The engineering landscape is shifting fast. Technologies keep evolving, and industries now want engineers who don’t just know ...

This study evaluates the performance of twenty-four machine learning models (MLMs) in forecasting SPVP in Bamenda, Cameroon. The study uses data from Photovoltaic Geographical Information System with ...

Learn how DenseNet works and why it’s a powerful architecture in deep learning. This tutorial breaks down DenseNet’s key concepts, including dense connections, feature reuse, and parameter ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Objectives: Gain hands-on, code-first experience with deep learning theories, models, and skills that are useful for applications and for advancing science. We focus on how to decide which problems ...

About this issue Abstract Assuming that a smoothness condition and a suitable restriction on the structure of the regression function hold, it is shown that least squares estimates based on multilayer ...

Symbolic regression is a powerful technique to discover analytic equations that describe data, which can lead to explainable models and the ability to predict unseen data. In contrast, neural networks ...

Based on the collected data, it is possible to identify the normal and malicious behavior of consumers by analyzing the data using machine learning (ML) and deep learning methods. This paper proposes ...