Data Visualization Process Research Articles

Biofuser: a multi-source data fusion platform for fusing the data of fermentation process devices.

In the past decade, the progress of traditional bioprocess optimization technique has lagged far behind the rapid development of synthetic biology, which has hindered the industrialization process of synthetic biology achievements. Recently, more and more advanced equipment and sensors have been applied for bioprocess online inspection to improve the understanding and optimization efficiency of the process. This has resulted in large amounts of process data from various sources with different communication protocols and data formats, requiring the development of techniques for integration and fusion of these heterogeneous data. Here we describe a multi-source fusion platform (Biofuser) that is designed to collect and process multi-source heterogeneous data. Biofuser integrates various data to a unique format that facilitates data visualization, further analysis, model construction, and automatic process control. Moreover, Biofuser also provides additional APIs that support machine learning or deep learning using the integrated data. We illustrate the application of Biofuser with a case study on riboflavin fermentation process development, demonstrating its ability in device faulty identification, critical process factor identification, and bioprocess prediction. Biofuser has the potential to significantly enhance the development of fermentation optimization techniques and is expected to become an important infrastructure for artificial intelligent integration into bioprocess optimization, thereby promoting the development of intelligent biomanufacturing.

A review: Data visualization for COVID-19

As COVID-19 raged across the globe, it seriously disrupted social health security, daily work, and the rest of the people, which aroused great panic among all classes. These facts extensively affected the regional circulation and economic growth of the epidemic area. Data visualization, an advanced technology, can help humans understand epidemic transmission trends, identify high-risk areas of outbreak, and evaluate prevention and control policies. Previous essays immediately established databases to organize and build datasets. Other researchers built new visualization models to analyze the causes of virus transmission and pointed out the low efficiency of the existing COVID-19 system response. Although various research and data visualization methods have been used on COVID-19, there has not been a comprehensive, systematic essay that covers the entire span of COVID-19 and its data visualization usage. Therefore, this paper will provide a new systematic and comprehensive perspective to explore the analysis of data visualization to solve COVID-19 data challenges and provide research methods and applications for COVID-19. To be specific, this paper first introduces the characteristics and challenges of COVID-19 epidemic data people may face during the data visualization process, which includes data complexity problems, data timeliness problems, data regional problems, and data quality problems. Then, this paper discusses data visualization methods used by predecessors in previous research. After that, this paper describes the urgent data visualization analysis requirements of COVID-19, including regional differences in virus spread, the influence of temperature on the virus, transmission trends, and death rates for different genders and ages, etc. Finally, this paper reviews applications of data visualization in COVID-19 analysis, followed by earlier-stage prevention, mid-term containment, and later-term reduction of impact. This paper provides an important reference for further research and application of data visualization technology in COVID-19 prevention and control, which brings new ideas for strengthening future epidemic surveillance and data visualization for global epidemic prevention and control.

Data Visualization of Budgeting Assumptions: An Illustrative Case of Trans-disciplinary Applied Knowledge

Trans-disciplinary research combines different fields into new conceptual and methodological frameworks. In this study, the SECI model of knowledge creation, which consists of Socialization, Externalization, Combination, and Internalization conversion modes, is used to analyze the implementation of a structured budgeting visualization system by a trans-disciplinary team. Through applied research in implementing a global budgeting system, budgeting assumptions are made explicit through visualization, transforming the approach to the budgeting process and its accuracy. This visualization, in turn, is enabled by assumptions underlying revenue planning, business services and employee compensation, and a visual process. The system displays a stepped approach, indicated by icons, representing the tasks involved in the budget process. For example, the system requires uploading the previous year’s information, setting the assumptions, calculating the suggested figures based on assumptions, and amending the proposed outcome. As adapted by Rice and Rice (2005), SECI is applied as the socialization of tacit-to-tacit budgeting assumption knowledge is solidified during the design phase of this transformation exercise. The externalization phase, in which budgeting assumptions are transformed from tacit to explicit, is evidenced during the configuration phase of the new system. The systemic collaboration results in the explicit assumptions being collectively leveraged across the regions during and after the “go-live” phase of system development. Finally, the internalization phase involves the explicit assumptions being transformed into new tacit knowledge as the experts evolve new assumptions derived from the transformation process. Semiotics provides variance information through hue, with, for example, darker colours indicating higher variances. This trans-disciplinary communication provides the means for increased efficiency and effectiveness. The resulting budget framework is visually validated through a heatmap by comparing the budgeting accuracy and assumption 1 Contact author 2 Peer-Editor: Prof D Sewry. Dean of Commerce, Rhodes University. complexity between the different regions where it was implemented. In summary, value is added in developing a new data visualization process, focusing on the role of budgeting assumptions and using planning process visualizations. This approach improves communication efficiency, effectiveness, and understanding of budgeting while enhancing accuracy.

