Mental well-being is a significant resource for athletes about their success and growth. Athletes are now facing additional risk factors in mental health in the sporting community, such as heavy workout loads, rough races, and demanding lifestyles. The great difficulty is to diagnose conditions and acquire sport and exercise features that contribute to daily or long-term practice to detrimental emotional reactions. In this paper, the sports activity session monitoring system (SASMS) has been proposed using wearable devices and EEG signal by monitoring the sports person’s heart rate and psychological behaviour. The proposed SASMS mental-health analysis focused on model spectrum forms representing the best results, mental illness, and mental health. The paper’s key conclusions concerned with the athletes’ performance, occupational and personal advancement of athletes in mental health problems, strategies intended to track and sustain athletes’ mental health, and outflow of different mental illness types. This research’s findings provide the basis for implementing actions that promote a healthy emotional state in the sport to enhance activity and fitness.
{"title":"Sports person psychological behaviour signal analysis during Thfeir activity session","authors":"Yu Zhang, P. Kumar, Adhiyaman Manickam","doi":"10.3233/JIFS-219018","DOIUrl":"https://doi.org/10.3233/JIFS-219018","url":null,"abstract":"Mental well-being is a significant resource for athletes about their success and growth. Athletes are now facing additional risk factors in mental health in the sporting community, such as heavy workout loads, rough races, and demanding lifestyles. The great difficulty is to diagnose conditions and acquire sport and exercise features that contribute to daily or long-term practice to detrimental emotional reactions. In this paper, the sports activity session monitoring system (SASMS) has been proposed using wearable devices and EEG signal by monitoring the sports person’s heart rate and psychological behaviour. The proposed SASMS mental-health analysis focused on model spectrum forms representing the best results, mental illness, and mental health. The paper’s key conclusions concerned with the athletes’ performance, occupational and personal advancement of athletes in mental health problems, strategies intended to track and sustain athletes’ mental health, and outflow of different mental illness types. This research’s findings provide the basis for implementing actions that promote a healthy emotional state in the sport to enhance activity and fitness.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"1 1","pages":"1-12"},"PeriodicalIF":1.3,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79467428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers and scientists in practical sports psychology are involved in the sports psychology practice process. Current models of training appear unsatisfied to assist trainees in psychology to learn the necessary humanistic skills for the requirement of athlete-centered services. This article aims to include an example of the value of Deep Neural Network Assisted Reflective Approaches (DNARA) as an alternative to clinical training, which may enable practitioners to manage themselves better in action. It addresses the essence of professional understanding; To describe reflection and present common examples of a reflective method in the “education professions” during the creation of reflective practice. It discusses how reflective exercise can support a clinician’s professional and personal growth within the field of sport psychology and illustrate how reflective practice may improve. Finally, there is a discussion about appropriate platforms for the distribution of insightful content. DNARA method achieves the highest classification accuracy of 94.12%, and error rate is reduced to 0.40, and DNARA method is more efficient for student health concepts.
{"title":"The application of deep learning in college students’ sports cognition and health concept","authors":"Ping Wang, Xiaopeng Chi, Yue-Xun Yu","doi":"10.3233/JIFS-219014","DOIUrl":"https://doi.org/10.3233/JIFS-219014","url":null,"abstract":"Researchers and scientists in practical sports psychology are involved in the sports psychology practice process. Current models of training appear unsatisfied to assist trainees in psychology to learn the necessary humanistic skills for the requirement of athlete-centered services. This article aims to include an example of the value of Deep Neural Network Assisted Reflective Approaches (DNARA) as an alternative to clinical training, which may enable practitioners to manage themselves better in action. It addresses the essence of professional understanding; To describe reflection and present common examples of a reflective method in the “education professions” during the creation of reflective practice. It discusses how reflective exercise can support a clinician’s professional and personal growth within the field of sport psychology and illustrate how reflective practice may improve. Finally, there is a discussion about appropriate platforms for the distribution of insightful content. DNARA method achieves the highest classification accuracy of 94.12%, and error rate is reduced to 0.40, and DNARA method is more efficient for student health concepts.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"36 1","pages":"1-13"},"PeriodicalIF":1.3,"publicationDate":"2021-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87767198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional teaching methods are limited to time and place, and the performance of dance teaching resources management is poor. Design a computer-assisted dance teaching resource management system. The functional structure of the system includes core computer-assisted teaching and teaching management applications. The data management module is used to store the processed data in data files, and the dance teaching content release module retrieves requests and multimedia. The remote image resource location request of the management module responds to the feedback. In order to improve the management of computer-aided dance teaching resources, this article takes dance robots as the research object, takes dance video information as input, uses deep learning methods to estimate the human body posture in the video, and obtains the key point position coordinates of the human body; The inverse kinematics calculation of the robot obtains the angle values of each joint of the robot, and the angle values of the lower body joints are adjusted to maintain the balance of the robot. In addition, this paper also proposes a method to automatically generate robot dance sequence. Gated cyclic unit (GRU) network is used to learn the correlation between the global characteristics of music and dance gesture relationship characteristics, the correlation between music local characteristics and dance movement density characteristics, and then combine the dance movement graphs to sample and plan Robot dance moves synchronized with the beat. Experimental results show that whether it is robot dance movement imitation or dance movement generation, it can improve the computer-aided management of dance teaching.
