In the current context of sustainable development and environmental protection issues, enterprises are paying more and more attention to green technology innovation. For this purpose, we introduced a composite neural network model, including the Siamese Network, Temporal Convolutional Networks (TCN) and Random Forests technology. First, the Siamese Network is used to measure the green technology investment similarities between enterprises to better understand the connections between them. Second, Temporal Convolutional Networks (TCN) are applied to process time series data to capture the time evolution trend of green technology investment. Finally, we use Random Forests technology to integrate the output of the Siamese Network and TCN to classify enterprises. Experimental results show that our method is effective in green technology investment classification and financial performance prediction, can more accurately assess the financial performance of enterprises, and can also help enterprises better understand the effects of their green technology investments.
{"title":"Application of Compound Neural Networks to Classifying Corporate Green Technology Investments","authors":"Zhenlin Dong, Muhammad Asif","doi":"10.4018/joeuc.348654","DOIUrl":"https://doi.org/10.4018/joeuc.348654","url":null,"abstract":"In the current context of sustainable development and environmental protection issues, enterprises are paying more and more attention to green technology innovation. For this purpose, we introduced a composite neural network model, including the Siamese Network, Temporal Convolutional Networks (TCN) and Random Forests technology. First, the Siamese Network is used to measure the green technology investment similarities between enterprises to better understand the connections between them. Second, Temporal Convolutional Networks (TCN) are applied to process time series data to capture the time evolution trend of green technology investment. Finally, we use Random Forests technology to integrate the output of the Siamese Network and TCN to classify enterprises. Experimental results show that our method is effective in green technology investment classification and financial performance prediction, can more accurately assess the financial performance of enterprises, and can also help enterprises better understand the effects of their green technology investments.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanmin Li, Chao Meng, Jintao Tian, Zhengyang Fang, Huimin Cao
In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. Experimental results show that this model has achieved excellent performance on each data set, has higher accuracy and AUC value than the baseline method, and has advantages over other control models in comparative experiments. This research provides solid support for the implementation of personalized marketing strategies.
{"title":"Data-Driven Customer Online Shopping Behavior Analysis and Personalized Marketing Strategy","authors":"Yanmin Li, Chao Meng, Jintao Tian, Zhengyang Fang, Huimin Cao","doi":"10.4018/joeuc.346230","DOIUrl":"https://doi.org/10.4018/joeuc.346230","url":null,"abstract":"In today's highly competitive market environment, personalized marketing has become an important means for enterprises to gain competitive advantages. In order to better meet customer needs, companies need to accurately identify and classify customers to implement more refined market strategies. This study focuses on the customer classification problem. Based on several classic deep learning models, the BiLSTM-TabNet model is designed, and the Whale Optimization Algorithm (WOA) is introduced to further improve the model performance, thereby improving classification accuracy and practicality. Experimental results show that this model has achieved excellent performance on each data set, has higher accuracy and AUC value than the baseline method, and has advantages over other control models in comparative experiments. This research provides solid support for the implementation of personalized marketing strategies.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.
{"title":"A Consumer Trust Assessment Model for Online Shopping Based on Fuzzy Fusion Decision-Making","authors":"Mengtian Zhang, Di Wu, Hui Xu, Zheng Chao","doi":"10.4018/joeuc.349730","DOIUrl":"https://doi.org/10.4018/joeuc.349730","url":null,"abstract":"With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu-Min Wang, Chung-Lun Wei, Hsin‐Hui Lin, Yi-Shun Wang
This study identifies the determinants of Internet entrepreneurial commitment by integrating the theory of planned behavior (TPB) and diffusion of innovation theory (DOI). It hypothesizes six determinants—relative advantage/perceived desirability, complexity, compatibility, attitude, subjective norm, and perceived behavioral control/perceived feasibility—alongside two moderators: job type and personal innovativeness. The research model was empirically tested with data collected from 220 respondents using multiple regression analysis. The findings endorse the integration of TPB and DOI in analyzing Internet entrepreneurial commitment determinants. However, the significance of these six determinants varies according to job type and personal innovativeness. Educators, policy makers, and venture investors can use the findings to design fostering programs and curriculums that are customized to individuals according to their different personal characteristics to enhance Internet entrepreneurial commitment.
