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160 results for “linear models”
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Research ArticleOpen accessIn the abstractImproved Steering and Adaptive Cruise Control for Autonomous Vehicles Using Model Predictive ControlSai Charan Dekkata, Sun YiThe research on autonomous vehicles was started nearly a century ago but the major parts of significant advance have been done over the past few decades. In this paper, designing and simulating of a Model Predictive Controller (MPC) and comparing it to a classical feedback controller, which was designed and implemented on a car for Lane Keeping Assist (LKA) and … …Journal of Mechatronics and Robotics, Vol. 3, Issue 1 · 26 Jun 2019 · 8,019 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of a Beta Regression Model for Covariate Adjusted ROCXing Meng, J.D. TubbsThe Receiver Operating Characteristic (ROC) curve and the area under the ROC (AUC) are widely used in determining the diagnostic capability of a binary classification procedure. Since the test performance is affected by covariates, the ROC and AUC have been utilized in a Generalized Linear Regression (GLM) setting. In this study, we revisit a problem where the AUC regression model … …Current Research in Biostatistics, Vol. 10, Issue 1 · 18 Jun 2020 · 13,966 accessesView PDFCite
Research ArticleOpen accessIn the abstractGlobal Supply Chain: Enhance Production Cost Efficiency Through Machine LearningLoubna Moumeni, Mohammed SaberMachine learning has enabled the discovery of various disciplines and patterns in Supply Chain Management (SCM). The entire industrial sector is striving to harness the contextual intelligence provided by machine learning by exploring new areas within the supply chain network. This article delves into Supply Chain Management (SCM) and its broader implications beyond logistics. SCM involves the resources, methods, and … …Journal of Computer Science, Vol. 20, Issue 9 · 14 Jun 2024 · 5,316 accessesView PDFCite
Research ArticleOpen accessIn the abstractUnivariate Generalized Additive Models for Simulated Stationary and Non-Stationary Generalized Pareto DistributionMostafa Behzadi, Mohd Bakri Adam, Anwar FitriantoGeneralized additive models as a predictor in regression approaches, are made up over cubic spline basis and penalized regression splines. Despite of linear predictor in GLM, generalized additive models use a sum of smooth functions of covariates as a predictor. The data which are used in this study have generalized Pareto distribution and have been simulated by inversion method. The … …Journal of Mathematics and Statistics, Vol. 13, Issue 2 · 3 Jun 2017 · 7,430 accessesView PDFCite
EditorialOpen accessIn the abstractChasing the Fat Demon: Fat Chance, Buddy?David L. Katz, Joachim Storsberg, Christian SchmidtScience is an interesting profession. At times, one could get caught up in his or her own world of thoughts, perception, far away from reality, sometimes referred to as the proverbial ivory tower (Lederberg, 1972). Science for the sake of science is, per se, not without merit, with our fragmented understanding of a given issue providing ample justification for experimentation … …American Journal of Immunology, Vol. 13, Issue 2 · 29 May 2017 · 8,134 accessesView PDFCite
Research ArticleOpen accessIn the abstractRobust Linear Discriminant AnalysisSharipah Soaad Syed Yahaya, Yai-Fung Lim, Hazlina Ali, Zurni OmarLinear Discriminant Analysis (LDA) is the most commonly employed method for classification. This method which creates a linear discriminant function yields optimal classification rule between two or more groups under the assumptions of normality and homoscedasticity (equal covariance matrices). However, the calculation of parametric LDA highly relies on the sample mean vectors and pooled sample covariance matrix which are sensitive … …Journal of Mathematics and Statistics, Vol. 12, Issue 4 · 4 Jan 2017 · 7,649 accessesView PDFCite
Research ArticleOpen accessIn the abstractEscalating SVM-Efficiency by Sequential QP LP and Slack Variable AnalysisAmit Kumar Kundu, Rezaul Karim, Ali Ahmed AveSupport Vector Machine (SVM) is a highly attractive algorithm among many machine learning models due to its generalization power and classification performance based on sound mathematical formulation being convex that offers global minimum. However, despite being sparse, its high classification cost from kernel execution with Support Vectors (SVs) reduces the user's interest when there are hard computational constraints in the … …Journal of Computer Science, Vol. 19, Issue 10 · 6 Sep 2023 · 5,913 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Toolkit Approach for Carbon Capture and Storage in Offshore Depleted Gas FieldRaj Deo Tewari, Tan Chee Phuat, Mohd Faizal SedaralitFossil fuels are considered a dependable, cost-effective, and efficient energy source and their utilization has resulted in tremendous growth for humanity. However, it has its downside also. Experts are of the view that present energy systems are unsustainable due to their detrimental impacts on the environment. Oil and gas producers are charged that their infrastructure, utilization of materials and technologies … …American Journal of Environmental Sciences, Vol. 19, Issue 1 · 17 May 2023 · 6,196 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultilevel Mixed Analysis and Mapping on Divorce Problem in UAEFaisal G. KhamisDespite the wealth of research investigating the divorce problem in the developed countries, few and inconsistent studies investigated this problem at emirate-level in developing countries, such as United Arab Emirates (UAE). The Questions are raised whether divorce is changed over time and whether clustering in divorce exists within emirates in UAE. The objectives are to investigate the change in divorce … …Journal of Mathematics and Statistics, Vol. 12, Issue 4 · 31 Oct 2016 · 7,296 accessesView PDFCite
Research ArticleOpen accessIn the abstract3D-QSAR and SVM Prediction of BRAF-V600E and HIV Integrase Inhibitors: A Comparative Study and Characterization of Performance with a New Expected Prediction Performance MetricLeonard Wesley, Saihitha Veerapaneni, Rachana Desai et al.The results of directly comparing the prediction accuracy of optimized 3D Quantitative Structure-Activity Relationship (3D-QSAR) models and linear Support Vector Machine (SVM) classifiers to identify small molecule inhibitors of the BRAF-V600E and HIV Integrase targets are reported. Performance comparisons were carried out using 303 compounds (68 active) against BRAF-V600E and 204 compounds (159 active) against HIV Integrase. A SVM prediction … …American Journal of Biochemistry and Biotechnology, Vol. 12, Issue 4 · 17 Oct 2016 · 7,802 accessesView PDFCite