Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

Abu-Alhaija, Mwaffaq; Turab, Nidal M.

doi:10.32604/iasc.2022.021426

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…These techniques rely on sensors to determine the heart rate [9,10]. In addition, mobility is not a concern since these techniques require no constraints or boundaries [11][12][13][14]. Individuals who suffer from skin irritations can utilize these techniques freely.…”

Section: Research Problemmentioning

confidence: 99%

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms

et al. 2023

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ECG provides critical information in a waveform about the heart’s condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians.

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Section: Research Problemmentioning

confidence: 99%

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms

et al. 2023

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“…The ROC curve, also known as the error curve, is a graph that shows the relationship between the algorithm's sensitivity (TPR, True Positive Rate) and the proportion of objects in a negative class that the algorithm predicted incorrectly (FPR, False Positive Rate) when the threshold of the decisive rule is changed [45]:…”

Section: Evaluation Metricsmentioning

confidence: 99%

CNN-BiLSTM Hybrid Model for Network Anomaly Detection in Internet of Things

Omarov¹,

Auelbekov²,

Suliman³

et al. 2023

IJACSA

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Anomaly detection in internet of things network traffic is a critical aspect of intrusion and attack detection, in which a deviation from typical behavior signals the existence of malicious or inadvertent assaults, faults, flaws, and other issues. The necessity to examine a large number of security events to identify anomalous behavior of smart devices adds to the urgency of addressing the challenge of picking machine-learning and deep learning models for identifying anomalies in network traffic. For the challenge of binary data categorization, a software implementation of an intrusion detection system based on supervised-learning algorithms has been completed. The UNSW-NB15 open dataset, which contains 2,540,044 records -vectors of TCP/IP network connection signals and their associated class labels are used to train and test the system. This research compares different machine-learning models and proposes CNN-BiLSTM hybrid model for IoT network intrusion detection. The metrics for measuring the quality of classification and the running duration of algorithms for different ratios of train and test samples are the result of the built framework testing.

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“…Some model trainers would like to train their models based on pre-trained models acquired from outsourced code societies. An adversary may spread technically modified models through the Internet [35]. When a model trainer uses these modified models as samples of their model, the robustness and security of machine learning models may be compromised.…”

Section: Other Attacksmentioning

confidence: 99%

Ensuring User Privacy and Model Security via Machine Unlearning: A Review

Tang,

Cai,

Liu

et al. 2023

CMC

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As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy. Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The data provider hopes that the model trainer can prove to them the confidentiality of the model. Trainer will be required to withdraw data when the trust collapses. In the meantime, trainers hope to forget the injected data to regain security when finding crafted poisoned data after the model training. Therefore, we focus on forgetting systems, the process of which we call machine unlearning, capable of forgetting specific data entirely and efficiently. In this paper, we present the first comprehensive survey of this realm. We summarize and categorize existing machine unlearning methods based on their characteristics and analyze the relation between machine unlearning and relevant fields (e.g., inference attacks and data poisoning attacks). Finally, we briefly conclude the existing research directions.

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Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

Cited by 12 publications

References 22 publications

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms

CNN-BiLSTM Hybrid Model for Network Anomaly Detection in Internet of Things

Ensuring User Privacy and Model Security via Machine Unlearning: A Review

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