Parallel Coordinates Research Articles

A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis

Breast cancer (BC) is a catastrophic global health concern that causes numerous fatalities worldwide. Early detection of breast cancer may mitigate death rates; however, the prevailing diagnostic procedure for the malignancy necessitates numerous multifaceted laboratory tests that must be performed by medical professionals. In this article machine learning, a branch of Artificial Intelligence (AI), has been employed to improve cancer diagnosis, prognoses and survival rates while reducing the vulnerability of humans. Support Vector Machine (SVM), K-nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Grey Wolf Optimizer (GWO) are implemented to prognosticate breast cancer. Comprehensive insights into the efficacy of these approaches for breast cancer prognosis are provided by the performance assessment that is accomplished using the confusion matrix, Receiver Operating Characteristic (ROC) curves and parallel coordinate plots. Both UCI (University of California Irvine) and SEER (Surveillance, Epidemiology and End Results) datasets have been utilized to confirm the investigation's findings and ensure their generalizability across diverse data sources. The results conclusively demonstrate that SVM is the cohort's most accurate classifier. With a stupendous accuracy rate of 99.1 %, the GWO-SVM compares favorably to all other algorithms. Furthermore, feature reduction approaches such as Minimum Redundancy Maximum Relevance (mRMR), ReliefF and Principal Component Analysis (PCA) are utilized. ReliefF has demonstrated exceptional effectiveness with a maximum accuracy of 98.2 %.

Exploratory Data Analysis of Adverse Birth Outcomes and Exposure to Oxides of Nitrogen Using Interactive Parallel Coordinates Plot Technique

Exploratory Data Analysis of Adverse Birth Outcomes and Exposure to Oxides of Nitrogen Using Interactive Parallel Coordinates Plot Technique

Towards Better Utilization of Haptic Interaction in Visualization: Design Space and Knob Prototype

Humans encounter a vast array of sensory stimuli in their everyday lives. However, many visualization techniques primarily utilize visual feedback, which may disregard certain intricate details. Relying on a single visual channel may overlook complex layouts. However, how haptic force feedback can be used to assist visualization remained under-explored. In this work, we initially conducted a literature review to identify potential problems in the visualization of large datasets and engaged in discussions with domain experts to explore the potential of haptic force feedback and visual collision representation. Subsequently, we designed an innovative haptic force feedback knob, which included 3 primary modules and 29 elements. To evaluate the clarity and usefulness of this design space, we conducted a workshop and devised “recommended solutions” for the identified visualization problems. Finally, we implemented a prototype of the haptic force feedback knob and assessed its performance on scatterplot and parallel coordinate plot tasks using large datasets. The results indicated that the knob prototype could reduce visual strain and enhance the efficiency of visualization tasks.

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

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.

Hammock Plots: Visualizing Categorical and Numerical Variables

I discuss the hammock plot for visualizing categorical or mixed categorical/numeric data. Hammock plots can be viewed as a generalization of parallel coordinate plots where the lines are replaced by boxes (or plotting elements) and the width of the boxes is proportional to the number of observations they represent. The article also introduces a modification to the hammock plot to avoid what Hoffman et al. termed the reverse line width illusion. Further, I give an historical overview over hammock-type plots such as common angle, GPCP, parsets, and alluvial plots and discuss the type of boxes used to connect adjacent variables. Supplementary materials for this article are available online.

A Versatile Deposition Model for Natural and Processed Surfaces

This paper introduces a robust deposition model designed for exploring the growth dynamics of deposits on surfaces under practical conditions. The study addresses the challenge of characterizing the intricate morphology of deposits, exhibiting significant visual variations. A generative approach is deployed to create diverse natural and engineered surface textures, governed by probabilistic principles. The model’s formulation addresses key questions related to deposition initiation, nucleation point behaviour, spatial scaling, deposit growth rates, spread dynamics, and surface mobility. A versatile algorithm, relying on six parameters and employing nested loops and Gaussian sampling, is developed. The algorithm’s efficacy is examined through extensive simulations, involving variations in nucleation scaling densities, aggregate scaling scenarios, spread factors, and diffusion rates. Surface statistics are computed for simulated deposits and analyzed using Fast Fourier Transform (FFT). The resulting database enables quantitative comparisons of surfaces generated with different parameters, where the database-derived parallel coordinates offer guidance for selecting optimal model parameters to achieve desired surface morphologies. The proposed approach is validated against urea-derived deposits, exhibiting statistical consistency and agreement with experimental observations. Overall, the model’s adaptable framework holds promise for understanding and predicting deposit growth on surfaces in diverse practical scenarios.

