Comparing machine learning, deep learning, and reinforcement learning performance in Culex pipiens predictive modeling.

ABSTRACT: Several machine learning (ML) and deep learning (DL) methods have been used to predict the presence of species in classification problems. Another set of methods, called reinforcement learning (RL), has been used in training agents to perform various tasks, but not in predicting species distribution. Culex pipiens (Diptera: Culicidae), commonly known as the common house mosquito, is a globally distributed species prevalent in temperate and subtropical regions. They serve as a primary vector for West Nile Virus (WNV), a mosquito-borne pathogen that affects humans and other animals. The study objective is to compare the performance of logistic regression, random forest classifier, deep neural networks, and the RL methods, including Q-learning, deep Q-network (DQN), REINFORCE, and Actor-Criti

SUBMITTER: Yin W 

PROVIDER: S-EPMC12614784 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature