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Journal of Organizational and End User Computing最新文献

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A Deep Learning-Based Animation Video Image Data Anomaly Detection and Recognition Algorithm 基于深度学习的动画视频图像数据异常检测与识别算法
Journal of Organizational and End User Computing Pub Date : 2024-07-16 DOI: 10.4018/joeuc.345929
Cheng Li, Qiguang Qian
{"title":"A Deep Learning-Based Animation Video Image Data Anomaly Detection and Recognition Algorithm","authors":"Cheng Li, Qiguang Qian","doi":"10.4018/joeuc.345929","DOIUrl":"https://doi.org/10.4018/joeuc.345929","url":null,"abstract":"Anomaly detection plays a crucial role in the field of machine learning, as it involves constructing detection models capable of identifying abnormal samples that deviate from expected patterns, using unlabeled or normal samples. In recent years, there has been a growing interest in integrating anomaly detection into image processing to tackle challenges related to target detection, particularly when dealing with limited sample availability. This paper introduces a novel fully connected network model enhanced with a memory augmentation mechanism. By harnessing the comprehensive feature capabilities of the fully connected network, this model effectively complements the representation capabilities of convolutional neural networks. Additionally, it incorporates a memory module to retain knowledge of normal patterns, thereby enhancing the performance of existing models for video anomaly detection. Furthermore, we present a video anomaly detection system designed to identify abnormal image data within surveillance videos, leveraging the innovative network architecture described above.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"11 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141640585","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}
引用次数: 0
Investigating the Moderating Effects of Context-Aware Recommendations on the Relationship Between Knowledge Search and Decision Quality 调查情境感知建议对知识搜索与决策质量之间关系的调节作用
Journal of Organizational and End User Computing Pub Date : 2024-07-16 DOI: 10.4018/joeuc.345930
Chang Liu, Hong Jin, Jianbo Wang
{"title":"Investigating the Moderating Effects of Context-Aware Recommendations on the Relationship Between Knowledge Search and Decision Quality","authors":"Chang Liu, Hong Jin, Jianbo Wang","doi":"10.4018/joeuc.345930","DOIUrl":"https://doi.org/10.4018/joeuc.345930","url":null,"abstract":"The paper applied a quantitative method to the impact of context-aware recommendations on decision quality and used partial least squares (PLS) to test the hypotheses of the study. The paper examines how context-aware recommendations affect the knowledge integration and decision-making, offering a valuable contribution to the existing body of knowledge and a framework for understanding knowledge management within a multi-dimensional setting when combined with context-aware technology. This paper provides designers of context-aware recommender systems with ideas to broaden the scope of services and refine learning applications.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641896","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}
引用次数: 0
Cracking the Code 破解密码
Journal of Organizational and End User Computing Pub Date : 2024-06-07 DOI: 10.4018/joeuc.345933
A. Almuqrin, I. Mutambik, J. Zhang, Hashem Farahat S. (28a8b678-e83f-4cad-bcb3-08b2bbe, Zahyah H. Alharbi
{"title":"Cracking the Code","authors":"A. Almuqrin, I. Mutambik, J. Zhang, Hashem Farahat S. (28a8b678-e83f-4cad-bcb3-08b2bbe, Zahyah H. Alharbi","doi":"10.4018/joeuc.345933","DOIUrl":"https://doi.org/10.4018/joeuc.345933","url":null,"abstract":"With the expanding reach of the Internet of Things, information security threats are increasing, including from the very professionals tasked with defending against these threats. This study identified factors impacting information security behavior among these individuals. Protection motivation theory and the theory of planned behavior were employed along with work-related organizational factors as a theoretical framework. Data were collected through a survey of 595 information security professionals working in Saudi information technology companies. Structural equational modeling was used to analyze the data. Threat susceptibility, threat severity, self-efficacy, response cost, fear attitude, behavioral control, subjective norms, and organizational commitment were found to play a significant role in information security protection motivation and behavior, while job satisfaction did not.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":" 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371927","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}
引用次数: 0
Time Series Trends Forecasting for Manufacturing Enterprises in the Digital Age 数字化时代制造企业的时间序列趋势预测
Journal of Organizational and End User Computing Pub Date : 2024-06-06 DOI: 10.4018/joeuc.345242
Chaolin Yang, Jingdong Yan, Guangming Wang
