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13th International conference on Computer Engineering and Networks (CENet)

作者： Sun, Yaqi Bu, Jiayi Tang, Peng Ou, Yangze Liang, Xiaoman Hengyang Normal Univ Coll Comp Sci & Technol Hengyang 421008 Hunan Peoples R China

ISBN: (纸本)9789819992454;9789819992430;9789819992423

The system is based on the improvement and optimization of the existing workflow of the cinema, which has been found to have a large number of mechanical and time-consuming tasks. In line with the current trend of "electronic information", the system firstly introduces text recognition technology, which enables the rapid conversion of paper and electronic information. Secondly, the data visualization technology helps the cinema to have a better understanding of the recent sales situation. Compared to the previous system, this system avoids the boring and tedious mechanical manual entry work and is more in line with modern information management, which can effectively increase the efficiency and promote the long-term development of the cinema.

关键词： Intelligent recognition technology Text recognition technology data visualization technology data analysis technology

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Machine Learning Modeling in Industrial Processes for Visual analysis 25th

Machine Learning Modeling in Industrial Processes for Visual...

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25th International conference on Engineering Applications of Neural Networks (EANN)

作者： Moran, Antonio Alonso, Serafin Fuertes, Juan J. Prada, Miguel A. Roca, Lidia Dominguez, Manuel Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain CIEMAT Plataforma Solar Almeria Ctra Senes S-N Almeria 04200 Spain

ISBN: (纸本)9783031624940;9783031624957

The use of visualization tools makes it easy to analyze the operation of an industrial process. Given the large number of variables involved in today's industrial systems, it is necessary to use techniques that reduce both the size of the data and the number of samples. In addition, since industrial systems involve similar processes running in parallel, this information can be added to analyze the processes. This paper proposes the use of a variant of self-organizing maps, Env-SOM, which allows conditioning the projection of these maps based on a set of variables. This variant is applied to operational data from AQUA-SOL II pilot plant located at the Plataforma Solar de Almer ' ia (PSA), which consists of several flat-plate collector loops that share a common water distribution pipe. Projecting the data in a conditional manner visualization maps are generated based on differences that allow to determine the existing differences between the heating loops.

关键词： Environmental conditions data mining Exploratory analysis Self-Organizing Maps envSOM

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analysis of Solar Radiation on Facades Using Mobile Augmented Reality 18th

Analysis of Solar Radiation on Facades Using Mobile Augmente...

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18th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 18th International conference on Computer Graphics Theory and Applications

作者： Meireles, Carolina Carmo, Maria Beatriz Claudio, Ana Paula Ferreira, Antonio Afonso, Ana Paula Redweik, Paula Catita, Cristina Brito, Miguel Centeno Soares, Daniel Univ Lisbon Fac Ciencias Dept Informat LASIGE P-1749016 Lisbon Portugal Univ Lisbon Fac Ciencias Dept Engn Geog Geofis & Energia P-1749016 Lisbon Portugal Univ Lisbon Fac Ciencias Inst Dom Luiz P-1749016 Lisbon Portugal

ISBN: (纸本)9783031667428;9783031667435

Recent advancements in mobile device technology have revolutionized the potential uses of Augmented Reality (AR), particularly in the domain of data visualization. In this chapter, we present a prototype application specifically designed for Android tablet devices, enabling users to access financial and energy feedback from photovoltaic (PV) systems. By simulating in AR the placement of PV systems on building facades, this solution serves a dual purpose: supporting technicians and researchers in their work while also empowering everyday users to actively contribute to the widespread adoption of renewable energy sources. SolAR offers an augmented view of the real world, enriched with graphical representations of aggregated irradiation data seamlessly overlaid onto building facades. To evaluate its effectiveness, a user study involving 32 volunteers was conducted, revealing promising results. Participants successfully optimized the positioning of PV modules to maximize energy efficiency, highlighting the application's practical value. Furthermore, the study demonstrated that the added functionalities were deemed relevant, and the overall usability of the application was positively assessed.