Insights from Turkey's big data: unraveling the preventability, pathogenesis, and risk management of Alzheimer's disease (AD)

Extensive research into dementia has more recently honed in on several key areas. These areas include the advancement of techniques such as the accumulation of amyloid-β and tau proteins, the monitoring of cerebral hypometabolism rates etc. The primary objective of this study is to explore the intricate interplay between Alzheimer's disease (AD)—other dementias (D) and various chronic illnesses in terms of time, intensity, and connectivity. In this context, we retrospectively examined data of 149,786 individuals aged 65 and above who received diagnoses of AD and D in the year 2020. At first, logistic regression (LR) analysis has been made with “sex”, “age” and “foreigner” (citizenship status) independent variables for AD and D. The LR models shows that while “sex” and “age” variables have a small rate on the risk of developing AD/D, it is detected that being a foreigner increase the risk of AD and D as 69.8% and 88.5% respectively. Besides, the LR models have middle-level success prediction rate for both of the two dependent variables. Additionally, we used the parallel coordinates graphs method within the R Studio to visualize their relationships and connections. The findings of this investigation strongly suggest that AD/D don’t stand as isolated conditions, but rather stem from intricate interactions and progressive processes involving diverse chronic diseases over time. Notably, ailments including hypertension, coronary artery disease, diabetes, hyperlipidemia, and psychological disorders, contribute substantially to the emergence of both AD and D. This study highlights that the fight against AD/D can only be possible with next-generation prophylactic interventions that can predict and manage risks. Such an approach holds the potential to potentially lower AD and dementia to levels that are amenable to treatment.

An experimental investigation into the influence of water column height in submerged mode of abrasive waterjet trepanning on the hole quality

Abrasive waterjet (AWJ) trepanning/drilling exhibits immense potential to realize holes of various shapes and sizes in a diverse range of materials, ranging from soft-and-ductile to hard-and-brittle, making it highly valuable for various industries. AWJ trepanning offers the advantage of not requiring tool changes to accommodate different hole shapes and sizes; adjusting the jet path can produce the desired hole geometry, proving exceptionally advantageous for mass production. However, the stochastic nature of the jet results in geometrical errors (diametric deviation, taper angle (α)), form errors (circularity error (Ce), cylindricity error (Cy)) and quality issues (damage region, edge radius (R), surface roughness (Ra), and waviness (Wa)). To address these issues, trepanning in submerged mode can be adopted as it attenuates the mist region and aggressiveness of the jet. This study analyzes the influence of the submerged mode of trepanning on the geometry, form, and quality of trepanned holes relative to the in-air mode. The study found that submerged trepanning produces a more consistent hole diametrical profile along the depth and minimizes the average Ce and α by 23.18 % and 29.20 %, respectively. The maximum Cy in in-air trepanning was reduced in submerged mode by 36.76 %. The average damage region and R reduced by 50.80 % and 35.49 %, respectively. The qualitative and quantitative analysis of the trepanned surface morphology is presented in-air and submerged mode: the results show that the Ra and Wa decrease at the hole entry and middle zone and increase at the exit zone. Finally, the process parameters level settings for achieving near-optimal trepanning responses (α, Cy, Ce, damage region, R, Ra, and Wa) suggested with the help of mean response- and parallel coordinate- plots. Micrographs of submerged trepanned surfaces show that the material removal mechanism (MRM) governs by shallower craters and grooves at the entry and middle zone of the hole, and at the exit zone, the MRM is random, in comparison to the in-air mode.

Low-frequency chatter suppression in robotic milling using Magnetorheological Joint Damper (MRJD)

Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic milling. Existing vibration suppression strategies primarily focus on passive vibration absorption at the robotic end and feedback control at the joint motor. Although these strategies have a certain vibration suppression effect, the limitations of robotic flexibility and the extremely limited applicable speed range remain to be overcome. In this study, a Magnetorheological Joint Damper (MRJD) is developed. The joint-mounted feature ensures machining flexibility of the robot, and the millisecond response time of the Magnetorheological Fluid (MRF) ensures a large effective spindle speed range. More importantly, the evolution law of the damping performance of MRJD was revealed based on a low-frequency chatter mechanism, which guarantees the application of MRJD in robotic milling machining. To analyze the influence of the robotic joint angle on the suppression effect of the MRJD, the joint braking coefficient and end braking coefficient were proposed. Parallel coordinate plots were used to visualize the joint range with the optimal vibration suppression effect. Finally, a combination of different postures and cutting parameters was used to verify the vibration suppression effect and feasibility of the joint angle optimization. The experimental results show that the MRJD, which directly improves the joint vibration resistance, can effectively suppress the low-frequency vibration of robotic milling under a variety of cutting conditions.

