Data Visualization Applications Research Articles

WordCanvas: Text-to-Image Generation

This project investigates the novel use of stable dif- fusion techniques to generate high-quality images from detailed text descriptions. The combination of natural language under- standing and computer vision in text-to-image conversion opens up new possibilities for content creation and communication. Using cutting-edge stable diffusion models, our project builds a solid foundation for the generation process, which includes tokenization, pre-processing, specialized architecture design, and post-processing techniques. The advantages include eye-catching images, increased user engagement, content personalization, and improved accessibility. Automation of content generation has applications in marketing, education, data visualization, and creative expression. However, challenges such as model accuracy, ethical concerns, and biases need addressing. Achieving a balance between automation and human supervision is critical for the responsible application of this transformative capability. Index Terms—Stable diffusion, Text-to-image conversion, Nat- ural language understanding, Pre - processing, Post-processing techniques, Content personalization

Implementation of a low-cost intelligent street light system using internet of things

In the contemporary world, science and technology are advancing swiftly to meet the growing human need for electricity. Within this framework, street lighting, serving as the most vital and ubiquitous element of urban lighting infrastructure, contributes significantly to public electricity consumption. Hence, enhancing the operational efficiency of street lamps becomes imperative to conserve energy. Nonetheless, traditional street lighting systems, being manually controlled, consuming excessive power, and entailing high installation expenses, present notable drawbacks and concerns. Leveraging the internet of things (IoT), advanced innovations automate various areas, including health monitoring, traffic management, agricultural irrigation, street lights, and classrooms. The current manual operation of street lights leads to substantial global energy waste. To address this, an integrated hardware and software solution is proposed. Practical implementation of the hardware devices employs a wireless sensor network, while the software focuses on developing an IoT application for data storage, analysis, and visualization. The proposed system enables effective monitoring of parameters such as ambient temperature, current, voltage, and energy consumption of photovoltaic street lights, which are used as an indicator of the lamp status. Using Xbee modules, a configuration by the X-CTU software is necessary to communicate between all street lights wirelessly. These Xbee modules are used as a leading technology for wireless sensor networks due to its low power and low cost. Experimental results demonstrate that the proposed system is energy-efficient and cost-effective, and further can be implemented in real street light systems.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Unveiling Financial Insights: Business Analytics, Graphs, and Key Metrics in Stock Trading

Abstract: This comprehensive journal explores the dynamic world of stock trading, emphasizing the pivotal role of business analytics, graphical analyses, and key financial metrics. The focus extends to the intricate details of price ratios, debt metrics, liquidity ratios, and turnover. Additionally, it delves into the applications of data visualization and manipulation in creating realtime dashboards and bar charts, providing valuable insights into their purpose and mechanics. The journal further discusses the integration of business analytics in analyzing chart patterns, leveraging machine learning for predictive trading strategies, and includes statistical formulas for a deeper understanding.

Application Analysis of Data Visualization in Tiktok E-commerce

With the rapid development of the e-commerce field, the application of data visualization technology is more extensive. Visual development in the field of Tiktok e-commerce can improve marketing accuracy and optimize consumers' shopping experience. However, visualization development in the field of Tiktok e-commerce has been affected. Based on this, the paper proposes to optimize Tiktok e-commerce by means of data optimization and integration. This paper uses the literature research method to analyze the application of data visualization in Tiktok e-commerce. Through reading a large number of literature, it is found that data visualization is of great help to the development of e-commerce. The technical realization and function summary of data visualization in Tiktok e-commerce are studied, but there are still some problems in commodity information, commodity quality, user positioning, logistics, and other aspects. Finally, some solutions are proposed.

Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms

Due to increasing herd sizes and automation on dairy farms there is an important need for automated monitoring of cow production, health, and welfare. Despite much progress in automatic monitoring techniques, there is still a need to integrate data from multiple sources to create a comprehensive overview and accurate diagnosis of a cow’s state. To aid the technological development of data integration, a prototype of an open and customizable automatic system that integrates data from multiple sensors relating to barn environment and cow behaviour was developed. The system integrates data from sensors that measure barn climate (e.g., temperature, humidity, wind speed), air quality (e.g., CO2 concentration), water use and temperature, the moisture and temperature of the litter and cow behaviour (e.g., lying, eating, ruminating). An external weather system and video recording system are also included. The system’s architecture consists of four main elements: sensors, nodes, gateways, and backend. The data are recorded by sensors, then locally processed on custom-developed sensor nodes, and then transmitted via radio channels to local gateways that combine the data from multiple nodes and transmit them to distributed digital storage (“the cloud”) via a 3G/4G cellular network. On the cloud, the data are further processed and stored in a database. The data are then presented to the user continuously and in real time on a dashboard that can be accessed via the internet. In the design of the local wireless network, care was taken to avoid data packet collision and thus to minimize data loss. To test the system’s performance, the system was installed and operated on three commercial dairy cattle farms for one year. The system provided high data stability with minimal loss and outliers, showing that the system is reliable and suitable for long term application on commercial dairy farms. The system’s architecture, communication network, and data processing and visualization applications form an open framework for research and development purposes, allowing it to be customized and fine-tuned before being deployed as a management assistant on commercial dairy farms. Missing elements that should be added in the future are the integration of the data from the milking parlour and cow identification. Algorithms to integrate information from multiple sensors can be added to provide a comprehensive system that monitors all aspects related to cow welfare, health, and production automatically, remotely and in real time, thereby supporting farmers in important management decision-making.

Supporting Literary Criticism with Data Visualization: Four Design Guidelines for Facilitating Interdisciplinary Collaborations

The application of data visualization in the context of literary criticism opened promising research directions but may raise frictions due to different traditions and approaches into play. Visualizations are commonly designed to promote a “view from nowhere” and appear objective, transparent, and factual. Literary criticism, instead, valorizes individual viewpoints and interpretations. The article presents an inquiry based on Action Research, Participant Observation, and Structured Interviews to explore divergencies between core methodologies of literary criticism (i.e., close reading and interpretation) and data visualization. It proposes four guidelines to orient design work conducted in this interdisciplinary area: embed situatedness, negotiate elusive concepts, use overview as a companion, support thinking with visual manipulation.

Electromyography and kinematics data of the hand in activities of daily living with special interest for ergonomics

This work presents a dataset of human hand kinematics and forearm muscle activation collected during the performance of a wide variety of activities of daily living (ADLs), with tagged characteristics of products and tasks. A total of 26 participants performed 161 ADLs selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices). The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. Data of more than 4100 ADLs is presented in this dataset as MATLAB structures with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour. The dataset is accompanied with a custom data visualization application (ERGOMOVMUS) as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or participants’ characteristics.

Providing software support for economic analysis

Today, modern software and hardware of information technologies are used to automate production processes, starting from modeling of technical developments and automated computer systems. This study is aimed at describing the process of implementing software support for economic analysis of the enterprise, which will calculate the main economic indicators and informatively display the calculated data. The object of the study is an automated system for calculating and maintaining the main economic indicators of the enterprise. The subject of research is methods of database design and development, data visualization and web applications. A review of literature sources allowed to conclude that most often to assess the economic performance of the enterprise, standard technologies are used, which the authors adapt to specific economic problems. In addition, the analysis of modern research has not identified a single software product that would collect, accumulate, analyze information and calculate the main indicators of economic activity of the enterprise, as well as monitor changes in such indicators in the dynamics. Automation of calculation of economic indicators, statistical analysis and visualization of data gives the chance to carry out effective management of the enterprise in modern economic conditions. The developed system allows to analyze the use of fixed assets; working capital analysis; analysis of the use of labor resources; profitability analysis; analysis of financial stability of enterprises. The paper shows the process of developing a functional model of a web application for the automated calculation of key economic indicators, and presents the advantages and disadvantages of working with the PostgreSQL database management system. The structure of the developed database, which consists of four tables, is given. An example of the work of the developed web application is presented.

