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.
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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.
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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.
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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
Hierarchical clustering of self-organizing maps for cloud classification
TL;DR: A new method for segmenting multispectral satellite images using Agglomerative Hierarchical Clustering and Probabilistic Self-Organizing Map is presented.
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Real-time computing platform for spiking neurons (RT-spike)
TL;DR: The overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important is evaluated.
Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges This paper reviews challenges and progress in implementing timing-based neuronal learning mechanisms in silicon.
TL;DR: In this paper, analog very large-scale integration (VLSI) circuit implementations of multiple synaptic plasticity rules, ranging from phenomenological ones (e.g., based on spike timing, mean firing rates, or both) to biophysically realistic ones, were described.
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Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks.
Antreas Afantitis,Georgia Melagraki,Panayiotis A. Koutentis,Haralambos Sarimveis,George Kollias +4 more
TL;DR: An in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules is developed and a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described.
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Optical neural networks
TL;DR: A systematic morphology of optical neural networks is presented and the state of the art of their implementation is indicated, and some supportable speculations on their future are given.