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Journal of Organizational and End User Computing最新文献

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Visual-and-Language Multimodal Fusion for Sweeping Robot Navigation Based on CNN and GRU 基于 CNN 和 GRU 的视觉语言多模态融合技术，用于扫地机器人导航
Journal of Organizational and End User Computing Pub Date : 2024-02-20 DOI: 10.4018/joeuc.338388
Yiping Zhang, Kolja Wilker
{"title":"Visual-and-Language Multimodal Fusion for Sweeping Robot Navigation Based on CNN and GRU","authors":"Yiping Zhang, Kolja Wilker","doi":"10.4018/joeuc.338388","DOIUrl":"https://doi.org/10.4018/joeuc.338388","url":null,"abstract":"Effectively fusing information between the visual and language modalities remains a significant challenge. To achieve deep integration of natural language and visual information, this research introduces a multimodal fusion neural network model, which combines visual information (RGB images and depth maps) with language information (natural language navigation instructions). Firstly, the authors used faster R-CNN and ResNet50 to extract image features and attention mechanism to further extract effective information. Secondly, GRU model is used to extract language features. Finally, another GRU model is used to fuse the visual- language features, and then the history information is retained to give the next action instruction to the robot. Experimental results demonstrate that the proposed method effectively addresses the localization and decision-making challenges for robotic vacuum cleaners.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447758","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
Application of Computer Vision on E-Commerce Platforms and Its Impact on Sales Forecasting 计算机视觉在电子商务平台上的应用及其对销售预测的影响
Journal of Organizational and End User Computing Pub Date : 2024-01-31 DOI: 10.4018/joeuc.336848
Wei-Dong Liu, Xi-Shui She
{"title":"Application of Computer Vision on E-Commerce Platforms and Its Impact on Sales Forecasting","authors":"Wei-Dong Liu, Xi-Shui She","doi":"10.4018/joeuc.336848","DOIUrl":"https://doi.org/10.4018/joeuc.336848","url":null,"abstract":"In today's digital age, the e-commerce industry continues to grow and flourish. The widespread application of computer vision technology has brought revolutionary changes to e-commerce platforms. Extracting image features from e-commerce platforms using deep learning techniques is of paramount importance for predicting product sales. Deep learning-based computer vision models can automatically learn image features without the need for manual feature extractors. By employing deep learning techniques, key features such as color, shape, and texture can be effectively extracted from product images, providing more representative and diverse data for sales prediction models. This study proposes the use of ResNet-101 as an image feature extractor, enabling the automatic learning of rich visual features to provide high-quality image representations for subsequent analysis. Furthermore, a bidirectional attention mechanism is introduced to dynamically capture correlations between different modalities, facilitating the fusion of multimodal features.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"668 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140479383","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
Improving Robot-Assisted Virtual Teaching Using Transformers, GANs, and Computer Vision 利用变形器、广义泛函模型和计算机视觉改进机器人辅助虚拟教学
Journal of Organizational and End User Computing Pub Date : 2024-01-17 DOI: 10.4018/joeuc.336481
Li Xiong, Yuanyuan Chen, Yi Peng, Y. Ghadi
{"title":"Improving Robot-Assisted Virtual Teaching Using Transformers, GANs, and Computer Vision","authors":"Li Xiong, Yuanyuan Chen, Yi Peng, Y. Ghadi","doi":"10.4018/joeuc.336481","DOIUrl":"https://doi.org/10.4018/joeuc.336481","url":null,"abstract":"This study aims to enhance the efficacy of personalized learning paths by amalgamating transformer models, generative adversarial networks (GANs), and reinforcement learning techniques. To refine personalized learning trajectories, the authors integrated the transformer model for enhanced information assimilation and learning path planning. Through generative adversarial networks, the authors simulated the fusion and interaction of multi-modal information, refining the training of virtual teaching assistants. Lastly, reinforcement learning was employed to optimize the interaction strategies of these assistants, aligning them better with student needs. In the experimental phase, the authors benchmarked their approach against six state-of-the-art models to assess its effectiveness. The experimental outcomes highlight significant enhancements achieved by the authors' virtual teaching assistant compared to traditional methods. Precision improved to 95% and recall to 96%, and an F1 score exceeding 95% was attained.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"54 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526916","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 Multimodal Understanding With LIUS 利用 LIUS 增强多模态理解能力