本研究通过整合计划行为理论（TPB）和创新扩散理论（DOI）来确定互联网创业承诺的决定因素。研究假设了六个决定因素--相对优势/感知可取性、复杂性、兼容性、态度、主观规范和感知行为控制/感知可行性--以及两个调节因素：工作类型和个人创新性。研究模型采用多元回归分析法对从 220 名受访者那里收集到的数据进行了实证检验。研究结果支持将 TPB 和 DOI 结合起来分析互联网创业承诺的决定因素。然而，这六个决定因素的重要性因工作类型和个人创新能力而异。教育工作者、政策制定者和风险投资者可以利用研究结果，根据个人的不同特点，设计出适合个人的培养计划和课程，以增强互联网创业承诺。
{"title":"What Drives Internet Entrepreneurial Commitment in Taiwan","authors":"Yu-Min Wang, Chung-Lun Wei, Hsin‐Hui Lin, Yi-Shun Wang","doi":"10.4018/joeuc.348956","DOIUrl":"https://doi.org/10.4018/joeuc.348956","url":null,"abstract":"This study identifies the determinants of Internet entrepreneurial commitment by integrating the theory of planned behavior (TPB) and diffusion of innovation theory (DOI). It hypothesizes six determinants—relative advantage/perceived desirability, complexity, compatibility, attitude, subjective norm, and perceived behavioral control/perceived feasibility—alongside two moderators: job type and personal innovativeness. The research model was empirically tested with data collected from 220 respondents using multiple regression analysis. The findings endorse the integration of TPB and DOI in analyzing Internet entrepreneurial commitment determinants. However, the significance of these six determinants varies according to job type and personal innovativeness. Educators, policy makers, and venture investors can use the findings to design fostering programs and curriculums that are customized to individuals according to their different personal characteristics to enhance Internet entrepreneurial commitment.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Zheng, Fuqing Li, Cui Li, Zheyuan Zhang, Rui Cao, Noman Sohail
This paper investigates text similarity methods in the field of NLP, improves upon the WMD, and develops the SWC-WMD distance, forming the basis for a clustering method for long ICH texts. Clustering experiments on the constructed ICH long text dataset using WMD, SWC-WMD, and TF-IDF-WMD distances were conducted. The impact of the number of feature words on clustering results and the effect of different distances on clustering outcomes were assessed based on accuracy and F1 values from the evaluation criteria. The final results show that the SWC-WMD distance improves the accuracy and F1 values of the ICH long text clustering results compared to the other two distances, thereby proving the effectiveness of the methods proposed in this paper.
本文研究了 NLP 领域的文本相似性方法，改进了 WMD，并开发了 SWC-WMD 距离，为非物质文化遗产长文本的聚类方法奠定了基础。使用 WMD、SWC-WMD 和 TF-IDF-WMD 距离对构建的非物质文化遗产长文本数据集进行了聚类实验。根据评价标准中的准确率和 F1 值，评估了特征词数量对聚类结果的影响以及不同距离对聚类结果的影响。最终结果表明，与其他两种距离相比，SWC-WMD 距离提高了非物质文化遗产长文本聚类结果的准确率和 F1 值，从而证明了本文所提方法的有效性。
{"title":"A Natural Language Processing Model for Automated Organization and Analysis of Intangible Cultural Heritage","authors":"Yan Zheng, Fuqing Li, Cui Li, Zheyuan Zhang, Rui Cao, Noman Sohail","doi":"10.4018/joeuc.349736","DOIUrl":"https://doi.org/10.4018/joeuc.349736","url":null,"abstract":"This paper investigates text similarity methods in the field of NLP, improves upon the WMD, and develops the SWC-WMD distance, forming the basis for a clustering method for long ICH texts. Clustering experiments on the constructed ICH long text dataset using WMD, SWC-WMD, and TF-IDF-WMD distances were conducted. The impact of the number of feature words on clustering results and the effect of different distances on clustering outcomes were assessed based on accuracy and F1 values from the evaluation criteria. The final results show that the SWC-WMD distance improves the accuracy and F1 values of the ICH long text clustering results compared to the other two distances, thereby proving the effectiveness of the methods proposed in this paper.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the exacerbation of the rural aging population trend, home-based health monitoring for the rural elderly has become a societal focal point, demanding an effective technological means to elevate the level of rural elderly health management. However, traditional algorithms for monitoring rural elderly behavior face myriad challenges, such as effectively capturing temporal and spatial features. Consequently, addressing the need to enhance the accuracy and robustness of rural elderly behavior recognition has become an urgent problem to solve. This study responds to this challenge by comprehensively employing deep learning and temporal modeling techniques, designing, and validating a short-term and long-term dual-layer home-based health monitoring system for the rural elderly.In the short-term layer, the model utilizes smartphones to collect health information from the rural elderly in various ways and performs real-time anomaly behavior detection.
