Artificial Neural Networks Tutorial

By Crescenzio Gallo. 2015

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Management Science and Operations Research

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Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “With cross validation, the accuracy estimate is the average accuracy for the k folds.”
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR) (2020, background): “Feed forward networks / Multi Layer Perceptron (MLP) achieved renewed interest of research community in mid 1980s as by that time “Hopfield network” provided the way to understand human memory and calculate state of a neuron [58].”
A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, unknown): “desired) output and the ANN output [32]–[34].”
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation (2021, background): “There are two publications A and B, describing two different classifiers, for example neural networks with different architectures [56], listing the corresponding confusion matrices and metrics (Table 2).”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, unknown): “[107] e: number of epochs k: number of neurons Association Rules >> O(n(3)) low Agrawal et al.”
Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, unknown): “Also, Jonsson et al. (2013) successfully use a similarly simple AR model (with lags of 24, 48 and 168 h) to account for residual autocorrelation and seasonal dynamics, and use it for short-term EPF. Conejo, Plazas et al. (2005) propose a wavelet-ARIMA technique, which consists of (i) decomposing the”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, unknown): “A single layer perceptron (SLP) consists of an input and an output layer, and it is the simplest form of ANN model [11, 12].”