{"title":"Design and implementation of computer aided resource management system for dance teaching","authors":"Peng Huang","doi":"10.3233/JIFS-219026","DOIUrl":"https://doi.org/10.3233/JIFS-219026","url":null,"abstract":"Traditional teaching methods are limited to time and place, and the performance of dance teaching resources management is poor. Design a computer-assisted dance teaching resource management system. The functional structure of the system includes core computer-assisted teaching and teaching management applications. The data management module is used to store the processed data in data files, and the dance teaching content release module retrieves requests and multimedia. The remote image resource location request of the management module responds to the feedback. In order to improve the management of computer-aided dance teaching resources, this article takes dance robots as the research object, takes dance video information as input, uses deep learning methods to estimate the human body posture in the video, and obtains the key point position coordinates of the human body; The inverse kinematics calculation of the robot obtains the angle values of each joint of the robot, and the angle values of the lower body joints are adjusted to maintain the balance of the robot. In addition, this paper also proposes a method to automatically generate robot dance sequence. Gated cyclic unit (GRU) network is used to learn the correlation between the global characteristics of music and dance gesture relationship characteristics, the correlation between music local characteristics and dance movement density characteristics, and then combine the dance movement graphs to sample and plan Robot dance moves synchronized with the beat. Experimental results show that whether it is robot dance movement imitation or dance movement generation, it can improve the computer-aided management of dance teaching.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"3 1","pages":"1-12"},"PeriodicalIF":1.3,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84130151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lei Wu, Juan Wang, Long Jin, P. Hemalatha, R. Premalatha
Artificial intelligence (AI) is an excellent potential technology that is evolving day-to-day and a critical avenue for exploration in the world of computer science & engineering. Owing to the vast volume of data and the eventual need to turn this data into usable knowledge and realistic solutions, artificial intelligence approaches and methods have gained substantial prominence in the knowledge economy and community world in general. AI revolutionizes and raises athletics to an entirely different level. Although it is clear that analytics and predictive research have long played a vital role in sports, AI has a massive effect on how games are played, structured, and engaged by the public. Apart from these, AI helps to analyze the mental stability of the athletes. This research proposes the Artificial Intelligence assisted Effective Monitoring System (AIEMS) for the specific intelligent analysis of sports people’s psychological experience. The comparative analysis suggests the best AI strategies for analyzing mental stability using different criteria and resource factors. It is observed that the growth in the present incarnation indicates a promising future concerning AI use in elite athletes. The study ends with the predictive efficiency of particular AI approaches and procedures for further predictive analysis focused on retrospective methods. The experimental results show that the proposed AIEMS model enhances the athlete performance ratio of 98.8%, emotion state prediction of 95.7%, accuracy ratio of 97.3%, perception level of 98.1%, and reduces the anxiety and depression level of 15.4% compared to other existing models.