Metode Peningkatan Literasi pada Pendidikan: Analisis Bibliometrik pada Publikasi 2013 – 2023

This study aims to determine the conditions in the range of 2013-2023 related to the scientific map, publication trends, and thematic research coverage of literacy learning methods in education, especially articles published in Scopus, Google Schoolar, and Crossref indexed journals. The research process focused on the following questions: (1). How are literacy improvement methods in education seen from the publication map; (2). What is of interest to academics regarding literacy improvement methods in education? and (3). What topics are often written about in publications related to methods of improving literacy in education? In addition to the research and data sources to be used in the bibliometric analysis, 1200 articles were found through the keywords "literacy, education" in the "article title &; abstract" published in Scopus, Google Schoolar and Crossref indexed journals. To explore and identify the main thematic areas and topics that appear in the publications using keyword occurrence analysis. To support the data analysis and visualization process obtained from Harzing's Publish or Perish and VOSviewer applications. The results of this study show that the method of improving literacy in education is a research trend that has been in great demand in the last 10 years and is still growing. In the visualization section, the high keyword density is presented in 8 clusters, namely: (1) literacy; (2) student; (3) study; (4) ability; and (5) technique. Bibliometric analysis in the form of mapping provides information about the development pattern of publications in the field of literacy in education. These findings provide contributions for future research: 1) The need for holistic research on improving literacy in education; and, 2) the need for in-depth research that focuses on one or several themes that are still rarely studied such as: (1) literacy culture; (2) early age; (3) student response; (4) literacy numeracy; (5) scientific literacy skills; (6) literacy tree; (7) culture and (8) habituation. Keywords: methods, literacy, education

Understanding Breast Cancer Detection: A Comprehensive Survey

The potentialities of improving the penetration of millimeter waves for breast cancer imaging are here explored. Methods: A field shaping technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification. Results: The theoretical results are numerically validated via the design and simulation of two circularly polarized antennas. The experimental validation of the designed antennas, using tissue-mimicking phantoms, is provided, being in good agreement with the theoretical predictions. Conclusion: The possibility of focusing, within a lossy medium, the electromagnetic power at millimeter-wave frequencies is demonstrated. Significance: Field shaping can be a key for using millimeter waves for breast cancer detection. Index Terms—Breast Cancer, Microwave Imaging, Millimeter Waves, Radial Line Slot Arrays, Near-Field Optimization. A field focusing technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification. A focusing technique based on convex optimization has been used here to increase the penetration in breast cancer imaging scenarios at millimeter waves. Significant enhancements of the field level inside a breast model have been achieved by employing focused apertures. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can dramatically improves the accuracy of diagnosis. As the methods itself show that it’s much complicated in designing and implementation. The data visualization process itself is a complicated process which can generate false results if the image has noise, so the result won’t be correct. It usually contains two major steps but the further steps are much similar to the above methods ,which makes it further more complicated as it including converting patched images to real images which makes the result inaccurate in diagnosis.

Pemodelan Numerik Hubungan Pola Curah Hujan Wilayah Equatorial di Pulau Sumatera Terhadap Fenomena ENSO dan IOD

A numerical modeling has been built for the relationship between rainfall in the equatorial region of Sumatra to the ENSO and IOD phenomena by taking data from 1991 to 2020 at three stations located in the western region of Sumatra Island, namely SM Japura, SM Minangkabau and SM Hang Nadim. The method used in this study is the method of correlation and linear regression on the Niño3.4 Index variable and the Dipole Mode Index on the amount of rainfall. The data visualization process is carried out with the output in the form of plots and graphs with Goole Colab coding. The resulting graph is a graph of the ENSO and IOD phenomena, as well as a regression graph between The Niño3.4, DMI and the amount of rainfall per month. This study was conducted with the aim of knowing how much influence the El Niño Southern Oscillation (ENSO) variable represented by the Niño3.4 Index and the Indian Ocean Dipole (IOD) represented by DMI had on rainfall in the equatorial region in the 1991 – 2020 period and to see the trend of significance between the two towards the equatorial region on the island of Sumatra. Rainfall in Indonesia's equatorial region tends to be influenced by IOD compared to ENSO. The combination of the two at the same time can cause a significant change in the amount of rainfall. La-Niña and negative IOD occurred in 1992, 1996, 1999, 2000, 2005, 2008, 2011 and 2016. The average rainy day index (SDII) tended to decrease in Japura Island in 1996, 2005, 2011, 2016 , at SM Minangkabau in 1992, 1996, 1999, 2000, 2016 and at SM Hang Nadim in 1996, 1999 and 2008. Meanwhile the longest series of wet days tended to decrease in SM Japura in 1992, 1996, 1999, 2005, in SM Minangkabau in 1992, 1996, 2005, 2008, and in SM Hang Nadim in 1992, 2000, 2005, 2008 and 2011. The longest series of dry days tended to increase in SM Japura in 1996, 2011, in SM Minangkabau in 1996, 2000, 2011, and at SM Hang Nadim in 1996.

