The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification...
In this paper, a statistical method for automatic detection of seizure and epilepsy in the dual-tree complex wavelet transform(DT-CWT) domain is proposed. Variances calculated from the EEG signals and their DT-CWT sub-bands are utilized as features in the classifiers such as artificial neural network(ANN) and support vector machine(SVM). Studies are conducted using EEG signals from a publicly available...
This paper presents a system for detecting breast cancer based on moments. Instead of trying to improve the applied classifier we focused on improving the input attributes. We extracted new features from database samples using the first four moments namely, mean, variance, skewness and kurtosis. Through simulations, 10-fold cross validation method was applied to the Wisconsin breast cancer database...
Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional...
Automatic gender detection through facial features has become a critical component in the new domain of computer human observation and computer human interaction (HCI). Automatic gender detection has numerous applications in the area of recommender systems, focused advertising, security and surveillance. Detection of gender by using the facial features is done by many methods such as Gabor wavelets,...
Signal of humming sound is the input which is important for the Query-by-Humming system. This input signal which has variable dimension depend on humming time interval will always affect the feature vector. It cannot be used with some classifiers, which require non-variable dimension of feature vector, such as Artificial Neural Network (ANN) or Support Vector Machine (SVM). Especially, SVM is good...
Document Script Identification (DSI) is a very useful application in document processing. This paper presents a method for this application that uses a new noise tolerant feature, the Downgraded Pixel Density feature. Compared to other features widely used in existing DSI solutions, this new feature is much more robust to variations in slant, font and style of printed documents. Experimental results...
Constructing a recognition system based on raw measurements for different objects usually requires expert knowledge of domain specific data preprocessing, feature extraction, and classifier design. We seek to simplify this process in a way that can be applied without any knowledge about the data domain and the specific properties of different classification algorithms. That is, a recognition system...
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently. Existing methods for supervised dimensionality reduction often presume that the data is densely sampled...
In order to resolve the problem incurred by low efficient manual classification of tremendous aurora images, an automatic aurora images classification system for huge dataset application is proposed. First, static aurora images are decomposed into texture part and cartoon part with a method called Morphological Component Analysis (MCA). Then features extracted from texture part are classified by three...
Recently a new method for recognition of isolated handwritten Persian digits, based on support vector machines (SVMs), has been introduced. In this research, this method was implemented for the same task with three new modifications, i.e. only one popular shape was considered for digits written in different shapes; sizes of glyphs normalized to digit boundaries; MLP (multi-layer perceptron), SVM/MLP...
A novel method of feature extraction form protein sequences, structures and physicochemical properties has been proposed and obtained a better classification results by the key eigenvector obtained form knowledge reduction combined with the algorithm of support vector machine. Based on Jackknife detecting methods, the comprehensive classification results 78.3% and 90.9% for all-??, all-??, ??+?? and...
The technological changes over the time, have allowed today's society focuses on the acquisition of all types of electronic documents, which is why there is a need to implement new systems to help us in the handwriting characters recognition field, since 70's years have been made research in this area but there are still problems without a solution, especially in cursive handwriting characters recognition...
This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply principal component analysis to reduce feature vectors in several dimensionalities...
The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is...
This paper addresses the problem of improving the generalization ability for gender classification. An approach based on Fuzzy SVM (FSVM) is developed to improve it. The fuzzy membership used in FSVM indicates the degree of one person's face belonging to female/male faces. Based on Learning Vector Quantization (LVQ) learning process, a novel method of generating fuzzy membership function automatically...
We propose a novel target recognition algorithm for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition public release database. Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. A real application of AdaBoost for synthetic...
Premature ventricular contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
In this paper, we present a biologically inspired method for detecting pedestrians in images. The method is based on a convolutional neural network architecture, which combines feature extraction and classification. The proposed network architecture is much simpler and easier to train than earlier versions. It differs from its predecessors in that the first processing layer consists of a set of pre-defined...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.