Waste sorting is highly recommended for municipal solid waste (MSW) management.
Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW …

Machine learning algorithms learn a desired input-output relation from examples in order to
interpret new inputs. This is important for tasks such as image and speech recognition or …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

This study uses three machine learning algorithms including, support vector machine (SVM),
random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices …

Adoption of digital information tools in the construction sector provides fertile ground for the
birth and growth of companies that specialize in applications of technologies to design and …

We present a novel, accurate, fast, and robust physics-informed neural network method for
solving problems involving differential equations (DEs), called Extreme Theory of Functional …

Many contact-sensor-based methods for structural damage detection have been developed.
However, these methods have difficulty compensating for environmental effects, such as …

Whole-genome enabled prediction of complex traits has received enormous attention in
animal and plant breeding and is making inroads into human and even Drosophila genetics …

This book introduces machine learning for readers with some background in basic linear
algebra, statistics, probability, and programming. In a coherent statistical framework it covers …

In order to meet stringent performance requirements, system administrators must effectively
detect undesirable performance behaviours, identify potential root causes, and take …