Development of a Voice Virtual Assistant for the Geospatial Data Visualization Application on the Web

Voice assistants can elevate interaction in geospatial data web platforms. This research introduces a voice assistant in the BStreams platform and focuses on understanding user commands in the geospatial domain. We developed a specialised geospatial discourse framework through structured prototyping. A survey with 66 participants revealed prevalent English geospatial terminologies. Using ChatGPT, we found the term suggestions aligned with survey results, with a notable correlation (r = 0.81, p < 0.01) between the NPL model’s probability scores and term prevalence in survey data. Our study also incorporated usability tests on the application, which uses tools like Web Speech API, Leaflet, and Mapbox geocoding. Results from these tests reaffirm the potential of voice assistants in enhancing geospatial data visualisation, though challenges persist in areas like language understanding and domain knowledge. The paper advocates for further research to refine the integration of voice technology in this domain.

Monitoring System for Temperature and Humidity Sensors in the Production Room Using Node-Red as the Backend and Grafana as the Frontend

The client from the TRIAS project is facing issues with a decrease in production quality or defective products. They require a monitoring system for temperature and humidity in the production area, providing real-time notifications in case of any anomalies in temperature and humidity. However, the project has a limited budget, which poses a challenge for the contractor in developing a monitoring system that tracks temperature and humidity changes using temperature and humidity sensors as the data source. It should also provide alarms if the temperature and humidity values exceed the standard values for the room. Additionally, the pricing offer should be adjusted using information technology. The research methodology used in this study includes qualitative methods such as observation, literature review, and interviews to gather data on the mentioned issues. The SWOT method is used to analyze business process problems, while the Waterfall method is employed for system development. Based on the research findings, the researcher concludes that this project requires cost reduction in material usage and also needs a data visualization application for the mentioned sensors. The visualization application system utilizes Grafana as the frontend, chosen for its high flexibility in processing. The temperature and humidity data obtained from the sensors will be recorded by Node-Red as the backend and synchronized on the server. The data stored on the server will be saved in a MySQL database. The data from the database will be synchronized with Grafana for processing and visualization, presenting the data in easily understandable graphical forms.

Leveraging electronic medical records for HIV testing, care, and treatment programming in Kenya—the national data warehouse project

BackgroundAggregate electronic data repositories and population-level cross-sectional surveys play a critical role in HIV programme monitoring and surveillance for data-driven decision-making. However, these data sources have inherent limitations including inability to respond to public health priorities in real-time and to longitudinally follow up clients for ascertainment of long-term outcomes. Electronic medical records (EMRs) have tremendous potential to bridge these gaps when harnessed into a centralised data repository. We describe the evolution of EMRs and the development of a centralised national data warehouse (NDW) repository. Further, we describe the distribution and representativeness of data from the NDW and explore its potential for population-level surveillance of HIV testing, care and treatment in Kenya.Main bodyHealth information systems in Kenya have evolved from simple paper records to web-based EMRs with features that support data transmission to the NDW. The NDW design includes four layers: data warehouse application programming interface (DWAPI), central staging, integration service, and data visualization application. The number of health facilities uploading individual-level data to the NDW increased from 666 in 2016 to 1,516 in 2020, covering 41 of 47 counties in Kenya. By the end of 2020, the NDW hosted longitudinal data from 1,928,458 individuals ever started on antiretroviral therapy (ART). In 2020, there were 936,869 individuals who were active on ART in the NDW, compared to 1,219,276 individuals on ART reported in the aggregate-level Kenya Health Information System (KHIS), suggesting 77% coverage. The proportional distribution of individuals on ART by counties in the NDW was consistent with that from KHIS, suggesting representativeness and generalizability at the population level.ConclusionThe NDW presents opportunities for individual-level HIV programme monitoring and surveillance because of its longitudinal design and its ability to respond to public health priorities in real-time. A comparison with estimates from KHIS demonstrates that the NDW has high coverage and that the data maybe representative and generalizable at the population-level. The NDW is therefore a unique and complementary resource for HIV programme monitoring and surveillance with potential to strengthen timely data driven decision-making towards HIV epidemic control in Kenya.Database link(https://dwh.nascop.org/).