An Efficient Big Data Visualization Deep Learning Architecture Model for Path Selection of College Students through Moral Education

Visualization technology can be used to present the analysis results in a more intuitive and easy-to-understand way, which can help educators to better understand the moral education needs of college students, and adjust their teaching strategies accordingly. The combination of big data analysis and visualization technology can also help to improve the efficiency and effectiveness of moral education in colleges and universities. The research on the moral education path selection of college students based on big data visualization has great significance for promoting the development of moral education in colleges and universities, and for cultivating high-quality talent with good moral character. This paper proposed an Optimization model for big data analytics for moral education. The data associated with moral education and information are stored in cloud with the big data. The stored big data visualization process is performed with the optimization model for the feature extraction. The optimization is performed with an integrated Flamingo and weighted black widow Optimization model. The proposed model is stated as the Integrated Flamingo Black Widow (IFBW) model. The performance of the IFBW model is implemented with the deep learning Restricted Boltzmann Machine (RBM) architecture. Simulation analysis stated that IFBW model achieves a higher classification accuracy rate of 99% with a minimal error rate.

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Networks

항공영어 교과목의 웹 콘텐츠 구축에 관한 탐색적 연구

The purpose of this study is to succeed in English interview in aviation-related colleges and aviation English courses. Simultaneous expression of English and Korean by topic, subtitled video using YouTube, and proper arrangement of video sound and subtitles were utilized. As described above, the online web content of the data visualization process was constructed as follows so that aviation service-related learners can learn as easily and as interestingly as possible. BR First, the subtitles were extracted and refined through the Language Learning with Youtube extension program from the YouTube video used for the subject of the airline interview English course opened at J University in Korea. After extracting the sound source from the video using 4K Video Downloader, the sound source was edited in sentences or verse units through Cool Edit Pro 2.1. Second, reflect the opinions of the Focused Group Interview experts and select the H5P content applicable to the vocabulary and sentence practice of the airline interview English course. Third, the produced H5P contents were serviced through Notion, a collaboration platform that enables project management and recording.

Adaptation of the Pedestrian Walkways Design along the Trans Semarang Bus Corridor to COVID-19

COVID-19 cases continue to increase in Indonesia, with an estimated value of 719,000 and an 8.37% mortality rate, due to the severity and potency of the deadly disease. One of the provinces that mostly contributes to the high mortality rate is Central Java, especially in populous areas like Semarang City. The spread of the virus occurs through direct contact between humans, forcing physical and social distances to become more important, especially in public spaces. Based on these conditions, city planning experts reportedly suggests a public space re-design process, adaptable to the current pandemic conditions, in order to provide a comfortable, safer, and sustainable urban life. Therefore, this study aims to analyze the integration of the mass transportation system (the Bus Trans Semarang) with the readjustment and redesign potentials of pedestrian walkways, in response to the COVID-19 pandemic. For the past two years, the development of pedestrian walkways has become an integral part of the Semarang City Government, in order to realize a sustainable transportation system. Furthermore, the pandemic has presently made pedestrian walkways less functional, due to public concerns on its spread in external areas. The retrieval spatial-based data of this study focused on the bus stops for the Trans Semarang Bus Corridor, which were potential points for the emergence of walking activities. The method used in the measurement of pedestrian activity included behavioral mapping and urban design analysis. This was to identify the pedestrian design indicators that were adaptive to the pandemic conditions. The simulation methods also assisted the data visualization process, by using spatial applications such as ArcGIS and Sketchup. After this, the calculation of the capacity was carried out, to determine the maximum limit of the available spaces. The results were in the form of indicators, such as additional width, lane division, as well as provision of health and hygiene facilities. These were the main recommendations applied on the pedestrian walkways' environmental characteristics, at various points in Semarang City.

