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Eye tracking data analytics in virtual reality training: Application in Deepwater Horizon oil drilling operation

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 821 - 825页

作者： Ziho Kang Jiwon Jeon Saeed Salehi Industrial and Systems Engineering University of Oklahoma Norman OK Data Science and Analytics Program University of Oklahoma Norman OK Petroleum and Geological Engineering University of Oklahoma Norman OK

Virtual reality (VR) enable us to train in a safe environment using computer-generated simulations. One such simulated environment is the Deepwater Horizon operation, and VR enables us to evaluate trainees’ and operators’ situation awareness (SA) in a non-hazardous environment. One unobtrusive and viable SA evaluation method might be the use of eye movements, specifically the time-ordered visual scan paths. In this research, we investigated how SA can be associated with visual scan paths in an anomaly detection task within the oil drilling rig virtual reality simulator. The results show that the trainees having lower SA tended to create random visual scan paths whereas the trainees having higher SA tended to create concentrated and refined visual scan paths. The results show promise in developing timely intervention methods through analyzing the visual scan path characteristics of the trainees.

关键词： Eye tracking visual scanning patterns virtual reality training Deepwater Horizon operations situation awareness

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Conjugacy properties of multivariate unified skew-elliptical distributions

arXiv

引用

arXiv 2024年

作者： Karling, Maicon J. Durante, Daniele Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Decision Sciences Institute for Data Science and Analytics Bocconi University Milan Italy

The broad class of multivariate unified skew-normal (SUN) distributions has been recently shown to possess important conjugacy properties. When used as priors for the coefficients vector in probit, tobit, and multinomial probit models, these distributions yield posteriors that still belong to the SUN family. Although this result has led to advancements in Bayesian inference and computation, its applicability beyond likelihoods associated with fully-observed, discretized, or censored realizations from multivariate Gaussian models remains yet unexplored. This article covers such a gap by proving that the wider family of multivariate unified skew-elliptical (SUE) distributions, which extends SUNs to more general perturbations of elliptical densities, guarantees conjugacy for broader classes of models, beyond those relying on fully-observed, discretized or censored Gaussians. Such a result leverages the closure under linear combinations, conditioning and marginalization of SUE to prove that this family is conjugate to the likelihood induced by multivariate regression models for fully-observed, censored or dichotomized realizations from skew-elliptical distributions. This advancement enlarges the set of models that enable conjugate Bayesian inference to general formulations arising from elliptical and skew-elliptical families, including the multivariate Student’s t and skew-t, among others. Copyright © 2024, The Authors. All rights reserved.

关键词： Normal distribution

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Tweets Sentiment Analysis During COVID-19 Pandemic

Tweets Sentiment Analysis During COVID-19 Pandemic

引用

data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Maha A. Alanezi Nabil M. Hewahi Big Data Science & Analytics Master Program Collage of Science University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.

关键词： COVID-19 Social networking (online) Pandemics Sentiment analysis Blogs Elbow Viruses (medical)

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Prediction of Stock Price and Direction Using Neural Networks: datasets Hybrid Modeling Approach

Prediction of Stock Price and Direction Using Neural Network...

引用

data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Mohamed Al Aradi Nabil Hewahi Big Data Science & Analytics Master Program Collage of Science Univestity of Bahain Sakhir Kingdom of Bahrain Collage of Information Technology Univestity of Bahain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [ 75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock.

关键词： Predictive models Machine learning Companies Neural networks Market research data models Recurrent neural networks

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Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment

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Biomedical Materials and Devices 2024年第2期2卷 778-810页

作者： Bryan, Anastasia C. Heinz, Michael V. Salzhauer, Abigail J. Price, George D. Tlachac, M.L. Jacobson, Nicholas C. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Department of Psychiatry Geisel School of Medicine Dartmouth College Quantitative Biomedical Sciences Program Dartmouth College Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Department of Information Systems and Analytics Bryant University Center for Health and Behavioral Sciences Bryant University

Mental health disorders—including depression, anxiety, trauma-related, and psychotic conditions—are pervasive and impairing, representing considerable challenges for both individual well-being and public health. Often the first challenges to treatment include financial, geographic, and stigmatic barriers, which limit the accessibility of traditional assessment measures. Further, compounded by frequent misdiagnosis or delayed detection, there is a need for effective, accessible, and scalable approaches to identification and management. Considering advances in computing and the ubiquitous nature of personal mobile and wearable technology, this narrative review examines the utilization of passive sensor data as a screening and diagnostic tool for mental disorders. As an alternative to traditional screening measures, passive sensing offers a tool to overcome barriers that prevent many from seeking services. We critically assess the literature up to September 2023, exploring the use of passive data—such as heart rate variability, movement patterns, and geolocation—to predict mental health outcomes across a spectrum of disorders. From a translational perspective, our review explores the state of passive sensing science, with special emphasis on the capacity for the science to be implemented in real world clinical and general populations, a novelty specific to this review to the best of our knowledge. Toward this aim, we consider multiple study factors, including participant demographics, data collection methods, sensor modalities, outcome measures, and analytic modeling approaches. We find that passive sensing features, such as GPS, heart rate, and actigraphy offer promise for enhancing early detection and improving the diagnostic process for mental disorders. Despite this promise, however, our findings highlight important limitations in passive sensing research including (1) a trend toward smaller, specialized samples, (2) a predominance of data collection apps built on

关键词： Digital biomarkers Digital mental health Digital phenotyping Mental health assessment Passive sensing Ubiquitous sensing

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

引用

data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Maha A. Alanezi Mawra T. Homeed Zahra S. Mohamed Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others.

