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检索条件"任意字段=Conference on Visual Data Exploration and Analysis II"

共 2787 条记录，以下是21-30 订阅

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Empowering Social Sciences with data visualization - An Insights and explorations into Behavioral Patterns and Urban Dynamics

Empowering Social Sciences with Data Visualization - An Insi...

引用

Pervasive Computational Technologies (ICPCT), International conference on

作者： Raihana Parveen Yeo Boon Chin K. Naveen B. Sonia B. Ramana G. LakshmiAlekhya Visual Media Science Research Laboratory QIS College of Engineering and Technology Ongole India Faculty of Engineering and Technology Multimedia University

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

visual data is an integral and essential aspect of any science, a powerful tool for description and exploration in social sciences005. Abstract: In this study, we apply new advanced ways of visualization to bring light on steps helping policy makers better understand multifaceted social phenomena using datasets publicly available. Through combining geospatial, temporal and network visualizations, the framework provides an intimate understanding of urban growth patterns of i-Social networks and behavior characteristics. Findings show that attractive visualizations make interpreting and revealing otherwise hidden patterns easier and provide insight into policy. Such an approach would finally close the gap between data complexity and actionable insights, offering social scientists a solid foundation for research with real impact.

关键词： visualization Fluctuations data analysis Correlation Geology Urban planning data visualization Solids Market research Geospatial analysis

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exploration and Practice of data visualization 24

Exploration and Practice of Data Visualization

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Proceedings of the 2024 5th International conference on Big data Economy and Information Management

作者： Gongzheng Yu Shaowu Yang College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9798400711862

Currently, data visualization is widely used in industries such as smart city display, rail transit monitoring and network traffic analysis, public opinion analysis. The data sources are relatively stable and the data formats are relatively regular. However, in some scenarios, the data is relatively scattered and of low quality, and there is limited introduction to the entire process of collecting, circulating, and aggregating this data into visual applications. Based on this, a comprehensive implementation method for metadata collection, raw data aggregation, and data visualization was explored and practiced, and a clear and scalable solution was proposed, from the backend to the frontend, the ideas and methods of data collection and aggregation, as well as the methods of building websites using the Django framework and implementing typical visualization applications using Echarts tools, were introduced.

关键词： data aggregation

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Satellite-Based Monitoring of a Multi-span Pre-stressed Concrete Bridge Crossing the Po River 5th

Satellite-Based Monitoring of a Multi-span Pre-stressed Conc...

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5th International conference on Protection of Historical Constructions, PROHITECH 2025

作者： Miano, Andrea Giordano, Pier Francesco Di Martire, Diego Giulivo, Federico Liuzzo, Riccardo Tufano, Rita Bausilio, Giuseppe Ballio, Francesco Calcaterra, Domenico Landolfo, Raffaele Limongelli, Maria Pina Prota, Andrea Department of Engineering Pegaso University Naples80143 Italy Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Piazza Leonardo da Vinci 32 Milan20133 Italy Department Earth Sciences Environment and Resources University of Naples Federico II Naples80126 Italy Department of Structures for Engineering and Architecture University of Naples Federico II Naples80125 Italy Department of Civil and Environmental Engineering Politecnico di Milano Piazza Leonardo da Vinci 32 Milan20133 Italy Faculty of Engineering Lisa Meitner Guest Professor at Lund University John Ericssons Väg 1 Lund Sweden

ISBN: (纸本)9783031873119

Satellite-based Synthetic Aperture Radar Interferometry (InSAR) monitoring is a potential complement to conventional Structural Health Monitoring (SHM) methods, due to its non-invasive nature and high spatial coverage. These characteristics allow for continuous monitoring without disrupting bridge operations and enable the exploration of historical data for trend analysis and proactive maintenance. In this paper, a multi-span pre-stressed concrete bridge crossing the Po River is investigated using InSAR data. The bridge has a total length of approximately 650 m, divided into 9 spans of about 60 m each. The bridge was decommissioned following structural damage due to subsidence. The objective of this work is to study, through the use of satellite data - both from COSMO-SkyMed (CSK) and European Ground Motion Service (EGMS) - the displacements suffered by the bridge. The comparison between the ascending and descending datasets provided by CSK and EGMS is also shown. Finally, the relationship between displacements and environmental parameters, e.g. temperature and water height, is investigated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Structural health monitoring

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Enhancing Human - Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: Application to Collision Detection 5th

Enhancing Human - Robot Collaboration in the Industry 5.0 Fr...

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5th International conference on Innovative Intelligent Industrial Production and Logistics

作者： Ciampi, Francesco G. Diallo, Thierno M. L. Mhenni, Faida Patalano, Stanislao Choley, Jean-Yves Univ Naples Federico II Dept Ind Engn I-80125 Naples Italy ISAE Supmeca Quartz Lab EA7393 3 Rue Fernand Hainaut F-93400 St Ouen France

ISBN: (纸本)9783031807596;9783031807602

In the context of rapidly evolving technology and increased attention to social and environmental dimensions, the industrial sector is transitioning from Industry 4.0 to Industry 5.0. This new paradigm emphasizes human centrality in highly automated environments, necessitating the exploration of collaboration mechanisms between humans and robots. This study investigates the application of Physics-Informed Neural Networks (PINNs) to enhance Human-Robot Collaboration (HRC). PINNs integrates the traditional data-driven approach based on machine learning models with a prior physical knowledge of the system, providing a valuable solution when data are scarce or physical models are too complex or incomplete. The study first focuses on the main aspects of interest in the field of HRC through a state-of-the-art analysis, evaluating the application of Physics-Informed Neural Networks (PINNs) in HRC and robotics. It then delves into the definition of PINNs and their implementation. Finally, as a proof of concept, the model is applied to a case study concerning collision detection in a 6 DoF robotic arm. This is achieved by predicting the joint currents and comparing them with the measured values to identify the contribution due to external forces such as collisions. The results demonstrate that the PINNs model outperforms a traditional neural network, achieving an average error below 10%. Additionally, the collision detection application shows an f1_score of 0.80, indicating strong performance.