Applications of MATLAB in Natural Sciences: A Comprehensive Review

In the natural sciences, MATLAB is a versatile and essential tool that has revolutionized research across various disciplines, including physics, chemistry, biology, geology, and environmental sciences. This review paper provides a comprehensive overview of MATLAB's applications in data analysis, modeling, simulation, image processing, computational chemistry, environmental sciences, physics, engineering, and data visualization. MATLAB simplifies data analysis by handling complex datasets, performing statistical analyses, and aiding in tasks like curve fitting and spectral analysis. In modeling and simulation, it enables the creation of predictive models for intricate systems, facilitating simulations of physical processes, ecological dynamics, and chemical reactions. In image processing, MATLAB enhances and analyzes images, benefiting fields such as medical imaging and remote sensing. For computational chemistry, MATLAB offers a rich library of tools for exploring molecular structures and simulating chemical reactions. Environmental sciences rely on MATLAB for climate data analysis and ecological modeling. In physics and engineering, it is invaluable for simulating complex systems and analyzing experimental data. Additionally, MATLAB's data visualization capabilities allow scientists to create compelling visuals for effective communication. While challenges like licensing costs exist, efforts are underway to address these issues and enhance integration with other software, including artificial intelligence and machine learning tools. Overall, MATLAB's computational power and versatility are fundamental to advancing natural sciences research, making it an invaluable resource for scientists and researchers across various disciplines.

Application of clustering methods and data visualization for decision making in higher education

This paper aims to present a case study to demonstrate the practical application of data visualization techniques and machine learning algorithms. Examining the data using various algorithms enables us to make predictions about how data visualization affects decision-making. Our study’s results support the notion that data visualization influences decision-making. Moreover, we delve into the implications of employing data visualization technology in the academic community, including faculty and students. Furthermore, we evaluate how data visualization influences decision making in higher education institutions, showcasing its potential to enhance the efficiency and speed of decision-making for stakeholders.

COVID-19 Vaccine Tracking Geospatial Application using GIS and GPS

One of the problems in addressing the COVID -19 epidemic is that the coverage of COVID -19 vaccination data in each area cannot be immediately displayed. In addition, the COVID-19 vaccination data presented do not include spatial data, which means that the data cannot be used for decision making to address the problem. Although COVID -19 vaccination data are presented through digital platforms. However, a current limitation is that the platforms used to manipulate COVID-19 vaccination data have not supported real-time data manipulation, data processing, or spatial data visualization. As a result, the distribution of vaccinated individuals in each area cannot be tracked. Therefore, this research developed a geospatial data application for real-time data processing and visualization of COVID -19 vaccination using a Geographic Information System (GIS) and Global Positioning System (GPS) for data processing to plan and support decision making by people and officials so that COVID -19 operations can be conducted efficiently and data collection time can be reduced. The software used to develop this platform includes PHP, MySQL, Google Maps API and Leaflet. The results show that the developed platform can be used to monitor and track vaccination data in real time COVID-19.

Local nonlinear dimensionality reduction via preserving the geometric structure of data

Dimensionality reduction has many applications in data visualization and machine learning. Existing methods can be classified into global ones and local ones. The global methods usually learn the linear relationship in data, while the local ones learn the manifold intrinsic geometry structure, which has a significant impact on pattern recognition. However, most of existing local methods obtain an embedding with eigenvalue or singular value decomposition, where the computational complexities are very high in a large amount of high-dimensional data. In this paper, we propose a local nonlinear dimensionality reduction method named Vec2vec, which employs a neural network with only one hidden layer to reduce the computational complexity. We first build a neighborhood similarity graph from the input matrix, and then define the context of data points with the random walk properties in the graph. Finally, we train the neural network with the context of data points to learn the embedding of the matrix. We conduct extensive experiments of data classification and clustering on nine image and text datasets to evaluate the performance of our method. Experimental results show that Vec2vec is better than several state-of-the-art dimensionality reduction methods, except that it is equivalent to UMAP on data clustering tasks in the statistical hypothesis tests, but Vec2vec needs less computational time than UMAP in high-dimensional data. Furthermore, we propose a more lightweight method named Approximate Vec2vec (AVec2vec) with little performance degradation, which employs an approximate method to build the neighborhood similarity graph. AVec2vec is still better than some state-of-the-art local dimensionality reduction methods and competitive with UMAP on data classification and clustering tasks in the statistical hypothesis tests.