Technology Trends and Challenges for Large-Scale Scientific Visualization.

Scientific visualization is a key approach to understanding the growing massive streams of data from scientific simulations and experiments. In this article, I review technology trends including the positive effects of Moore's law on science, the significant gap between processing and data storage speeds, the emergence of hardware accelerators for ray-tracing, and the availability of robust machine learning techniques. These trends represent changes to the status quo and present the scientific visualization community with a new set of challenges. A major challenge involves extending our approaches to visualize the modern scientific process, which includes scientific verification and validation. Another key challenge to the community is the growing number, size, and complexity of scientific datasets. A final challenge is to take advantage of emerging technology trends in custom hardware and machine learning to significantly improve the large-scale data visualization process.

RApid Data Integration and Visualization (RADIV) in Subsurface Operations

AbstractComplex subsurface operations are characterized by a life‐threatening environment, a skilled and initiative opponent, and the absence of predictability of the events due to a high level of interdependencies. The reduction of complexity by provision of essential information is crucial for decision‐making and rapid integration and visualization of heterogeneous data is essential for successful mission accomplishment. Currently, only standalone applications are available for the underground operational environment, and collaborative planning and working spaces in command and control are missing. The RApid Data Integration and Visualization (RADIV) process addresses exactly this challenge and ensures the lateral continuity of visualization systems across the entire reality‐virtuality continuum (2D ⇔ 3D ⇔ mixed reality). It provides a comprehensive command and control system for subterranean operations by processing and visualizing data in different views for different purposes. Integration of these data within the Subsurface Operations Mission Tool (SOMT) will increase the decision quality by improved perception and collaboration. Close cooperation and information exchange between operators and action forces is a prerequisite for success by displaying the relevant information within the truly comprehensive common operational picture, thereby enabling more accurate and precise action reducing own losses and collateral damage.

Application for polymer industry to hold nearly 40% of the bio-based platform chemicals market; demand to grow by 8% CAGR through 2031

Today, more leaders, managers, and directors can decide, even though they cannot decide for every situation. When decisions are scattered and unstructured, decisions become complicated and so that a single decision-maker based on expertise alone cannot address them adequately. This research proposes the Electronic Decision System (EDS) for effective data visualization and analysis process. EDS helps to decide during a complicated situation using Big Data Analytics-based Artificial Neural Network Framework (BDA-ANNF). The BDA-ANNF collects and analyses the data provided, and Artificial Neural Network (ANN) is used to predict and take accurate decisions in every complicated situation. These systems allow decision-makers to understand the market, direct and indirect climate, analyze the numerous trade-offs in the sector, evaluate costs linked to the various choices for decision-making, and eventually help people improve their decision-making outcome. EDS's approaches and techniques share one key challenge that must successfully provide consumers and policymakers with an appropriate and understandable simulation solution. The finding indicates that the BDA-ANNF decides the complicated situation up to the accuracy rate of 95.23%, which is not attainable by existing methods, and the error rate is 4.77%.

Industrial process data visualization based on a deep enhanced t-distributed stochastic neighbor embedding neural network

PurposeThe purpose of this paper is to propose a approach for data visualization and industrial process monitoring.Design/methodology/approachA deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph.FindingsThe proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring.Originality/valueThis paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

Research on electronic decision system for effective data visualization and analysis process

Today, more leaders, managers, and directors can decide, even though they cannot decide for every situation. When decisions are scattered and unstructured, decisions become complicated and so that a single decision-maker based on expertise alone cannot address them adequately. This research proposes the Electronic Decision System (EDS) for effective data visualization and analysis process. EDS helps to decide during a complicated situation using Big Data Analytics-based Artificial Neural Network Framework (BDA-ANNF). The BDA-ANNF collects and analyses the data provided, and Artificial Neural Network (ANN) is used to predict and take accurate decisions in every complicated situation. These systems allow decision-makers to understand the market, direct and indirect climate, analyze the numerous trade-offs in the sector, evaluate costs linked to the various choices for decision-making, and eventually help people improve their decision-making outcome. EDS's approaches and techniques share one key challenge that must successfully provide consumers and policymakers with an appropriate and understandable simulation solution. The finding indicates that the BDA-ANNF decides the complicated situation up to the accuracy rate of 95.23%, which is not attainable by existing methods, and the error rate is 4.77%.

