Electronic Stethoscope for Heartbeat Abnormality Detection

Omarov, Batyrkhan; Batyrbekov, Aidar; Dalbekova, Kuralay; Abdulkarimova, Glyussya; Berkimbaeva, Saule; Kenzhegulova, Saya; Gusmanova, Faryda; Yergeshova, Aigerim; Omarov, Bauyrzhan

doi:10.1007/978-3-030-74717-6_26

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…To combat crime, several cities use video surveillance systems. Nevertheless, prior research claimed that using video surveillance alone is insufficient for deterring and averting crimes [39][40][41]. Today, the most crucial elements of urban security solutions are threat detection and data analysis technology, such as motion sensors, thermal imaging systems, and license plate recognition software.…”

Section: Discussionmentioning

confidence: 99%

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Momynkulov¹,

Dosbayev²,

Suliman³

et al. 2023

Computers, Materials &Amp; Continua

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Video analytics is an integral part of surveillance cameras. Compared to video analytics, audio analytics offers several benefits, including less expensive equipment and upkeep expenses. Additionally, the volume of the audio datastream is substantially lower than the video camera datastream, especially concerning real-time operating systems, which makes it less demanding of the data channel's bandwidth needs. For instance, automatic live video streaming from the site of an explosion and gunshot to the police console using audio analytics technologies would be exceedingly helpful for urban surveillance. Technologies for audio analytics may also be used to analyze video recordings and identify occurrences. This research proposed a deep learning model based on the combination of convolutional neural network (CNN) and recurrent neural network (RNN) known as the CNN-RNN approach. The proposed model focused on automatically identifying pulse sounds that indicate critical situations in audio sources. The algorithm's accuracy ranged from 95% to 81% when classifying noises from incidents, including gunshots, explosions, shattered glass, sirens, cries, and dog barking. The proposed approach can be applied to provide security for citizens in open and closed locations, like stadiums, underground areas, shopping malls, and other places.

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Section: Discussionmentioning

confidence: 99%

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Momynkulov¹,

Dosbayev²,

Suliman³

et al. 2023

Computers, Materials &Amp; Continua

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“…In the last ten years, numerous deep learning algorithms have produced impressive results in computer vision, speech recognition, and intelligent robot control. Geohazard experts have gradually become aware of these algorithms' ability to exploit the potential of multiple relationships in massive data [23]. As a result, deep neural networks (DNNs), convolutional neural networks (CNNs), and their derivate models have been successfully used for landslide recognition and forecasting [24].…”

Section: Introductionmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields. When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support. In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline. In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system. The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods. All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022. The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively. Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting realworld earthquakes in seismic zones.

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Electronic Stethoscope for Heartbeat Abnormality Detection

Cited by 2 publications

References 14 publications

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

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