Visualization Techniques Research Articles

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.

Velocity (μPIV) and temperature/concentration (μLIF) measurements in a dual serpentine microchannel of a micro DMFC stack

Herein, we present an experimental study of the flow, as well as the thermal and concentration characteristics of two serpentine microchannels, via micro particle image velocity/micro laser induced fluorescence optical visualization techniques, which simulate the flow channels at the anode of a typical micro direct methanol fuel cell stack (two cells: Cell A/B). The flow channel consists of two serpentine microchannels. Each channel has one channel and six paths. Channels A and B were connected by a connecting tube to create an entire flow loop with methanol as the working fluid. The velocity, temperature and concentration distribution along the channel downstream for 5.7 ≤ Re ≤ 594 were visualized and measured. At a particular operation condition of methanol flow rate of 1 ml/min and concentration of 1 M at stack temperature of 25 °C, both in series and parallel arrangements were examined with a peak power density of 61.4 mW/cm2 (current density 120 mA/cm2 and voltage 1.0 V) and of 75 mW/cm2 (current density 120 mA/cm2 and voltage 1.2 V), respectively. CO2 ellipse-like bubbles were found at the up/down stream corners with a definite major/minor axis length. Furthermore, relevant (f, Nu, and Sh) correlations regarding the relationship of the methanol solution pumping power, heat transfer and utilization with Reynolds number in form of power law were developed with 95 % accuracy of experimental data. With these related results, it might be beneficial to the configuration design of the μDMFC stack.

Visualization and Analysis of Vocal Fold Dynamics in Various Voice Disorders

Objectives/HypothesisThis study aims to compare the glottal dynamics of vocal fold leukoplakia, laryngitis, and papilloma using various visualization techniques, including phonovibrograms (PVG) and glottovibrograms (GVG). It hypothesizes that these techniques can provide understanding of the vibratory dynamics of these voice disorders, which can aid in their objective diagnosis and deriving visual features for computer-assisted classifications. Study DesignThis work employs a comparative analyses of vocal fold disorders using visualization techniques. The study examines phonovibrograms (PVG), derivative of PVG (PVG-1), digital kymograms (DKG) and glottovibrograms (GVG) to analyze the glottal dynamics of vocal fold leukoplakia, laryngitis, and papilloma. MethodsThe study utilizes high-speed video (HSV) endoscopy to capture vocal fold behavior in different pathological conditions. PVG, GVG and DKG techniques are applied to visualize and analyze the vibratory patterns of the vocal folds. PVG offers a comprehensive portrayal of vocal fold motion, while GVG presents the distance between vocal fold contours. The study also examines PVG-1 as a derivative of PVG. ResultsDistinct modifications in vocal fold vibrations across various stages (open, closed, closing, and opening) are documented using PVG, PVG-1, GVG, DKG, and PVG trajectory. Healthy vocal folds exhibit consistent periodic patterns, while disorders like leukoplakia, laryngitis, and papilloma manifest unique features due to structural and functional variations. The methods employed identify both structural irregularities and functional deviations in vocal fold vibrations. ConclusionsComparative analyses using PVG and other visualization techniques aids in understanding the glottal dynamics of voice disorders. While laryngoscopy and stroboscopy are valuable for real-time assessment, offline analysis techniques like PVG and GVG are useful for tracking glottal dynamics during phonation and assessing treatment effectiveness over time. Studies like this will contribute to standardizing the PVG based diagnostic criteria for its clinical utility, and development of classification tools for improved diagnosis and management of vocal fold pathologies.

Recognition of EEG-based movement intention combined with channel selection adopting deep learning methods

Brain-computer interface (BCI) is an emerging technology which provides a road to control communication and external devices. Electroencephalogram (EEG)-based motor imagery (MI) tasks recognition has important research significance for stroke, disability and others in BCI fields. However, enhancing the classification performance for decoding MI-related EEG signals presents a significant challenge, primarily due to the variability across different subjects and the presence of irrelevant channels. To address this issue, a novel hybrid structure is developed in this study to classify the MI tasks via deep separable convolution network (DSCNN) and bidirectional long short-term memory (BLSTM). First, the collected time-series EEG signals are initially processed into a matrix grid. Subsequently, data segments formed using a sliding window strategy are inputted into proposed DSCNN model for feature extraction (FE) across various dimensions. And, the spatial-temporal features extracted are then fed into the BLSTM network, which further refines vital time-series features to identify five distinct types of MI-related tasks. Ultimately, the evaluation results of our method demonstrate that the developed model achieves a 98.09% accuracy rate on the EEGMMIDB physiological datasets over a 4-second period for MI tasks by adopting full channels, outperforming other existing studies. Besides, the results of the five evaluation indexes of Recall, Precision, Test-auc, and F1-score also achieve 97.76%, 97.98%, 98.63% and 97.86%, respectively. Moreover, a Gradient-class Activation Mapping (GRAD-CAM) visualization technique is adopted to select the vital EEG channels and reduce the irrelevant information. As a result, we also obtained a satisfying outcome of 94.52% accuracy with 36 channels selected using the Grad-CAM approach. Our study not only provides an optimal trade-off between recognition rate and number of channels with half the number of channels reduced, but also it can also advances practical application research in the field of BCI rehabilitation medicine, effectively.

Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023.

Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser. In this survey article, we present the updated findings of new analyses of this dataset as of fall 2023 and discuss trends, insights, and eight open challenges for using visualizations in machine learning. Our results corroborate the rapidly growing trend of visualization techniques for increasing trust in machine learning models in the past three years, with visualization found to help improve popular model explainability methods and check new deep learning architectures, for instance.

Visualization and Visual Analytics in Autonomous Driving.

Autonomous driving is no longer a topic of science fiction. Advancements of autonomous driving technologies are now reliable. Effectively harnessing the information is essential for enhancing the safety, reliability, and efficiency of autonomous vehicles. In this article, we explore the pivotal role of visualization and visual analytics (VA) techniques used in autonomous driving. By employing sophisticated data visualization methods, VA, researchers, and practitioners transform intricate datasets into intuitive visual representations, providing valuable insights for decision-making processes. This article delves into various visualization approaches, including spatial-temporal mapping, interactive dashboards, and machine learning-driven analytics, tailored specifically for autonomous driving scenarios. Furthermore, it investigates the integration of real-time sensor data, sensor coordination with VA, and machine learning algorithms to create comprehensive visualizations. This research advocates for the pivotal role of visualization and VA in shaping the future of autonomous driving systems, fostering innovation, and ensuring the safe integration of self-driving vehicles.

Multivariable correlations of estimated glomerular filtration rate in 1020 consecutive PCIs in 16 months from a single center

ABSTRACT Background: Contrast-induced nephropathy can occur post percutaneous coronary intervention (PCIs). Objectives: To establish the multivariable relationships in all post-PCI patients. Methods: Renal function assessed by calculating estimated glomerular filtration rate using the modification of diet in renal disease formula and then indexed to respective BSA in consecutive 1020 post-PCI patients from June 2021 to September 2022. The complete raw data of various variables were segregated and analyzed in Microsoft Excel under various headings. Then studied for their clinical significance and interrelationships. Statistical significance was studied with number, percentages, maximum, minimum, mean, standard deviation etc., and specific statistics (includes: descriptive statistics provides number, %, interquartile range, etc., correlation statistics-provides Pearson ‘r’ correlation and regression statistics-provides the relationships). Finally, all the results were presented in the form of tables and figures from the data visualization technique. Results: The complete analysis of all 1020 PCI patient’s data suggests that age, left ventricular ejection fraction by two-dimensional echocardiography, body mass index, pre-procedural serum creatinine values and volumes of contrast used, and diastolic blood pressure (BP) has directly related to good cardiorenal outcome. Conclusions: Post-PCI acute kidney injury is a known entity. Renal functions were affected by multiple variables. Prerenal assessment and management, adequate (pre and post-PCI) hydration therapy, avoiding or minimizing nephrotoxic agents, BP control, staged PCI, precision PCI with additional procedures will not only reduce the contrast agent burden on kidney and will have good cardiorenal outcome.

Analysis and design of broadband Doherty power amplifier using parameterized load modulation network and complex combining load

This paper presents a novel approach to design broadband Doherty power amplifiers (DPAs) with extended fractional bandwidth (FBW). A parameterized load modulation network (LMN) and complex combining loads are proposed that can extend the FBW while providing more accurate back-off impedance. To examine the influence of parameter variations in the LMN on the broadband performance of the DPA, we utilize the parameter scanning method and subsequently select the optimal parameters in the LMN. Additionally, three-dimensional computer visualization techniques is invoked to visually examine the impedance solution space of the complex combining loads. A DPA prototype with a bandwidth of 0.85–1.8 GHz is then designed and implemented using commercial GaN transistors to validate the proposed architecture. The fabricated DPA attains a measured gain of 14.2–15.7 dB and a saturated power of 43.0–44.9 dBm. Across the designed operation band, the measured drain efficiency reached 51.6%–73.6% at saturation and 43.7%–56.7% at 6 dB output power back-off. When driven by a 20 MHz modulated signal with a peak-to-average power ratio (PAPR) of 8 dB, the average efficiency of the fabricated DPA is higher than 46.7%.

