By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.
{"title":"Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things","authors":"Yi Tang, Xiaoning Wang, Wenyan Wang","doi":"10.4018/ijitwe.347880","DOIUrl":"https://doi.org/10.4018/ijitwe.347880","url":null,"abstract":"By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822418","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}
With the development of computer technology innovation, be able to deal with the media comprehensive information and real-time information interaction with the computer multimedia technology arises at the historic moment, it promotes the application fields of computer widen to industrial all aspects of life. As the product of digital technology, animation technology plays an irreplaceable role in the production of multimedia courseware. However, the existing human-computer interaction methods have shortcomings such as incomplete extraction of video features and poor human-computer interaction effect. In this context, this paper designs a multimedia human-computer interaction method for animation works based on CNN model. First of all, the original video data is collected and preprocessed. Then it is input into the HCI framework based on CNN model for feature extraction. Finally, the effectiveness and practicability of the proposed method are proved by simulation experiments, which provides a reference and basis for the research of modern human-computer interaction.
{"title":"Multimedia Human-Computer Interaction Method in Video Animation Based on Artificial Intelligence Technology","authors":"Linran Sun, Nojun Kwak","doi":"10.4018/ijitwe.344419","DOIUrl":"https://doi.org/10.4018/ijitwe.344419","url":null,"abstract":"With the development of computer technology innovation, be able to deal with the media comprehensive information and real-time information interaction with the computer multimedia technology arises at the historic moment, it promotes the application fields of computer widen to industrial all aspects of life. As the product of digital technology, animation technology plays an irreplaceable role in the production of multimedia courseware. However, the existing human-computer interaction methods have shortcomings such as incomplete extraction of video features and poor human-computer interaction effect. In this context, this paper designs a multimedia human-computer interaction method for animation works based on CNN model. First of all, the original video data is collected and preprocessed. Then it is input into the HCI framework based on CNN model for feature extraction. Finally, the effectiveness and practicability of the proposed method are proved by simulation experiments, which provides a reference and basis for the research of modern human-computer interaction.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099788","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 comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.
{"title":"Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network","authors":"Shilin Liu, Guangbin Yu, Youngchul Kim","doi":"10.4018/ijitwe.339186","DOIUrl":"https://doi.org/10.4018/ijitwe.339186","url":null,"abstract":"The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260175","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}
This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.
{"title":"Manufacturing Process Optimization in the Process Industry","authors":"Shilin Liu, Hanlie Cheng","doi":"10.4018/ijitwe.338998","DOIUrl":"https://doi.org/10.4018/ijitwe.338998","url":null,"abstract":"This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442928","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}
Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.
热电堆采用非接触式红外测温原理，被广泛应用于各种精密温度测量仪器中。本文分析了环境温度对热电堆测量精度的影响，并提出了一种基于 GA-BP（遗传算法-反向传播）神经网络的环境温度补偿方法。GA 算法弥补了 BP 算法迭代速度慢、易陷入局部优化的缺陷。实验仿真结果表明，基于 GA-BP 的环境温度补偿能够准确修正由环境温度和其他因素引起的测量误差。
{"title":"GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation","authors":"Ye Aifen, Shuwan Lin, Wang Huan","doi":"10.4018/ijitwe.337491","DOIUrl":"https://doi.org/10.4018/ijitwe.337491","url":null,"abstract":"Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795350","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}
Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.
热电堆采用非接触式红外测温原理，被广泛应用于各种精密温度测量仪器中。本文分析了环境温度对热电堆测量精度的影响，并提出了一种基于 GA-BP（遗传算法-反向传播）神经网络的环境温度补偿方法。GA 算法弥补了 BP 算法迭代速度慢、易陷入局部优化的缺陷。实验仿真结果表明，基于 GA-BP 的环境温度补偿能够准确修正由环境温度和其他因素引起的测量误差。
{"title":"GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation","authors":"Ye Aifen, Shuwan Lin, Wang Huan","doi":"10.4018/ijitwe.337491","DOIUrl":"https://doi.org/10.4018/ijitwe.337491","url":null,"abstract":"Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855117","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}
This article takes college students' learning adaptability as the research object, adopts B/S structure to develop a learning adaptive platform, designs a learner data model, a learning style model, a learning resource presentation module, and an ability level test module; tests the platform through simulated data; and analyzes college students' learning style, knowledge level and learner collaboration level. The results show that college students' learning adaptation has the characteristics of flexibility, individuality, initiative, and reflection. A self-adaptive learning platform can understand its learning state and effect through learning evaluation, adjust its learning strategies and methods in time, and help college students better understand and master knowledge. The research results provide theoretical data support for the exploration of college students' learning adaptability under the background of wisdom education.
