.

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IoT-Inspired Smart Toilet System for Home-Based Urine Infection Prediction

Cited by 31 publications

References 41 publications

A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

A novel IoT–fog–cloud-based healthcare system for monitoring and predicting COVID-19 outspread

Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Fog-Assisted Healthcare Framework for Smart Hospital Environment

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