Expert exploranation for communicating scientific methods — A case study in conflict research

Science communication aims at making key research insights accessible to the broad public. If explanatory and exploratory visualization techniques are combined to do so, the approach is also referred to as exploranation. In this context, the audience is usually not required to have domain expertise. However, we show that exploranation can not only support the communication between researchers and a broad audience, but also between researchers directly.With the goal of communicating an existing method for conducting causal inference on spatio-temporal conflict event data, we investigated how to perform exploranation for experts, i.e., expert exploranation. Based on application scenarios of the inference method, we developed three versions of an interactive visual story to explain the method to conflict researchers. We abstracted the corresponding design process and evaluated the stories both with experts who were unfamiliar with the explained method and experts who were already familiar with it.The positive and extensive feedback from the evaluation shows that expert exploranation is a promising direction for visual storytelling, as it can help to improve scientific outreach, methodological understanding, and accessibility for researchers new to a field.

Spam SMS Classifier using Machine Learning Algorithms

Mobile SMS communication is insecure as a result of a significant problem with spam detection. A technique or model with high accuracy and precision is required to address this spam SMS issue. The amount of spam emails has dramatically increased over the last few years. SMS spam has major negative impacts since it harms both consumers and service providers, eroding their mutual trust to a great extent. Different types of classifier algorithm have been implemented like Naïve bayes, Random Forest, KNN and Support vector classifier on a raw dataset collected from UCI repository in this research. Metrices like Accuracy, Precision and Recall are takes as performance metrics for calculating the efficiency of the algorithm. After experimenting, the result of these algorithms and compared them with another models. We showed the comparison using Visualization Techniques.

Standards for providing emergency care at the prehospital and early hospital stages for patients with mechanical jaundice

Mechanical jaundice is a condition characterized by obstructive disorders in the biliary system, posing a diagnostic challenge that requires advanced visualization techniques and laboratory tests for accurate diagnosis. This article explores the role of biochemical markers, presents an analysis of the algorithm for laboratory diagnosis and visualization techniques in order to stratify patients with jaundice syndrome, and conduct timely and effective conservative treatment with further surgical interventions. A comprehensive diagnostic and therapeutic algorithm has been developed whose use by emergency physicians at the prehospital and early hospital stages will lead to a rapid and qualitative elimination of biliary obstruction, a reduction in the level of complications, and the achievement of optimal treatment outcomes for patients.

Deep-learning based 3D birefringence image generation using 2D multi-view holographic images

Refractive index stands as an inherent characteristic of a material, allowing non-invasive exploration of the three-dimensional (3D) interior of the material. Certain materials with different refractive indices produce a birefringence phenomenon in which incident light is split into two polarization components when it passes through the materials. Representative birefringent materials appear in calcite crystals, liquid crystals (LCs), biological tissues, silk fibers, polymer films, etc. If the internal 3D shape of these materials can be visually expressed through a non-invasive method, it can greatly contribute to the semiconductor, display industry, optical components and devices, and biomedical diagnosis. This paper introduces a novel approach employing deep learning to generate 3D birefringence images using multi-viewed holographic interference images. First, we acquired a set of multi-viewed holographic interference pattern images and a 3D volume image of birefringence directly from a polarizing DTT (dielectric tensor tomography)-based microscope system about each LC droplet sample. The proposed model was trained to generate the 3D volume images of birefringence using the two-dimensional (2D) interference pattern image set. Performance evaluations were conducted against the ground truth images obtained directly from the DTT microscopy. Visualization techniques were applied to describe the refractive index distribution in the generated 3D images of birefringence. The results show the proposed method’s efficiency in generating the 3D refractive index distribution from multi-viewed holographic interference images, presenting a novel data-driven alternative to traditional methods from the DTT devices.

Positional normalization-based mixed-image data augmentation and ensemble self-distillation algorithm

The prior approach to data augmentation using positional normalization necessitated manual determination of weight parameters for blending two images. However, theoretically, these parameters cannot cater to all samples. We propose a novel data augmentation approach called pnMix (Positional normalization-based data mixing) based on data blending and positional normalization to address these issues. Moreover, we have improved the teacher model of an existing network structure based on self-distillation technology using an ensembling approach called ESKD. Our method enhances performance across multiple datasets and tasks. We outperform previous methods in complex real-world datasets such as imagenet-a and imagenet-o, which we believe are highly meaningful for practical applications. Specifically, we obtain good results on the CIFAR100 and Imagenet image classification datasets, the Pascal VOC and COCO object detection datasets, and the COCO and Cityscapes image segmentation datasets. Furthermore, we analyze the pnMix method's advantages compared to other data augmentation methods through visualization techniques, demonstrating its effectiveness and interpretability. Our source code and pre-trained models are available at https://github.com/sydney72380/pnMix.