Journal of Organizational and End User Computing Pub Date : 2024-01-12 DOI: 10.4018/joeuc.336276
Chunlai Song
{"title":"Enhancing Multimodal Understanding With LIUS","authors":"Chunlai Song","doi":"10.4018/joeuc.336276","DOIUrl":"https://doi.org/10.4018/joeuc.336276","url":null,"abstract":"VQA (visual question and answer) is the task of enabling a computer to generate accurate textual answers based on given images and related questions. It integrates computer vision and natural language processing and requires a model that is able to understand not only the image content but also the question in order to generate appropriate linguistic answers. However, current limitations in cross-modal understanding often result in models that struggle to accurately capture the complex relationships between images and questions, leading to inaccurate or ambiguous answers. This research aims to address this challenge through a multifaceted approach that combines the strengths of vision and language processing. By introducing the innovative LIUS framework, a specialized vision module was built to process image information and fuse features using multiple scales. The insights gained from this module are integrated with a “reasoning module” (LLM) to generate answers.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"49 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531977","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
Intelligent Productivity Transformation 智能生产力转型
Journal of Organizational and End User Computing Pub Date : 2024-01-10 DOI: 10.4018/joeuc.336284
Bojing Liu, Mengxiang Li, Zihui Ji, Hongming Li, Ji Luo
{"title":"Intelligent Productivity Transformation","authors":"Bojing Liu, Mengxiang Li, Zihui Ji, Hongming Li, Ji Luo","doi":"10.4018/joeuc.336284","DOIUrl":"https://doi.org/10.4018/joeuc.336284","url":null,"abstract":"With the penetration of deep learning technology into forecasting and decision support systems, enterprises have an increasingly urgent need for accurate forecasting of time series data. Especially in fields such as finance, retail, and production, immediate and accurate predictions of market trends are the key to maintaining a competitive advantage. This study aims to address the limitations of traditional time series forecasting methods, such as the difficulty in adapting to the nonlinearity and non-stationarity of the data, through an innovative deep learning framework. The authors propose a Prophet model that combines deep learning with LSTNet and statistics. In this way, they combine the ability of LSTNet to handle complex time dependencies and the flexibility of the Prophet model to handle trends and periodicity. The particle swarm optimization algorithm (PSO) is responsible for tuning this hybrid model, aiming to improve the accuracy of predictions. Such a strategy not only helps capture long-term dependencies in time series, but also models seasonality and holiday effects well.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439969","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 Learners' Performance in Contest Through Knowledge Mapping Algorithm 通过知识图谱算法提高学生的竞赛成绩
Journal of Organizational and End User Computing Pub Date : 2024-01-10 DOI: 10.4018/joeuc.336277
Zhilin Luo, Xuefeng Shao, Xiaochun Ma
{"title":"Enhancing Learners' Performance in Contest Through Knowledge Mapping Algorithm","authors":"Zhilin Luo, Xuefeng Shao, Xiaochun Ma","doi":"10.4018/joeuc.336277","DOIUrl":"https://doi.org/10.4018/joeuc.336277","url":null,"abstract":"The fairness of vocational contest scoring is key to generating reliable competency assessments. This study examined the performance impact of the motivation of English-as-a-foreign-language learners in contests with vocabulary knowledge antecedents in the contexts of artificial intelligence (AI) and blockchain (BC). The sample comprised 185 participants of an oral English contest at higher vocational institution in China. AI-powered scoring of learners' contest performance and a survey were used to collect data. The findings revealed that learners' intrinsic drive was the main positive factor, outweighing their extrinsic motivation, and that AI and BC increased the trustworthiness and integrity of contest records, thus providing new opportunities to build learner trust and form psychological incentives. This study enriches foreign language motivation theory in the context of contest research and highlights the importance of using AI and BC to enhance the scoring accuracy and credibility of contests as authoritative evaluation instruments in vocational education.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"48 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440866","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
An Empirical Study on Personalized Product Recommendation Based on Cross-Border E-Commerce Customer Data Analysis 基于跨境电子商务客户数据分析的个性化产品推荐实证研究
Journal of Organizational and End User Computing Pub Date : 2024-01-10 DOI: 10.4018/joeuc.335498
Wanwan Li, Ying Cai, Mohd Hizam Hanafiah, Zhenwei Liao
