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

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A platform to support the visual analysis of the SALMANTICOR study outcomes: conveying cardiological data to lay users 21

A platform to support the visual analysis of the SALMANTICOR...

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9th International conference on Technological Ecosystems for Enhancing Multiculturality (TEEM)

作者： Vazquez-Ingelmo, Andrea Garcia-Penalvo, Francisco Jose Sanchez-Puente, Antonio Vicente-Palacios, Victor Laffond, Ana Garcia-Galindo, Alberto Sampedro-Gomez, Jesus Ignacio Dorado-Diaz, P. Sanchez, Pedro L. Univ Salamanca Res Inst Educ Sci Dept Comp Sci GRIAL Res Grp Salamanca Spain Hosp Univ Salamanca SACyL Cardiol Dept Salamanca Spain Univ Salamanca Fac Med IBSAL Salamanca Spain CIBERCV ISCiii Madrid Spain Philips Healthcare Salamanca Spain Hosp Univ Salamanca Cardiol Dept Salamanca Spain IBSAL Salamanca Spain

ISBN: (纸本)9781450390668

Cardiovascular diseases are the largest risk factor for mortality in developed countries, and hospitals are facing an increasing workload in relation to cardiovascular diseases. In this regard, policymaking related to these diseases and based on epidemiological knowledge of the population can be a powerful means to prevent and treat them. The SALMANTICOR study was conceived in this context. This study had the purpose of collecting data concerning the prevalence and incidence of structural heart disease in the province of Salamanca (Spain). However, the amount of data and variables collected (more than 300), can make the understanding of the results cumbersome for non-experience users. This work overviews the design and architecture of a data visualization platform to support the exploration of the SALMANTICOR study results, with a special focus on conveying this information to lay users.

关键词： data visualization Health domain Cardiology Human-Computer Interaction

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Towards Accurate Text-based Image Captioning with Content Diversity exploration

Towards Accurate Text-based Image Captioning with Content Di...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu, Guanghui Niu, Shuaicheng Tan, Mingkui Luo, Yucheng Du, Qing Wu, Qi South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Adelaide Adelaide SA Australia Minist Educ Key Lab Big Data & Intelligent Robot Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase;2) it is non-trivial to capture the complex relationship between diverse texts in an image;3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct the multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images.

关键词： visualization Computer vision Image resolution Benchmark testing Pattern recognition Proposals Optical character recognition software

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A comparative analysis of different terrain correction methods based on Landsat-8 OLI data 2

A comparative analysis of different terrain correction metho...

引用

2021 2nd International conference on Geology, Resources exploration and Development, ICGRED 2021

作者： Zhang, Ying Ai, Yunfei Su, Fei Su, Hang He, Zhixian Wang, Changhai Ying, Jun Zhou, Minlu China Traffic Telecommunication and Information Center Beijing100011 China College of Forestry Beijing Forestry University Beijing100083 China GuangXi Communications Design Group CO. LTD. 530029 China

The topographic effect has long been recognized as one of problems for quantitative remote sensing. The paper compares the correction results of OLI data based on C model, Cosine model, SCS model, Minnaert model and pixel-based Minnaert Correction Method. The visual inspection, statistical analysis and regression analysis of images were used to analyze the results before and after correction of image. The results show that the C model has a better correction effect. The Cosine model and SCS model were significantly overcorrected. However, C model, Minnaert model and slope-based Minnaert model weakened the influence of terrain. By comparative analysis, the C correction method was optimal model for OLI image. © Published under licence by IOP Publishing Ltd.

关键词： Regression analysis

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Breast Cancer Classification: In-depth exploration of Different Paradigms and Ensemble Technique

Breast Cancer Classification: In-depth Exploration of Differ...

