The customer personality analysis serves as an in-depth exploration into the realm of a company's ideal customers, meticulously examining their characteristics, behaviors, and preferences. This paper's analyti...
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ISBN:
(纸本)9798350385304;9798350385298
The customer personality analysis serves as an in-depth exploration into the realm of a company's ideal customers, meticulously examining their characteristics, behaviors, and preferences. This paper's analytical approach aims to empower businesses by unraveling the intricacies of customer bases and enabling the customization of products based on diverse customer segments' specific needs and concerns. By leveraging advanced visualization techniques like histograms, bar charts, donut charts, and table charts within Tableau, this study seeks to enhance the understanding of customer attributes, facilitating more targeted and effective marketing strategies. Understanding customers is pivotal for businesses seeking to tailor their products and marketing efforts. Customer personality analysis delves into the wealth of data available, allowing for a nuanced understanding of customer segments. The objective is to shift from a generalized approach to a more targeted one, where resources are allocated efficiently and marketing efforts are tailored to resonate with specific customer groups.
The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the propose...
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ISBN:
(纸本)9798350391749
The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).
With Generation Z's rise in technology use, recognising their specific cybersecurity risks is vital. Adopting effective cybersecurity practices is essential for their protection, yet the factors influencing these ...
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With Generation Z's rise in technology use, recognising their specific cybersecurity risks is vital. Adopting effective cybersecurity practices is essential for their protection, yet the factors influencing these protective behaviours still need further exploration. This study proposed an adapted Theory of Planned Behaviour model, testing it with data from 123 participants. analysis using SPSS for descriptive statistics and PLS-SEM for data evaluation revealed that knowledge and self-efficacy significantly predict Gen Z's cybersecurity practices. Contrarily, their attitudes, awareness, and subjective norms showed no substantial influence on these practices. This research highlights the need for increased practical knowledge and efficacy in cybersecurity measures among Gen Z technology users.
In field of air quality research, it is essential to scientifically reflect the internal structure of air quality distribution and reveal the dynamic evolution of air pollution. In this study, a novel visual analytics...
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ISBN:
(纸本)9783031500749;9783031500756
In field of air quality research, it is essential to scientifically reflect the internal structure of air quality distribution and reveal the dynamic evolution of air pollution. In this study, a novel visual analytics method is proposed to address these challenges. Initially, the spatio-temporal features of air quality data are mined to complete urban agglomeration division based on dimensionality reduction and clustering. Subsequently, the air pollution transmission network (APTN) is constructed through particle transport and correlation analysis. A progressive explorationanalysis method based on multidimensional space transformation is then employed to explore the process of air pollution transmission. Furthermore, a visual analytics system is developed to facilitate the interpretation of the results. Finally, we demonstrate the effectiveness of our proposed methodology using real data sets and case studies, and receive positive feedback from domain experts.
Parallel coordinate plots (PCPs) have been widely used for high-dimensional (HD) data storytelling because they allow for presenting a large number of dimensions without distortions. The axes ordering in PCP presents ...
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Parallel coordinate plots (PCPs) have been widely used for high-dimensional (HD) data storytelling because they allow for presenting a large number of dimensions without distortions. The axes ordering in PCP presents a particular story from the data based on the user perception of PCP polylines. Existing works focus on directly optimizing for PCP axes ordering based on some common analysis tasks like clustering, neighborhood, and correlation. However, direct optimization for PCP axes based on these common properties is restrictive because it does not account for multiple properties occurring between the axes, and for local properties that occur in small regions in the data. Also, many of these techniques do not support the human-in-the-loop (HIL) paradigm, which is crucial (i) for explainability and (ii) in cases where no single reordering scheme fits the users' goals. To alleviate these problems, we present PC-Expo, a real-time visual analytics framework for all-in-one PCP line pattern detection and axes reordering. We studied the connection of line patterns in PCPs with different dataanalysis tasks and datasets. PC-Expo expands prior work on PCP axes reordering by developing real-time, local detection schemes for the 12 most common analysis tasks (properties). Users can choose the story they want to present with PCPs by optimizing directly over their choice of properties. These properties can be ranked, or combined using individual weights, creating a custom optimization scheme for axes reordering. Users can control the granularity at which they want to work with their detection scheme in the data, allowing exploration of local regions. PC-Expo also supports HIL axes reordering via local-property visualization, which shows the regions of granular activity for every axis pair. Local-property visualization is helpful for PCP axes reordering based on multiple properties, when no single reordering scheme fits the user goals. A comprehensive evaluation was done with r
Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visualexploration of hierarchical genome structures in molecular biology. However, cre...
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Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visualexploration 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.
Psychometric research relies on latent variables analysis, employing techniques like factor analysis to explore test structures. However, such methods often assume linear dimensionality reduction, not always reflectiv...
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The proceedings contain 43 papers. The topics discussed include: GenLens: a systematic evaluation of visual GenAI model outputs;evaluating how interactive visualizations can assist in finding samples where and how com...
ISBN:
(纸本)9798350393804
The proceedings contain 43 papers. The topics discussed include: GenLens: a systematic evaluation of visual GenAI model outputs;evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes;MaugVLink: augmenting mathematical formulas with visual links;A-Map: interactive visualexploration of intercity accessibility dynamics based on railway network data;understanding and automating graphical annotations on animated scatterplots;efficient level-crossing probability calculation for Gaussian process modeled data;CommentVis: unveiling comment insights through interactive visualization tool;and GraphFederator: federated visualanalysis for multi-party graphs.
Over recent years, we witnessed an astonishing growth in production and consumption of Linked data (LD), which contains valuable information to support decision-making processes in various application domains. In this...
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ISBN:
(数字)9783031241970
ISBN:
(纸本)9783031241963;9783031241970
Over recent years, we witnessed an astonishing growth in production and consumption of Linked data (LD), which contains valuable information to support decision-making processes in various application domains. In this context, datavisualization plays a decisive role in making sense of the large volumes of data created every day and in effectively communicating structures, processes, and trends in data in an accessible way. In this paper, we present LDViz, a visualization tool designed to support the exploration of knowledge graphs via multiple perspectives: (i) RDF graph/vocabulary inspection, (ii) RDF summarization, and (iii) exploratory search. We demonstrate the usage and feasibility of our approach through a set of use case scenarios showing how users can perform searches through SPARQL queries and explore multiple perspectives of the resulting data through multiple complementary visualization techniques. We also demonstrate the reach and generic aspects of our tool through an evaluation that tests the support of 419 different SPARQL endpoints.
The proceedings contain 19 papers. The topics discussed include: visualexploration of indirect bias in language models;RiskFix: supporting expert validation of predictive timeseries models in high-intensity settings;...
ISBN:
(纸本)9783038682196
The proceedings contain 19 papers. The topics discussed include: visualexploration of indirect bias in language models;RiskFix: supporting expert validation of predictive timeseries models in high-intensity settings;a business intelligence dashboard for the phone: small-scale visualizations embedded into a mobile analysis and monitoring solution;Loki: reusing custom concepts in interactive analytic workflows;effect of color palettes in heatmaps perception: a study;detection and visualanalysis of pathological abnormalities in diffusion tensor imaging with an anomaly lens;accelerated volume rendering with volume guided neural denoising;level of detail visualanalysis of structures in solid-state materials;and CatNetVis: semantic visualexploration of categorical high-dimensional data with force-directed graph layouts.
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