{"title":"Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors","authors":"Yao Zhang, Guangji Tong, Chun Lin","doi":"10.4018/joeuc.345927","DOIUrl":"https://doi.org/10.4018/joeuc.345927","url":null,"abstract":"With the exacerbation of the rural aging population trend, home-based health monitoring for the rural elderly has become a societal focal point, demanding an effective technological means to elevate the level of rural elderly health management. However, traditional algorithms for monitoring rural elderly behavior face myriad challenges, such as effectively capturing temporal and spatial features. Consequently, addressing the need to enhance the accuracy and robustness of rural elderly behavior recognition has become an urgent problem to solve. This study responds to this challenge by comprehensively employing deep learning and temporal modeling techniques, designing, and validating a short-term and long-term dual-layer home-based health monitoring system for the rural elderly.In the short-term layer, the model utilizes smartphones to collect health information from the rural elderly in various ways and performs real-time anomaly behavior detection.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Facial expression recognition technology can improve the possibility of fugitives and persons subject to execution being discovered by public security and judicial personnel in the virtual world, thereby improving the actual effectiveness and credibility of the law. There may also be a risk of portrait rights infringement with this technology, and the user of the technology needs to inform the person whose facial expression is being extracted in advance and obtain permission. With the rapid progression of deep learning and artificial intelligence, 3D facial expression modeling has garnered increased significance in computer vision and graphics. We propose an innovative approach combining Transformer models with cloud computing to simulate facial expression evolution in virtual social environments. Leveraging Transformer-based feature extraction, our model integrates emotional cues from various modalities to accurately capture subtle changes over time.
{"title":"An Empirical Study of Portrait Rights on Emotional Evolution in Virtual Social Scenarios by Transformer and Cloud Computing","authors":"Guimei Jia, Xiaoyuan Gao","doi":"10.4018/joeuc.347217","DOIUrl":"https://doi.org/10.4018/joeuc.347217","url":null,"abstract":"Facial expression recognition technology can improve the possibility of fugitives and persons subject to execution being discovered by public security and judicial personnel in the virtual world, thereby improving the actual effectiveness and credibility of the law. There may also be a risk of portrait rights infringement with this technology, and the user of the technology needs to inform the person whose facial expression is being extracted in advance and obtain permission. With the rapid progression of deep learning and artificial intelligence, 3D facial expression modeling has garnered increased significance in computer vision and graphics. We propose an innovative approach combining Transformer models with cloud computing to simulate facial expression evolution in virtual social environments. Leveraging Transformer-based feature extraction, our model integrates emotional cues from various modalities to accurately capture subtle changes over time.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Artificial Intelligence (AI)-driven smart learning management system (SLMS) innovates the knowledge production and dissemination, and reshapes the teacher roles and the school ecosystem. This study advances prior research by examining the impact of teacher digital leadership (TDL) based on SLMS on the campus organizational culture (COC), and how this relationship is mediated by two key abilities. Result shows that TDL has a profound impact on how much they influence the curriculum design and integration from knowledge-based perspective. It also indicates that their manner of curriculum construct in the SLMS has a significant effect on COC. The teachers awarded by the Teaching Ability Competition are particularly effective in operating a curriculum in AI-powered SLMS, and show effective informal leadership. The conclusion presents feasible measures for TDL in blended curriculum construct for innovative AI-teacher collaboration from micro perspective under reforms in recent China.