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International conference on Computing for Sustainable Global Development (INDIACom)

作者： Abdulahi Mahammed Adem Ravi Kant Sonia Sudesh Kumari C. Naga Swaroopa Gaurav Gupta Yogananda School of AI Computer and Data Sciences Shoolini University Solan India ECE Department Maharishi Markandeshwar (Deemed to be University) Mullana Ambala BPSMV Khanpur Kalan Sonipat India Annamacharya Institute of Technology and Sciences Rajampet Andhra Pradesh India

Many lives are lost to breast cancer every year. Prognosis and timely identification of cancer types are vital components of cancer research, as early diagnosis is critical to effective treatment. This study presents a comprehensive investigation into breast cancer classification, employing an extensive suite of datasets from Wisconsin Breast Cancer for machine learning models. Rigorous preprocessing techniques were applied to refine the dataset, segmented into groups of testing and training. The ensemble of models, comprising Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine (SVM), Gaussian Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, together with Deep Neural Network (DNN), underwent meticulous evaluation. For a more in-depth analysis, performance metrics like ROC-AUC scores, F1 Score, accuracy, precision, and recall were used. The ROC curve, or receiver operating characteristic provided visual insights, highlighting the trade-offs involving specificity and sensitivity. The ensemble model emerged as a promising approach, showcasing collaborative strengths across diverse algorithms, and achieves an accuracy of 98.2%. Confusion matrices and running time comparisons further elucidated model intricacies and computational efficiencies. This study will contribute valuable insights into breast cancer diagnostics, emphasizing the importance of tailored model selection and collaboration in clinical decision-making.

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visual analysis of Intertextuality Based on Knowledge Graph 2021

Visual Analysis of Intertextuality Based on Knowledge Graph

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2021 3rd International conference on Artificial Intelligence and Advanced Manufacture

作者： Yi Gao Chenming Duan Qiuyang Cao Dalian Polytechnic University China Dalian University of Technology China

ISBN: (纸本)9781450385046

The rapid growth of the scientific literature is presenting researchers with increasing challenges. That means relying on human resources to complete the mining of massive resources or the exploration of cutting-edge trends in a short period of time is impossible. To address this problem, this paper presents a method for visual literature research based on knowledge graph. The method is to use the software CiteSpace to make a comprehensive visual analysis of the origin, development, themes and frontier trends of intertextuality theory, and use artificial intelligence to process data, information and images, so as to complete the construction of scientific knowledge graph of intertextuality theory, enrich literature resources and improve the scientific research environment. The knowledge graph is a scientific measurement method which can visually present literature research results and ultimately realize the grand goal of artificial intelligence serving the development of scientific research.

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Sparsity exploration for Structured and Unstructured Weight Formations in CNN Architecture

Sparsity Exploration for Structured and Unstructured Weight ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and conference (APSIPA)

作者： Devi Noor Endrawati Infall Syafalni Nana Sutisna Trio Adiono University Center of Excellence on Microelectronics Bandung Institute of Technology School of Electrical Engineering and Informatics

The Convolutional Neural Network (CNN) is a frequently used algorithm used technique for processing pixel data and visual images. Nevertheless, the utilization of extensive network sizes poses significant obstacles to the underlying technology’s throughput and energy efficiency. Various methods have been devised to enhance the efficiency and computing speed of Convolutional Neural Network (CNN) acceleration, improving the accuracy of the CNN process. Compression techniques have been employed in the algorithmic aspect to minimize the number of parameters in each layer, enhancing the detection process’s efficiency. Pruning is a compression technique that involves the removal of neurons and synapses that do not play a substantial role in the detection process, hence minimizing their impact on the Neural Network layer. The compression method generates sparse data, wherein a significant amount of data has a value of zero. Various pruning techniques provide distinct types of sparse data, encompassing organized structured sparse and unstructured sparse *** consequent form of sparse data structure has many advantages and downsides. Based on an analysis of various extant sparse data structures, an innovative accelerator has been developed to perform convolution operations using various types of sparse data obtained via distinct pruning procedures. The design architecture outlined in this study involves the removal of all zero-valued data weights through a pruning technique following the training process. The accelerator will process all non-zero weights by performing the convolution process promptly, utilizing the "weight stream" data. The findings from the conducted design testing indicate that the utilization of trimmed sparse data leads to improved performance in terms of faster output during the convolution process. The researchers conducted experiments utilizing diverse types of sparse weighted data, encompassing both structured and unstructured forms. These experi

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ConceptExplorer: visual analysis of Concept Drifts in Multi-source Time-series data 15

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-...