关键词： Support vector machines Business Bayes methods Hidden Markov models Logistics data models Credit cards

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Descriptive analytics Analysis as Support for Ancestral Domain Preservation: Empirical Evidence Generation as support for Decision Making

Descriptive Analytics Analysis as Support for Ancestral Doma...

引用

2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： Castro, Eltimar T. Romano, Nephi P. Odchigue, Rev. F.R. Randy Jasper C. Young, Rev. F.R. John Christian U. Alfante, Jannina De Jesus Valencia, Rochelle A. Father Saturnino Urios University Data Science Analytics Center Butuan City Philippines Father Saturnino Urios University Office of VP Academic Affairs and Research Butuan City Philippines Father Saturnino Urios University Office of the President Dept. Butuan City Philippines Father Saturnino Urios University Arts and Science Program Butuan City Philippines

ISBN: (纸本)9798350321302

Ancestral domain refers to the lands, territories, and resources possessed and administered collectively by indigenous peoples, such as the indigenous communities of the Philippines. Indigenous peoples who have inhabited and cared for these regions for generations attach great cultural, spiritual, and economic significance to them. Indigenous Peoples Rights Act (IPRA) of 1997 acknowledged for the first time the idea of ancestral domain in the Philippines. However, the IPRA's implementation and the preservation of ancestral properties in the Philippines have encountered obstacles. There have been instances of unlawful logging, mining, and land appropriation in ancestral domains, typically with the participation of local government officials and influential persons. This has resulted in the uprooting of indigenous populations and the devastation of their land and resources. This research aimed to analyze the data in relation to tree planting activities initiated by several advocates, especially the Father Saturnino Urios University Foundation. Which aims, helping to preserve and saving Ancestral Land. This research employs data mining activity and develops insights according to generated patterns. Descriptive analytics is the branch of data analysis that involves summarizing and describing data. It typically involves using statistics, data visualization, and other techniques to identify patterns and trends in data. Descriptive analytics can be used to understand the characteristics of data and to communicate those characteristics to others. Further, the results show that tree planting activities conducted by some advocates resulted in good results, since planted trees near the watershed have had a survival rate of 92.28 (%) percent and around 43,270 seedlings planted in 88.05 hectares. On the other hand, abaca have been planted in the area for the livelihood of IP situated in the area, have a survival rate of 96 (%) percent. This result is the output of an ongoing reh

关键词： data visualization

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Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors

arXiv

引用

arXiv 2020年

作者： Cao, Jian Durante, Daniele Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Saudi Arabia Department of Decision Sciences Bocconi Institute for Data Science and Analytics Bocconi University Italy Statistics Program King Abdullah University of Science and Technology Saudi Arabia

Predictive models for binary data are fundamental in various fields, and the growing complexity of modern applications has motivated several flexible specifications for modeling the relationship between the observed predictors and the binary responses. A widely-implemented solution is to express the probability parameter via a probit mapping of a Gaussian process indexed by predictors. However, unlike for continuous settings, there is a lack of closed-form results for predictive distributions in binary models with Gaussian process priors. Markov chain Monte Carlo methods and approximation strategies provide common solutions to this problem, but state-of-the-art algorithms are either computationally intractable or inaccurate in moderate-to-high dimensions. In this article, we aim to cover this gap by deriving closed-form expressions for the predictive probabilities in probit Gaussian processes that rely either on cumulative distribution functions of multivariate Gaussians or on functionals of multivariate truncated normals. To evaluate these quantities we develop novel scalable solutions based on tile-low-rank Monte Carlo methods for computing multivariate Gaussian probabilities, and on mean-field variational approximations of multivariate truncated normals. Closed-form expressions for the marginal likelihood and for the posterior distribution of the Gaussian process are also discussed. As shown in simulated and real-world empirical studies, the proposed methods scale to dimensions where state-of-the-art solutions are impractical. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Statistical Modeling: A New Regression Curve Approximation using Mixed Estimators Truncated Spline and Epanechnikov Kernel

引用

Engineering Letters 2023年第4期31卷 1649-1655页

作者： Sifriyani Dani, Andrea Tri Rian Fauziyah, Meirinda Budiantara, I. Nyoman Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

In the era of computation, researchers have paid significant attention to the nonparametric regression method. Nonparametric regression has the benefit of a high degree of modeling flexibility. Developing a mixed estimator truncated spline-Epanechnikov kernel is the most recent innovation in this study. The truncated spline estimator excels at handling data whose behavior varies at predetermined intervals. In contrast, the Epanechnikov kernel estimator has a more flexible structure and excels at modeling data that does not adhere to a particular pattern. Maximum Likelihood Estimation is utilized to estimate parameters. The concluding section of this study will discuss the estimator properties of the mixed estimators truncated spline and Epanechnikov kernel models. The proposed can be utilized for additional analysis in the field of nonparametric regression. © 2023, International Association of Engineers. All rights reserved.

关键词： Maximum likelihood estimation

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Multivariate Conway-Maxwell-Poisson distribution: Sarmanov method and doubly-intractable Bayesian inference

arXiv

引用

arXiv 2021年

作者： Piancastelli, Luiza S.C. Friel, Nial Barreto-Souza, Wagner Ombao, Hernando School of Mathematics and Statistics University College Dublin Dublin Ireland Insight Centre for Data Analytics Ireland Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

In this paper, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive non-diagonal entries;(ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals;(iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose a Bayesian inferential approach where the resulting doubly-intractable posterior is dealt with via the exchange algorithm and the Grouped Independence Metropolis-Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We analyze the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts. Copyright © 2021, The Authors. All rights reserved.

关键词： Monte Carlo methods

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