It is well known that urbanization and industrialization have resulted in the rapidly increasing
emissions of volatile organic compounds (VOCs), which are a major contributor to the …

This paper presents an efficient algorithm for learning Bayesian belief networks from databases.
The algorithm takes a database as input and constructs the belief network structure as …

This paper performs a comprehensive study on the deep-learning-based computer-aided
diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by …

Natural killer (NK) cells provide a central defense against viral infection by using inhibitory
and activation receptors for major histocompatibility complex class I molecules as a means of …

In this paper, we empirically evaluate algorithms for learning four types of Bayesian network
(BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes …

This paper investigates the methods for learning predictive classifiers based on Bayesian
belief networks (BN) — primarily unrestricted Bayesian networks and Bayesian multi-nets. We …

This paper addresses the problem of learning Bayesian network structures from data by
using an information theoretic dependency analysis approach. Based on our three-phase …

This paper presents an efficient algorithm for constructing Bayesian belief networks from
databases. The algorithm takes a database and an attributes ordering (ie, the causal attributes …

Low-temperature catalysts of mesoporous Co 3 O 4 and Au/Co 3 O 4 with high catalytic
activities for the trace ethylene oxidation at 0 C are reported in this paper. The catalysts were …

In this study, N/B co-doped biochars were employed as metal-free activators of peroxydisulfate
(PDS) for tetracycline degradation, more importantly, the roles of dopants and the relative …