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Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular,...
the objective to develop clinical decision support system (CDSS) tools is to help physicians making faster and more reliable clinical decisions. The first step in their development is choose a machine learning classifier as the system core. Previous works reported implementation of artificial neural networks, support vector machines, genetic algorithms, etc. as core classifiers for CDSS; however,...
Many computer-based devices are now connected to the internet technology. These devices are widely used to manage critical infrastructure such energy, aviation, mining, banking and transportation. The strategic value of the data and the information transmitted over the Internet infrastructure has a very high economic value. With the increasing value of the data and the information, the higher the...
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...
Outlier detection is a method to improve performances of machine learning models. In this paper, we use an outlier detection method to improve the performance of our proposed algorithm called decision boundary making (DBM). The primary objective of DBM algorithm is to induce compact and high performance machine learning models. To obtain this model, the DBM reconstructs the performance of support...
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 research paper proposes an intelligent classification technique to recognize normal and abnormal MRI brain image. Medical image like ECG, MRI and CT-scan images are important way to diagnose disease of human being efficiently. The manual analysis of tumor based on visual inspection by radiologist/physician is the conventional method, which may lead to wrong classification when a large number...
Many features extraction method and classifiers are used singly, with modest results, for automatic image annotation. In order to improve the semantic image annotation accuracy, this document provides an automatic system to annotate image content by using a fusion of 3 classifier and a combination of some features extraction methods; multiclass support vector machine, multilayer neural network and...
Drug discovery research has progressed to a place where it essentially counts on high performance computer systems and huge databases for its victory. As such, Virtual Screening (VS), a computationally intensive process, plays a major role in the systematic drug designing process for pressing diseases. Therefore, it is imperative that the VS process has to be made as fast as possible in order to efficiently...
This paper presents results of our study in order to build an automatic alarm system using sound analysis which can be used for medical telemonitoring. The system will raise the alarm if it detects abnormal sounds coming from abnormal situations in a patient/aged person's room. Our work consists of building three sound corpora and developing a sound analysis algorithm consisting of three sound discriminators...
The goal of this research is to design a multimedia analyzer (MA) that can be embedded in portable devices. This MA can recognize different multimedia (e.g. text and image) patterns and help the user to analyze the multimedia contents more efficiently. To realize the MA in an environment with limited computing resource, we propose a new concept called decision boundary learning (DBL). The basic idea...
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...
The dependency function proposed in the rough set model and fuzzy rough set model is widely employed in feature evaluation and attribute reduction. It is shown that the relations between fuzzy memberships haven't used in the function. We introduce the concept of fuzzy similarity measure and propose a new model in evaluating feature dependency. The properties of the model were discussed. Experimental...
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...
Biometric authentication based on iris patterns is used for personal identification. Important attributes to identity applications include accuracy, speed and template size. Iris patterns are segmented by considering the maximum area of the connected components in the binary images. The iris region is decomposed into subregions. Hu moments are applied to the minimum variance subregions (MVS). The...
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,...
In this paper, we propose a pedestrian analysis solution helpful for adaptive content delivery and interest measurement for outdoor advertisement displays. The proposed system has built-in camera on the top panel of such displays which capture the real time viewers' frames. The captured frames have been analyzed for detection of faces using Viola-Jones algorithm. The detected faces have been processed...
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...
In this paper, we propose a novel approach for identity verification based on the directional analysis of velocity-based partitions of an on-line signature. First, inter-feature dependencies in a signature are exploited by decomposing the shape (horizontal trajectory, vertical trajectory) into two partitions based on the velocity profile of the base-signature for each signer, which offers the flexibility...
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