A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

A team of MIT researchers developed a new algorithm that could eventually allow drones to constantly learn to adapt to ...

The predictive model also incorporates an improved fruit fly optimization algorithm (IFOA) in conjunction with support vector machine (SVM) techniques. The actual instances of lost circulation serve ...

The fusion of remote sensing techniques with sophisticated machine learning (ML) algorithms promises transformative advancements for plant breeding programs. Once ML-driven workflows achieve ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Abstract: The purpose of this research was to optimize the backpropagation algorithm process by adding the Nguyen-Widrow method in input layer of feed-forward process and adapting the learning rate ..

In order to ensure the prediction accuracy of mechanical properties, a TCMSSA-ELM model, which is a hybrid of the sparrow search algorithm (SSA) optimized by the tent chaotic mapping (TCM) algorithm ...

To address this limitation, we propose ELM-DeepONets, an Extreme Learning Machine (ELM) framework for DeepONets that leverages the backpropagation-free nature of ELM. By reformulating DeepONet ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

Objectives This study aimed to employ machine learning algorithms ... 24 KNN: An instance-based learning algorithm that classifies data points based on the classes of their nearest neighbours, making ...