"Visualizing Success: Unveiling Sales Insights through Tableau's Lens"

In today's dynamic business environment, data-driven decision-making has become imperative for organizations aiming to stay competitive. The field of sales, in particular, relies heavily on insights derived from vast amounts of data to drive strategies and enhance performance. Leveraging advanced visualization tools such as Tableau presents an opportunity to transform raw data into actionable insights, empowering sales teams to make informed decisions efficiently. This research paper delves into the realm of sales insight using Tableau, exploring how visualization techniques can unlock hidden patterns, trends, and correlations within sales data. By harnessing Tableau's capabilities, businesses can gain a comprehensive understanding of their sales performance across various dimensions, including products, regions, customer segments, and sales channels. Through a combination of literature review, case studies, and practical demonstrations, this paper elucidates the power of Tableau in providing intuitive and interactive visualizations that facilitate deeper exploration of sales data.

Differential Colonization and Mucus Ultrastructure Visualization in Bovine Ileal and Rectal Organoid-Derived Monolayers Exposed to Enterohemorrhagic Escherichia coli.

Enterohemorrhagic Escherichia coli (EHEC) is a critical public health concern due to its role in severe gastrointestinal illnesses in humans, including hemorrhagic colitis and the life-threatening hemolytic uremic syndrome. While highly pathogenic to humans, cattle, the main reservoir for EHEC, often remain asymptomatic carriers, complicating efforts to control its spread. Our study introduces a novel method to investigate EHEC using organoid-derived monolayers from adult bovine ileum and rectum. These polarized epithelial monolayers were exposed to EHEC for four hours, allowing us to perform comparative analyses between the ileal and rectal tissues. Our findings mirrored in vivo observations, showing a higher colonization rate in the rectum compared with the ileum (44.0% vs. 16.5%, p < 0.05). Both tissues exhibited an inflammatory response with increased expression levels of TNF-a (p < 0.05) and a more pronounced increase of IL-8 in the rectum (p < 0.01). Additionally, the impact of EHEC on the mucus barrier varied across these gastrointestinal regions. Innovative visualization techniques helped us study the ultrastructure of mucus, revealing a net-like mucin glycoprotein organization. While further cellular differentiation could enhance model accuracy, our research significantly deepens understanding of EHEC pathogenesis in cattle and informs strategies for the preventative measures and therapeutic interventions.

A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.

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.

Thermal removal mechanism of paraffin deposits: Impact of flowrate on removal efficiency

Active heating is increasingly explored as a cost-effective strategy for managing wax deposition in production flowlines. While these methods are commonplace throughout the industry to maintain or raise the temperature of the production fluid above the wax appearance temperature (WAT), there are no clear guidelines for wax removal using active heating. In this study, a novel dynamic microscopic visualization technique is used to observe the effects of the flow regime on the removal process. At high enough temperatures, 50.6 °C and 43.3 °C, 100% removal is observed in both turbulent and laminar cases. A removal temperature of 37.0 °C resulted in 72.5% removal under turbulent conditions, and 97% removal under laminar conditions. No reduction in thickness was observed in either case when removing at a temperature of 30.6 °C. Turbulent conditions were also shown to significantly increase the heating time required before detachment compared to laminar conditions at the same removal temperature. The turbulent experiments required 2.7 times as long for detachment to occur when removal temperatures were raised to 43.3 °C, and 4.6 times as long when temperatures were raised to 37.0 °C. Turbulent conditions are also shown to require higher temperatures to trigger the removal process, likely due to the harder deposits formed at higher flow rates. This analysis sheds light on the impact flow regime and deposit properties have on the removal process. The results obtained in this study enhance the understanding of the impact of flow on thermal removal and this knowledge is necessary for accurate removal modeling.

Mapping the Landscape of Electronic Health Records and Health Information Exchange Through Bibliometric Analysis and Visualization.

The adoption of Electronic Health Records (EHRs) and the establishment of Health Information Exchange (HIE) systems have significantly transformed healthcare delivery and management. This study presents a comprehensive bibliometric analysis and visualization of the landscape surrounding EHRs and HIE to provide insights into the current state and emerging trends in this field. Leveraging advanced bibliometric methodologies, including co-citation analysis, keyword co-occurrence analysis, and network visualization techniques, we systematically map the scholarly literature spanning several decades. Our analysis reveals key thematic clusters, influential publications, prolific authors, and collaborative networks within the domain of EHRs and HIE. Furthermore, we identify significant research gaps and areas for future exploration, including interoperability challenges, privacy concerns, and the integration of emerging technologies such as artificial intelligence and blockchain. The findings of this study offer valuable insights for researchers, policymakers, and healthcare practitioners seeking to navigate and contribute to the ongoing evolution of EHRs and HIE systems, ultimately enhancing the quality, efficiency, and accessibility of healthcare services.

PLANT LEAF DISEASE DETECTION

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly Keywords: Plant leaf disease detection, leaf disease detection, convolutional neural network, deep learning