Anticipation of Living Status of Hepatitis B Patient by using Machine Learning

Abstract: Recently the methods of Data mining and machine learning are widely used in medical field. These methods/techniques have given better results in the prediction of respective diseases. Hepatitis B is a Liver inflammation; it can affect people of all age groups. Lakhs of people across the globe are thought to be affected by Hepatitis B. Early prediction of Hepatitis B with accurate results can save many people. Hepatitis B is a tough challenge for public health care system because of limited clinical diagnosis in the early stages of disease. This paper presents the decision tree algorithm to diagnose the Hepatitis B. The proposed algorithm includes collection of datasets, pre-processing, EDA (Exploratory Data Analysis), Feature Selection, data visualizing, Interpreting, saving and evaluating the model. After the data visualization process decision tree algorithm is implemented to diagnose the disease along with the patient chances of living. Keywords: Hepatitis B virus, Machine Learning, Decision Tree, Public Health, EDA

Activity Worksheets for Teaching and Learning Data Visualization.

In this work, the data visualization activity (DVA) worksheet method for teaching and learning data visualization is presented. The DVA worksheet method consists of a series of activity worksheets developed to guide novice instructors and students through the data visualization process. The activity worksheets help new faculty and visualization instructors, lacking formal training in pedagogy and data visualization, learn the data visualization process, design course work, and develop curriculum. The worksheets can be used for individual activities, or as a collection of activities to support data visualization capacity building. Each worksheet focuses on an individual step in the process, allowing the worksheets to be tailored to discipline-specific data visualization needs. We share the motivation and evolution of the worksheets from paper-based to the semi-automated process utilized in fall 2020. We conclude this work with a discussion and areas for improvement.

COVID-19 and Bangladesh: Situation report, comparative analysis, and case study.

This paper describes different aspects of COVID-19 in Bangladesh and other countries with visualization and infographic images containing the virus's spreading, other information from the statistical format, and some case studies based on various important fields for a country educational, economic, etc. Firstly, literature review section this paper on COVID-19 related works and its type with previous virus transmission reports and its effect. Secondly, completing work describes through algorithms and methodology diagram, which contains the overall data analytics process, including data wrangling. In this phase, the total working procedure to complete further process. Thirdly, a complete situation report analysis on Bangladesh by infographics through dataset containing every perspective of COVID-19 in Bangladesh. Comparative analysis with other top infected countries visualizes through the Data Visualization process and statistical analytics and case studies based on various impacts of lockdown on Bangladesh. Finally, a discussion presents the statistical summarization of COVID-19 in Bangladesh.

Implementation SEMAR-IoT-Platform for Vehicle as a Mobile Sensor Network

With the rapid development of IoT technology in various fields such as smart cities and industry 4.0, the need for wireless sensor network-based systems has increased, one of which is the concept of using a vehicle as a mobile sensor network or known as VaaMSN. Many developers use the IoT platform as a cloud computing service in developing the VaaMSN system. However, not all IoT platform service providers provide monitoring features on every device and provide information such as device location, purpose, condition. Therefore, this research aims to develop an IoT Platform that can receive data and provide information on each device, making it easier to process data and control devices. Therefore, this research aims to develop an IoT platform called the SEMAR-IoT-Platform that able to received data and provide information on each device for easier data processing and control devices. The SEMAR-IoT-Platform integrates Big Data, Data Analytics, Machine Learning, using the principles of Extract, Transfer, and Load (ETL) for data processing and provides communication services using HTTP-POST, MQTT, and NATS. The test results show that the system has been successfully implemented to complement a simple IoT system with an average delay time of HTTP, NATS, and MQTT communications of less than 150ms for the data storage process, and for the data visualization process has an average delay time of less than 300ms.

VISUALISASI DATA E-DAGANG MENGGUNAKAN AUGMENTASI REALITI

Data visualization is viewed as a significant element in data analysis and communication. As the data engagement becomes more and more complex, visual presentation of data does help users understand the data. So far, two-dimensional (2D) data visuals are often used for the data visualization process, but the lack of depth dimension leads to inefficient and limited understanding of the data. Therefore, the effectiveness of augmented reality (AR) in data visualization was studied through the development of an AR Data Visualization application using E-commerce data. Machine learning models are also involved in the development of this AR application for the provision of data using predictive analysis functions. To provide quality E-commerce data and an optimal machine learning model, the data science process is carried out using the python programming language. The E-commerce data selected for this study is open data taken through the Kaggle Website. This database has 9994 data numbers and 21 attributes. This AR data visualization application will make it easier for users to understand the E-commerce data in-depth through the use of AR technology and be able to visualize the forecasts for sales profit based on the algorithm model "Auto-Regressive Integrated Moving Average" (ARIMA).