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.
{"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}
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.
{"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}
A. Almuqrin, I. Mutambik, J. Zhang, Hashem Farahat S. (28a8b678-e83f-4cad-bcb3-08b2bbe, Zahyah H. Alharbi
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.
{"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}
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.
{"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}
Anzhong Huang, Qiuxiang Bi, Mengen Chang, Xuan Feng, Anqi Zhang
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.
{"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}
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.
在当今以深度学习为主导的时代，突发公共事件的实时分类是一个重要的研究领域。然而，现有的方法往往不能全面考虑时间和空间方面。本研究介绍的 GEDNAS 是一种结合了无序卷积神经网络（DCNN）、门控递归单元（GRU）和神经结构搜索（NAS）的新型模型，旨在解决这些局限性。GEDNAS 利用 DCNN 捕捉局部时空特征，整合 GRU 进行时间序列建模，并利用 NAS 进行整体结构优化。该方法大大提高了实时公共应急分类性能，展示了其在应对实时场景时的效率和准确性，并为应急响应工作提供了有力支持。这项研究为公共安全引入了一种创新解决方案，推动了深度学习在应急管理中的应用，启发了实时分类模型的设计，最终提升了整体社会安全。
{"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}
Assion Lawson-Body, Abdou Illia, Laurence Lawson-Body, K. Rouibah, Gurkan I Akalin, E. M. Tamandja
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.
现有的大数据分析措施是在没有考虑发展中国家文化层面的情况下制定的。本研究旨在开发和验证大数据价值和文化大数据分析的测量方法，并研究它们对发展中国家大数据价值主张的影响。按照 MacKenzie 和 Shiau、Huang 的量表开发流程，两次从发展中国家的个人中收集数据，以完善量表并重新审查其属性。使用 PLS 方法研究了大数据 Vs 和文化大数据分析对价值主张的影响。研究结果表明，大数据分析的势利主义和墨守成规会对大数据价值主张产生积极影响。同样，大数据的数量、速度和种类也对价值主张产生积极影响。矛盾的是，大数据的真实性和可变性对价值主张没有显著影响。令人惊讶的是，大数据分析宿命论对价值主张产生了负面影响。该报告提供了理论和实践方面的贡献。
{"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}
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.
{"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}
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.
{"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}
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.
{"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}
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