Data Visualization Techniques Research Articles

Data Visualization & Analysis of Disease X [Nipah Virus

Abstract: This article is about performing mathematical analysis and visualization of data that was researched and collected about Nipah Virus; which is treated as Disease X[deadly disease] by World Health Organization. It includes information about the field of Data Analysis and Visualization techniques which associate closely with current era and the project itself. Furthermore, there is description which provides information about the main application developed and implemented. The variety of research papers that were made use of, along-with significantly valuable data are cited too. Data Analyses has very close connection with data science, however, the key difference lies in its actual working, as Data Analysis makes extensive use of historically available data & generates important insights from them, which are very considerable for future operations. Thus, due to such complex level usage, Data Analysis has gained recognition in the recent years, which makes it important for professionals to add it as a part of daily life tasks. When it comes to real time usage, Data Analyses is used for many purposes which includes but is not limited to customer preferences, finance sectors, research in markets and also in assessment of variety of risks. Data Analysis thus, has introduced a massive number of futuristic achievable tasks by taking important notes from the ones that were conducted in the past

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Abstract: In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

Exploring Healthcare Trends: A Python-Powered Analysis of Doctor Visits

This project delves into an analysis of the "Dr.Visits" dataset using Python tools and libraries, aiming to uncover insights into patterns and relationships related to doctor visits and health conditions. Through data visualization techniques and statistical methods, the project seeks to reveal key trends and correlations within the dataset. Initial steps involve importing the dataset and exploring its characteristics, including variables like gender, age, income, and illness distribution. The analysis focuses on understanding how these variables impact doctor visits and health-related activities. Notably, the project highlights gender-based variations in reduced activity due to illness, prompting further exploration of potential contributing factors. In summary, this project provides valuable insights into healthcare and patient behavior through the lens of the "Dr.Visits" dataset.

Smart Report for Healthcare System

In the ever-evolving landscape of healthcare, the effective interpretation and presentation of pathology patient data play a pivotal role in diagnosis, treatment, and research. Recognizing the transformative power of advanced data visualization techniques, this project endeavors to forge a comprehensive and user-friendly a platform dedicated to illuminating pathology patient data. By leveraging cutting-edge visualization tools, our the initiative aspires to furnish healthcare professionals, researchers, and patients with an intuitive and holistic representation of intricate pathology data. As healthcare continues to evolve, the potential benefits of Smart Healthcare Systems are evident, including enhanced patient outcomes, reduced costs, and improved healthcare delivery. In conclusion, this report underscores the profound impact of Smart Healthcare Systems on the future of medical services. It provides valuable insights for healthcare professionals, policymakers, and stakeholders to navigate the changing landscape and maximizing the benefits of smart technologies in healthcare.

Comparative Analysis of 4G and 5G Networks: A Review Study

The transition from 4G to 5G networks marks a significant leap in telecommunications technology, promising faster speeds, lower latency, and enhanced connectivity. This research paper conducts a thorough comparative analysis and visualization of key attributes between 4G and 5G networks. Leveraging data visualization techniques and Python libraries such as Pandas, Matplotlib, Seaborn, Plotly, and Bokeh, the study aims to provide insights into the advancements and benefits offered by 5G technology over its predecessor, 4G. Additionally, the paper explores broader implications, including economic, societal, and environmental impacts of 5G adoption.

Analyzing SME's Data Visualization, Business Challenges, and Solutions: A Seven Stars Review

This comprehensive report encapsulates a thorough analysis conducted on the extensive Seven Stars dataset. Leveraging advanced data visualization techniques, the analysis has been meticulously executed to extract meaningful insights and elucidate intricate patterns within the dataset. By delving deep into the data, the report identifies a spectrum of potential business challenges that the organization may encounter, both in the present and future landscapes. These challenges encompass diverse realms such as market fluctuations, resource allocation, and operational inefficiencies. In response to the identified challenges, a robust set of solutions is proposed, tailored to address each issue methodically. Drawing upon best practices and industry expertise, these solutions aim to bolster the organization's resilience and competitiveness in the dynamic business environment. Moreover, to facilitate seamless data management and decision-making processes, a bespoke dashboard has been meticulously crafted. This intuitive dashboard serves as a centralized platform, enabling stakeholders to effortlessly manipulate and analyze data from disparate sources, thereby fostering informed decision-making and strategic planning. In essence, this paper serves as a comprehensive roadmap for the organization's data-driven journey, guiding it towards sustainable growth and success in an ever-evolving business landscape. Through diligent analysis, strategic foresight, and proactive problem-solving, the organization can chart a course towards long-term prosperity and resilience.

