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Research ArticleOpen accessIn the abstractAn Ensemble of Gaussian Mixture Model and Support Vector Machines for Network Intrusion DetectionOlujimi Daniel Alao, Sheriff Alimi, Shade Oluwakemi Kuyoro et al.NetworkIntrusion Detection Systems (NIDS) can protect computer networks and computersystems by detecting abnormal network packets and taking agreed action plans,such as notifying an administrator or rejecting the network packets. In this study,the aim is the implementation of NIDS with improved performance using anensemble of Support Vector Machines (SVMs) and the Gaussian Mixture Model(GMM). Four SVMs with Radial Basis Function (RBF), … …Journal of Computer Science, Vol. 18, Issue 9 · 21 Sep 2022 · 6,114 accessesView PDFCite
Research ArticleOpen accessIn the abstractFeasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, NigeriaNajashi B. Gafai, Asia'u Talatu BelgoreThe extremely variable nature of solar radiation makes it difficult for solar power plants to keep up with predicted power output and demand curves. As a result, solar radiation simulation is crucial to the efficient design, administration, and operation of any solar power plant. With only partially satisfactory results, empirical models have been routinely employed in Nigeria to predict solar … …Energy Research Journal, Vol. 13, Issue 1 · 21 Jul 2022 · 10,032 accessesView PDFCite
Research ArticleOpen accessIn the abstractUncertainty Assessment for the Buckling Analysis of Glass Columns with Random ParametersMohammad Momeni, Chiara BedonLoad-bearing elements composed of glass, as known, are often susceptible to buckling collapse mechanisms. This intrinsic characteristic (and thus potential limitation for design) typically derives from the use of relatively small thicknesses to cover large spans and surfaces, thus resulting in a multitude of columns, beams, or plates that are characterized by high slenderness. In the literature, accordingly, several and … …International Journal of Structural Glass and Advanced Materials Research, Vol. 4, Issue 1 · 22 Dec 2020 · 6,295 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Probit and Logistic Regression Models in the Analysis of Dichotomous OutcomesAmrutha Jose, Mariyamma Philip, Lavanya Tumkur Prasanna, M. ManjulaProbit and logistic regression models are members of the family of generalized linear models, used for estimating the functional relationship between the dichotomous dependent and independent variables. The current study is designed to find the performance of logistic and probit regression models in different conditions under multivariate normality. The objective of the study is to compare the performance of probit … …Current Research in Biostatistics, Vol. 10, Issue 1 · 26 Mar 2020 · 17,036 accessesView PDFCite
Research ArticleOpen accessIn the abstractTilted Beta Binomial Linear Regression Model: A Bayesian ApproachEdilberto Cepeda-Cuervo, Maria Victoria Cifuentes-AmadoThis paper proposes new linear regression models to deal with overdispersed binomial datasets. These new models, called tilted beta binomial regression models, are defined from the tilted beta binomial distribution, proposed assuming that the parameter of the binomial distribution follows a tilted beta distribution. As a particular case of this regression models, we propose the beta rectangular binomial regression models, … …Journal of Mathematics and Statistics, Vol. 16, Issue 1 · 5 Jan 2020 · 8,784 accessesView PDFCite
Research ArticleOpen accessIn the abstractTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDFCite
Research ArticleOpen accessIn the abstractAssociation Between Particulate Matter (PM10) Exposure and Cardiorespiratory Hospital Admissions: A Time Series AnalysisNur Nabila Abd Rahim, Abqariyah Yahya, Wan Rozita Wan MahiyuddinThis study aimed to evaluate the association between particulate matter (PM10) exposure and cardiorespiratory hospital admissions and develop a time series model for cardiorespiratory hospital admissions in Johor, Malaysia. Retrospective time series analysis was utilised to study cardiorespiratory hospital admissions for the study period 2015-2016. Over-dispersed Poisson generalized linear models with parametric smoothing functions were used to examine the association … …American Journal of Environmental Sciences, Vol. 17, Issue 2 · 27 May 2021 · 6,917 accessesView PDFCite
Research ArticleOpen accessIn the abstractBeatbox Classification to Distinguish User Experiences Using Machine Learning ApproachesJason Martanto, Iman Herwidiana KartowisastroResearch regarding beatbox classification has generated a relatively significant growth in the past decade. Although the differences between contributors’ expertise within a vocal percussion dataset have been mentioned in previous works, the impact of those discrepancies has not been thoroughly investigated. In this study, the authors explore performances of machine learning algorithms for beatbox classification, with an emphasis on prior … …Journal of Computer Science, Vol. 21, Issue 4 · 18 Mar 2025 · 5,208 accessesView PDFCite