Journal ArticleDOI
Neural networks
Alberto Prieto,Beatriz Prieto,Eva M. Ortigosa,Eduardo Ros,Francisco J. Pelayo,Julio Ortega,Ignacio Rojas +6 more
TLDR
The development and evolution of different topics related to neural networks is described showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems.About:
This article is published in Neurocomputing.The article was published on 2016-11-19. It has received 184 citations till now. The article focuses on the topics: Neural modeling fields & Nervous system network models.read more
Citations
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Journal ArticleDOI
Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
TL;DR: The proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database and can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
Journal ArticleDOI
Recommendation system based on deep learning methods: a systematic review and new directions
TL;DR: This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications and indicated that autoencoder models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks and the Recurrent Neural Networks.
Journal ArticleDOI
Deep learning approach for microarray cancer data classification
TL;DR: A deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes using a 7-layer deep neural network architecture having various parameters for each dataset is developed.
Journal ArticleDOI
Supervised learning in spiking neural networks: A review of algorithms and evaluations
TL;DR: This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively, and provides five qualitative performance evaluation criteria and presents a new taxonomy for supervisedLearning algorithms depending on these five performance evaluated criteria.
Journal ArticleDOI
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
TL;DR: A thorough review on the development of ML-ELMs, including stacked ELM autoencoder, residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications, and the connection between random neural networks and conventional deep learning.
References
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Journal ArticleDOI
Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings
TL;DR: A comprehensive experimental comparison study over the effectiveness of four learning algorithms, i.e., BP, ELM, I-ELM, and SVM over a data set consisting of real financial data for corporate credit ratings.
Journal ArticleDOI
Choquet fuzzy integral-based hierarchical networks for decision analysis
TL;DR: A Choquet fuzzy integral-based approach to hierarchical network implementation is investigated and the fuzzy integral as an excellent component for decision analysis is generalized, resulting in increased flexibility.
Journal ArticleDOI
Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong
TL;DR: A newly developed method, particle swarm optimization (PSO) model, is adopted to train the perceptron and to predict the pollutant levels, establishing a new neural network model, PSO-based approach, established and completed.
Journal ArticleDOI
Rough sets as a front end of neural-networks texture classifiers
Roman W. Swiniarski,Larry Hargis +1 more
TL;DR: The numerical experiments show the ability of rough sets to select reduced set of pattern's features (minimizing the pattern size), while providing better generalization of neural-network texture classifiers.
Proceedings ArticleDOI
Efficient simulation of large-scale Spiking Neural Networks using CUDA graphics processors
Jayram Moorkanikara Nageswaran,Nikil Dutt,Jeffrey L. Krichmar,Alexandru Nicolau,Alexander V. Veidenbaum +4 more
TL;DR: This paper demonstrates an efficient, Izhikevich neuron based large-scale SNN simulator that runs on a single GPU and presents a collection of new techniques related to parallelism extraction, mapping of irregular communication, and compact network representation for effective simulation of SNNs on GPUs.