Exploring Celestial Object Characteristics: An In-depth Analysis of Quasars, Stars, and White Dwarfs Using the Sloan Digital Sky Survey (SDSS) Dataset

This research utilizes the Sloan Digital Sky Survey (SDSS) dataset, examining 12,884 observations to explore quasars, stars, and white dwarf objects. Magnitude data and coordinates across five filter bands are analyzed, revealing unique features through statistical methods. The identification of 77,429 quasars with 15 dimensions enhances the dataset. Thorough analyses of stellar and white dwarf classes, coupled with visualization techniques, unveil variable relationships. Residual validation and Gaussian kernel density plots confirm significant class differences. Non-linear regression and a normal distribution mixture model depict complex variable relationships. A parallel coordinates plot aids in interpreting data patterns, while predictive modeling via regression exposes meaningful coefficients. Logistic regression effectively classifies astronomical objects in the SDSS training data. This research contributes to understanding celestial object characteristics, offering valuable insights for astronomers and astrophysicists in analyzing large-scale astronomical datasets.

Электоральные циклы в Грузии: опыт пространственного анализа

The article discusses three electoral cycles in Georgia: 2012, 2016, and 2020. Among the main issues in the political party programs that make the parties different are the territorial integrity of the country, relations with Russia and the collective West, as well as issues of economic stability that has become even more sensitive in the context of the coronavirus pandemic. Given these facts, the author seeks to understand how electoral support for the parties changed during the last three electoral cycles in Georgia municipalities. Besides, the author tries to uncover some possible patterns of electoral behavior that are closely connected with the present cleavages in Georgian society and economic situation. In order to answer the questions, the author used non-spatial models of spatial analysis. Firstly, the transformation of electoral preferences in Georgian municipalities is characterized with the use of cartograms of box blots of voting for Georgian Dream, United National Movement, and Alliance of Patriots of Georgia. Secondly, the author used multi-variable analysis on the basis of parallel coordinate plot (PCP) in order to analyze the voting patterns. When conducting the analysis, such indicators were included as the unemployment rate in municipalities, Georgian, Armenian, Azerbaijani, Russian, and Ossetia population, as well as Orthodox, Armenian Orthodox, and Muslim population. Electoral choice in Georgia is determined, among others, by the spatial factor, the ethnic and religious composition of Georgian municipalities, their economic indicators, as well as their position on the map, i.e., which country borders a particular Georgian municipality. Little support for United National Movement is shown by municipalities that border the Russian Federation excluding Kakheti. Besides, municipalities with a big proportion of Armenian population also show low electoral support for the party. At the same time, municipalities bordering Turkey actively vote for United National Movement. As for Georgian Dream, Georgians in municipalities bordering Abkhazia and South Ossetia, as well as along the Russian border, highly support the party since one of its core issues on the agenda is recreating the territorial integrity of the state. However, Kakheti and Mtskheta-Mtianeti show low electoral support for the party. In addition, this party promotes the idea of expanding economic and political ties with Turkey, albeit the bordering municipalities are not enthusiastic about the idea and grant lower support for the party as a result. Municipalities on the Russian, Turkish, and Armenian border at the same time show high support for Alliance of Patriots of Georgia, which is connected with religious, ethnic, political, and economic cleavages.

How just is the low-carbon transition in coastal areas? Development of a composite vulnerability index for coastal low-carbon transition

The transition toward a carbon–neutral society requires social and economic sacrifices in certain regions, and the vulnerability of specific areas to low-carbon transitions may be particularly pronounced in coastal regions. This study focuses on the development and application of a composite vulnerability index that can assess climate justice in coastal areas from a low-carbon transition perspective. Facilitated by the developed indices, this study, focuses on investigating regional disparities stemming from the transition and exploring various policy options for alleviating the resulting burdens or damages associated with these inequalities.The initial step in developing indicators involved creating a scoping diagram to define the scope of vulnerability and to identify specific indicators for coastal low-carbon transition vulnerability. Subsequently, based on the parameters, dimensions, and measurement factors presented in the diagram, composite vulnerability indices were formulated. In the process of calculating the vulnerability index, weights were assigned to the indicators at both quantitative and qualitative levels, reflecting vulnerability factors and dimensions. Finally, the Coastal Low-Carbon Transition Vulnerability Index (CLCTVI) was established for three sectors: marine fisheries, carbon sink, and energy development. The CLCTVI was then applied to coastal regions in South Korea, and its regional and sectoral patterns were analyzed using basic statistics, scatter plots, parallel coordinate plots, and the geodetector method.The findings of this study highlight the following key points: the degree of vulnerability in coastal areas to low-carbon transitions varies across regions; the main factors contributing to high or low vulnerability in low-carbon transitions differ for each sector; only a few regions are simultaneously vulnerable across all sectors. In particular, various interactive effects among factors were identified in each sector, underscoring the need for considering the interactions among factors alongside dominant factors for sector-specific vulnerabilities in policy support for regions exhibiting low-carbon transition vulnerabilities. This study showcases how CLCTVI can effectively capture interregional disparities resulting from low-carbon transitions in coastal areas, thus providing insight to decision-makers regarding previously unaddressed regional disparities associated with low-carbon transitions.

