IoT-Based Smart Health Monitoring System for COVID-19

SN Computer Science, Journal Year: 2022, Volume and Issue: 3(2)

Published: Jan. 20, 2022

Language: Английский

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IoT-Enabled Social Relationships Meet Artificial Social Intelligence

IEEE Internet of Things Journal, Journal Year: 2021, Volume and Issue: 8(24), P. 17817 - 17828

Published: May 18, 2021

With the recent advances of Internet Things (IoT), and increasing accessibility to ubiquitous computing resources mobile devices, prevalence rich media contents, ensuing social, economic, cultural changes, technology applications have evolved quickly over past decade. They now go beyond personal computing, facilitating collaboration social interactions in general, causing a quick proliferation relationships among IoT entities. The number these their heterogeneous features led communication bottlenecks that prevent network from taking advantage improve offered services customize delivered content, known as explosion. On other hand, artificial intelligence emerging promising research field (ASI) has potential tackle explosion problem. This article discusses role management, problem IoT, reviews proposed solutions using ASI, including social-oriented machine-learning deep-learning techniques.

Language: Английский

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Towards development of IoT-ML driven healthcare systems: A survey

Journal of Network and Computer Applications, Journal Year: 2021, Volume and Issue: 196, P. 103244 - 103244

Published: Oct. 20, 2021

Language: Английский

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A Taxonomic Review of the Use of IoT and Blockchain in Healthcare Applications

IRBM, Journal Year: 2021, Volume and Issue: 43(5), P. 511 - 519

Published: May 20, 2021

Language: Английский

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Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

Artificial Intelligence in Medicine, Journal Year: 2022, Volume and Issue: 134, P. 102431 - 102431

Published: Oct. 22, 2022

Language: Английский

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A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Sensors, Journal Year: 2022, Volume and Issue: 22(14), P. 5327 - 5327

Published: July 16, 2022

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency critical; this may suffer as a result of cloud latency because the delay caused by cloud. Therefore, fog computing is utilized in healthcare applications. There still limitations time, latency, energy consumption. Thus, proper architecture good task scheduling algorithms should be developed to minimize these limitations. study, an Energy-Efficient Internet Medical Things Fog Interoperability Task Scheduling (EEIoMT) framework proposed. This schedules tasks efficient way ensuring critical executed shortest possible time within their deadline while balancing consumption when processing other tasks. our architecture, Electrocardiogram (ECG) sensors used monitor heart health at home smart city. ECG send sensed data continuously ESP32 microcontroller through Bluetooth (BLE) for analysis. also linked scheduler via Wi-Fi results analysis (tasks). The appropriate node carefully selected execute giving each special weight, which formulated on basis expected amount consumed executing choosing with lowest weight. Simulations were performed iFogSim2. simulation outcomes show suggested has superior performance reducing usage energy, network utilization weighed against CHTM, LBS, FNPA models.

Language: Английский

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IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 76, P. 103638 - 103638

Published: March 18, 2022

Language: Английский

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An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis

Informatics, Journal Year: 2023, Volume and Issue: 10(1), P. 21 - 21

Published: Feb. 6, 2023

Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog with IoT applications. In this study, IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in patients using ENsemble Deep learning (FRIEND), introduced that can instantaneously facilitate patients. proposed system was trained on the combined dataset Long-Beach, Cleveland, Switzerland, Hungarian disease datasets. We first tested model eight basic ML approaches, decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, XGBoost then applied ensemble methods bagging classifiers, weighted averaging, soft hard voting achieve enhanced outcomes deep neural network, approach, methods. These models were validated 16 performance 9 network parameters justify work. accuracy, PPV, TPR, TNR, F1 scores experiments reached 94.27%, 97.59%, 96.09%, 75.44%, 96.83%, respectively, which comparatively higher when assembled hard-voting classifiers. user-friendliness inclusion principles, instantaneous cardiac patient diagnosis, low etc., are advantages confirmed according achieved experimental results.

Language: Английский

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IoT-based eHealth using blockchain technology: a survey

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(6), P. 7083 - 7110

Published: April 4, 2024

Abstract The eHealth sector has witnessed significant growth due to technological advancements, facilitating care delivery in patients' homes and moving away from traditional hospital settings. Blockchain the Internet of Things (IoT) play pivotal roles enhancing healthcare services, offering features such as remote patient monitoring, streamlined electronic medical record (EMR) management, drug traceability, effective disease control, particularly during events like COVID-19 pandemic. growing utilization IoT devices brings about security challenges, including concerns related data integrity device authentication. This paper proposes integration blockchain technology a robust solution. Leveraging its decentralized tamper-resistant features, establishes trust among diverse devices, ensuring data. Additionally, smart contracts enhance authentication, fortifying overall by addressing vulnerabilities associated with centralization. Regarding management eHealth, this survey begins an overview industry, highlighting IoT-related challenges healthcare. It explores various applications discusses how can effectively address obstacles through IoT. Notably, provides insights into examining consensus algorithm parameters within systems, clarifying methodology used assess optimize these critical components. extends thorough review existing research on integrating blockchain-based eHealth. Finally, it presents potential solutions for implementing sector. comprehensive aims empower stakeholders providing dynamic evolving field.

Language: Английский

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A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(3), P. 035209 - 035209

Published: June 28, 2024

Abstract Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within heart, leading irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is significant cause morbidity mortality global scale. About 80% deaths related disease are caused by ventricular arrhythmias. research investigated application an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated model’s performance dataset from UCI ML repository varying train-test splits (70:30, 80:20, 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, directory analysis were applied along Pearson correlation feature selection, all aimed enhancing model performance. proposed RF achieved impressive metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), specificity (100%). Furthermore, study compared approach existing models, demonstrating improvements across various measures.

Language: Английский

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