{"title":"An Empirical Study on Personalized Product Recommendation Based on Cross-Border E-Commerce Customer Data Analysis","authors":"Wanwan Li, Ying Cai, Mohd Hizam Hanafiah, Zhenwei Liao","doi":"10.4018/joeuc.335498","DOIUrl":"https://doi.org/10.4018/joeuc.335498","url":null,"abstract":"Thanks to the rapid growth of cross-border e-commerce platforms, numerous cross-border items are now available to customers. Several serious issues with cross-border e-commerce platforms related to item promotion and consumer product screening have arisen. Particular importance should be placed on studying and implementing personalized recommendation systems based on international e-commerce. In light of the quick expansion of commodities, when making individualized suggestions, traditional recommendation algorithms have had to deal with issues such as scant data, a chilly start to the market, and trouble identifying user preferences. To automatically mine the implicit and latent relationships between users and objects in recommendation systems, this study employs deep learning with nonlinear learning capabilities, which resolves the challenges of user interest mining. The weaknesses of the existing global recommendation research are emphasized, the study of conventional recommendation algorithms mixed with deep learning technology is deep factorization machine (DeepFM) and neural matrix factorization (NeuMF) models. Both models excel in recommending implicit feedback data. The DeepFM model yields the lowest loss function values, while the NeuMF model outperforms the competing models in terms of HR@20 (a commonly used indicator to measure the recall rate) and loss functions. In summary, this research addresses critical issues in cross-border e-commerce by developing personalized recommendation systems and integrating deep learning with traditional recommendation algorithms to enhance global recommendations.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"76 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440818","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 Reinforcement Learning for Adaptive Stock Trading 自适应股票交易的深度强化学习
Journal of Organizational and End User Computing Pub Date : 2024-01-07 DOI: 10.4018/joeuc.335083
Lei Zhao, Bowen Deng, Liang Wu, Chang Liu, Min Guo, Youjia Guo
{"title":"Deep Reinforcement Learning for Adaptive Stock Trading","authors":"Lei Zhao, Bowen Deng, Liang Wu, Chang Liu, Min Guo, Youjia Guo","doi":"10.4018/joeuc.335083","DOIUrl":"https://doi.org/10.4018/joeuc.335083","url":null,"abstract":"In this study, the authors explore how financial institutions make decisions about stock trading strategies in a rapidly changing and complex environment. These decisions are made with limited, often inconsistent information and depend on the current and future strategies of both the institution itself and its competitors. They develop a dynamic game model that factors in this imperfect information and the evolving nature of decision-making. To model reward transitions, they utilize a combination of t-Copula simulation of a non-stationary Markov chain, probabilistic fuzzy regression, and chaos optimization algorithms. They then apply deep q-network, a method from deep reinforcement learning, to ensure the effectiveness of the chosen strategy during ongoing decision-making. The approach is significant for both researchers across fields and practical professionals in the finance industry.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"65 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449032","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
Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management 通过用于需求预测和库存管理的 BO-CNN-LSTM 优化供应链管理
Journal of Organizational and End User Computing Pub Date : 2024-01-07 DOI: 10.4018/joeuc.335591
Rong Liu, Vinay Vakharia
{"title":"Optimizing Supply Chain Management Through BO-CNN-LSTM for Demand Forecasting and Inventory Management","authors":"Rong Liu, Vinay Vakharia","doi":"10.4018/joeuc.335591","DOIUrl":"https://doi.org/10.4018/joeuc.335591","url":null,"abstract":"This project addresses demand forecasting and inventory optimization in supply chain management. Traditional methods have limitations with complex demand patterns and large-scale data. Deep learning techniques are employed to enhance accuracy and efficiency. The project utilizes BO-CNN-LSTM, leveraging Bayesian optimization for hyperparameter tuning, Convolutional Neural Networks (CNNs) for spatiotemporal feature extraction, and Long Short-Term Memory Networks (LSTMs) for modeling sequential data. Experimental results validate the effectiveness of the approach, outperforming traditional methods. Practical implementation in supply chain management improves operational efficiency and cost control.","PeriodicalId":504311,"journal":{"name":"Journal of Organizational and End User Computing","volume":"20 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448436","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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