Interpretable self-organizing map assisted interactive multi-criteria decision-making following Pareto-Race

The problem-solving task of the multi-criteria decision-making (MCDM) approach involves decision makers’ (DMs’) interaction by incorporating their preferences to arrive at one or more preferred near Pareto-optimal solution(s). The Pareto-Race is an interactive MCDM approach that relies on finding preferred solutions as the DM navigates on the Pareto surface by selecting the preferred reference direction and uniform/varying speed using the concept of Achievement Scalarization Function (ASF) or its augmented version (AASF). Selecting a reference direction or speed is possible, algorithmically, but the effectiveness of the Pareto-Race concept relies on suitable visualization methods that present the series of past solutions and convey the current objective trade-off information to assist the DM’s in decision-making task. Traditional multi-dimensional Pareto-optimal front visualization methods like parallel coordinate plots, radial visualization, and heat maps are deemed insufficient for this purpose. Therefore, the paper proposes integrating the concept of the Pareto-Race MCDM approach with a recently developed interpretable self-organizing map (iSOM) based visualization method. The iSOM maps high-dimensional data into lower-dimensional space, facilitating visual convenience for the DM. The proposed method requires a finite-size representation of non-dominated solutions near the complete Pareto Front to generate iSOM plots of objectives. We also propose visualizing the metrics such as closeness to constraint boundaries, trade-off value, and robustness using iSOM to check the quality of the preferred solutions, which is rarely considered in existing MCDM approaches. iSOM plots of objective functions, closeness to constraint, trade-off, and robustness metrics assist the DM in effectively choosing new reference direction and step size in each iteration to arrive at the most preferred solution. The proposed iSOM-enabled Pareto-Race approach is demonstrated on benchmark analytical and real-world engineering examples with 3 to 10 objectives.

Robust Predictive Model for Different Cancers using Biomarker Proteins

Background: When analyzing multivariate data, it can be challenging to quantify and pinpoint relationships between a collection of consistent characteristics. Reliable computational prediction of cancer patient's response to treatment based on their clinical and molecular profiles is essential in this era of precision medicine. This is essential in helping doctors choose the least contaminated and most potent restorative therapies that are now available. Better patient monitoring and selection are now possible in clinical trials. Methods: This research proposes a novel robust model to aid in the diagnosis of various cancers induced by biomarker proteins (Protein Kinase B, MAPK, and mammalian Target of Rapamycin). Later, various medications (Perifosine, Wortmannin, and Rapamycin) were proposed to cure cancer. Various studies were carried out to obtain all of the results, which aid in the identification of various types of cancer. The drugs mentioned in this essay help to ward off cancer. Scaling and normalization were carried out using parallel coordinates plots and correlation tests, respectively. The boosted tree method and kNN with multiple distance approaches were also used to generate a solid model. The medical diagnosis system was enhanced by training the boosted tree technique to identify various tumors. A robust model was validated by predicting various values that were displayed against the observed value and agreed with the advised strategy to locate biomarkers to show the value of our method. Results: The results show that the predicted and observed values agree with each other, especially for MAPK pathways. The observed correlation coefficient (r2) is 0.9847 without intercept and 0.9849 with intercept. The precise computational prediction of the reaction of cancer patients to treatment based on the patient's clinical and molecular profiles is vital in the period of exactitude medicine. Conclusion: A robust model was validated by predicting the different values that were plotted with the observed value, which agrees with the results of the proposed technique to uncover biomarkers and shows the effectiveness of our technique.