Application of Data Analysis and Data Visualization in Medical Equipment: Take Infusion Pump as Example

To combine data analysis and data visualization with medical devices, provide a set of data analysis and data visualization solutions. This solutions can deeply excavate the data of the whole life cycle of medical equipment, and then play a guiding role in business. With the help of mature tools in the Internet field, YIYI and YOUSHU, we can quickly complete data collection and rich visualization effects, and introduce the concept of data analysis to deeply mine data. Taking the maintenance data of infusion pump as an example, the data collection is completed through YIYI, and the maintenance system is built on YOUSHU. The maintenance system of infusion pump system is simple and clear, with good visualization effect. It can quickly analyze maintenance failures, reduce maintenance times and maintenance costs, ensure the safety of the equipment. Moreover, the system can be easily transferred to other medical equipment, and can carry on the whole life cycle data research.

Analisis Implementasi Komunikasi Modbus TCP/IP dalam Penerapan Visualisasi Data Hasil Produksi pada Sistem Andon Line Production

Andon refers to an information system aimed at management, maintenance, planners, or other workers regarding quality, process, or quantity issues. To support accurate data communication, a reliable protocol and high precision are required. The Modbus TCP/IP protocol is compatible with Andon systems. This study aims to analyze the implementation of Modbus TCP/IP communication in the application of production data visualization in online production systems. At a certain distance, the antenna's data must display the data's precision in real time. The average data transfer time at the farthest distance tested, 50 meters, was 0.0043 seconds, indicating that the longer the distance between the client and server, the longer the waiting time tends to be, but does not exceed 0.01 seconds if it does not affect production activities. These results are still pertinent to the information required by the organization.

Application of Data Visualization and Big Data Analysis in Intelligent Agriculture

Intelligent agriculture can renovate agricultural production and management, making agricultural production truly scientific and efficient. The existing data mining technology for agricultural information is powerful and professional. But the technology is not well adapted for intelligent agriculture. Therefore, this paper introduces data visualization and big data analysis into the application scenarios of intelligent agriculture. Firstly, an intelligent agriculture data visualization system was established, and the RadViz data visualization method was detailed for intelligent agriculture. Moreover, the intelligent agriculture data were processed using dimensionality reduction through principal component analysis (PCA) and further optimized through k-means clustering (KMC). Finally, the crop yield was predicted using the multiple regression algorithm and the residual principal component regression algorithm. The crop yield prediction model was proved effective through experiments.

Data Analytics and Visualization Application for Asset Health Monitoring

Much of the research on predictive maintenance has focused on statistical and machine learning techniques, while there has been significantly less focus on the human computer interaction or visualization aspects of PHM.  Human computer interaction and visualization techniques can quickly help identify interesting data sub-domains, both time periods and sensors, provided the data can be queried, retrieved and displayed in a timely manner. Augmenting visualization and interaction with a visual, aggregative fleet-based query system adds a further dimension, highlighting the ability of the fleet to carry out its mission. In the age of big data, effectively visualizing data from an asset with a multitude of sensors can be difficult, and that difficulty is compounded as you add additional assets.
 In this paper, we propose a scalable framework that is capable of visualizing past, current, and prognosticated health from the individual sensor up to the fleet or group level.   In addition to viewing near real time sensor data, maintenance logs, fault information, and data aggregations will be merged with the sensor data to make the analysis and visualizations more valuable.  This framework is scalable in regard to how much data can be collected, stored, and processed, as well as to the different organizational levels within a fleet of assets.  The framework is built as a web-application primarily using the following visualizations: a collapsible tree graph for asset information; 2D charts for temporal sensor data, fault data, and maintenance data; and 3D digital twins of critical components. 
 The current dataset used to build and demonstrate the capability of the web-application has data from over 3000 vehicles and contains approximately 9TB of data. Vehicle information such as model, make, sub-component, and fleet organization are presented in a configurable, collapsible tree structure. This allows the user to visualize the fleet and to select the asset and sensor combinations needed to display the temporal sensor data that will answer the question at hand. Information regarding each vehicle’s health status is displayed then aggregated and displayed for each of the higher tree nodes.   A 3D digital twin is also available to highlight sensor locations and current health status of the component.  These component models can be viewed and manipulated with and without a virtual reality headset. As health status monitoring for asset subcomponents are developed, they can be added to the system, allowing for complete health status reporting.