Analysis and Visualization of Advertising Sales Data Using Python Software Through an Internship Program

This research discusses the use of data visualization as a tool for analyzing and presenting information effectively. The main goal of this research is visualization that allows better understanding of complex data. In this context, research explores various data visualization techniques, including the use of bar charts, correlation heatmaps, and interactive technology to present information intuitively. In improving decision-making abilities, identifying patterns or trends, and exploring relationships and insights contained in large and heterogeneous datasets. The research methodology includes comparative analysis of various visualization methods, user experiments, and the application of new techniques to evaluate the effectiveness and usefulness of data visualization in advertising video sales data at PT XXX. Based on the research results, it was found that data visualization involves presenting data information in graphic or image form to facilitate understanding. This helps in explaining the facts and determining the steps that need to be taken. The research results are expected to provide in-depth insight into the development of data visualization techniques. The results of this data visualization can be widely applied in various fields, especially creative, marketing and sales management.

Parallel Chords: an audio-visual analytics design for parallel coordinates

AbstractOne of the commonly used visualization techniques for multivariate data is the parallel coordinates plot. It provides users with a visual overview of multivariate data and the possibility to interactively explore it. While pattern recognition is a strength of the human visual system, it is also a strength of the auditory system. Inspired by the integration of the visual and auditory perception in everyday life, we introduce an audio-visual analytics design named Parallel Chords combining both visual and auditory displays. Parallel Chords lets users explore multivariate data using both visualization and sonification through the interaction with the axes of a parallel coordinates plot. To illustrate the potential of the design, we present (1) prototypical data patterns where the sonification helps with the identification of correlations, clusters, and outliers, (2) a usage scenario showing the sonification of data from non-adjacent axes, and (3) a controlled experiment on the sensitivity thresholds of participants when distinguishing the strength of correlations. During this controlled experiment, 35 participants used three different display types, the visualization, the sonification, and the combination of these, to identify the strongest out of three correlations. The results show that all three display types enabled the participants to identify the strongest correlation — with visualization resulting in the best sensitivity. The sonification resulted in sensitivities that were independent from the type of displayed correlation, and the combination resulted in increased enjoyability during usage.

Explaining vulnerabilities of heart rate biometric models securing IoT wearables

In the field of health informatics, extensive research has been conducted to predict diseases and extract valuable insights from patient data. However, a significant gap exists in addressing privacy concerns associated with data collection. Therefore, there is an urgent need to develop a machine-learning authentication model to secure the patients’ data seamlessly and continuously, as well as to find potential explanations when the model may fail. To address this challenge, we propose a unique approach to secure patients’ data using novel eigenheart features calculated from coarse-grained heart rate data Various statistical and visualization techniques are utilized to explain the potential vulnerabilities of the model. Though it is feasible to develop continuous user authentication models from readily available heart rate data with reasonable performance, they are affected by factors such as age and Body Mass Index (BMI). These factors will be crucial for developing a more robust authentication model in the future.

The impact of data visualization technique on decision making in business analytics Vaibhav,