{"title":"Time Series Trends Forecasting for Manufacturing Enterprises in the Digital Age","authors":"Chaolin Yang, Jingdong Yan, Guangming Wang","doi":"10.4018/joeuc.345242","DOIUrl":"https://doi.org/10.4018/joeuc.345242","url":null,"abstract":"In the digital age, manufacturing enterprises face challenges like information overload and data fragmentation. To address these issues, this paper proposes a novel method that integrates the Improved Whale Optimization Algorithm (IWOA), Bidirectional Long Short-Term Memory (BILSTM), and Temporal Pattern Attention (TPA) for analyzing time series data. IWOA optimizes hyperparameters, BILSTM captures temporal dependencies, and TPA enhances interpretability. Experimental results show the method's effectiveness in market trend prediction, production planning, and supply chain management. It enables accurate forecasts in a competitive environment, enhancing flexibility and foresight. This research overcomes existing limitations, offering a valuable analytical tool for understanding the digital economy's impact on manufacturing enterprises. It provides guidance for the industry's development in the digital era.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"16 s23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377961","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}
引用次数: 0
Predicting Corporate Financial Risk Using Artificial Bee Colony-Attention-Gated Recurrent Unit Model 利用人工蜂群-注意力门控递归单元模型预测企业财务风险
Journal of Organizational and End User Computing Pub Date : 2024-06-06 DOI: 10.4018/joeuc.345244
Anzhong Huang, Qiuxiang Bi, Mengen Chang, Xuan Feng, Anqi Zhang
{"title":"Predicting Corporate Financial Risk Using Artificial Bee Colony-Attention-Gated Recurrent Unit Model","authors":"Anzhong Huang, Qiuxiang Bi, Mengen Chang, Xuan Feng, Anqi Zhang","doi":"10.4018/joeuc.345244","DOIUrl":"https://doi.org/10.4018/joeuc.345244","url":null,"abstract":"Corporate financial risk prediction is a critical task for ensuring the stability and success of businesses in today's dynamic economic landscape. However, existing models often fall short in accurately assessing and managing these risks. They often rely on historical financial data alone, which fails to account for sudden market fluctuations or unforeseen external events, leading to suboptimal risk assessments. Recognizing the paramount importance of time series analysis in financial risk prediction, we introduce a novel approach to the ABC-Attention-GRU combination model. This innovative model leverages the strengths of Artificial Bee Colony (ABC), the attention mechanism, and Gated Recurrent Unit (GRU) to enhance predictive accuracy and robustness. In our experiments, the ABC-Attention-GRU model consistently outperformed state-of-the-art methods across various financial datasets. It effectively captured complex temporal dependencies, resulting in superior Precision, Recall, F1 Score, and AUC metrics.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"190 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376000","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}
引用次数: 0
Real-Time Classification Model of Public Emergencies Using Fusion Expansion Network 利用融合扩展网络的突发公共事件实时分类模型
Journal of Organizational and End User Computing Pub Date : 2024-06-05 DOI: 10.4018/joeuc.345245
Haiou Xiong, Gang Wang
{"title":"Real-Time Classification Model of Public Emergencies Using Fusion Expansion Network","authors":"Haiou Xiong, Gang Wang","doi":"10.4018/joeuc.345245","DOIUrl":"https://doi.org/10.4018/joeuc.345245","url":null,"abstract":"In today's deep learning-dominated era, real-time classification of public emergencies is a critical research area. Existing methods, however, often fall short in considering both temporal and spatial aspects comprehensively. This study introduces GEDNAS, a novel model that combines atrous convolutional neural network (DCNN), gated recurrent unit (GRU), and neural structure search (NAS) to address these limitations. GEDNAS utilizes DCNN to capture local spatio-temporal features, integrates GRU for time series modeling, and employs NAS for overall structural optimization. The approach significantly enhances real-time public emergency classification performance, showcasing its efficiency and accuracy in responding to real-time scenarios and providing robust support for emergency response efforts. This research introduces an innovative solution for public safety, advancing the application of deep learning in emergency management and inspiring the design of real-time classification models, ultimately enhancing overall societal safety.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"88 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385448","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}
引用次数: 0
Big Data Analytics and Culture 大数据分析与文化
Journal of Organizational and End User Computing Pub Date : 2024-05-24 DOI: 10.4018/joeuc.344453
Assion Lawson-Body, Abdou Illia, Laurence Lawson-Body, K. Rouibah, Gurkan I Akalin, E. M. Tamandja
{"title":"Big Data Analytics and Culture","authors":"Assion Lawson-Body, Abdou Illia, Laurence Lawson-Body, K. Rouibah, Gurkan I Akalin, E. M. Tamandja","doi":"10.4018/joeuc.344453","DOIUrl":"https://doi.org/10.4018/joeuc.344453","url":null,"abstract":"The existing big data analytics measures were developed without considering the cultural dimensions of developing countries. This research aims to develop and validate measures for big data Vs and cultural big data analytics and study their impacts on the developing countries' big data value proposition. Following MacKenzie's and Shiau and Huang's scale development procedures, data was collected twice from individuals in a developing country to refine the scale and reexamine its properties. PLS methods were used to study the impacts of big data Vs and cultural big data analytics on the value proposition. The findings revealed that big data analytics snobbism and conformism positively impact big data value proposition. Similarly, big data volume, velocity, and variety positively impact the value proposition. Paradoxically, big data veracity and variability do not significantly affect the value proposition. Surprisingly, big data analytics fatalism negatively impacts the value proposition. Theoretical and practical contributions were offered.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"15 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100059","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}
