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检索条件"任意字段=2020 Conference on Visualization and Data Analysis, VDA 2020"

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DECODE: A New Cloud-Based Framework for Advanced visualization, Simulation, and Optimization Treatment of Peripheral Artery Disease 23

DECODE: A New Cloud-Based Framework for Advanced Visualizati...

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23rd IEEE International conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AboArab, Mohammed A. Potsika, Vassiliki T. Gkois, George Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece Electronics and Electrical Communication Engineering Department Faculty of Engineering Tanta Egypt Biomedical Research Institute Foundation for Research and Technology-Hellas IoanninaGR 45110 Greece

ISBN: (纸本)9798350393118

Recent research indicates a worrisome surge in the prevalence of Peripheral Artery Disease (PAD). This alarming rise underscores the pressing need for new approaches to PAD treatment. Consequently, this paper introduces the DECODE platform, a cloud-based solution aimed at revolutionizing the treatment of PAD using Drug-Coated Balloons (DCBs). DECODE's main contribution lies in its integration of cutting-edge technologies, including 3D-enabled visualization, augmented and virtual reality, investigation of drug release mechanisms, in-vitro and in-silico simulations, computational modeling, multiscale analysis, and data analysis, into a cohesive system. This integration facilitates collaboration among researchers, clinicians, and medical professionals and provides a holistic approach to PAD treatment, encompassing visualization, simulation, optimization, and outcome analysis. DECODE's impressive performance metrics, assessed by Google's Lighthouse tool, further emphasize its efficiency: a performance score of 91%, accessibility at 90%, adherence to best practices at 83%, SEO-friendliness with a score of 100%, and optimization as a progressive web app. These metrics highlight its strengths as a comprehensive and effective solution for PAD treatment. © 2023 IEEE.

关键词： data visualization

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Non-destructive Prediction of Loquat Sugar Content and Hardness Using Hyperspectral Imaging Combined with Weighted Band Selection 17

Non-destructive Prediction of Loquat Sugar Content and Hardn...

引用

17th International conference on Advanced Computer Theory and Engineering

作者： Zhang, Longfeng Chen, Chong Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798331539894;9798331539887

Sugar content and firmness are important indicators for measuring the quality of loquats. In order to perform non-destructive testing on the sugar content and hardness of loquat, the hyperspectral image information of 262 loquats was obtained by using a visible light/near infrared (400-1167 nm) hyperspectral imaging system. The original spectra were preprocessed using moving average smoothing (MA) and convolution smoothing (SG). On this basis, the weighted band selection algorithm (WBS), successive projection algorithm (SPA), principal component analysis (PCA) and SPA-WBS were used to reduce the dimension of hyperspectral data and extract characteristic variables. Random forest (RF) and partial least squares regression (PLSR) prediction models were established to detect the sugar content and hardness of loquat, and the optimal prediction model was established. Based on the optimal prediction model, the sugar content and firmness of loquat fruits were fitted, enabling the visualization of the internal distribution of sugar content and firmness within the fruits. The results indicate that: The RF model based on SPA-WBS has the best prediction effect on sugar content, with correlation coefficients of the training set and test set being 0.9269 and 0.9162, and root mean square errors being 0.4366 and 0.4336, respectively;The RF model based on the full spectrum band achieves the best hardness prediction effect, and the correlation coefficient of the test set can reach *** methods proposed in this work enable non-destructive prediction and visualization research of sugar content and hardness in loquat.

关键词： loquats sugar content and firmness hyperspectral image non-destructive testing visualization

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A Deep-Learning-Based Novel Method to Classify Breast Cancer 2

A Deep-Learning-Based Novel Method to Classify Breast Cancer

引用

2nd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saha, Soham Dutta, Ahona Choudhury, Sabarna St. Thomas' College of Engineering and Technology Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350327533

With the global health crisis of breast cancer, which is expected to affect about 2.3 million people by 2020, it is clear that there is increasing need for developing early detection tools. This study has achieved 98.25% diagnosis accuracy by using the combination of Deep Neural Network (DNN) and Machine Learning (ML) algorithms, signifying a major improvement in breast cancer detection. This study represents the recent advances in breast cancer detection research and has the potential to totally revolutionize and redefine the current techniques. The proposed approach has the potential to transform breast cancer diagnosis and enhance patient care and outcomes by utilizing data vi su a liz ation and analytical techniques. © 2024 IEEE.

