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

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An Intelligent visual Big data Analytics Framework for Supporting Interactive exploration and visualization of Big OLAP Cubes 24

An Intelligent Visual Big Data Analytics Framework for Suppo...

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24th International conference Information visualisation (IV)

作者： Ordonez, Carlos Chen, Zhibo Cuzzocrea, Alfredo Garcia-Garcia, Javier Univ Houston Houston TX 77004 USA Haliburton Houston TX USA LORIA Villers Les Nancy France Univ Calabria Commenda Di Rende Italy Univ Nacl Autonoma Mexico C3 Mexico City DF Mexico

ISBN: (纸本)9781728191348

visual big data analytics aims at supporting big data analytics via visual metaphors, with a plethora of applications in modern settings and scenarios. In all these domains, visual big data analytics paradigms offer several advantages, among which some noticeable ones are: (i) fast knowledge understanding from big data sets;(ii) pattern and trend discovery from big data sets;(iii) entity and model discovery from big data sets;(iv) sharing insights among organizations. Among several proposals, OLAP-based visual big data analytics methodologies and tools represents a successful case of visual big data analytics frameworks, which is entirely based on OLAP analysis. In this context, an OLAP cube is typically explored with multiple aggregations selecting different subsets of cube dimensions to analyze trends or to discover unexpected results. Unfortunately, such analytic process is generally manual and fails to statistically explain results. On the basis of these considerations, in this paper we propose an innovative OLAP-shaped visual big data analytics framework that incorporates a state-of-the-art statistical technique for supporting exploration and visualization of OLAP data cubes. An experimental evaluation with a medical data set presents statistically significant results and interactive visualizations, which link risk factors and degree of disease.

关键词： OLAP Cubes visual Big data Analytics Big data analysis Intelligent Big data Systems

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Implementing Agile Task Management System: Leveraging Web Development and Power BI for Enhanced Decision-Making

Implementing Agile Task Management System: Leveraging Web De...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International conference on

作者： M. Jaishree V. Anupriya M Sukitha Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Developing a Task management system can benefit organizations in several ways which offers a comprehensive solution that enhances productivity, improves collaboration, provides visibility, supports Agile practices, ensures accountability, and allows for seamless integration and scalability. This helps organizations manage their tasks and projects more efficiently, ultimately leading to better outcomes and increased success. The Agile Task Management system can be developed by combining both the front-end and back-end along with a database for storing and organizing the company project details like the status, project timeline, and project team details like their mail IDs and the particular projects they belong to. The task management web application can be developed in various languages and frameworks for managing and aggregating projects with specific employees. This research study focuses on two main areas: developing a task management web application and creating a dashboard using Power BI for in-depth analysis of the organization’s project dataset. Power BI plays a crucial role in data analytics by integrating data from multiple sources, enabling interactive exploration, and facilitating collaborative decision-making through its powerful visualization and reporting tools. The discussion includes using HTML, JavaScript, Ajax, and Bootstrap for the front end, PHP for the backend, and MySQL for the database to create a comprehensive Task Management web application. Additionally, it covers the development of a simple task management system with a Python Flask application using SQL Alchemy and PyQt5 for GUI creation with an SQLite database. Additionally, the system is designed to handle complete project-related datasets by incorporating Power BI Desktop to create interactive dashboards for visualizing project data which is a separate part of the task management system.

关键词： Accuracy Decision making Standards organizations data visualization Collaboration Termination of employment visual databases Security Reliability Graphical user interfaces

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Interactive, visual-Learning based Tool for Hearing Impaired Children to Improve Language and Cognitive Skills

Interactive, Visual-Learning based Tool for Hearing Impaired...

