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检索条件"任意字段=2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025"

共 614 条记录，以下是271-280 订阅

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Comparative study on green logistics based on neural network word embedding 4

Comparative study on green logistics based on neural network...

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2024 4th international conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Chen, Meng School of Management Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9781510686304

Green logistics present rapid development trend in China, this study combined with academic platforms and media platforms, using neural network word2vec word embedded algorithm to carry out comparative study of green logistics, choose China network and China logistics and purchasing network as a research object, to collect "green logistics" corpus, build academic platforms and media platforms two corpus and word2vec modeling. On this basis, compare the top 20 words with the top 20 words most similar to "green logistics" with the attention topics of the two platforms. Then, using the t-SNE algorithm, visually displaying and comparing through the *** package, and the results show that there are differences between green logistics in academic and media platforms. This study innovatively uses the word2vec algorithm to compare the differences between academic platforms and media platforms in green logistics from the semantic level, thus providing a new perspective for green logistics research. © 2025 SPIE.

关键词： Education and training Carbon Neural networks visualization Industry data modeling Evolutionary algorithms Reflection visual process modeling Transportation

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st international conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Classification of Cacao Pods Ripeness Using Convolutional Neural Networks 16

Classification of Cacao Pods Ripeness Using Convolutional Ne...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

作者： Borromeo, Karl O. Bonotan, Melbert R. Llagas, Archie P. Juan, Joana Marie O. Sanchez, Philip Donald C. Berro, Nathan Kit A. Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City Philippines Center for Resource Assessment Analytics and Emerging Technologies College of Engineering and Geo-Sciences Caraga State University Butuan City Philippines Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City Philippines

This paper presents methods for the development of Ripeness Identification of Cacao Pods using Convolutional Neural Network (CNN) and precision agriculture employed in a Mobile Application. This research explores if there is a correlation between the color dynamics of cacao pods and its physiological characteristics, and the possibility of using this correlation to develop Artificial Intelligence (AI) model for ripeness and variety identification, which conventional eye estimation resulted to unripe and overripe harvesting of Cacao Pods. data analysis and correlation was developed, integrating detection and classification models based on the Single Shot Detection (SSD) MobileNet and visual Geometry Group (VGG) 16 or VGG16 layers network architectures, respectively. The mobile application utilizes the TensorFlow template for object detection and incorporates image preprocessing techniques for enhanced accuracy. The successful integration of the model into a mobile application demonstrates its potential for practical applications in the cacao industry. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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visual analysis of ecological environment monitoring data based on SpringBoot 24

Visual analysis of ecological environment monitoring data ba...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Shi, Jinmei Wen, Xinyuan Liu, Delong Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

Based on SpringBoot and Echarts technology, this study conducted in-depth analysis and visual display of ecological environment data. Firstly, rich environmental data, including air quality and water quality monitoring, are obtained through multiple data sources. Secondly, data cleaning and preprocessing techniques are used to ensure the quality and integrity of the data and provide a reliable basis for subsequent analysis. In terms of research methods, combined with literature research method, statistical analysis method and comparative analysis method, the ecological environment data were comprehensively analyzed and compared, and the key trends and problems in the data were revealed. In terms of system design, a complete database structure is established, the system interface is designed in line with the principles of web design, and the visual display of ecological environment data is realized. Finally, through the data analysis of multiple aspects, some important environmental change trends and problems are found, which provides an important reference for environmental protection and improvement. Future research directions include further integration of data sources and algorithmic models, integration of real-time data monitoring systems, promotion of public participation and awareness, continuous improvement of waste disposal technologies, and strengthening of data monitoring and management. In summary, this study provides novel methods for understanding and managing complex ecological environmental data, which provides strong support for environmental protection and sustainable development. © 2024 Copyright held by the owner/author(s).

关键词： data visualization

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Optimization of the integrated video surveillance system with elements of data analysis 3

Optimization of the integrated video surveillance system wit...

