Development of a System for Ensuring Humidity in Sport Complexes

Murzamadieva, Mainura; Ivashov, Arslan; Omarov, Bakhytzhan; Omarov, Bauyrzhan; Kendzhayeva, Balnur; Abdrakhmanov, Rustam

doi:10.1109/confluence51648.2021.9377175

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…For each class c, the matrix shows the true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN) values. The confusion matrix is used to calculate many efficiency metrics, including accuracy, precision, recall, and F1-score [28]. Eqs.…”

Section: Discussionmentioning

confidence: 99%

A Skeleton-based Approach for Campus Violence Detection

Omarov¹,

Narynov²,

Zhumanov³

et al. 2022

Computers, Materials &Amp; Continua

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In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate highprocessing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method was trained and tested using the collected dataset. The results indicate the accuracy of 97% was achieved in identifying aggressive conduct in video sequences. Furthermore, the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

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Section: Discussionmentioning

confidence: 99%

A Skeleton-based Approach for Campus Violence Detection

Omarov¹,

Narynov²,

Zhumanov³

et al. 2022

Computers, Materials &Amp; Continua

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“…As a result, the question of employing different methods for identifying irregular incursions becomes significant. The use of machine learning algorithms [5] is one of these ways. Machine learning is used because it can help automate threat processing and keep the system up to date by studying and detecting threats.…”

Section: Introductionmentioning

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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“…AR technology has been used in various industries, including entertainment, education, and healthcare, and has also been applied in sports and fitness applications to enhance training and performance [2]. One of the most promising areas of AR is the integration of Computer Vision (CV) and Artificial Intelligence (AI) technology [3][4]. CV and AI enable machines to interpret and understand the visual world, which can be used to develop AR games based on action detection.…”

Section: Introductionmentioning

confidence: 99%

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Doskarayev¹,

Omarov²,

Omarov³

et al. 2023

IJACSA

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This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports. With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation. To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game. These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity. The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports. The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.

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Development of a System for Ensuring Humidity in Sport Complexes

Cited by 11 publications

References 13 publications

A Skeleton-based Approach for Campus Violence Detection

A Skeleton-based Approach for Campus Violence Detection

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

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

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