关键词： Augmented reality Mobile devices data visualization Solar radiation Photovoltaic modules

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AppMAIS Simple data visualization App

AppMAIS Simple Data Visualization App

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IEEE SoutheastCon conference

作者： Richardson, Logan Campell, Christopher Tashakkori, Rahman O'Brien, Will Davis, Sarah Elizabeth Appalachian State Univ Boone NC 28608 USA

ISBN: (纸本)9781665476119

Due to the fact that a large number of honey bee hives have been lost in recent years, there have been some efforts to collect various data to assist with monitoring the hives and determine their health status. The Appalachian Multi-purpose Apiary Informatics Systems (AppMAIS) was funded by the North Carolina General Assembly in 2020 to establish a collaborative research project to monitor the health of honey bee hives. The AppMAIS Simple data visualization App greatly increases our ability to analyze data, detect patterns, and train our ML swarm-like detection models. The app utilizes tooltips, hyperlinking, and highlight functionalities to significantly improve the efficiency of data analysis.

关键词： Precision apiculture data visualization Swarm detection Robbery Dash

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Multiscale visualization: A Structured Literature analysis

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第12期28卷 4918-4929页

作者： Cakmak, Eren Jaeckle, Dominik Schreck, Tobias Keim, Daniel A. Fuchs, Johannes Univ Konstanz Dept Comp & Informat Sci D-78547 Constance Germany Graz Univ Technol Inst Comp Graph & Knowledge Visualisat A-8010 Graz Austria

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this article, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference articles between 1995 and 2020. We organized the reviewed articles in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed articles, we examine research trends and highlight open research challenges.

关键词： data visualization Navigation visualization Taxonomy Encoding Molecular biology Libraries Multiscale visualization multiscale navigation multiscale exploration literature analysis taxonomy survey

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Modelling and visualization of spatial data: A case study of COVID-19 data in Maine,USA 3

Modelling and visualization of spatial data: A case study of...

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3rd conference on Information Technology and data Science

作者： Abdi, Ismail H. Univ Debrecen Doctoral Sch Informat Debrecen Hungary Garissa Univ Sch Pure & Appl Sci Garissa Kenya

ISBN: (纸本)9798350387896;9798350387889

The coronavirus pandemic has rapidly spread worldwide and significantly disrupted the daily lives of people globally. This paper presents the results of a detailed spatial analysis of the COVID-19 cases reported between March 2020 and December 2022 in Maine, USA. The aim of this study was to estimate and test the spatial autocorrelation of disease cases among the counties of the state, determine relative risks, and assess excess risk in each county. Using the adjacency matrix, Moran's I statistic was computed, and a significant positive spatial autocorrelation was found. The study employed Bayesian Conditional Autoregressive (CAR) model with Integrated Nested Laplace Approximation (INLA) to map the relative risk of COVID-19 cases, and identified that 56.25% of the counties in the state had a relative risk greater than one. The study further found clustering of disease cases in the state, which may indicate areas that need intervention measures in the future.

关键词： Spatial autocorrelation conditional autoregressive model BYM model COVID-19 relative risk disease mapping INLA

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visualization and Statistical analysis of Research Pillar of Top Five THE (Times Higher Education)-Ranked Universities for the Years 2020–2023 3rd

Visualization and Statistical Analysis of Research Pillar of...

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3rd International Semantic Intelligence conference, ISIC 2023

作者： Das, Susanta Kulkarni, Shravani Dsouza, Jenisia Bhosale, Piyush Dhanwade, Ritul Trivedi, Khushbu Kelkar, Parineeta Roy, Debanjali Barman Kumar, Ranjit School of Engineering Ajeenkya DY Patil University Maharashtra Charoli Bk.Via Lohegaon District Pune412105 India

