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Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks
(Elsevier Science, 2022-05)
In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, ...
Robust timing and motor patterns by taming chaos in recurrent neural networks
(Nature Publishing Group, 2013-07)
The brain's ability to tell time and produce complex spatiotemporal motor patterns is critical for anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities ...
MicroRNAs and the neural crest: From induction to differentiation
(Elsevier Science, 2018-12)
MicroRNAs are small noncoding RNAs that can control gene expression by base pairing to partially complementary mRNAs. Regulation by microRNAs plays essential roles in diverse biological processes such as neural crest ...
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
(Elsevier, 2020-04)
In this work convolutional-fully connected neural networks were designed and trained to predict the glass transition temperature of polymers based only on their chemical structure. This approach has shown to successfully ...
Neural networks and wireless communications modeling
(Institute of Electrical and Electronics Engineers, 2010-09)
This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables ...
Probing the structure–function relationship with neural networks constructed by solving a system of linear equations
(Nature Research, 2021-12)
Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which ...
Identification and adaptive PID Control of a hexacopter UAV based on neural networks
(John Wiley & Sons Ltd, 2019-01)
In this paper, a novel adaptive PID controller for trajectory-tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear ...
Learning styles' recognition in e-learning environments with feed-forward neural networks
(Blackwell Publishing, 2006-05-10)
People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the ...
Artificial neural networks to evaluate the boron concentration decreasing profile in Blood-BPA samples of BNCT patients
(Pergamon-Elsevier Science Ltd, 2011-12)
For the prediction of decay concentration profiles of the p-boronophenylalanine (BPA) in blood during BNCT treatment, a method is suggested based on Kohonen neural networks. The results of a model trained with the concentration ...
Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
(Elsevier, 2021-03)
The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. ...