scholarly journals Identification and Modeling of College Students’ Psychological Stress Indicators for Deep Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Yuan Tian

Aiming at the problems of low accuracy of recognition results, long recognition time, and easy interference in traditional methods, a deep learning-oriented recognition modeling method of college students' psychological stress indicators is proposed. First, the ECG signal is collected by the ECG signal acquisition system, and the wavelet transform method is used to denoise the collected ECG signal. Then, the sequential backward selection algorithm is used to select the features of psychological stress indicators to reduce the feature dimension. Finally, based on the convolutional neural network in deep learning technology, a mental pressure indicator recognition model is established and the model parameters are optimized to realize the recognition of college students’ mental pressure indicators. Experimental results show that the method in this paper has high recognition accuracy, has high recognition efficiency, is not susceptible to interference, and has certain feasibility and effectiveness.

2020 ◽  
Vol 9 (6) ◽  
pp. 63
Author(s):  
Xiangsen Liu ◽  
Zhenzhen Ye ◽  
Dongmei Jiang

As the primary productive force, education plays a huge role in the development of modern society. Traditional educational activities mainly focus on the teaching of students’ superficial knowledge and skills, which are no longer able to meet the current society’s demand for talents. At present, it is required to promote the development of in-depth thinking of college students, stimulate their innovation and creativity, and enable students to better contribute to social production. Therefore, it is very important to introduce the concept of deep learning for college students and establish a student-based teaching model. The author mainly uses mobile learning technology to analyze the functional role of mobile learning technology in promoting deep learning activities for college students, and proposes ways to effectively grasp mobile learning technology in future university informatization education activities.


2021 ◽  
Vol 11 (9) ◽  
pp. 3838
Author(s):  
Pengfei Zhang ◽  
Fenghua Li ◽  
Rongjian Zhao ◽  
Ruishi Zhou ◽  
Lidong Du ◽  
...  

Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications.


2021 ◽  
Vol 12 ◽  
Author(s):  
Ting Wang ◽  
Jinkyung Park

In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server (B/S) structure, the intelligent sports management system is constructed. The basic framework of Spring Cloud is used to integrate the framework and components of each part, and a distributed microservice system is built. The artificial intelligence recommendation algorithm is used to analyze the user's age, body mass index (BMI), and physical health status, and recommend sports programs suitable for students, thus realizing the intelligent sports program recommendation function. At the same time, the recommendation algorithm is used to complete the course recommendation according to the students' preferences, teaching distance, opening time, course evaluation, and other indexes, and the course registration system is constructed; after the analysis of the entity and the relationship between the entities of the intelligent sports system, the database relational model of the system is designed with the entity relationship (E-R) diagram. The results of the functional test show that the system can run well. In conclusion, the sports training environment instructional system based on artificial intelligence and deep learning technology can meet the teaching needs of colleges, improve the sports' quality for college students, and promote psychological education.


Author(s):  
Georgy V. Ayzel ◽  
◽  

For around a decade, deep learning – the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers – modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources, identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of “Gartner Hype Curve”, which in the general details describes a life cycle of modern technologies.


2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.


Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.


Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Qinghua Chen ◽  
Wenqing Zhao ◽  
Qun Li ◽  
Harnof Sagi

BACKGROUND: with the increase of study and life pressure, the number of depressed college students showed an increasing trend year by year, and the drug treatment alone could not achieve a comprehensive recovery of depression patients, so it was more necessary to pay attention to the spiritual treatment. OBJECTIVE: this research aimed to better understand the relationship between college students’ depression and life events, social support, psychological pressure, and coping style, and the influence of systematic family therapy on depression degree, psychological stress, and social adaptability of college students with depression. METHODS: in this study, 105 college students with depression were selected as the research object, and healthy college students were taken as the control group. Through questionnaire, the differences in life events, social support, psychological stress, and coping styles between the groups were compared. The correlation between the degree of depression and various variables were analyzed, and the impact path of each variable on depression was analyzed using the path analysis model. Depression patients were then divided into a conventional group treating with conventional medications and an observation group treating with systematic family interventions. Differences in Hamilton Depression Scale-17, (HAMD-17), CPSS, and Social Adaptive Functioning Evaluation (SAFE) scores were compared and analyzed between the two groups before treatment (T1), during the treatment (T2), and after treatment(T3). RESULTS: there were significant differences in scores of life events, social support, psychological stress, and coping styles between the healthy control group and the depressed patients (P <  0.05). There was an obvious correlation between different depression degrees and life events, social support, psychological stress, and coping styles (P <  0.05). Life events, social support, and psychological stress had a direct and significant impact on depression (0.250, 0.218, and 0.392; P <  0.05), and they also had an indirect and significant impact on depression through coping styles (P <  0.05). The systematic family treatment model could significantly reduce HAMD-17 and CPSS scores (P <  0.05), and significantly improve SAFE scores (P <  0.05). CONCLUSIONS: adverse life events, lack of social support, excessive psychological stress, and negative coping styles can aggravate college students’ depression. Systematic family therapy can improve the degree of depression, reduce the psychological stress, and enhance the social adaptability of college students with depression.


2021 ◽  
Author(s):  
Zhiting Chen ◽  
Hongyan Liu ◽  
Chongyang Xu ◽  
Xiuchen Wu ◽  
Boyi Liang ◽  
...  

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