Data visualization plays a crucial role in the field of business analytics, significantly influencing decision-making processes. By presenting complex data in a visually appealing and easily understandable format, data visualization helps stakeholders and decision-makers to identify patterns, trends, and insights that would otherwise go unnoticed. This, in turn, aids in making informed decisions, improving business performance, and achieving strategic goals. Data visualization has emerged as a critical tool in the realm of business analytics, revolutionizing the way organizations interpret, analyse, and make decisions based on data. In an era inundated with vast amounts of data, the ability to transform complex datasets into meaningful visual representations has become indispensable for businesses seeking to gain actionable insights. This introduction will delve into the profound impact of data visualization on decision-making in business analytics, exploring its role in enhancing comprehension, facilitating communication, and driving strategic outcomes. Enhancing Comprehension: One of the primary advantages of data visualization is Its ability to distil intricate datasets into intuitive visuals that are easily understandable to stakeholders across various levels of an organization. Unlike traditional tabular formats or raw data sets, visual representations such as charts, graphs, and dashboards offer a more accessible means of interpreting complex information. By leveraging graphical elements such as colour, shape, and size, data visualization enables users to discern patterns, trends, and correlations that might otherwise remain obscured within the data. This enhanced comprehension empowers decision-makers to grasp key insights swiftly and make informed judgments based on a comprehensive understanding of the underlying data. Facilitating Communication: Effective communication of insights derived from data analysis is essential for driving alignment and informed decision-making within organizations. Data visualization serves as a powerful medium for conveying complex findings in a concise and compelling manner. Visualizations not only provide a common language through which diverse stakeholders can interpret and discuss data but also foster collaboration by facilitating the sharing of insights across departments and teams. By presenting information in visually engaging formats, such as interactive dashboards or infographics, data visualization promotes clarity and transparency in communication, enabling decision-makers to convey key messages more effectively and garner consensus around strategic initiatives.

Preliminary Evidence for the Decline in Discharge at Falaj Daris, Nizwa, Oman

Falaj Daris is one of the oldest active aflāj located in Oman and the largest in the Dakhiliyah governorate. It is a 7,999-meter long Dāwūdī-type falaj. Ninety-five percent of water drawn from falaj Daris is used for agricultural irrigation and the remaining five percent is used for domestic purposes. In this paper, we analyzed several datasets including falaj Daris discharge, borehole water levels in wadi al-Abyadh, whose aquifer falaj Daris taps, and GRACE satellite data to determine the historical trend of falaj Daris discharge during 1982-2020. A set of standard statistical methods and data visualization techniques were applied including the Mann-Kendall trend test. Preliminary evidence shows a decline of 226,000 L/day in falaj Daris discharge between 1982-2020. This finding is supported by the overall trends in borehole water levels in wadi al-Abyadh. All these trends were confirmed using GRACE satellite data which shows a decrease in water volume of 6 (WET cm) in the larger basin between 2002-2021. Keywords: qanat; falaj Daris; wadi al-Abyadh; Nizwa, Oman; GRACE satellite.

Unveiling predictors influencing patent licensing: Analyzing patent scope in robotics and automation

Ensuring the sustainability of technology transfer offices depends on effective patent licensing strategies. This study investigates novel predictors for patent licensing prediction. It emphasizes the importance of judiciously selecting suitable patent-scope metrics to enhance the likelihood of successful patent licensing agreements. This work focuses on a critical aspect of patent scope, specifically examining the number of independent claims, the length of the first claim, the depth of the claim, the Cooperative Patent classification count, the non-US family count, and the family independent count. Additionally, we consider conventional metrics previously investigated in prior research, such as claim count, the count within the International Patent Classification, and Simple Family Application. Our empirical analysis harnesses a dataset comprising patents from university technology transfers within the robotics and automation domain. We analyze the relationship between patent scope measures and licensing outcomes using data visualization and statistical techniques, including the point-biserial correlation coefficient and the t-test. Comparative analysis of the statistical results is performed to identify the most impactful predictor. Our study reveals a correlation between the number of independent claims and the success of patent licensing. In contrast, the rest of the investigated measures do not impact the success of patent licensing.

Continuity with caveats in anesthesia: state and response entropy of the EEG.