关键词： Breast Cancer Detection data analysis data Pre-processing data visualization Deep Neural Networks Ensemble Learning Global Health Healthcare Technology Machine Learning Algorithms Random Forest Classification

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Context-situated visualization of biclusters to aid decisions: going beyond subspaces with parallel coordinates 2022

Context-situated visualization of biclusters to aid decision...

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16th Working conference on Advanced Visual Interfaces (ACM AVI)

作者： Goncalves, Daniel M. Costa, Rafael S. Henriques, Rui Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal Univ Lisbon IDMEC Inst Super Tecn Lisbon Portugal Univ Nova Lisboa LAQV REQUIMTE DQ NOVA Sch Sci & Technol Lisbon Portugal IDMEC Lisbon Portugal Univ Lisbon INESC ID Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9781450397193

Pattern discovery and subspace clustering are pervasive tasks across biological, biotechnological, and biomedical domains. Parallel co-ordinates plots and heatmaps are reference visualizations for individual biclusters. Both have been object of improvements over time, with a special emphasis on heatmaps, commonly used in gene expression analysis. However, the emphasis is solely placed on the corresponding subspace, preventing an assessment of biclusters' significance against global regularities. This work proposes an improvement on bicluster visualization by disruptively extending parallel coordinates representations with the means to compare the local bicluster against the remaining dataset instances helping in the contextualization of a pattern in the broader picture of an entire dataset. The proposed solution is the first able to deal with mixed data types and is independent from the underlying biclustering or pattern mining algorithm. Results in different data domains show the utility of the proposed visualization, especially in primary phases where visual inspection of biclusters is used.

关键词： biclustering omics/clinical data visualization subspace analysis bioinformatics tool data science

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visualization analysis of Land Use Dynamics and Landscape Type Transitions in the Yangtze River Delta Based on Geographic Information Big data 9

Visualization Analysis of Land Use Dynamics and Landscape Ty...

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9th IEEE International conference on Computational Intelligence and Applications, ICCIA 2024

作者： Zhang, Wei Zhai, Duanqiang Mao, Yanyun School of Architecture Tianjin University Tianjin China Zhejiang University Hangzhou ZJU-STEC Urban Development and Planning Innovation Joint Research Center Zhejiang China College of Architecture and Urban Planning Tongji University Shanghai China Shanghai Tongji Urban Planning & Design Institute Co. Ltd. Shanghai China Urban Mobility Institute Tongji University Shanghai China

ISBN: (纸本)9798350352214

Studying the spatiotemporal changes in land use and landscape types is a crucial method for analyzing human utilization of natural resources. Geographic information big data analysis can effectively enhance rational land resource allocation and improve the utilization rate of territorial space resources. With China's rapid socio-economic development in recent years, significant changes in land use and landscape types have occurred in many regions, profoundly impacting urban construction and development. Taking the Yangtze River Delta in China as an example, this study proposes a method for visualizing land use dynamics and landscape type transitions based on geographic information big data and applies it to an empirical analysis from 2000 to 2020. The study shows a continuous increase in construction land, a decline in arable land, a slight decrease in forest land, and scattered, insignificant changes in grassland and water bodies. The main areas of landscape type transitions are around Taihu Lake and Hangzhou Bay, with the largest transition type being the conversion of arable land to construction land, severely affecting agricultural space. Future land use patterns should focus on balancing urban development with the protection of ecological and agricultural spaces. © 2024 IEEE.