{"title":"Research on the strategy of intelligent analysis to improve sports person psychological experience in the era of artificial intelligence","authors":"Lei Wu, Juan Wang, Long Jin, P. Hemalatha, R. Premalatha","doi":"10.3233/JIFS-219009","DOIUrl":"https://doi.org/10.3233/JIFS-219009","url":null,"abstract":"Artificial intelligence (AI) is an excellent potential technology that is evolving day-to-day and a critical avenue for exploration in the world of computer science & engineering. Owing to the vast volume of data and the eventual need to turn this data into usable knowledge and realistic solutions, artificial intelligence approaches and methods have gained substantial prominence in the knowledge economy and community world in general. AI revolutionizes and raises athletics to an entirely different level. Although it is clear that analytics and predictive research have long played a vital role in sports, AI has a massive effect on how games are played, structured, and engaged by the public. Apart from these, AI helps to analyze the mental stability of the athletes. This research proposes the Artificial Intelligence assisted Effective Monitoring System (AIEMS) for the specific intelligent analysis of sports people’s psychological experience. The comparative analysis suggests the best AI strategies for analyzing mental stability using different criteria and resource factors. It is observed that the growth in the present incarnation indicates a promising future concerning AI use in elite athletes. The study ends with the predictive efficiency of particular AI approaches and procedures for further predictive analysis focused on retrospective methods. The experimental results show that the proposed AIEMS model enhances the athlete performance ratio of 98.8%, emotion state prediction of 95.7%, accuracy ratio of 97.3%, perception level of 98.1%, and reduces the anxiety and depression level of 15.4% compared to other existing models.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"178 1","pages":"1-11"},"PeriodicalIF":1.3,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85040716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the fast pace of modern online English teaching and the complicated teaching relationship, English teachers face increasing pressure in teaching work, which easily leads to various psychological problems. In view of this, based on theoretical results and expert experience in the field of psychological pressure of English teachers, this paper uses fuzzy processing systems and fuzzy weighted logic inference theory to establish a knowledge base in the field of psychological health and an evaluation model for psychological pressure of English teachers. Moreover, this paper carries out knowledge representation and reasoning on the knowledge of psychological health theory, and applies the evaluation reasoning model to the expert system of psychological health evaluation of English teachers to finally design and realize the expert system of psychological health evaluation of English teachers based on fuzzy weighted logic. Finally, this paper designs experiments to verify the system performance. The research results show that the system’s data processing speed and the accuracy of the evaluation results of psychological stress of English teachers meet the actual needs.
{"title":"Fuzzy processing system for psychological pressure of English teachers at work based on analysis of online teaching video","authors":"Zheng Jin","doi":"10.3233/JIFS-219055","DOIUrl":"https://doi.org/10.3233/JIFS-219055","url":null,"abstract":"Due to the fast pace of modern online English teaching and the complicated teaching relationship, English teachers face increasing pressure in teaching work, which easily leads to various psychological problems. In view of this, based on theoretical results and expert experience in the field of psychological pressure of English teachers, this paper uses fuzzy processing systems and fuzzy weighted logic inference theory to establish a knowledge base in the field of psychological health and an evaluation model for psychological pressure of English teachers. Moreover, this paper carries out knowledge representation and reasoning on the knowledge of psychological health theory, and applies the evaluation reasoning model to the expert system of psychological health evaluation of English teachers to finally design and realize the expert system of psychological health evaluation of English teachers based on fuzzy weighted logic. Finally, this paper designs experiments to verify the system performance. The research results show that the system’s data processing speed and the accuracy of the evaluation results of psychological stress of English teachers meet the actual needs.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"96 1","pages":"1-10"},"PeriodicalIF":1.3,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76730953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, many exciting results have been achieved in the application of deep learning to image recognition. However, there are still many problems to be overcome before deep learning is used in practical applications such as image retrieval, image annotation, and image-text conversion. This paper studies the structure of deep learning, improves the commonly used training algorithms, and proposes two new neural network models for different application scenarios. This paper uses Support Vector Machine (SVM) as the main classifier for Internet of Things image recognition and uses the database of this paper to train SVM and CNN. At the same time, the effectiveness of the two for image recognition is tested, and the trained classifier is used for image recognition. The result surface: In the labeled data set, the rank-1 accuracy of CNN is 85.77%, which is higher than 90.28% of the SVM method. In the detection data, CNN’s rank-1 accuracy rate is 83.11%, which also exceeds SVM’s 80.22%. SVM+CNN has a rank 1 value of 84.69% for the detection data set. This shows that deep learning can map the feature representation of the image and the feature representation of the word to the same space, making the calculation of the similarity and correlation between the image and the text easier and more straightforward.