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15th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Wang, Xumeng Chen, Wei Xia, Jiazhi Chen, Zexian Xu, Dongshi Wu, Xiangyang Xu, Mingliang Schreck, Tobias Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China Hangzhou Dianzi Univ Inst Graph & Image Hangzhou Peoples R China Zhengzhou Univ Zhengzhou Peoples R China Graz Univ Technol Graz Austria

ISBN: (纸本)9781728180090

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

关键词： Temporal data data analysis reasoning problem solving and decision making machine learning techniques

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MIVA: Multimodal Interactions for Facilitating visual analysis with Multiple Coordinated Views 24

MIVA: Multimodal Interactions for Facilitating Visual Analys...

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24th International conference Information visualisation (IV)

作者： Chowdhury, Imran Moeid, Abdul Hoque, Enamul Kabir, Muhammad Ashad Hossain, Md Sabir Islam, Mohammad Mainul Chittagong Univ Engn & Technol Chittagong Bangladesh York Univ Toronto ON Canada Charles Sturt Univ Bathurst NSW Australia Verizon Media Sunnyvale CA USA

ISBN: (纸本)9781728191348

Typically, people perform visual data analysis using mouse and touch interactions. While such interactions are often easy to use, they can be inadequate for users to express complex information and may require many steps to complete a task. Recently natural language interaction has emerged as a promising technique for supporting exploration with visualization, as the user can express a complex analytical question more easily. In this paper, we investigate how to synergistically combine language and mouse-based direct manipulations so that weakness of one modality can be complemented by the other. To this end, we have developed a novel system, named Multimodal Interactions System for visual analysis (MIVA), that allows user to provide input using both natural language (e.g., through speech) and direct manipulation (e.g., through mouse or touch) and presents the answer accordingly. To answer the current question in the context of past interactions, the system incorporates previous utterances and direct manipulations made by the user within a finite-state model. We tested the applicability of MIVA on several dashboards including a COVID-19 dashboard that visualizes coronavirus cases around the globe. Our demonstration provides initial indication that the MIVA system enhances the flow of visual analysis by enabling fluid, iterative exploration and refinement of data in a dashboard with multiple-coordinated views.

关键词： Multimodal interaction multiple-coordinated views natural language interface direct manipulation

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visual Recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning 1

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1st International conference on Pattern analysis and Machine Intelligence, ICPAMI 2024

作者： Li, Si Yang, Xiaoqi Guangzhou College of Commerce Guangzhou China

ISBN: (数字)9789819633494

ISBN: (纸本)9789819633487

This article designs and constructs a deep prediction model that combines deep neural networks and Transformer encoders. Build a prediction model using deep neural networks and Transformer encoders. Due to the powerful feature extraction ability of deep neural networks and their ability to fuse heterogeneous data from multiple sources, they are used to process basic information about users and scenic spots. The Transformer encoder is used to process user comment data, modeling long-distance dependencies between words in sentences through position encoding and self attention at the encoding end, thereby fully mining the comprehensive features of comment data and obtaining more potential user interest features. The features of the last three networks are input into the factorization machine to further learn their potential interaction relationships, thereby improving the prediction accuracy of the model and alleviating the problem of data sparsity. Finally, the effectiveness and accuracy of this recommendation model were verified through experimental comparison with traditional recommendation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Combining visual analytics and machine learning for reverse engineering in assembly quality control

Combining visual analytics and machine learning for reverse ...

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2021 conference on visualization and data analysis, VDA 2021

作者： Ruediger, Patrick Claus, Felix Hamann, Bernd Hagen, Hans Leitte, Heike Technische Universität Kaiserslautern Germany

The authors introduce an integrative approach for the analysis of the high-dimensional parameter space relevant for decision-making in the context of quality control. Typically, a large number of parameters influence the quality of a manufactured part in an assembly process, and our approach supports the visual exploration and comprehension of the correlations among various parameters and their effects on part quality. We combine visualization and machine learning methods to help a user with the identification of important parameter value settings having certain effects on a part. The goal to understand the influence of parameter values on part quality is treated from a reverse engineering perspective, driven by the goal to determine what values cause what effects on part quality. The high-dimensional parameter value domain generally cannot be visualized directly, and the authors employ dimension reduction techniques to address this problem. Their prototype system makes possible the identification of regions in a high-dimensional parameter value space that lead to desirable (or non-desirable) parameter value settings for quality assurance. They demonstrate the validity and effectiveness of our methods and prototype by applying them to a sheet metal deformation example. © 2020 Society for Imaging Science and Technology.

关键词： Quality control

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