关键词： Abiotic

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Modeling and Assessing User Interaction in Big data visualization Systems 1

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19th International-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) International conference on Human-Computer Interaction (INTERACT)

作者： Benvenuti, Dario Filosa, Matteo Catarci, Tiziana Angelini, Marco Sapienza Univ Rome Rome Italy

ISBN: (数字)9783031422836

ISBN: (纸本)9783031422829;9783031422836

When users interact with large data through a visualization system, its response time is crucial in keeping engagement and efficacy as high as possible, and latencies as low as 500 ms can be detrimental to the correct execution of the analysis. This can be due to several causes: (i) for large data or high query rates, database management systems (DBMS) may fail to meet the performance needs;(ii) modeling all the interactions with a visualization system is challenging due to their exploratory nature, where not all of them are equally demanding in terms of computation time;(iii) there is a lack of models for integrating optimizations in a holistic way, hampering consistent evaluation across systems. In response to these problems, we propose a conceptual interaction-driven framework that enhances the visualization pipeline by adding a new Translation layer between the data-, visualization- and Interaction- layers, leveraging the modeling of interactions with augmented statecharts. This new layer aims to collect information about queries and rendering computations, linking such values to interactions in the statechart. To make the Translation layer actionable, we contribute a software component to automatically model the user interactions for a generic web-based visualization system through augmented statecharts, in which each interaction is labeled with its latency threshold. We first demonstrate its generality on ten state-of-the-art visualization systems. Then we perform a user study (n=50), collecting traces by asking users to perform already established exploratory tasks on the well-known Crossfilter interface. Finally, we replay those traces over its generated statechart, assessing the capability to model the user interactions correctly and describing violations in the latency thresholds.

关键词： visualization Systems visualization Pipeline User Interaction Modeling

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analysis and visualization of Stock data Based on BP Neural Network 24

Analysis and Visualization of Stock Data Based on BP Neural ...

引用

2024 International conference on Intelligent Education and Computer Technology, IECT 2024

作者： Deng, Chao Peng, Jiao College of Computer Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400709920

In the field of finance, the analysis and prediction of stock data have always been a hot topic of research. Back Propagation (BP) neural networks have shown great potential in handling complex financial time series data due to their excellent nonlinear mapping capabilities. In this paper, a model for analyzing and visualizing stock data based on BP neural network is constructed to predict the closing price of the stock market. A multi-layer feedforward neural network is designed, with the cross-entropy loss function selected and the stochastic gradient descent (SGD) algorithm used to update parameters. By computing the gradient of the loss function with respect to each parameter, the direction and magnitude of parameter updates can be determined. After multiple rounds of iterative training, the model gradually approaches the optimal solution. Detailed preprocessing and analysis of historical stock data are conducted, and the trained BP model is used to predict the closing price. The results show that the model can effectively learn and predict stock price trends, with certain practical value. © 2024 ACM.

关键词： Prediction models

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Graph Summarization for Human-Understandable visualization towards CVE data analysis

Graph Summarization for Human-Understandable Visualization t...

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IEEE International conference on Big data and Smart Computing (BigComp)

作者： Park, Ji Sun Kang, Mingu Lee, Sungryoul Chae, Dong-Kyu Hanyang Univ Seoul South Korea Zhejiang Univ Hangzhou Peoples R China Affiliated Inst ETRI Daejeon South Korea

ISBN: (纸本)9781665421973

Graph is one of the data structures used to represent various real-world data effectively. It is easy to analyze complex relationships between objects through graphs, and the structure of data can be understood at a glance through visualization. As the size of the data increases, the scale of the graph representing the data also increases. However, for large-scale graphs, not only is it difficult to analyze patterns, but it is also expensive in terms of storage space and computational speed. In this case, by applying a graph summary method that can extract only the core information of the graph, the aforementioned problem can be solved. This paper studies various graph summarization techniques and applied them to the CVE data. For effective analysis, we first converted the CVE data into a graph, then summarized it with graph embedding and clustering methods. By visualizing the results, we confirmed that we could successfully derive a brief view of the data through this process.

