scholarly journals Virtual Reality-Based Parallel Coordinates Plots Enhanced with Explainable AI and Data-Science Analytics for Decision-Making Processes

2021 ◽  
Vol 12 (1) ◽  
pp. 331
Author(s):  
Szymon Bobek ◽  
Sławomir K. Tadeja ◽  
Łukasz Struski ◽  
Przemysław Stachura ◽  
Timoleon Kipouros ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Data Science ◽  
Clustering Algorithms ◽  
Multi Objective Optimization ◽  
Parallel Coordinates ◽  
Multidimensional Datasets ◽  
Analytical Work ◽  
Importance Analysis ◽  
Scatter Plots ◽  
Decision Making Processes

We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.

scholarly journals Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Marco Recenti ◽  
Carlo Ricciardi ◽  
Romain Aubonnet ◽  
Ilaria Picone ◽  
Deborah Jacob ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motion Sickness ◽  
Muscle Activation ◽  
Brain Activity ◽  
Conflict Theory ◽  
Visual Signals ◽  
Physiological Factors ◽  
Biomedical Sensors ◽  
Dry Electrode ◽  
Importance Analysis

Motion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (IMS). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for IMS). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.

A Multi-task Comparative Study on Scatter Plots and Parallel Coordinates Plots

2015 ◽  
Vol 34 (3) ◽  
pp. 261-270 ◽  
Author(s):  
Rassadarie Kanjanabose ◽  
Alfie Abdul-Rahman ◽  
Min Chen
Keyword(s):  
Comparative Study ◽  
Parallel Coordinates ◽  
Scatter Plots

scholarly journals Theoretical signposts for tracing spirituality within the fluid decision-making of a mobile virtual reality

2012 ◽  
Vol 68 (2) ◽  
Author(s):  
Jan-Albert Van den Berg
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Practical Theology ◽  
Information Age ◽  
Mobile Platforms ◽  
Decision Making Processes ◽  
Use Of The Internet ◽  
Using Data ◽  
Mobile Communication Technology

In the context of the interconnected world of the information age, and demarcated by a virtual existence through the use of the Internet, decision-making has become even more dynamic. In an evolving era of virtuality, with special emphasis on the increasing role of mobile communication technology, it is indicated that decision-making has become fluid. As part of the phenomenon of fluid decision-making, not only is the evolutionary character of virtual connectivity acknowledged, but the ever-increasing and important role of mobile platforms is also emphasised. In a hermeneutical practical theology of lived spirituality, focusing on the praxis of everyday living, the possible role of spirituality in informing the fluid decision-making processes in a mobile virtual world was traced. A qualitatively inspired analysis, using data collected from various virtual forums, was proposed. In the description of these contours, special emphasis was placed on narrative-inspired biographical accents. The research made a contribution in terms of possible new articulations of the language of faith as embodied in fluid decision-making in a mobile virtual reality.

scholarly journals Data Science in Decision-Making Processes: A Scientometric Analysis

2021 ◽  
Vol XXIV (Issue 3B) ◽  
pp. 1061-1074
Author(s):  
Wieslawa Gryncewicz ◽  
Monika Sitarska-Buba
Keyword(s):  
Decision Making ◽  
Data Science ◽  
Scientometric Analysis ◽  
Decision Making Processes

Towards Autonomous Machine Learning in Chemistry via Evolutionary Algorithms

2019 ◽  
Author(s):  
Gaurav Vishwakarma ◽  
Mojtaba Haghighatlari ◽  
Johannes Hachmann
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Model Selection ◽  
Data Science ◽  
Multi Objective Optimization ◽  
Rational Model ◽  
Hyperparameter Optimization ◽  
Promising Tool ◽  
Science Community ◽  
Autonomous Machine

Machine learning has been emerging as a promising tool in the chemical and materials domain. In this paper, we introduce a framework to automatically perform rational model selection and hyperparameter optimization that are important concerns for the efficient and successful use of machine learning, but have so far largely remained unexplored by this community. The framework features four variations of genetic algorithm and is implemented in the chemml program package. Its performance is benchmarked against popularly used algorithms and packages in the data science community and the results show that our implementation outperforms these methods both in terms of time and accuracy. The effectiveness of our implementation is further demonstrated via a scenario involving multi-objective optimization for model selection.

