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Recognition of Plant Diseases Using Convolutional Neural Networks
(ITESO, 2020-02)
Recognition of Plant Diseases Using Convolutional Neural Networks
(ITESO, 2020-02)
Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks
Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition ...
Multi-label Classification of Panoramic Radiographic Images Using a Convolutional Neural Network
(2020-01-01)
Dentistry is one of the areas which mostly present potential for application of machine learning techniques, such as convolutional neural networks (CNNs). This potential derives from the fact that several of the typical ...
Denoising digital breast tomosynthesis projections using convolutional neural networks
(2021-01-01)
The Digital Breast Tomosynthesis (DBT) projections are obtained with low quality, being essential to use denoising methods to increase the quality of the projections. Currently, deep learning methods have become the ...
Two-level genetic algorithm for evolving convolutional neural networks for pattern recognition
(IEEE-Inst Electrical Electronics Engineers, 2021)
The aim of Neuroevolution is to nd neural networks and convolutional neural network (CNN)
architectures automatically through evolutionary algorithms. A crucial problem in neuroevolution is search
time, since multiple ...
Deep neural network approaches for Spanish sentiment analysis of short texts
(Springer Verlag, 2018)
Sentiment Analysis has been extensively researched in the last years. While important theoretical and practical results have been obtained, there is still room for improvement. In particular, when short sentences and low ...
Transfer learning between texture classification tasks using convolutional neural networks
(IEEE, 2015)
Convolutional Neural Networks (CNNs) have set the state-of-the-art in many computer vision tasks in recent years. For this type of model, it is common to have millions of parameters to train, commonly requiring large ...