Weakly supervised deep network for spatiotemporal localization and detection of human actions in wild conditions

Kumar, Naresh; Sukavanam, N.

doi:10.1007/s00371-019-01777-5

Cited by 8 publications

(4 citation statements)

References 58 publications

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“…People will generate a large amount of health data during exercise, such as the current pulse rate, blood circulation, body water, and fat changes. The data sources are stored in different formats, forming a multi-source and heterogeneous complex data source ( Elumalai and Ramakrishnan, 2020 ; Kumar and Sukavanam, 2020 ). To extract useful mental health data from complex data sources, deep learning algorithms are used to fuse multi-source and heterogeneous mental health data and extract mental health mutual information features.…”

Section: Recognition and Analysis Of Sports On Mental Healthmentioning

confidence: 99%

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Li²

2022

Front. Psychol.

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This paper presents the purpose of sport recognition of mental health for users and analyzes and studies the recognition of mental health by sports based on deep learning. The recognition model of sport mental health state composed of data layer, logic layer and display layer is built. After fusing human health data with deep learning algorithm, the feature of human health mutual information is extracted, the feature into the recognition model of mental health state is inputted, and the recognition results of sport mental health mode after forward and reverse operation are outputted. The recognition data of sports on mental health status are obtained, which correspond to the link flowing through during multi-level transmission, calibrate the multi-level transmission point, and fuse and process the recognition information of sports on mental health status. The experimental results show that the loss value of the research method when analyzing the effect of sports on mental health enhancement is the smallest, the output result is reliable, can effectively improve the body mass index (BMI) of the human body, has the most controllable amount of data, and has good performance.

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Section: Recognition and Analysis Of Sports On Mental Healthmentioning

confidence: 99%

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Li²

2022

Front. Psychol.

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“…Step 4: analyze the distance from ðx z , ŷz Þ to ðx z ′ , y z ′ Þ, and the human body key points close to each other are the same human body. Take the ðx z ′ , y z ′Þ pointed by each ðx z , ŷz Þ as the feature, use the unsupervised clustering algorithm [22,23] to group all ðx z , ŷz Þ, construct a sports action posture through the ðx z , ŷz Þ of each group, complete the sports action posture estimation [24], and obtain the sports action posture video sequence q.…”

Section: Sports Posture Estimation Based On Cnnmentioning

confidence: 99%

“…where the learning rate is γ. In formula (22), the gradient term represents a large cumulative reward that can be improved τ. In the direction of occurrence times, continuously optimize the strategy in the form of gradient rise to obtain the best strategy.…”

Section: Offline Trainingmentioning

confidence: 99%

Accurate Recognition Method of Continuous Sports Action Based on Deep Learning Algorithm

Zhengfeng

2022

Wireless Communications and Mobile Computing

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In order to improve the recognition effect of continuous sports action, an accurate recognition method of continuous sports action based on deep learning is proposed. The convolution neural network is used to predict the key points and center vector of human body in grid mode, and the key points of human body are grouped according to the distance from the key points of the human body to the center of human body, so as to obtain the sequence diagram of sports posture, input the posture sequence diagram of sports action in the deep learning network, and apply the best strategy of continuous action recognition obtained in the deep learning training stage to the accurate recognition of continuous sports action. The position of the rectangular frame of the attitude instead of the target attitude is predicted online by the convolution neural network, and the recognition is completed when the attitude stops. The experimental results show that this method can effectively recognize continuous sports actions in real time. The expected average overlap rate of different video attributes is high, which has better recognition effect. At different position error thresholds, it has high recognition accuracy.

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“…The important branches of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) have greatly influenced every research domain connected with human profession. The technologies used in Ml and DL have several popular applications such human action recognition [8], security and surveillance, robotics, and many decision-making stochastic process with big data analytic [9]. These technologies are limited to domain specific research [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Hussain,

Razak,

Firdaus

2023

JCSANDM

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Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems.

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Weakly supervised deep network for spatiotemporal localization and detection of human actions in wild conditions

Cited by 8 publications

References 58 publications

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Accurate Recognition Method of Continuous Sports Action Based on Deep Learning Algorithm

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

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