{"title":"Internet of things image recognition system based on deep learning","authors":"Jing Li, Xinfang Li, Yuwen Ning","doi":"10.3233/JIFS-219080","DOIUrl":"https://doi.org/10.3233/JIFS-219080","url":null,"abstract":"At present, many exciting results have been achieved in the application of deep learning to image recognition. However, there are still many problems to be overcome before deep learning is used in practical applications such as image retrieval, image annotation, and image-text conversion. This paper studies the structure of deep learning, improves the commonly used training algorithms, and proposes two new neural network models for different application scenarios. This paper uses Support Vector Machine (SVM) as the main classifier for Internet of Things image recognition and uses the database of this paper to train SVM and CNN. At the same time, the effectiveness of the two for image recognition is tested, and the trained classifier is used for image recognition. The result surface: In the labeled data set, the rank-1 accuracy of CNN is 85.77%, which is higher than 90.28% of the SVM method. In the detection data, CNN’s rank-1 accuracy rate is 83.11%, which also exceeds SVM’s 80.22%. SVM+CNN has a rank 1 value of 84.69% for the detection data set. This shows that deep learning can map the feature representation of the image and the feature representation of the word to the same space, making the calculation of the similarity and correlation between the image and the text easier and more straightforward.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"41 3","pages":"1-9"},"PeriodicalIF":1.3,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JIFS-219080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72463168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ching-Hsien Hsu, AmAmir H. Alavi, M. Dong, Gunasekaran Manogaran
{"title":"Fuzzy systems for innovations in healthcare","authors":"Ching-Hsien Hsu, AmAmir H. Alavi, M. Dong, Gunasekaran Manogaran","doi":"10.3233/JIFS-219007","DOIUrl":"https://doi.org/10.3233/JIFS-219007","url":null,"abstract":"","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"8 1","pages":"1-2"},"PeriodicalIF":1.3,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73870769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The secure operation of critical infrastructures is 8 essential to the security of a nation, its economy, and 9 the public’s health and safety. Security incidents in 10 critical infrastructures can directly lead to a viola11 tion of users’ safety and privacy, physical damages, 12 significant economic impacts for individuals and 13 companies, and threats to human life, while decreas14 ing trust in institutions and bringing their social value 15 into question. Because of the increasing interconnec16 tion between the digital and physical worlds, these 17 infrastructures and services are more critical, sophis18 ticated, and interconnected than ever before. This 19 makes them increasingly vulnerable to attacks, as 20 confirmed by the steady rise of cybersecurity inci21 dents. The consequences of these attacks are almost 22 always invasive and disruptive for the computerized 23 systems and can be fatal if the attacks are performed 24 in the domain of smart health devices. The need 25 therefore arises to research effective security-based 26 solutions for nullifying these risks or countering the 27 attacks. In recent years, this has been suc essfully 28 accomplished using intelligent technologies, includ29 ing artificial intelligence, machine learni g and deep
{"title":"New trends of intelligent systems-based secure critical infrastructure in smart city","authors":"A. El-latif, L. Tawalbeh","doi":"10.3233/JIFS-219058","DOIUrl":"https://doi.org/10.3233/JIFS-219058","url":null,"abstract":"The secure operation of critical infrastructures is 8 essential to the security of a nation, its economy, and 9 the public’s health and safety. Security incidents in 10 critical infrastructures can directly lead to a viola11 tion of users’ safety and privacy, physical damages, 12 significant economic impacts for individuals and 13 companies, and threats to human life, while decreas14 ing trust in institutions and bringing their social value 15 into question. Because of the increasing interconnec16 tion between the digital and physical worlds, these 17 infrastructures and services are more critical, sophis18 ticated, and interconnected than ever before. This 19 makes them increasingly vulnerable to attacks, as 20 confirmed by the steady rise of cybersecurity inci21 dents. The consequences of these attacks are almost 22 always invasive and disruptive for the computerized 23 systems and can be fatal if the attacks are performed 24 in the domain of smart health devices. The need 25 therefore arises to research effective security-based 26 solutions for nullifying these risks or countering the 27 attacks. In recent years, this has been suc essfully 28 accomplished using intelligent technologies, includ29 ing artificial intelligence, machine learni g and deep","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"25 1","pages":"1-1"},"PeriodicalIF":1.3,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80990037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How local industrial clusters break through the lock-in status of low end of value chains and realize industrial upgrading in the development process of embedded global value chain is the central topic of current industrial development research. To explore how industrial clusters achieve the enhancement of their innovation capability and value chains when they are embedded in the global value chain, from the perspective of knowledge transfer and according to the differences in the knowledge levels of the local industrial clusters, three fuzzy game models of knowledge transfer paths were constructed, and the model of the realization mechanism of knowledge transfer and its stability condition was analyzed, which make clear the path of cluster growth under different embedding modes. Results show that although the mode of embedding and the path of knowledge transfer is different, the local industrial clusters can obtain external knowledge transfer by embedding in the global value chain; the knowledge transformation ability of local industrial clusters is the determining factor that the knowledge transfer can smoothly achieve and become stable. The conclusion also shows that the feasibility of the cross-sectional growth of industrial clusters by actively embed the global value chain and acquiring external knowledge transfer if the industrial clusters want to enhance their technology accumulation, their innovation ability, and their position in the global value chain.