{"title":"Innovative Teacher Leadership in Curriculum Construction","authors":"Zhilin Luo, Yan Wang","doi":"10.4018/joeuc.348333","DOIUrl":"https://doi.org/10.4018/joeuc.348333","url":null,"abstract":"The Artificial Intelligence (AI)-driven smart learning management system (SLMS) innovates the knowledge production and dissemination, and reshapes the teacher roles and the school ecosystem. This study advances prior research by examining the impact of teacher digital leadership (TDL) based on SLMS on the campus organizational culture (COC), and how this relationship is mediated by two key abilities. Result shows that TDL has a profound impact on how much they influence the curriculum design and integration from knowledge-based perspective. It also indicates that their manner of curriculum construct in the SLMS has a significant effect on COC. The teachers awarded by the Teaching Ability Competition are particularly effective in operating a curriculum in AI-powered SLMS, and show effective informal leadership. The conclusion presents feasible measures for TDL in blended curriculum construct for innovative AI-teacher collaboration from micro perspective under reforms in recent China.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study employs a novel Markov jump system model to address complexities and uncertainties in modern logistics management, particularly in supply chain logistics information networks. It introduces dynamic memory to tackle issues in traditional static networks, enabling modeling and control of this intricate system. By assessing decision node importance, a novel strategy optimization method is devised. Through information exchange and decision adjustments among cooperating nodes, the overall decision system performance is enhanced, resulting in a comprehensive logistics information coordination mechanism for production supply chains based on the Markov jump system. The research demonstrates that this approach considers node interactions and information exchange, using dynamic memory to improve system adaptability and robustness, ultimately enhancing overall decision performance and stability. This has practical value for decision support and system optimization in production supply chain logistics information networks, offering fresh insights into Markov jump system control.
{"title":"Optimizing Production Supply Chain With Markov Jump System for Logistics Collaboration","authors":"Rong Liu, Vinay Vakharia","doi":"10.4018/joeuc.344452","DOIUrl":"https://doi.org/10.4018/joeuc.344452","url":null,"abstract":"This study employs a novel Markov jump system model to address complexities and uncertainties in modern logistics management, particularly in supply chain logistics information networks. It introduces dynamic memory to tackle issues in traditional static networks, enabling modeling and control of this intricate system. By assessing decision node importance, a novel strategy optimization method is devised. Through information exchange and decision adjustments among cooperating nodes, the overall decision system performance is enhanced, resulting in a comprehensive logistics information coordination mechanism for production supply chains based on the Markov jump system. The research demonstrates that this approach considers node interactions and information exchange, using dynamic memory to improve system adaptability and robustness, ultimately enhancing overall decision performance and stability. This has practical value for decision support and system optimization in production supply chain logistics information networks, offering fresh insights into Markov jump system control.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ji Liu, Zheng Xu, Ying Yang, Kun Zhou, Munish Kumar
Predicting financial market volatility is essential for investors and risk management. This study proposes a dynamic prediction model for financial asset volatility, with a Bi-directional Recurrent Neural Network (Bi-RNN) utilized to cleverly address market complexity. Our framework integrates Bi-RNN and gated recurrent units (GRU) to perform global optimization via particle swarm optimization algorithm (PSO). Bi-RNN combines historical data and future expectations, while GRU effectively solves long-term dependency issues through a gating mechanism, which enhances model generalization. Experimental results show that the model exhibits significant performance advantages on different financial datasets, along with strong learning and generalization capabilities superior to traditional methods. This research provides advanced and practical solutions for financial asset fluctuation prediction and is of positive significance for the greater accuracy of investment decisions and risk mitigation.
{"title":"Dynamic Prediction Model of Financial Asset Volatility Based on Bidirectional Recurrent Neural Networks","authors":"Ji Liu, Zheng Xu, Ying Yang, Kun Zhou, Munish Kumar","doi":"10.4018/joeuc.345925","DOIUrl":"https://doi.org/10.4018/joeuc.345925","url":null,"abstract":"Predicting financial market volatility is essential for investors and risk management. This study proposes a dynamic prediction model for financial asset volatility, with a Bi-directional Recurrent Neural Network (Bi-RNN) utilized to cleverly address market complexity. Our framework integrates Bi-RNN and gated recurrent units (GRU) to perform global optimization via particle swarm optimization algorithm (PSO). Bi-RNN combines historical data and future expectations, while GRU effectively solves long-term dependency issues through a gating mechanism, which enhances model generalization. Experimental results show that the model exhibits significant performance advantages on different financial datasets, along with strong learning and generalization capabilities superior to traditional methods. This research provides advanced and practical solutions for financial asset fluctuation prediction and is of positive significance for the greater accuracy of investment decisions and risk mitigation.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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