The growing use of neuromonitoring in general anesthesia provides detailed insights into the effects of anesthetics on the brain. Our study focuses on the processed EEG indices State Entropy (SE), Response Entropy (RE), and Burst Suppression Ratio (BSR) of the GE EntropyTM Module, which serve as surrogate measures for estimating the level of anesthesia. While retrospectively analyzing SE and RE index values from patient records, we encountered a technical anomaly with a conspicuous distribution of index values. In this single-center, retrospective study, we analyzed processed intraoperative electroencephalographic (EEG) data from 15,608 patients who underwent general anesthesia. We employed various data visualization techniques, including histograms and heat maps, and fitted custom non-Gaussian curves. Individual patients' anesthetic periods were evaluated in detail. To compare distributions, we utilized the Kolmogorov-Smirnov test and Kullback-Leibler divergence. The analysis also included the influence of the BSR on the distribution of SE and RE values. We identified distinct pillar indicesfor both SE and RE, i.e., index values with a higher probability of occurrence than others. These pillar indexvalues were not age-dependent and followed a non-equidistant distribution pattern. This phenomenon occurs independently of the BSR distribution. SE and RE index values do not adhere to a continuous distribution, instead displaying prominent pillar indiceswith a consistent pattern of occurrence across all age groups. The specific features of the underlying algorithm responsible for this pattern remain elusive.

COVID-19 Pandemic Risk Assessment: Systematic Review.

The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era. We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning. This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts. Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles. Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

Personalized Learning Models Using Decision Tree and Random Forest Algorithms in Telecommunication Company

In response to the rising popularity of online training, this study addresses the crucial need for effective assessment methods at PT XYZ. The research focuses on developing a comprehensive solution through a data visualization dashboard and a machine learning model. The data visualization dashboard, created using Tableau, provides an interactive platform for exploring training data. It offers valuable insights into employees learning progress and needs, empowering them to monitor their advancement and identify areas for improvement effectively. Simultaneously, a machine learning model was developed using Python and Google Collab, employing decision trees and random forest algorithms. The model exhibited promising results with an accuracy rate of 69% for decision trees and 70% for random forests, indicating its proficiency in predicting skill groups. Furthermore, the study rigorously evaluated the dashboard and machine learning model using a 20% holdout dataset, affirming their effectiveness. The dashboard, deployed on a web server, ensures accessibility to all PT XYZ employees, enhancing user experience and engagement. Notably, the dashboard's user-friendly interface allows employees to actively participate in their learning journey, while the machine learning model generates personalized training recommendations based on their progress and needs. In summary, this research provides a practical and innovative solution to the challenge of online training assessment at PT XYZ. By combining data visualization techniques and machine learning algorithms, the developed tools significantly enhance the efficiency and effectiveness of training programs. These findings contribute valuable insights into online training assessment methodologies and pave the way for improved learning experiences in the digital age.

Crisis talk: analysis of the public debate around the energy crisis and cost of living

A prominent media topic in the UK in the early 2020s is the energy crisis affecting the UK and most of Europe. It brings into a single public debate issues of energy dependency and sustainability, fair distribution of economic burdens and cost of living, as well as climate change, risk, and sustainability. In this paper, we investigate the public discourse around the energy crisis and cost of living to identify how these pivotal and contradictory issues are reconciled in this debate and to identify which social actors are involved and the role they play. We analyse a document corpus retrieved from UK newspapers from January 2014 to March 2023. We apply a variety of natural language processing and data visualisation techniques to identify key topics, novel trends, critical social actors, and the role they play in the debate, along with the sentiment associated with those actors and topics. We combine automated techniques with manual discourse analysis to explore and validate the insights revealed in this study. The findings verify the utility of these techniques by providing a flexible and scalable pipeline for discourse analysis and providing critical insights for cost of living—energy crisis nexus research.

Stress Detection of Autistic Adults during Simulated Job Interviews Using a Novel Physiological Dataset and Machine Learning

The interview process has been identified as one of the major barriers to employment of autistic individuals, which contributes to the staggering rate of under and unemployment of autistic adults. Decreasing stress during the interview has been shown to improve interview performance. However, in order to effectively provide insights on stress to both interviewees and interviewers, it is necessary to first effectively measure stress. This work explores physiological stress detection through wearable sensing as a means of obtaining quantitative stress measures from young autistic adults undergoing a virtual simulated interview using supervised machine learning techniques. Several supervised learning models were explored and it was found that Elastic Net Regression had the best accuracy with individual models with an accuracy of 84.8% while Support Vector Regression models evaluated with leave-one-out cross validation had a group accuracy of 75.4%. The predictions from the stress model were used with data visualization techniques in order to provide insights on the interview process from both a group and individual viewpoint, showing that stress trends can be found and evaluated using the stress model. This work also addresses a major gap in physiological stress detection literature by presenting a novel dataset of physiological data and ground truth labels for 15 autistic young adults undergoing a simulated interview.

