Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM

Zhao, Mengyang; Chang, Chih‐Ju; Xie, Wenbin; Xie, Zhou; Hu, Jiemiao

doi:10.1109/access.2020.2978090

Cited by 84 publications

(61 citation statements)

References 26 publications

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“…With the development of data information technology such as big data and machine learning [28,29], using data algorithms to mine valuable information from large amounts of data has become a hot topic of data research [30,31]. e sports health application, which collects a large number of users' sports and physical signs data, can use data information technology to dig out valuable information, so that the sports assistant service can be more intelligent and personalized.…”

Section: Introductionmentioning

confidence: 99%

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Xiao³

2021

Journal of Healthcare Engineering

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Intelligent sports equipment and software have emerged in the field of sports as a result of the advancement of information technology, allowing professional athletes to collect and visually display the movement and physical signs of the human body to aid in the planning of sports strategies. Intuitive data, on the other hand, cannot assist ordinary people who lack professional knowledge in exercising correctly. As a result, in the field of intelligent sports and health, effective use of collected exercise and physical sign data to analyze the user’s personal physical condition and generate reasonable exercise suggestions has emerged as a research direction. In humans, the heart sound signal is a biological signal. It can help people detect and monitor heart health problems by analyzing the characteristics of heart sound signals. The goal of this paper is to use heart sound to identify and analyze athletes’ training health. It provides a revolutionary health analysis algorithm based on heart rhythm feature extraction and convolutional neural networks, which is based on exercise training. It greatly improves the accuracy of the recognition and prediction of the athlete’s training health status.

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Section: Introductionmentioning

confidence: 99%

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Xiao³

2021

Journal of Healthcare Engineering

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“…Then, we used the NeuralNetTools package ( Beck, 2018 ) to obtain the relative importance (weight) of input variables (smile, eye contact, gesture, and tone) in the neural network ( Zhao et al, 2020 ; Chu et al, 2021 ; You et al, 2021 ; Zhang et al, 2021 ) through garson algorithm ( Ghanizadeh et al, 2020 ). As shown in Figure 3 , it can be found that the foreign language teacher’s eye contact and gesture have a greater influence on the decision of whether to improve students’ classroom learning efficiency (the weight of each variable is above 30%), followed by tone and smile (the weight of each variable is between 10 and 20%).…”

Section: Methods and Resultsmentioning

confidence: 99%

Foreign Language Teachers’ Emotion Recognition in College Oral English Classroom Teaching

Dai¹

2021

Front. Psychol.

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One of the significant courses in Chinese universities is English. This course is usually taught by a foreign language instructor. There will, however, necessarily be some communication hurdles between “foreign language teachers” and “native students.” This research presents an emotion recognition method for foreign language teachers in order to eliminate communication barriers between teachers and students and improve student learning efficiency. We discovered four factors of emotion recognition through literature analysis: smile, eye contact, gesture, and tone. We believe that differences in foreign language teachers’ performance in these four areas will have an impact on students’ emotion recognition and, as a result, on their learning efficiency. The influence of the foreign language teacher’s eye contact and gestures is larger (the weight of a single variable accounted for 30% or more) in the decision whether can improve the students’ classroom learning efficiency, according to 43 of the questionnaire data analysis. The second is the tone and smile (the weight of a single variable accounted for between 10 and 20%). Our research contributes to the body of knowledge on emotion recognition in university foreign language teachers by presenting a practical method for recognizing emotion in foreign language teachers. We recommend that college foreign language teachers pay attention to eye and gesture communication with students in English classrooms based on the findings. By enriching the style of emotional expression in class, college language teachers, particularly foreign language teachers, can improve communication, and connection with students.

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“…e realization of high-precision cost forecasting through mathematical modeling has piqued the interest of industry professionals and academics [21,22], thanks to the rapid advancement of computer and neural network technologies [23][24][25]. Certain mathematical models and related historical engineering data are used to make the construction cost forecast.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Construction Project Cost Forecast Based on Particle Swarm Optimization-Guided BP Neural Network

2021

Scientific Programming

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Construction project cost prediction is an important function in construction-related fields; it can provide an important basis for project feasibility study and design scheme comparison and selection, and its accuracy will directly affect the investment decision of the project. The successful realization of construction cost prediction can bring great convenience to the control and management of construction cost. The purpose of this paper is to study a fast, accurate, convenient, deducible, and rational construction project cost prediction method, to provide a basis for the cost management of the whole life cycle of the project. Therefore, this paper uses particle swarm optimization algorithm to improve BP neural network and proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network. Aiming at the defects of BP neural network updating weights and thresholds with the gradient descent method, this paper uses the advantages of particle swarm optimization in the field of parameter optimization to optimize BP neural network with PSO algorithm. The structure of BP neural network weights and the threshold of each neuron in the coding, through intelligent search for each particle, find the most suitable weights and thresholds, so that the BP neural network has faster convergence speed, better generalization ability, and higher prediction precision. Simulation results also show that the proposed algorithm is competitive enough.

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Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM

Cited by 84 publications

References 26 publications

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Foreign Language Teachers’ Emotion Recognition in College Oral English Classroom Teaching

An Algorithm for Construction Project Cost Forecast Based on Particle Swarm Optimization-Guided BP Neural Network

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