Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be
useful in many agroforestry applications. However, it lacks the spectral range and precision …

In the last decade a large number of supervised learning methods have been introduced in
the field of the machine learning. Supervised learning became an area for a lot of research …

Supervised Machine Learning (SML) is the search for algorithms that reason from externally
supplied instances to produce general hypotheses, which then make predictions about …

Integrating the various embedded devices and systems in our environment enables an
Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that …

In this paper, a survey of the literature of the past 15 years involving machine learning (ML)
algorithms applied to self-organizing cellular networks is performed. In order for future …

Traditional approaches to the prediction of suicide attempts have limited the accuracy and
scale of risk detection for these dangerous behaviors. We sought to overcome these …

The Internet of Things (IoT) is set to become one of the key technological developments of
our times provided we are able to realize its full potential. The number of objects connected …

Natural language processing (NLP) techniques can be used to make inferences about
peoples' mental states from what they write on Facebook, Twitter and other social media …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

Machine learning is becoming a popular and important approach in the field of medical
research. In this study, we investigate the relative performance of various machine learning …