引用次数: 0
Enhancing Logistics Optimization 加强物流优化
Journal of Organizational and End User Computing Pub Date : 2024-05-24 DOI: 10.4018/joeuc.344039
Lei Wang, G. Liu, Habib Hamam
{"title":"Enhancing Logistics Optimization","authors":"Lei Wang, G. Liu, Habib Hamam","doi":"10.4018/joeuc.344039","DOIUrl":"https://doi.org/10.4018/joeuc.344039","url":null,"abstract":"With the expansion of the logistics network, enterprise logistics distribution faces increasing challenges, including high transportation costs, low distribution efficiency, and unstable distribution networks. To address these issues, this study focuses on optimizing enterprise logistics distribution using a double-layer (DL) model. In this paper, we propose a DL model for optimizing enterprise logistics distribution. The DL model is designed to find the optimal solution using the particle swarm optimization (PSO) algorithm. By leveraging location data from the region, the DL model evaluates and compares alternative distribution centers to determine the most efficient distribution strategy. The results demonstrate that the DL site selection model developed in this study effectively addresses the tasks of logistics center location and distribution optimization among alternative distribution centers. Comparison tests reveal that the distribution path proposed by the DL model is more accessible and cost-effective compared to alternative approaches.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"12 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141098529","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}
引用次数: 0
Deep Learning-Driven E-Commerce Marketing Communication for Recommending Shopping System and Optimizing User Experience 深度学习驱动的电子商务营销传播，用于推荐购物系统和优化用户体验
Journal of Organizational and End User Computing Pub Date : 2024-05-22 DOI: 10.4018/joeuc.343258
Qian Liu, Haibing Tang, Lufei Wu, Zheng Chao
{"title":"Deep Learning-Driven E-Commerce Marketing Communication for Recommending Shopping System and Optimizing User Experience","authors":"Qian Liu, Haibing Tang, Lufei Wu, Zheng Chao","doi":"10.4018/joeuc.343258","DOIUrl":"https://doi.org/10.4018/joeuc.343258","url":null,"abstract":"As competition in the realm of e-commerce escalates, the provision of personalized and precise shopping recommendations emerges as a pivotal strategy for e-commerce platforms striving to engage users effectively. Traditional recommendation systems often grapple with challenges such as the inability to capture intricate relationships, limited personalization, and issues concerning diversity. In response to these challenges, this study introduces cutting-edge deep learning techniques, namely Transformer models, Generative Adversarial Networks (GANs), and reinforcement learning, with the aim of bolstering the recommendation accuracy and user experience within e-commerce shopping systems.Initially, we harness Transformer models, capitalizing on their exceptional performance in processing sequential data to adeptly extract and learn representations of both product and user features. This facilitates a more profound understanding of the correlations between products and user shopping behaviors, thus empowering the system to offer more tailored recommendations.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"3 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108229","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}
引用次数: 0
Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital Marketing 整合视觉转换器和图神经网络，实现数字营销中的视觉分析
Journal of Organizational and End User Computing Pub Date : 2024-04-09 DOI: 10.4018/joeuc.342092
Yingna Chao, Hongfeng Zhu, Yueding Zhou
{"title":"Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital Marketing","authors":"Yingna Chao, Hongfeng Zhu, Yueding Zhou","doi":"10.4018/joeuc.342092","DOIUrl":"https://doi.org/10.4018/joeuc.342092","url":null,"abstract":"In today's digital economy, digital marketing has become a crucial means for businesses to drive growth and enhance brand exposure. However, with increasing competition, predicting and optimizing advertising effectiveness has become a pivotal component in formulating digital marketing strategies. In order to better understand ad creatives and deeply explore the information within them, this study focuses on integrating visual transformer (VIT) and graph neural network (GNN) methods. Additionally, the study leverages generative adversarial networks (GAN) to enhance the quality of visual features, aiming to achieve visual analysis, exploration, and prediction of advertising effectiveness in digital marketing. This approach begins by employing VIT, an emerging visual transformer technology, to transform image information into high-dimensional feature representations.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"76 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726509","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}
引用次数: 0
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