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3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks, CH and CMiGIN 2024

作者： Odarchenko, Roman Pevnev, Vladyslav Pinchuk, Alla Polihenko, Oleh National Aviation University Liubomyra Huzara Ave. 1 Kyiv03058 Ukraine

The video surveillance market is one of the most popular areas in information technology. Therefore, the question of creating an effective approach to the construction of video surveillance systems, a method of optimizing subsystems and their correct adaptation, which was solved in this work, is acute. To solve this problem, the task was decomposed into user groups and software and hardware levels, existing tools in the field of application and their analysis were identified. An analysis of new and unpopular tools was carried out to check the expediency of their use. Thus, several main levels of the video surveillance system were created, each of which had separate tools for solving problems. The following levels are defined: equipment selection, solution topology, network optimization, use of data processing hardware resources, and visualization of system operation. Each of these sections carries a global solution to problems, namely, reducing financial costs, reducing the number of equipment, eliminating unprofitability, thereby ensuring the use of a smaller number of environmental resources for specific tasks and saving the time and efficiency of the work of specialists. As a result, a method of optimizing integrated video surveillance systems was developed for two blocks of users: the administrator and the operator. The method is modular, so its further development and filling with new technologies is possible in the future. © 2025 Copyright for this paper by its authors.

关键词： Security systems

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Artificial Intelligence-based disinformation discovery for social networks 5

Artificial Intelligence-based disinformation discovery for s...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Zhu, Zhiying Zhujiang College South China Agricultural University Guangdong Guangzhou510900 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this era of rapid development of the Internet, disinformation, as a new public opinion phenomenon, is generated and disseminated through the Internet. The most important feature that distinguishes it from general disinformation is that the means of dissemination have changed from verbal transmission to the dissemination of textual information or multimedia information on the Internet. Its untruthful and deceptive information content has a great impact on society and people's lives. The wide range and fast speed of dissemination of false information makes it difficult to judge its dissemination channels and the scope of dissemination, and therefore it is impossible to detect false information in an effective and timely manner. In this paper, through the simulation of the BSS virtual community propagation model, the SIR model, we can discover its propagation mode in social networks. After getting a rough idea of the pattern of disinformation dissemination. From the perspective of artificial intelligence through the feature extraction model detection method and Web content filtering model method to effectively detect false information, to reduce the negative impact of false information dissemination on society. © 2025 SPIE.

关键词： Tunable filters Social networks Internet visual process modeling Artificial intelligence Feature extraction Optical filters data transmission Information visualization Virtual reality

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Software Engineering Courses Through the Lens of an Education Intelligence Tool

Software Engineering Courses Through the Lens of an Educatio...

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conference on Software Engineering Education & Training (CSEE&T)

作者： Manuel Stöger Johann Grabner Mario Bernhart Thomas Grechenig Research Group for Industrial Software (INSO) TU Wien Vienna Austria

ISBN: (数字)9798331537098

ISBN: (纸本)9798331537104

In software engineering education, continuous evaluation of student progress and project outcomes plays a vital role in improving student performance and course design. This paper presents the development and application of the “Education Intelligence (EI)” system, combining mining software repositories (MSR) techniques with data visualization and advanced analytical capabilities. Designed for software engineering courses, based on previous research and expert input, the developed dashboard provides visual analytics to track student engagement, repository contributions, and project management activities over time. Being deployed within the course of three academic years (2020-2023) at a large European university, detailed comparisons have been possible, contributing to a more refined and responsive educational approach. Findings revealed significant variations in performance and unexpected time commitments, uncovering previously unnoticed issues. The knowledge gained was then used to reduce time demands, distribute work more equally, and finally improve student outcomes. By integrating MSR and visual analytics, this paper highlights how EI contributes to a more adaptive, data-driven approach toward enhancing both student learning experiences and the overall quality of software engineering courses.

关键词： Pandemics visual analytics Education Prototypes data visualization Project management Software data mining Usability Software engineering

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visual analytics for Multivariate Time-Series data Using Interactive Dimensionality Reduction Methods

Visual Analytics for Multivariate Time-Series Data Using Int...

