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Journal ArticleDOI

Neural networks

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.

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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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Book ChapterDOI

Soft Computing Applications Developed by ECANSE

TL;DR: The presented applications are from power engineering, environment monitoring and telecommunication, and include neural networks, fuzzy logic, and genetic algorithms.