Future Challenges of Particulate Matters (PMs) Monitoring by Computing Associations Among Extracted Multimodal Features Applying Bayesian Network Approach

Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features

“… 58 The present research is consistent with similar studies investigated by utilizing BayesiaLab to determine the strength of relationship, causality among the features, node force, dynamic profile analysis, sensitivity analysis to further unfold the nonlinear hidden dynamics to further improve the diagnostic and prognostic such as brain tumor MRIs, 12 congestive heart failure, 13 prostate cancer, 59 and lungs infection due to particulate matters. 15 The results reveal that the proposed approach can be better utilized for early decision-making and improving the better diagnostic and prognostic system for patients suffering from lung pathologies.…”

“… 7 BNs have successfully been utilized in many studies by different researchers such as Kocian et al, 8 Amaral et al, 9 Laurila-Pant et al, 10 and Zhang et al 11 The Bayesian inference (BI) approach utilizing the BayesiaLab has gained its popularity to investigate the causal relationships, dynamic profile analysis, target analysis, posterior probability analysis, etc. in variety of different applications including empirical analysis of brain tumor MRIs, 12 dynamic profile analysis of congestive heart failure, 13 environmental sustainability indicators to manufacture, 14 dynamical analysis of particulate matters future challenges monitoring and evaluations, 15 predicting childhood lead exposure risk from community water system using Bayesian approach, 16 etc. There are many applications of BI approach such as intrahepatic cholangiocarcinoma patients’ microvascular invasion, 17 human factor analysis for sustainable hazardous cargo port operations, 18 gallbladder polyps with malignant potential based on preoperative ultrasound Bayesian prediction analysis, 19 decision-making, 20 industries 4.0 technologies selection, 21 transport vulnerability, 22 etc.…”