Journal: International Journal of Information Technology

Article Title: Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules

doi: 10.1007/s41870-022-01153-y

Figure Lengend Snippet: Significant review findings of various soft computing methods used in the Clinical domain predictive models

Article Snippet: 16 , Ershadi, M. M et al , 2020 , Bayesian Belief Network , Bayesian networks were constructed based on domain experts' knowledge to acquire accuracy of 87% using 10 sample datasets Matlab R2015a, 64-bit software, is a classification method based on the k-fold cross-validation technique Bayesian Network with experts' knowledge has an accuracy of 87% [ ] .

Techniques: Extraction, Construct, Modification, Diagnostic Assay, Software, Selection, Generated

Journal: ACS Omega

Article Title: Impact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases

doi: 10.1021/acsomega.1c02721

Figure Lengend Snippet: (a) Dynamic response curves acquired from an array of 16 commercial gas sensors (b) under exposure to mixture gases with diverse concentration. (c) Schematic illustration of the machine learning (ML) process with a support vector machine (SVM) model based on the k -fold ( k = 5) cross-validation protocol. (d) Schematic diagram of data preparation for ML.

Article Snippet: To predict the concentration of multiple gases using an ML technique, we adopted the SVM model with k -fold cross-validation ( k = 5) in MATLAB software, as represented in Figure c. Since two classes of datasets consisting of labeled data (or answer data) and feature data were needed for utilizing the ML method, we rationally assigned the target gas concentrations and gas response as the labeled data and feature data, respectively, as represented in Figure d. The details of the experimental conditions are described in the Supporting Information .

Techniques: Concentration Assay, Plasmid Preparation, Biomarker Discovery