关键词： Industry 5.0 Human-robot collaboration Collision detection Physics-informed neural networks Physics-informed machine learning

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Faces in the Fog: A Deep Dive into Face De-Identification Techniques and Their Comparative analysis

Faces in the Fog: A Deep Dive into Face De-Identification Te...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Shaonti Goswami Sudipty Konica Paul Sanyam Jawlia Anurag Goel Department of CSE Delhi Technological University New Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Face de-identification have drawn a lot of attention due to growing privacy concerns and the growing requirement for anonymization in visual data. This review suggests a systematic exploration of current face de-identification methods, emphasizing three main deep learning-based methods: diffusion models, latent Variable based transformations, and GAN-based models. The in-depth analysis of this literature provides a comparison of how well each strategies protect privacy while retaining data utility by focusing on the advantages and disadvantages of them. Our analysis delves deeper into the benefits and limitations of these models, focusing on important issues such as adversarial robustness, systematic biases, and trade-offs between realistic image generation and privacy preservation. This comprehensive overview synthesizes data from several studies, identifies important gaps in existing approaches and provides guidance to researchers and industry practitioners directing to more reliable and ethical de-identification frameworks.

关键词： Deep learning data privacy Privacy visualization Systematics Reviews Focusing Diffusion models Protection Faces

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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2025 International conference on visual Analytics and data visualization, ICVADV 2025

作者： Nagalakshmi, Thirumalasetty Sambasivudu, M. Sai, Thota Siva Ratna Dept. of Cse Telangana India

ISBN: (纸本)9798331521394

Modern agriculture relies on plant leaf disease detection to reduce crop losses and guarantee food security. This study takes a look at the three main types of methods used to identify plant leaf diseases: conventional methods, techniques based on Machine Learning (ML), and methodology based on Deep Learning (DL). Although they provide essential insights, the scalability and precision of traditional methods-which depend on chemical analysis, image processing, and human inspection-are severely lacking. ML techniques, which make use of algorithms such as Random Forests (RF) and Support Vector Machines (SVM), automate feature-based categorization, which improves detection. Automated disease identification with high accuracy has recently been made possible by DL, which uses sophisticated methods like semantic segmentation, object recognition, and transfer learning in conjunction with architectures like Convolutional Neural Networks (CNNs). This study examines all of these approaches in detail, comparing and contrasting their advantages, disadvantages, and possible uses. Emerging developments, such as eXplainable Artificial Intelligence (XAI) and Internet of Things (IoT)-based systems, are also highlighted in the discussion, with an emphasis on how they will influence smart and sustainable agriculture in the future. © 2025 IEEE.

关键词： Semantic Segmentation

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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visual Analytics and data visualization (ICVADV), International conference on

作者： Thirumalasetty Nagalakshmi M Sambasivudu Thota Siva Ratna Sai Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

关键词： Support vector machines Deep learning Reviews Explainable AI Semantic segmentation Plants (biology) Transfer learning Food security Object recognition Diseases

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Knowledge-based Graph exploration analysis on Customer’s Preference of Insurance Services

Knowledge-based Graph Exploration Analysis on Customer’s Pr...

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International ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Yingfan Chen Chalermpon Kongjit College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics.

关键词： visualization Sentiment analysis Text analysis Social networking (online) Knowledge based systems Insurance Knowledge graphs History

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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visual Analytics and data visualization (ICVADV), International conference on

作者： P. Suresh T Sivakumar N. Revathy M. Pandi Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Master of Computer Applications Hindustan College of Arts and Science Coimbatore Tamil Nadu India Department of Information Technology SRM Madurai College for Engineering and Technology Sivagangai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance.

关键词： Technological innovation IEEE merchandise Heuristic algorithms visual analytics Layout Reinforcement learning Real-time systems Internet of Things Artificial intelligence Optimization

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Developing a Comprehensive Task Framework for Effective Workforce analysis 25

Developing a Comprehensive Task Framework for Effective Work...

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Companion Proceedings of the ACM on Web conference 2025

作者： Rebecca Dew Mingzhao Li Weidong Liu Sandya Baratha Raj Pearson Melbourne Australia Pearson Sydney Australia

ISBN: (纸本)9798400713316

Effective workforce analysis, planning, and management require a deep understanding of the tasks and skills associated with different roles. This paper introduces a novel methodology for developing a comprehensive task framework that leverages Large Language Models (LLMs) and large-scale job ads data. We first propose an innovative approach to task taxonomy design, which involves the decomposition and reconstruction of tasks into a hierarchical structure based on action-object pairings, systematically refined using LLMs. The methodology extends to integrating the taxonomy with occupation and skill linkages derived from job ads, ensuring alignment with real-world workforce dynamics. Finally, we demonstrate the practical value of this framework through a visual analytics system that enables interactive exploration and analysis of tasks, occupations, and associated skills, highlighting its potential to transform workforce analysis. Demo video: https://***/41txBZK

关键词： gpt

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