{"title":"Growth path of industrial clusters embedded in global value chain from the perspective of knowledge transfer: A fuzzy game approach","authors":"B. He, W. Meng","doi":"10.3233/JIFS-189950","DOIUrl":"https://doi.org/10.3233/JIFS-189950","url":null,"abstract":"How local industrial clusters break through the lock-in status of low end of value chains and realize industrial upgrading in the development process of embedded global value chain is the central topic of current industrial development research. To explore how industrial clusters achieve the enhancement of their innovation capability and value chains when they are embedded in the global value chain, from the perspective of knowledge transfer and according to the differences in the knowledge levels of the local industrial clusters, three fuzzy game models of knowledge transfer paths were constructed, and the model of the realization mechanism of knowledge transfer and its stability condition was analyzed, which make clear the path of cluster growth under different embedding modes. Results show that although the mode of embedding and the path of knowledge transfer is different, the local industrial clusters can obtain external knowledge transfer by embedding in the global value chain; the knowledge transformation ability of local industrial clusters is the determining factor that the knowledge transfer can smoothly achieve and become stable. The conclusion also shows that the feasibility of the cross-sectional growth of industrial clusters by actively embed the global value chain and acquiring external knowledge transfer if the industrial clusters want to enhance their technology accumulation, their innovation ability, and their position in the global value chain.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"7 1","pages":"1-10"},"PeriodicalIF":1.3,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84667653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Belt and Road is the logistics and trade of China’s coastal areas, which has gained new development opportunities. To explore the Belt and Road strategy in the current situation of logistics and trade development in coastal areas, the coastal logistics and trade measurement indicators were selected, an evaluation model of the collaborative development of logistics and trade was built by using time-series data and fuzzy theory, and the causal relationship between the indicators was analyzed through the Granger causal model. Results show that from 2009 to 2018, the total logistics value and trade volume of the studied area show an increasing trend year by year, and the trade scale shows an upward trend year by year. The trade dependence of the coastal area reaches about 37.5%, and the key driving force of economic development is trade export. The total logistics value of the coastal area and the trade import, export, and total import and export of the region, there is a high positive correlation among the three indicators, and the correlation coefficient between the import volume and the export value is more than 0.84, reaching the “extra high” evaluation level, and there is a highly significant positive correlation between the two indicators. The logistics and trade in the region have a positive correlation and collaborative development relationship.
{"title":"Fuzzy evaluation of coordinated development of logistics and trade in coastal areas based on granger causal model","authors":"Lingli Chu","doi":"10.3233/JIFS-189927","DOIUrl":"https://doi.org/10.3233/JIFS-189927","url":null,"abstract":"The Belt and Road is the logistics and trade of China’s coastal areas, which has gained new development opportunities. To explore the Belt and Road strategy in the current situation of logistics and trade development in coastal areas, the coastal logistics and trade measurement indicators were selected, an evaluation model of the collaborative development of logistics and trade was built by using time-series data and fuzzy theory, and the causal relationship between the indicators was analyzed through the Granger causal model. Results show that from 2009 to 2018, the total logistics value and trade volume of the studied area show an increasing trend year by year, and the trade scale shows an upward trend year by year. The trade dependence of the coastal area reaches about 37.5%, and the key driving force of economic development is trade export. The total logistics value of the coastal area and the trade import, export, and total import and export of the region, there is a high positive correlation among the three indicators, and the correlation coefficient between the import volume and the export value is more than 0.84, reaching the “extra high” evaluation level, and there is a highly significant positive correlation between the two indicators. The logistics and trade in the region have a positive correlation and collaborative development relationship.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":"123 12 1","pages":"1-6"},"PeriodicalIF":1.3,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85383775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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