{"title":"Exploration of College Students' Learning Adaptability Under the Background of Wisdom Education","authors":"Henan Zhang, Xiangzhe Liu","doi":"10.4018/ijitwe.336486","DOIUrl":"https://doi.org/10.4018/ijitwe.336486","url":null,"abstract":"This article takes college students' learning adaptability as the research object, adopts B/S structure to develop a learning adaptive platform, designs a learner data model, a learning style model, a learning resource presentation module, and an ability level test module; tests the platform through simulated data; and analyzes college students' learning style, knowledge level and learner collaboration level. The results show that college students' learning adaptation has the characteristics of flexibility, individuality, initiative, and reflection. A self-adaptive learning platform can understand its learning state and effect through learning evaluation, adjust its learning strategies and methods in time, and help college students better understand and master knowledge. The research results provide theoretical data support for the exploration of college students' learning adaptability under the background of wisdom education.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526366","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}
This study investigates the method of analyzing emotional tendencies in music courses and its application in lesson plan evaluation. Using a weighted method to analyze emotional tendencies in music curriculum, the study compares the results with existing literature, demonstrating the superior accuracy of the proposed method. To evaluate lesson plan quality, a combination of self-assessment, mutual evaluation, group evaluation, and the middle school music lesson plan evaluation form is recommended for comprehensive assessment. The study's method for comment polarity achieves an accuracy rate of 69.19%, significantly outperforming other methods. Additionally, improvements in lexical feature extraction reduce computation complexity and interference factors in sentiment polarity analysis. In conclusion, this study offers valuable insights for enhancing teaching effectiveness, lesson plan quality, and understanding course feedback.
{"title":"Emotional Behavior Analysis of Music Course Evaluation Based on Online Comment Mining","authors":"Nan Li","doi":"10.4018/ijitwe.336287","DOIUrl":"https://doi.org/10.4018/ijitwe.336287","url":null,"abstract":"This study investigates the method of analyzing emotional tendencies in music courses and its application in lesson plan evaluation. Using a weighted method to analyze emotional tendencies in music curriculum, the study compares the results with existing literature, demonstrating the superior accuracy of the proposed method. To evaluate lesson plan quality, a combination of self-assessment, mutual evaluation, group evaluation, and the middle school music lesson plan evaluation form is recommended for comprehensive assessment. The study's method for comment polarity achieves an accuracy rate of 69.19%, significantly outperforming other methods. Additionally, improvements in lexical feature extraction reduce computation complexity and interference factors in sentiment polarity analysis. In conclusion, this study offers valuable insights for enhancing teaching effectiveness, lesson plan quality, and understanding course feedback.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527253","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}
Based on the fuzzy method, this paper establishes a ranking model of the psychological quality of college teachers and an interception model of assessment indicators. On this basis, a quantitative evaluation method of college teachers' psychological quality is proposed by using the principles of fuzzy psychological evaluation and fuzzy recognition. According to empirical study, this evaluation approach is capable of providing a theoretical foundation for the next teacher training as well as a thorough assessment of the psychological qualities of teachers. The research concludes by pointing out that the model and evaluation approach can also be used to introduce and train university teachers, and it makes some sound recommendations for their development. An empirical study on the quantitative evaluation method of college teachers' psychological quality based on fuzzy psychological evaluation and fuzzy recognition principle is beneficial to better build the foundation of college teachers' psychological quality under the concept of harmonious education.
{"title":"Quantitative Evaluation Method of Psychological Quality of College Teachers Based on Fuzzy Logic","authors":"Liangqun Yang, Jian Li","doi":"10.4018/ijitwe.335486","DOIUrl":"https://doi.org/10.4018/ijitwe.335486","url":null,"abstract":"Based on the fuzzy method, this paper establishes a ranking model of the psychological quality of college teachers and an interception model of assessment indicators. On this basis, a quantitative evaluation method of college teachers' psychological quality is proposed by using the principles of fuzzy psychological evaluation and fuzzy recognition. According to empirical study, this evaluation approach is capable of providing a theoretical foundation for the next teacher training as well as a thorough assessment of the psychological qualities of teachers. The research concludes by pointing out that the model and evaluation approach can also be used to introduce and train university teachers, and it makes some sound recommendations for their development. An empirical study on the quantitative evaluation method of college teachers' psychological quality based on fuzzy psychological evaluation and fuzzy recognition principle is beneficial to better build the foundation of college teachers' psychological quality under the concept of harmonious education.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448418","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 to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.
{"title":"Application of QGA-BP Neural Network in Debt Risk Assessment of Government Platforms","authors":"Qingping Li, Ming Liu, Yao Zhang","doi":"10.4018/ijitwe.335124","DOIUrl":"https://doi.org/10.4018/ijitwe.335124","url":null,"abstract":"How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145711","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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