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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Mizuki Emmei Naoki Okami Takanori Fujiwara Naohisa Sakamoto Jorji Nonaka Kobe University Linköping University RIKEN R-CCS

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

One advancing machine-learning-based analysis approach for multivariate time-series data is representing data as a third-order tensor and then applying dimensionality reduction (DR) methods. In this work, we introduce a visual analytics method that employs multiple interactive DR methods to support both extraction and interpretation of latent patterns of multivariate time-series data. Our method first allows analysts to select an analysis focus from three axes: instance, variable, and time axes. Then, the method applies a multi-step DR method to produce a 2D scatterplot that depicts latent patterns of the selected axis’s elements (e.g., time points). Afterward, the analysts interactively investigate data groups that appeared in the plot with a DR method designed for comparative analysis. The method can be further applied iteratively to perform more precise and detailed analyses. We implement a prototype system and demonstrate the effectiveness of our method by analyzing supercomputer log data.

关键词： Dimensionality reduction Tensors Reviews visual analytics data visualization Prototypes Supercomputers Iterative methods data mining Pattern analysis

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CoDAQ: Congressional-Based data Imputation for Aggregate Queries

CoDAQ: Congressional-Based Data Imputation for Aggregate Que...

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international conference on Big data and Smart Computing (BIGCOMP)

作者： Mohamed A. Sharaf Nishi Kochunni Heba Helal Omar El Harrouss College of Information Technology UAE University Al Ain United Arab Emirates

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

visual analytics are widely used across various domains, including business intelligence, healthcare, and e-commerce, to provide data-driven insights that enhance decision-making processes. These systems analyze large datasets to generate visualizations that help users identify trends, patterns, and actionable insights. These insights rely heavily on accurate and complete data. However, effectively addressing the challenges posed by missing data is crucial for ensuring the accuracy and reliability of data-driven insights and visualizations. Incomplete datasets often lead to reduced accuracy in visualizations, prompting users to adopt costly imputation strategies to clean the data. This paper presents an innovative approach, ‘CoDAQ’-Congressional-Based data Imputation for Aggregate Queries that explores the use of a stratified-based selection technique for imputation to handle missing values within a specified imputation budget effectively. CoDAQ strategically selects cells for imputation by analyzing missing data patterns across different groups, ensuring a structured and representative approach. This technique offers enhanced accuracy and consistency over baseline methods. Our findings illustrate how the proposed technique enhances the utilization of available data, improves the accuracy of visualizations, and reduces bias in analysis, ultimately increasing the performance of visual analytics.

关键词： Accuracy visual analytics data visualization Medical services data aggregation Sampling methods Market research Imputation Reliability Electronic commerce

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Bitcoin Forecasting Using Deep Learning and Time Series Ensemble Techniques 44th

Bitcoin Forecasting Using Deep Learning and Time Series Ense...

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44th SGAI international conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Zafar, Huma Kapetanakis, Stylianos Middlesex University LondonNW4 1DY United Kingdom Distributed Analytics Solutions LondonE14 5RE United Kingdom

ISBN: (纸本)9783031779145

This research investigates Bitcoin price prediction by reviewing the current state of the art, comparing Time Series and Deep Learning models, evaluating their performance on a range of metrics, and assessing the selected model’s real-world applicability. It reviewed existing studies on Deep Learning, Time Series, and Bitcoin, and utilised 44,419 hourly Bitcoin data points for data visualization and mining. The findings showed that ensemble models, particularly stacked ensemble, outperformed other models in predictive accuracy, with the lowest error metrics and highest R2 value. Deep learning models also performed well, but with slightly higher errors. Time Series models were inadequate for Bitcoin price prediction, as evidenced by their negative R2 values. These results contribute to our understanding of effective modelling approaches for Bitcoin price prediction. The study also suggests promising avenues for future research, such as fine-tuning ensemble models, incorporating advanced feature engineering techniques, and exploring volatility forecasting. Thus, the study offers a valuable contribution to the field of cryptocurrency research, advancing knowledge on Bitcoin price prediction and fostering a deeper comprehension of the intricacies underlying cryptocurrency markets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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