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International conference on Information Technology (ICIT)

作者： Saluka Udbhasa Parami Lelkada Modeesha Priyanka Esala Senarathna Samitha Vidhanaarachchi Jagath Wickramarathne Janaka L. Wijekoon Sri Lanka Institute of Information Technology Colombo Sri Lanka Military Technological College Muscat Oman

Hearing impairment is a common condition that affects millions of people worldwide, with approximately 1 in 1000 newborns experiencing some degree of hearing loss. This condition can significantly impact a person's quality of life, causing communication difficulties and social isolation. Early childhood hearing impairment can present significant challenges to a child's cognitive development, making it difficult for them to learn in a traditional educational setting. Consequently, there is an urge for effective learning tools that can assist hearing-impaired children in learning their first language. This paper introduces visual-based and interactive learning tools as a promising approach to enhancing the learning experience and engagement of hearing-impaired children. The proposed system utilizes machine learning to assess a child's initial status and subsequently generates adaptive content while continuously monitoring progress. Moreover, the system incorporates an object exploration feature that enables children to learn from their surroundings. Additionally, it employs natural language learning processes to present contextually similar content, supported by audio and lip movement analysis features that guide correct pronunciation. In response to the ML models utilized, object detection was accomplished through YOLO5, while lip movements were analyzed using LipNet. Regression models were deployed for assessing a child's initial status and subsequent progress. The data collection process involved comprehensive sources that covered a wide array of visual, auditory, and linguistic elements, with the dataset being rigorously tested and verified. Testing implementations were conducted in various real-world settings, encompassing diverse environmental factors and learning conditions. The YOLO model demonstrated an accuracy of 90.3%, LipNet achieved 92.2% accuracy in lip movement analysis, and the multivariate regression models showed a prediction accuracy of 92.8% in ev

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DOLAP 2021 - Proceedings of the 23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big data, co-located with the 24th International conference on Extending database Technology and the 24th International conference on database Theory, EDBT/ICDT 2021

DOLAP 2021 - Proceedings of the 23rd International Workshop ...

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23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big data, DOLAP 2021

The proceedings contain 14 papers. The topics discussed include: a foundation for spatio-textual-temporal cube analytics;estimating the job’s pending time on a high-performance computing cluster through a hierarchical data-driven methodology;towards local post-hoc recommender systems explanations;learning analysis behavior in SQL workloads;an in-depth investigation of large-scale RDF relational schema optimizations using spark-SQL;MDORG: annotation assisted rule agents for metadata files;adaptive indexing for in-situ visual exploration and analytics;using fuzzy vaults for privacy preserving record linkage;and exploring data using patterns: a survey and open problems.

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exploration of visual Interaction Method for Metro Station Space Guidance System Based on Computer Aided Design

Exploration of Visual Interaction Method for Metro Station S...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International conference on

作者： Yuwan Feng Kun Wu School Architeture & Urban Planning Shenzhen University Shenzhen Guangdong China

In recent years, the domain space interaction of subway stations based on computer-aided design has become a new trend. This article takes urban rail transit as an example to explain the importance of using computer to realize functions such as graphics and images. The system uses B/S structure to process graphic information, and stores the results in the server database for later analysis and feedback of changes in user needs. Through the simulation based on virtual sample technology, the effectiveness of the model and algorithm is verified. The visualization coefficient of the subway station domain space guidance system is between 0.7 and 0.9, the accuracy of landmarks is between 96% and 97%, the ease of use is between 84% and 89%, and the system operability is 80% above. Finally, in the computer environment, the interactive interface of the subway station domain space is designed to provide passengers with a better experience. This move means that the space interaction in the subway station has a higher technical level, and has played an important role in improving user satisfaction and convenience. In the research, it was found that the visual interaction method of the subway station spatial guidance system based on computer-aided design can be improved through the following aspects. These data may include passenger flow, train operation status, and safety information, and can be presented in the form of charts, heat maps, and animations. These research findings aim to continuously improve the spatial guidance system of subway station areas, enhance user experience and efficiency.

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International conference on Computer Graphics, visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

International Conference on Computer Graphics, Visualization...

