ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected Neural Network

Qin, Xinyi; Zhang, Lu; Liu, in; Xu, Ziwei; Liu, Guangzhong

doi:10.1109/tcbb.2021.3089168

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Yu et al [13] have proposed a motion state recognition method based on a multilayer neural network, which can use smartphones to collect data and accurately identify the motion state. e multilayer neural network model includes convolutional neural network (CNN)-image recognition and speech recognition [14], recurrent neural network (RNN)-natural language processing and speech audio recognition [15], deep neural network (DNN)-improving overfitting phenomenon [16]. Several studies have shown that the self-extraction and feature retention characteristics of CNN can effectively improve the accuracy and stability of motion recognition technology [17,18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Aerobic Exercise Recognition and Its Effect on Health Status of College Students

Zhang

Lai

et al. 2022

Mobile Information Systems

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It was to explore the role of motion recognition technology based on the convolutional neural network (CNN) model in guiding the aerobic exercise of college students and its impact on their physical and mental health, providing a scientific and effective method for students to have a healthy body and mood. 256 college students were randomly divided into control group (appropriate aerobic exercise was selected by self-evaluation, and exercise was performed spontaneously) and observation group (aerobic exercise under the guidance of machine learning model-based motion recognition technology), 128 cases in each group. The changes in the physical and psychological status of the two groups of college students were evaluated after one year. The recognition rate of CNN algorithm test results (92.98%) and the recognition accuracy of stationary, walking, running, squatting, hand raising, seated position, lunge, and other movement states were higher than those of recurrent neural network (RNN) (52.22%) and deep neural network (DNN) (40.21%) algorithms ( P < 0.05 ); after 1 year of exercise, the 800/1,000 m running performance, standing long jump performance, 1 min sit-up performance, vital capacity, maximal ventilator volume per minute (MVV) and vital capacity/body mass index, stroke volume (SV), cardiac output (CO), ejection fraction (EF), heart rate (HR), ejection time (ET), mean systolic ejection rate (MSER), mean velocity of circumferential fibre shortening (MVCF) of the observation group were superior to those of the control group, and tension, anger, depression, fatigue, panic, energy, self-related emotions, and the improvement of total mood disturbance (TMD) score were also superior to those before exercise. The improvement effect of physical fitness, cardiopulmonary function, and mentality of college students in the observation group was better than that in the control group ( P < 0.05 ). Aerobic exercise can effectively improve the physical fitness, cardiopulmonary function, and adverse mentality of college students, and through the assistance of machine learning, exercise recognition technology can further improve the effect of aerobic exercise, which is worthy of application and promotion.

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