Swarm intelligence techniques constitute a class of nature-inspired metaheuristics that model the collective behaviour of decentralised, self-organising systems. In feature selection, these algorithms ...

This repository implements a few-shot learning framework with reinforcement learning-based feature selection for SAR (Synthetic Aperture Radar) image classification. The model uses an RL agent to ...

Abstract: With a global search mechanism, particle swarm optimization (PSO) has shown promise in feature selection (FS). However, most of the current PSO-based FS methods use a fix-length ...

In the era of A.I. agents, many Silicon Valley programmers are now barely programming. Instead, what they’re doing is deeply, deeply weird. Credit...Illustration by Pablo Delcan and Danielle Del Plato ...

Abstract: In this study, a hybrid optimization model involving Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) is presented for feature selection and hyperparameter tuning of ...

ABSTRACT: The Rectified Linear Unit (ReLU) activation function is widely employed in deep learning (DL). ReLU shares structural similarities with censored regression and Tobit models common in ...

Adopting Artificial Intelligence (AI) models for financial applications presents significant challenges, as this domain demands high social and ethical standards. In such contexts, besides model ...

Unconventional reservoirs, including shale gas, tight oil, and coalbed methane formations, have emerged as vital contributors to global energy security, accounting for a substantial portion of the ...

🔧 Combine feature selectors with classifiers and regressors in a seamless pipeline using scikit-learn compatible meta-estimators for enhanced machine learning.

ABSTRACT: Accurately predicting individual responses to antidepressant treatment is a critical step toward achieving personalized psychiatry and minimizing the ...