Recognition of violations of individual labor protection rules using a convolutional neural network

Pronina, Olha; Piatykop, Olena

doi:10.1109/csit56902.2022.10000582

Cited by 4 publications

(9 citation statements)

References 7 publications

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“…However, the best balance between fewer features and high accuracy was found with 40 MFCCs. Tong et al Another study [9] focuses on exploring models and techniques involving convolutional neural networks for identifying speech disorders in children. Initial investigations were conducted on classes of speech disturbances.They performed 32 experiments, 8 experiment for each disorder.The study determined that the accurate recognition of trained categories-dyslexia, stuttering, dysphonia, and dyslalia-is around 77-79% .…”

Section: Results and Findingsmentioning

confidence: 99%

“…[3], [2], [5], [10], [9], [14], [16], [19], [20], [7] Long Short Term Memory (LSTM) [4], [3], [12], [2] Gated Recurrent Unit (GRU) [5], [3] Pyramid Bidirectional LSTM (PBLSTM) [13] Adaptive Optimization Artificial based Neural Network (AOANN) [21] Class Token(CT) Transformer [1] rics. These metrics include F-measure,accuracy, precision, recall, micro and macro averaging of accuracy, sensitivity, Unweighted Average and area under the curve.…”

Section: Review Of Performance Metricmentioning

confidence: 99%

“…, [8], [9], [11], [13], [14], [16], [17], [18] , [19], [20], [21] Accuracy 12 [7] , [10] Accuracy and F1 Score 2…”

Section: Review Of Performance Metricmentioning

confidence: 99%

“…They used Relu as an activation function with a drop out rate of 50% . Pronina et al[9] proposed a mathematical model to represent different types of speech defects in children. This mathematical model provides a structured framework for classifying and understanding different speech disorders in children, offering a way to categorize and study these issues in a systematic manner.…”

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confidence: 99%

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Automatic Speech and Voice Disorder Detection Using Deep Learning—A Systematic Literature Review

Sindhu,

Sainin

2024

IEEE Access

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Speech and voice disorders are highly prevalent and a significant concern, particularly among children. These disorders have a notable impact on individual's personalities, academic performance and overall development. Early detection and intervention of these disorders surely help out and generate an effective outcome. Automatic detection of speech and voice disorders at an early stage plays a crucial role in identifying and addressing these communication challenges because of their efficiency over traditional diagnostic methods. Over time, numerous techniques were developed in the past, but some of them have become less efficient in the present scenario. Deep Learning has surfaced as a recent and significant advancement in detecting speech and voice disorders. This paper provides a systematic review of the utilization of deep learning techniques for the detection of speech and voice disorders. The review encompasses studies published from 2018 to 2023 exploring various architectures of deep learning models for capturing complex patterns in speech data. Each deep learning based speech sound disorder detection technique is discussed with critical appraisal and relevant benchmarks available for evaluation of results. This review holds significance for new researchers who are interested in exploring the field of automatic speech disorder detection as the paper concludes by discussing future directions and potential areas of improvement in automatic speech sound disorder detection using deep learning.

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Section: Results and Findingsmentioning

confidence: 99%

Section: Review Of Performance Metricmentioning

confidence: 99%

“…, [8], [9], [11], [13], [14], [16], [17], [18] , [19], [20], [21] Accuracy 12 [7] , [10] Accuracy and F1 Score 2…”

Section: Review Of Performance Metricmentioning

confidence: 99%

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confidence: 99%

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Automatic Speech and Voice Disorder Detection Using Deep Learning—A Systematic Literature Review

Sindhu,

Sainin

2024

IEEE Access

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“…The results show that the proposed method can identify dyslexia, stuttering, difsonia, and dyslalia with a recognition accuracy of 77-79%. The authors' related works are referenced as [116,122,123]. The research paper "Enhancing out-of-class independent learning in a cloud-based information and communication learning environment: insights from students of a pedagogical university" [68] by Oleksandr H. Kolgatin, Larisa S. Kolgatina and Nadiia S. Ponomareva (figure 12) addresses the challenges associated with students' out-of-class independent work in an information and communication learning environment that leverages cloud technologies.…”

Section: Articles Overview 21 Digital Transformation Of Educationmentioning

confidence: 99%

ACNS Conference on Cloud and Immersive Technologies in Education: Report

et al. 2023

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This article serves as an introduction to a collection of selected papers presented at the ACNS Conference on Cloud and Immersive Technologies in Education (CITEd 2022), which took place in Kyiv, Ukraine, on December 21-22, 2022. The collection provides valuable insights into the latest advancements in cloud and immersive technologies in the field of education. The introductory text offers an overview of the conference events and provides concise summaries of the selected papers, offering a glimpse into the diverse topics and research areas covered. These selected papers reflect the cutting-edge research and innovative approaches showcased at CITEd 2022, making them a valuable resource for educators, researchers, and practitioners interested in exploring the potential of cloud and immersive technologies in education.

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Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning

Papadakis¹,

Ків²,

Kravtsov³

et al. 2023

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The article presents the proceedings of two workshops: Cloud-based Smart Technologies for Open Education Workshop (CSTOE 2022) and Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2022) held in Kyiv, Ukraine, on December 22, 2022. The CoSinE workshop focuses on computer simulation in education, including topics such as computer simulation in STEM education, AI in education, and modeling systems in education. The CSTOE workshop deals with cloud-based learning resources, platforms, and infrastructures, with topics including personalized learning and research environment design, big data and smart data in open education and research, machine learning for open education and research, and more. The article includes a summary of successful cases and provides directions for future research in each workshop’s respective topics of interest. The proceedings consist of several peer-reviewed papers that present a state-of-the-art overview and provide guidelines for future research. The joint program committee consisted of members from universities and research institutions worldwide.

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Recognition of violations of individual labor protection rules using a convolutional neural network

Cited by 4 publications

References 7 publications

Automatic Speech and Voice Disorder Detection Using Deep Learning—A Systematic Literature Review

Automatic Speech and Voice Disorder Detection Using Deep Learning—A Systematic Literature Review

ACNS Conference on Cloud and Immersive Technologies in Education: Report

Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning

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