Smart farming with artificial intelligence provides an efficient solution to today's agricultural
sustainability challenges. Machine learning, Deep learning, and time series analysis are …

Bayesian networks (BNs), also known as Bayesian belief networks or Bayes nets, are a kind
of probabilistic graphical model that has become very popular to practitioners mainly due to …

A wide range of quantitative and qualitative modelling research on ecosystem services
(ESS) has recently been conducted. The available models range between elementary …

Intrusion detection systems (IDS) are commonly categorized into misuse based, anomaly
based and specification based IDS. Both misuse based IDS and anomaly based IDS are …

Abstract Random Naïve Bayes (RNB) is a machine learning method that uses the Random
Forest (RF) structure to optimize Naïve Bayes (NB). It is interesting to see whether RNB …

Abstract Dynamic Bayesian networks (DBNs) as an extension of traditional Bayesian
networks have recently been paid great concern to environmental modeling to capture …

The application of machine learning to agriculture is currently experiencing a “surge of
interest” from the academic community as well as practitioners from industry. This increased …

Flooding hazard evaluation is the basis of flooding risk assessment which has significances
to natural environment, human life and social economy. This study develops a spatial …

This study sought to determine the features of an ideal serve in men's professional tennis. A
total of 25,680 first serves executed by 151 male tennis players during Australian Open …

Arsenic-contaminated groundwater is considered one of the world's largest environmental
health crises, as more than 300 million people in more than one-third of countries worldwide …