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Deep Learning in Chemistry

Adam C. Mater, Michelle L. Coote • Journal of Chemical Information and Modeling (2019)

Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns repres...

OPENALEX DOI: https://doi.org/10.1021/acs.jcim.9b00266

Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning

Jiuxin Wang, Yutian Luo, Zhengming Yang, Xinli Zhao et al. • Applied Sciences (2021)

In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in ...

DOAJ Open Access (DOAJ) DOI: 10.3390/app11083589

A metaheuristic with a neural surrogate function for Word Sense Disambiguation

Azim Keshavarzian Nodehi, Nasrollah Moghadam Charkari • Machine Learning with Applications (2022)

Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many task...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mlwa.2022.100369

Artificial Empathy and Imprecise Communication in a Multi-Agent System

Joanna Siwek, Konrad Pierzyński, Przemysław Siwek, Adrian Wójcik et al. • Applied Sciences (2024)

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applicatio...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15010008