A Multi-Model Machine Learning Approach for Monitoring Calories Being Burnt During Workouts Using Smart Calorie Tracer

INTRODUCTION: In today's health-conscious world, accurate calorie monitoring during exercise is crucial for achieving fitness goals and maintaining a healthy lifestyle. However, existing methods often lack precision, driving the need for more reliable tracking systems. This paper explores the use of a multi-model machine learning approach to predict calorie burn during workouts by utilizing a comprehensive dataset.
 OBJECTIVES: The objective of this paper is to develop a user-friendly program capable of accurately predicting calorie expenditure during exercise, leveraging advanced machine learning techniques.
 METHODS: Techniques from social network analysis were employed to analyze the dataset, which included information on age, gender, height, weight, workout intensity, and duration. Data preprocessing involved handling missing values, eliminating irrelevant columns, and preparing features for analysis. The dataset was then divided into training and testing sets for model development and evaluation. Machine learning models, including Neural Networks, AdaBoost, Random Forest, and Gradient Boosting, were chosen based on their performance in regression tasks.
 RESULTS: The neural network model demonstrated superior performance in predicting calorie burn, outperforming other models in terms of MSE, RMSE, and an R2 score. Data visualization techniques aided in understanding the relationship between variables and calorie burn, highlighting the effectiveness of the neural network model.
 CONCLUSION: The findings suggest that a multi-model machine learning approach offers a promising solution for accurate calorie tracking during exercise. The neural network model, in particular, shows potential for developing user-friendly calorie monitoring applications. While limitations exist, such as dataset scope and environmental factors, this study lays the groundwork for future advancements in calorie monitoring and contributes to the development of holistic fitness applications.

Optimizing the Sales Law and Replenishment Decision Analysis of Fresh Supermarket Products

In the field of fresh food supermarkets, due to the relatively limited shelf-life of vegetable products, their quality will gradually deteriorate with the sales time. Therefore, supermarkets need to perform daily rationing replenishment operations based on the historical sales data and demand of the products. This paper is based on the collected product information of vegetable categories, detailed vegetable sales flow data, wholesale prices of vegetable products, and recent vegetable product wastage rates. Data analysis and visualization techniques are used to analyze the distribution pattern of vegetable sales in each category and single product. Next, a functional relationship between total sales volume and cost-plus pricing of vegetable categories was constructed. Regression forecasts were used to simulate the future wholesale prices of vegetable categories.

A feasibility study for a unified, multimodal analysis of online information foraging in health-related topics.

Digital health literacy (DHL) is the ability to find, understand, and appraise online health-related information, as well as apply it to health behavior. It has become a core competence for navigating online information and health service environments. DHL involves solving ill-structured problems, where the problem and its solution are not clearcut and may have no single answer, such as in the process of sensemaking. We employ and expand on information foraging theory to address how experts and novices in information retrieval perform a search task. Our overarching aim is to pinpoint best practices and pitfalls in understanding and appraising health-related information online to develop a digital intervention to increase DHL and critical thinking. In this feasibility study, we recruited a total of twenty participants for our expert and novice subsamples. We collected sociodemographic data with a self-developed survey, video data through an observation protocol of a 10-minute search task, as well as audio-video data via a retrospective think-aloud. The three, multimodal data streams were transcribed and aligned. Codes were developed inductively in several iterations, then applied deductively to the entire dataset. Tabularized, coded and segmented qualitative data were used to create various quantitative models, which demonstrate viability for the qualitative and statistical comparison of our two subsamples. Data were visualized with Epistemic Network Analysis to analyze code co-occurrences in the three aligned data streams, and with Qualitative/Unified Exploration of State Transitions to examine the order in which participants in our two subsamples encountered online content. This paper describes our methods and planned analyses elaborated with mock figures. Quantifying qualitative data, aligning data streams, and representing all information in a tabularized dataset allows us to group data according to various participant attributes and employ data visualization techniques to pinpoint patterns therein.