关键词： Laplace equations data analysis conferences Clustering methods data visualization Clustering algorithms Inspection

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Incorporating Eye Tracking into an EEG-Based Brainwave visualization System

Incorporating Eye Tracking into an EEG-Based Brainwave Visua...

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Human-Computer Interaction Thematic Area conference (HCI) Held as Part of the 25th International conference on Human-Computer Interaction (HCII)

作者： Cavalcanti, Matheus Melo, Felipe Silva, Thiago Falcao, Matheus Cavalcanti, Daniel de Queiroz Becker, Valdecir Univ Fed Paraiba Informat Ctr Lab Interact & Media Joao Pessoa Paraiba Brazil

ISBN: (纸本)9783031355950;9783031355967

This article describes the incorporation of eye tracking into a brain-waves visualization and analysis system, based on electroencephalography (EEG), to map attention during fruition of audiovisual content. The visualization system was developed in Python, using an Emotiv Insight headset. During the tests, there was a need to identify whether the reactions mapped by the EEG were in fact related to the fruition of the content or whether they originated from elements external to the screen, with the individual looking away and, consequently, losing attention. Based on the Design Science Research methodology, eye tracking was incorporated into the system architecture. For validation, tests were performed with 10 users. Analyzing the generated data, it was possible to identify the correlation between the information presented by the EEG and the gaze of the individuals. In this way, it is possible to increase confidence about the origin of user's emotions during the fruition of audiovisual content.

关键词： Eye Tracking EEG Audiovisual Fruition

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Advancing precision livestock farming through ontology-driven interoperable health data management: Extending the Livestock Health Ontology (LHO) for enhanced disease surveillance 11

Advancing precision livestock farming through ontology-drive...

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11th European conference on Precision Livestock Farming

作者： Noor, Saba Degroote, Jeroen van Schaik, Gerdien Pardon, Bart Bokma, Jade Slegers, Yara Morante, Beatriz Garcia Hostens, Miel Department of Animal Sciences and Aquatic Ecology Faculty of Bioscience Engineering Ghent University Coupure Links 653 Ghent9000 Belgium Department of Internal Medicine Reproduction and Population Medicine Faculty of Veterinary Medicine Ghent University MerelbekeB-9820 Belgium Department of Population Health Sciences Unit Farm Animal Health Utrecht University Yalelaan 7 Utrecht3584 CL Netherlands Bellaterra Barcelona08193 Spain Department of Animal Science Cornell University IthacaNY14853 United States

ISBN: (纸本)9791221067361

Precision Livestock Farming (PLF) is a form of smart farming that advances agriculture by prioritizing animal care through real-time monitoring of individual animal health. Despite its potential, some challenges need attention. PLF faces challenges in integrating diverse data from different locations and formats, ensuring consistency and understanding for better animal health and welfare. To address these challenges, ontologies can play a central role. Ontologies are formal and explicit representations of knowledge that define concepts, relationships, and properties within a specific domain. In the context of PLF, ontologies serve as a structured framework, facilitating the integration and standardization of diverse data sources and ensuring a unified representation for seamless analysis and decision-making, ensuring interoperability. This paper describes Ontology-Driven Knowledge-based Farm Animal data Management (ODKFADM) framework demonstrating a case study for the development and application of Livestock Health Ontology (LHO) by integrating AGROVOC thesauruses, specifically designed for cattle, poultry, and pigs. The aim is to enhance integration and obtain a unified view of the data for analysis and visualization. This approach focuses on resource descriptive framework (RDF) data generation, utilizing SPARQL queries for data analysis, and data visualization to enhance LHO's applications in disease surveillance and early detection. The results are served to multiple data users, and showcased via a visualization tool to provide insights to veterinarians, researchers, and farmers. In the near future, we envision that this tool, or similar ones, capture pathogen trends, provide early warnings, support informed vaccination decisions, and facilitate effective disease control on farms. © 2024 11th European conference on Precision Livestock Farming. All rights reserved.

关键词： Agri Semantics Disease Surveillance Early Detection Infectious Diseases Livestock Health Ontology Precision Livestock Farming

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