Research ArticleOpen accessIn the keywordsAutomated Terrain Classification with a Bayesian Hyperparameter Optimized Deep Supervised Autoencoder ModelTiny Du Toit, Hennie Kruger, Annette Van Der MerweTerrain classification according to specific terrain attributes, has become increasingly important in certain decision-making scenarios. Automated robots are often utilized to traverse a specific surface to collect data that can be used in classification models to identify a specific terrain. In this study, a supervised autoencoder model (i.e., an autoencoder combined with a supervised learner such as a multilayer perceptron) … …Journal of Computer Science, Vol. 19, Issue 9 · 31 Aug 2023 · 5,019 accessesView PDFCite
Research ArticleOpen accessIn the keywordsHEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKSMariana D.C. Lima, Silvia M. Nassar, Pedro Ivo R.B.G. Rodrigues et al.Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In … …Journal of Computer Science, Vol. 10, Issue 5 · 18 Jan 2014 · 7,054 accessesView PDFCite
Research ArticleOpen accessIn the abstractActive Reset and Scheduling in Fault-Tolerant Quantum-Classical Cloud ComputingMohamed El-DosukyQuantum-classical cloud computing refers to a hybrid computing model that integrates quantum computing with classical computing resources, typically hosted on the cloud. The objective of this paper is threefold: First, to scrutinize the computing paradigm of quantum-classical cloud computing; second, to study the three pillars of the underpinning techniques: Active reset, scheduling, and fault tolerance; third, to demonstrate the realization … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Sep 2026 · 767 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimizing Bias Detection in Tweets Using Bayesian Probabilistic ModelPrasanth G Rao, Harsha Chigurupati, Krish Hashia et al.Content moderation on social media faces persistent challenges from inconsistent evaluation shaped by subjective judgment and subtle semantic variations. This work proposes a Bayesian probabilistic framework for detecting bias in tweets using WordNet-based vocabulary filtering, statistical normalization via z-scores, and threshold optimization. The system is stateless, scalable, and dataset-agnostic, requiring no session-specific information. Unlike complex models such as Support Vector … …Journal of Computer Science, Vol. 22, Issue 5 · 19 May 2026 · 1,009 accessesView PDFCite