From Sociology to ICTs

2021 ◽  
pp. 1-20
Author(s):  
Alicia Mon
Keyword(s):  
Information Technology ◽  
Virtual Reality ◽  
Human Computer Interaction ◽  
Interaction Design ◽  
Industry 4.0 ◽  
Data Science ◽  
Technological Development ◽  
Interdisciplinary Knowledge ◽  
The Creation

This chapter addresses the inclusion of women in the field of information technology from a perspective that promotes the creation of interdisciplinary spaces, making visible the knowledge inherent to the different professions capable of adding value to technological development. The model presented here was created to evaluate the level of technological development that will allow the determining of the components needed for the transformation towards Industry 4.0, from which you can determine the knowledge necessary for the development of products in their real context of use. Areas such as data science, virtual reality, or human-computer interaction design techniques, models, and/or tools for the construction of solutions that do not require strictly engineering-based knowledge. This chapter proposes a journey towards the development and adoption of technologies in the industry, which requires the inclusion of interdisciplinary knowledge, hence giving new meaning to the role of women in technological development.

scholarly journals The Fundamental Clustering and Projection Suite (FCPS): A Dataset Collection to Test the Performance of Clustering and Data Projection Algorithms

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 13 ◽  
Author(s):  
Alfred Ultsch ◽  
Jörn Lötsch
Keyword(s):  
Data Science ◽  
Clustering Algorithms ◽  
Cluster Structure ◽  
Projection Methods ◽  
Analysis Method ◽  
Clustering Methods ◽  
Projection Algorithms ◽  
Science Data ◽  
Data Density ◽  
Data Points

In the context of data science, data projection and clustering are common procedures. The chosen analysis method is crucial to avoid faulty pattern recognition. It is therefore necessary to know the properties and especially the limitations of projection and clustering algorithms. This report describes a collection of datasets that are grouped together in the Fundamental Clustering and Projection Suite (FCPS). The FCPS contains 10 datasets with the names “Atom”, “Chainlink”, “EngyTime”, “Golfball”, “Hepta”, “Lsun”, “Target”, “Tetra”, “TwoDiamonds”, and “WingNut”. Common clustering methods occasionally identified non-existent clusters or assigned data points to the wrong clusters in the FCPS suite. Likewise, common data projection methods could only partially reproduce the data structure correctly on a two-dimensional plane. In conclusion, the FCPS dataset collection addresses general challenges for clustering and projection algorithms such as lack of linear separability, different or small inner class spacing, classes defined by data density rather than data spacing, no cluster structure at all, outliers, or classes that are in contact. This report describes a collection of datasets that are grouped together in the Fundamental Clustering and Projection Suite (FCPS). It is designed to address specific problems of structure discovery in high-dimensional spaces.

scholarly journals A study on using data clustering for feature extraction to improve the quality of classification

2021 ◽  
Author(s):  
Maciej Piernik ◽  
Tadeusz Morzy
Keyword(s):  
Feature Extraction ◽  
Data Science ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Global Perspective ◽  
Separate Experiment ◽  
Essential Questions ◽  
Rigorous Study ◽  
Using Data

AbstractThere is a certain belief among data science researchers and enthusiasts alike that clustering can be used to improve classification quality. Insofar as this belief is fairly uncontroversial, it is also very general and therefore produces a lot of confusion around the subject. There are many ways of using clustering in classification and it obviously cannot always improve the quality of predictions, so a question arises, in which scenarios exactly does it help? Since we were unable to find a rigorous study addressing this question, in this paper, we try to shed some light on the concept of using clustering for classification. To do so, we first put forward a framework for incorporating clustering as a method of feature extraction for classification. The framework is generic w.r.t. similarity measures, clustering algorithms, classifiers, and datasets and serves as a platform to answer ten essential questions regarding the studied subject. Each answer is formulated based on a separate experiment on 16 publicly available datasets, followed by an appropriate statistical analysis. After performing the experiments and analyzing the results separately, we discuss them from a global perspective and form general conclusions regarding using clustering as feature extraction for classification.

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