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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Book
How to Build a Brain: A Neural Architecture for Biological Cognition
TL;DR: This chapter discusses Nengo: Advanced modeling methods, a framework for building a brain, and theories of cognition, which aim to clarify the role of language in the development of cognition.
Book
Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance
Robert R. Trippi,Efraim Turban +1 more
TL;DR: In Neural Networks in Finance and Investing, Robert Trippi and Efraim Turban have assembled a stellar collection of articles by experts in industry and academia on the applications of neural networks in this important arena.
Journal ArticleDOI
Memristor-based neural networks
Andy Thomas,Andy Thomas +1 more
TL;DR: This work presents and explains the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determines the minimal requirements for an artificial neural network.
Proceedings ArticleDOI
A DNA-Based Archival Storage System
TL;DR: An architecture for a DNA-based archival storage system is presented, structured as a key-value store, and leverages common biochemical techniques to provide random access, and a new encoding scheme is proposed that offers controllable redundancy, trading off reliability for density.
Journal ArticleDOI
Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain
TL;DR: This paper introduces a general class of neural networks with arbitrary constant delays in the neuron interconnections, and neuron activations belonging to the set of discontinuous monotone increasing and (possibly) unbounded functions, for which stability is instead insensitive to the presence of a delay.