A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines
David Charte,Francisco Charte,Salvador García,María José del Jesus,Francisco Herrera,Francisco Herrera +5 more
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TLDR
Autoencoders (AEs) as mentioned in this paper have emerged as an alternative to manifold learning for conducting nonlinear feature fusion, and they can be used to generate reduced feature sets through the fusion of the original ones.About:
This article is published in Information Fusion.The article was published on 2018-11-01 and is currently open access. It has received 209 citations till now. The article focuses on the topics: Isomap & Feature (computer vision).read more
Citations
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Journal ArticleDOI
Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models
TL;DR: The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features in the classification of waste types.
Journal ArticleDOI
The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges.
Amir Ahmad,Sunita Garhwal,Santosh Kumar Ray,Gagan Kumar,Sharaf Jameel Malebary,Omar M. Ba-Rukab +5 more
TL;DR: A detailed review of machine learning methods used to predict the number of confirmed cases of Covid-19 is presented in this article, where the authors present a taxonomy that groups them in four categories.
Journal ArticleDOI
Deep Learning Based Systems Developed for Fall Detection: A Review
Md. Milon Islam,Omar Tayan,Md. Repon Islam,Md. Saiful Islam,Sheikh Nooruddin,Muhammad Nomani Kabir,Md. Rabiul Islam +6 more
TL;DR: Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc.
Journal ArticleDOI
Real-time monitoring of high-power disk laser welding statuses based on deep learning framework
TL;DR: This study provides a novel and accurate method for high-power disk laser welding status monitoring that achieves higher accuracy and stronger robustness in monitoring welding status by comparing with the backpropagation neural network, support vector machine and random forest.
Journal ArticleDOI
A review on classifying abnormal behavior in crowd scene
A. A. Afiq,Mohd Azman Zakariya,Mohamad Naufal Mohamad Saad,A. A. Nurfarzana,M. H. Md Khir,A. F. Fadzil,A. Jale,W. Gunawan,Z. A. A. Izuddin,M. Faizari +9 more
TL;DR: A review of crowd behavior analysis methods including Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Optical Flow method and Spatio-Temporal Technique (STT) to provide insight on several detection methods.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.