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The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research...
Brain-Computer Interface (BCI) can be realized by translating user's thoughts into control commands to assist paralyzed persons to communicate and control electronic devices. In this work, Electroencephalographic (EEG) signals were recorded from four subjects while they perform different mental states. We present an Artificial-Neural-Network-based approach for the purpose of classifying Electroencephalographic...
To date, paper-based examinations are still in use worldwide on all levels of education levels (e.g. secondary, tertiary levels). However, literature regarding off-line automatic assessment systems employing off-line handwriting recognition is not numerous. This paper proposes an off-line automatic assessment system employing a hybrid feature extraction technique - a newly proposed Modified Direction...
In this paper, we have compared and analyzed the classification accuracies using two different classifiers for mental arithmetic and rest task using functional near-infrared spectroscopy (fNIRS) signals. Multi-channel continuous-wave imaging system was used to extract the signals from the prefrontal cortex of the brain of seven healthy subjects. The extracted signals were then converted into change...
In this paper, two neural network based methods were implemented for recognition of images showing 10 hand gestures. Images were available from 24 subjects and captured on two different backgrounds and with several space orientations. Firstly, Histogram of Oriented Gradients method was applied for feature extraction and training was performed with multilayer feed forward neural network with back propagation...
Nowadays there are numerous user-generated restaurant reviews available on the Internet, of which they are considered valuable resources for decision making to customers. In reality, not every reviews available online are helpful to users, so the need for filtering unqualified reviews is realized. There have been several studies on spam review detection that attempt to detect unqualified reviews using...
Deep neural network (DNN) has recently received much attention due to its superior performance in classifying data with complex structure. In this paper, we investigate application of DNN technique to automatic classification of modulation classes for digitally modulated signals. First, we select twenty one statistical features which exhibit good separation in empirical distributions for all modulation...
This paper presents a method for detecting anomalous power consumption patterns attacks, using a discrete wavelet transform, as well as the variance fractal dimension (VFD) and an artificial neural network (ANN) for a smart grid. The main procedure of the proposed algorithm consists of the following steps: (i) Finding normal and anomalous patterns of power consumption to train the proposed method,...
For cardiologists, the detection of cardiac abnormalities is a very delicate and crucial task for the treatment of a patient's condition. This task that requires electronic systems of medical assistance that is more precise, faster and reliable to help cardiologists to analyze and make the right decisions. These medical assistance systems tend to model the human expertise and perception using signal...
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature — all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found...
This paper presents a Face Detection System with Expression Recognition using Artificial Neural Networks. It is an automated vision system designed and implemented using MATLAB. The Face Detection with Expression Recognition system accomplishes facial expression recognition through two phases. The captured image is processed first to detect the face, and then the facial expression is recognized. These...
A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify...
In this article we applied Support Vector Machines to acoustic model of Speech Recognition System based on MFCC and LPC features for Azerbaijani DataSet. This DataSet has been used for speech recognition by Multilayer Artificial Neural Network and achieved some results. The main goal of this work is applying SVM techniques to the Azerbaijan Speech Recognition System. The variety of results of SVM...
Digital images have an important function in several fields like journalism, film industry and forensic investigations. Several image editing softwares can change the content of an image very easily. Attackers use contrast enhancement for avoiding the traces left by image forgery. So it is necessary to perform contrast enhancement detection for detecting an image forgery. In the proposed system, there...
Recently, Deep belief networks(DBNs) have been applied in classification and regression, proved to be superior to general algorithms. But its powerful deep feature extraction ability has not yet been fully played so that a novel algorithm, multi-scale DBNs fusing wavelet transform(WT), is proposed in this paper. Based on the advantages of predicting high frequency components from WT by DBN confirmed,...
Nowadays Opinion mining is given more important, since it provides decision makers to estimate the success of a newly proposed techniques, novel ad campaign or novel product launch. In general, supervised methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify the opinions. In some cases SVM performs better classification and some cases ANN performs better...
Linear discriminant analysis (LDA) and Gaussian probabilistic LDA (PLDA) have been shown to effectively suppress channel- and session-variability of i-vectors. But they suffer the following limitations: 1) In LDA, a single linear transformation may not be adequate to describe the nonlinear relationship of features and 2) Gaussian-PLDA assumes the speaker and channel factors follow a Gaussian distribution,...
We have proposed a spatial-cue based binaural noise reduction algorithm for hearing aids. However, in that algorithm, decision parameters are empirically selected. In this paper, we extend the work and propose a supervised classification algorithm for binaural speech enhancement/separation and dereverberation using a modified ideal binary mask (mIBM) as the training target and simple neural networks...
We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In this paper, we use neural networks as acoustic models to extract DNN/LSTM phoneme...
The physiological and pathological information obtained by the single-point or complex multi-point pressure sensor is still less. In this paper, we adopt the pulse image sensor which can reflect the change of the pulse-taking skin surface particularly and comprehensively, we use the MM-3 pulse model (Group A) as the subjects produced by Shanghai University of Traditional Chinese Medicine to study...
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