An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Digital processing of speech signals

Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on deep learning, and we propose a deep learning approach for intrusion detection using recurrent neural networks (RNN-IDS). Moreover, we study the performance of the model in binary classification and multiclass classification, and the number of neurons and different learning rate impacts on the performance of the proposed model. We compare it with those of J48, artificial neural network, random forest, support vector machine, and other machine learning methods proposed by previous researchers on the benchmark data set. The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification. The RNN-IDS model improves the accuracy of the intrusion detection and provides a new research method for intrusion detection.

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

International Joint Conference on Neural Networks

Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach

Intrusion detection is well-known as an essential component to secure the systems in Information and Communication Technology (ICT). Based on the type of analyzing events, two kinds of Intrusion Detection Systems (IDS) have been proposed: anomaly-based and misuse-based. In this paper, three-layer Recurrent Neural Network (RNN) architecture with categorized features as inputs and attack types as outputs of RNN is proposed as misuse-based IDS. The input features are categorized to basic features, content features, time-based traffic features, and host-based traffic features. The attack types are classified to Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). For this purpose, in this study, we use the 41 features per connection defined by International Knowledge Discovery and Data mining group (KDD). The RNN has an extra output which corresponds to normal class (no attack). The connections between the nodes of two hidden layers of RNN are considered partial. Experimental results show that the proposed model is able to improve classification rate, particularly in R2L attacks. This method also offers better Detection Rate (DR) and Cost Per Example (CPE) when compared to similar related works and also the simulated Multi-Layer Perceptron (MLP) and Elman-based intrusion detectors. On the other hand, False Alarm Rate (FAR) of the proposed model is not degraded significantly when compared to some recent machine learning methods.

Intrusion detection using reduced-size RNN based on feature grouping

Predicting blast-induced ground vibration using various types of neural networks

In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

Emotion recognition from speech has noticeable applications in the speech-processing systems. In this paper, the effect of using a rich set of features including formant frequency related, pitch frequency related, energy, and the two first mel-frequency cepstral coefficients (MFCCs) on improving the performance of speech emotion recognition systems is investigated. To do this, the different sets of features are employed, and by using the fast correlation-based filter (FCBF) feature selection method, some efficient feature subsets are determined. Finally, to recognize the emotion from speech, fuzzy ARTMAP neural network (FAMNN) architecture is used. Also, the genetic algorithm (GA) is employed to determine optimum values of the choice parameter (α), the vigilance parameters (ρ
 a, ρ
 b, and ρ
 ab), and the learning rate (β) of FAMNN. Experimental results show the improvement in emotion recognition rate of angry, happiness, and neutral states by using a subset of 25 selected features and the GA-optimized FAMNN-based emotion recognizer.

Mansour Sheikhan

Papers

Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach

Intrusion detection using reduced-size RNN based on feature grouping

Predicting blast-induced ground vibration using various types of neural networks

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

Speech emotion recognition using FCBF feature selection method and GA-optimized fuzzy ARTMAP neural network