Water Quality Assessment on Physicochemical and Biological Parameters of Selected Lentic Ecosystem in Aboh Mbaise Local Government Area, Imo State, Nigeria

The aim of this study is to determine the suitability of the ponds in Aboh Mbaise Local Government Area, Imo State, Nigeria for domestic use. A cross-sectional study was carried out for the study. Composite sampling method was used, where three water samples were collected randomly from each station to ensure that the samples were representative of the entire station. Samples were collected during the rainy season, given that the ponds always dry up during the dry season. Physicochemical and microbial analyses were carried out on water samples of selected lentic aquatic ecosystems in randomly identified ponds in five communities of Aboh Mbaise. These ponds are used by the residents of these villages for various domestic and anthropogenic activities. Statistical analyses include: descriptive statistics, Analysis of Variance, Parallel coordinate plot, Pearson correlation, Agglomerative Hierarchical Clustering (AHC) and Water Quality Index (WQI) were carried out. Results indicate that pond water within the study area did not fall within the bracket of good water quality, as per the WQI range of 0 to 50, thereby affirming the poor quality of water. The WQI for the five ponds ranges from 1338.71 - 3322.81. There is a direct correlation between the presence of Total Bacterial Counts from Shigella and Salmonella counts and the presence of fecal contamination from both human and animal wastes. Given the poor quality of the pond water, it is unhealthy for the inhabitants in the study area to use pond water for food preparation and other household tasks, except if it is treated.

COMPO*SED: Composite Parallel Coordinates for Co-Dependent Multi-Attribute Choices.

We propose Composite Parallel Coordinates, a novel parallel coordinates technique to effectively represent the interplay of component alternatives in a system. It builds upon a dedicated data model that formally describes the interaction of components. Parallel coordinates can help decision-makers identify the most preferred solution among a number of alternatives. Multi-component systems require one such multi-attribute choice for each component. Each of these choices might have side effects on the system's operability and performance, making them co-dependent. Common approaches employ complex multi-component models or involve back-and-forth iterations between single components until an acceptable compromise is reached. A simultaneous visual exploration across independently modeled but connected components is needed to make system design more efficient. Using dedicated layout and interaction strategies, our Composite Parallel Coordinates allow analysts to explore both individual properties of components as well as their interoperability and joint performance. We showcase the effectiveness of Composite Parallel Coordinates for co-dependent multi-attribute choices by means of three real-world scenarios from distinct application areas. In addition to the case studies, we reflect on observing two domain experts collaboratively working with the proposed technique and communicating along the way.

Proposal of a tool to assess human gait coordination using parallel-coordinates plot

Proposal of a tool to assess human gait coordination using parallel-coordinates plot

Smooth Transitions Between Parallel Coordinates and Scatter Plots via Polycurve Star Plots

AbstractThis paper presents new techniques for seamlessly transitioning between parallel coordinate plots, star plots, and scatter plots. The star plot serves as a mediator visualization between parallel coordinate plots and scatter plots since it uses lines to represent data items as parallel coordinates do and can arrange axes orthogonally as used for scatter plots. The design of the transitions also motivated a new variant of the star plot, the polycurve star plot, that uses curved lines instead of straight ones and has advantages both in terms of space utilization and the detection of clusters. Furthermore, we developed a geometrically motivated method to embed scatter points from a scatter plot into star plots and parallel coordinate plots to track the transition of structural information such as clusters and correlations between the different plot types. The integration of our techniques into an interactive analysis tool for exploring multivariate data demonstrates the advantages and utility of our approach over a multi‐view approach for scatter plots and parallel coordinate plots, which we confirmed in a user study and concrete usage scenarios.

Görselleştirme Okuryazarlığı Çalışmalarında Değerlendirmenin Karşılaştırmalı Bir İncelemesi

Data visualization is a powerful tool that simplifies complex datasets into easily comprehensible visual representations, making it easier to identify patterns and relationships within the data. To fully understand data visualization, individuals must develop visual literacy skills, which entails effectively understanding, interpreting, and creating visualizations. Assessing visualization literacy skills through literacy tests is crucial, and educational tools play a significant role in advancing these skills by providing guidance and resources. However, determining the suitable settings for data collection in literacy tests and evaluating educational tools is a complex and demanding task. This paper presents a comparative analysis based on two literacy studies introducing literacy tests and evaluating the effectiveness of pedagogical tools for Treemap and Parallel Coordinates Plot (PCP) through classroom and crowdsourcing experiments. The analysis focuses on key factors, including data collection, study time and resources, data quality, data validity, and challenges encountered during experiments. The findings underscore the significance of considering specific research questions and target populations when selecting experimental settings for visualization literacy and educational purposes.