引用

International conference on Computer Graphics, visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big data Analytics, data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on Computer Science and Information Systems, MCCSIS 2021

ISBN: (纸本)9789898704320

The proceedings contain 24 papers. The topics discussed include: using fast multidimensional projections to reveal band and circumplex patterns in reorderable matrices;a 3D spectral-spatial classification of hyperspectral remote sensing imagery using inception based network;3D face reconstruction from hard blended edges;capsule neural networks in classification of skin lesions;a single RGB image based 3D object reconstruction system;face features-based personality assessment;analysis of capsule networks for image classification;analysis and visual exploration of prediction algorithms for public bicycle sharing systems;and hello, my name is smarttram, human factors is on board, enjoy the ride! developing a human factors program for automatic trams.

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Differential Evolution Based Convolutional Neural Network for Hazy Image Classification

Differential Evolution Based Convolutional Neural Network fo...

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

作者： Sai Sandeep Reddy Thadasina Nikhita Reddy Ravula Akhitha Reddy Sangasani Ravi Kumar Jatoth Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

This research paper addresses the pivotal domain of image classification, a field with profound real-world applications. The significance of classifying hazy images into distinct categories lies in the ability to apply the most suitable dehazing algorithm for each class. Relying solely on human perception is deemed suboptimal due to its unpredictability and vulnerability to bias, underscoring the need for an algorithmic approach to image classification. The initial binary haze-clear classification is refined into six nuanced categories: dense fog, thick fog, moderate fog, light fog, thin fog, haze. While convolutional neural networks (CNNs) excel in image classification, manually constructing network structures for specific tasks requires numerous trials to fine-tune a multitude of hyperparameters. This process is time-consuming, and finding the fittest hyperparameters suitable for target data poses a challenge. This research paper introduces a Differential Evolution (DE) based automatic network evolution model, optimising hyper-parameters by exploring the fittest parameters. The proposed evolutionary algorithm identifies optimistic hyperparameters. By merging the capabilities of the Differential Evolution algorithm with a sophisticated classification system, this research promises to push the boundaries of hazy image classification. This approach enhances classification accuracy and empowers subsequent applications by delivering cleaner and more detailed images. The results showcase a more efficient and effective approach to tackle the challenges posed by atmospheric haze, ultimately contributing to advancements in computer vision, object recognition and various domains relying on visual data analysis.

关键词： Adaptation models Neural networks Evolutionary computation Filtering algorithms Classification algorithms Space exploration Convolutional neural networks

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Big data analysis, AI, and visualization Workshop: Road Mapping Infrastructures for Artificial Intelligence Supporting Advanced visual Big data analysis 20

Big Data Analysis, AI, and Visualization Workshop: Road Mapp...

引用

Working conference on Advanced visual Interfaces (AVI)

作者： Reis, Thoralf Bornschlegl, Marco X. Hemmje, Matthias L. Univ Hagen Fac Math & Comp Sci Hagen Germany

ISBN: (纸本)9781450375351

The overall scope and goal of the workshop is to bring together researchers active in the areas of Artificial Intelligence (AI), Big data analysis, and visualization to achieve a road map, which can support the acceleration in research and data science activities by means of transforming, enriching, and deploying AI models and algorithms as well as intelligent advanced visual user interfaces supporting creation, configuration, management, and usage of distributed Big data analysis. Big data analysis and AI mutually support each other: AI-powered algorithms empower data scientists to analyze Big data and thereby exploit its full potential whereas Big data enables AI experts to comfortably design, validate, and deploy AI models. One of the workshop's objectives is the examination of the importance and necessity of a third, a more straightforward relationship of Big data and AI: AI supporting all user stereotypes and organizations involved in Big data analysis on their exploration journey from raw input data to insight and effectuation.

关键词： AI Big data analysis visualization AI2VIS4Bigdata

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Designing a data visualisation and analysis tool for supporting decision-making with public transportation network 23

Designing a data visualisation and analysis tool for support...

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23rd International conference on Engineering Design, ICED 2021

作者： Vallet, Flore Khouadjia, Mostepha Amrani, Ahmed Pouzet, Juliette Université Paris-Saclay CentraleSupélec Laboratoire Genie Industriel France IRT SystemX Paris-Saclay France SNCF Innovation and Research France

Massive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?" The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport. © ICED *** right reserved.

关键词： data visualization

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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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