ISBN: (纸本)9789819773558

We conducted an analysis of the research pillar in the top five universities ranked by THE for the years 2020–2023 using data obtained from THE website. To derive meaningful insights, we calculated the average research data for each year across these universities. Subsequently, we compared the research data between the 1st-ranked university and the remaining four universities, as well as between consecutively ranked universities for each year. Our analysis demonstrated variations over these years. Initially, there was an upward trend in average research performance from 2020 to 2022, followed by a decline from 2022 to 2023. Interestingly, we observed instances where lower-ranked universities outperformed higher-ranked ones in specific years. Furthermore, we noted that the inclusion or exclusion of a particular university had a noteworthy impact on the research pillar, leading to fluctuations in the average values, both upward and downward. In our investigation, we also considered the potential influence of the COVID-19 pandemic on the research pillar and consequently on university rankings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Graphical visualization Research pillar Statistical analysis The World University ranking Top five university

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Understanding the Forest: A visualization Tool to Support Decision Tree analysis 27

Understanding the Forest: A Visualization Tool to Support De...

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27th International conference on Information Visualisation (IV) / 19th International conference Computer Graphics, Imaging and visualization (CGiV)

作者： Macas, Catarina Campos, Joao R. Lourenco, Nuno Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9798350341614

Decision Trees (DTs) are one of the most widely used supervised Machine Learning algorithms. The algorithm constructs binary tree data structures that partition the data into smaller segments according to different rules. Hence, DTs can be used as a learning process of finding the optimal rules to separate and classify all items of a dataset. Since the algorithm relies on a decision process similar to rule-based decisions, they are easily interpretable. However, DTs can be difficult to analyse when dealing with large datasets and/or with multiple trees, i.e. ensembles. To ease the analysis and validation of these models, we developed a visual tool which includes a set of visualizations that overview and give details of a set of trees. Our tool aims to provide different perspectives over the same data and provide further insights on how decisions are being made. In this article, we overview our design process, present the different visualization models and their iterative validation. We present a use case in the telecommunications domain. In concrete, we use the visual tool to help understand how a model based on DTs decides which is the best channel (i.e., phonecall, e-mail, SMS) to contact a client.

关键词： Visual Analytics Random Forest Decision Tree

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Understanding Comparative Genomics data visualization for Biodiversity Domain Through Persona-Based Interviews

Understanding Comparative Genomics Data Visualization for Bi...

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2024 IEEE International conference on Computing, ICOCO 2024

作者： Chan, Vei Siang Mohamed, Farhan Bin Salleh, Faezah Binti Mohd Iglesias, Andres Jamaludin, Najwa Ayuni Binti Mustafa Albakri, Ikmal Faiq Albakri Media and Game Innovation Center of Excellence Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Applied Mathematics and Computational Sciences Universidad de Cantabria Santander Spain Pervasive Computing & Educational Technology Department of Media Interactive Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9798331530303

Understanding the needs of genomic data visualization techniques is important to help bioinformaticians to perform their comparative analytical tasks effectively. DNA sequencing datasets consist of a long sequential string of nucleotide base pairs combination and are often involved in multiple sequences alignment for comparative analysis. Therefore, existing challenges include designing suitable visualization layout and the need of user-centered approach to ensure optimal user experience. Existing works also featured heavily on the qualitative evaluation of comparative visualization tools, such as use cases and case studies, which are difficult to quantify and conduct follow up evaluation for tool improvement. Hence, this paper aims to understand the needs of visualization tools in comparative genomic analysis to identify visualization tasks and visual design requirements. The methodology includes the creation of semi-structured interview based on personas template as guiding questions and the synthesis of thematic analysis results. These results presented the requirements of the visualization tasks for the biodiversity domain based on comparative targets. For future work, this research will design, develop, and validate the comparative visualization tool based on the discovered requirements. ©2024 IEEE.

关键词： Genome

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IPL data analysis and visualization for Team Selection and Profit Strategy 7

IPL Data Analysis and Visualization for Team Selection and P...

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7th International conference on Computing Methodologies and Communication, ICCMC 2023

作者： Saranya, G. Swaminathan, Aravind Joel Benjamin, J. Surendran, R. Nelson, Leema Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India Data Science Middlesex University Dubai United Arab Emirates Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9781665464086

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) data analysis 2022 and data visualization for the duration (2008-2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for S cientific Computing, Pandas used to perform data analysis, and finally Matplotlib and S eaborn to enable data visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report. © 2023 IEEE.

关键词： data visualization

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