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In this paper, we study the performance of different classifiers on the CIFAR-10 dataset, and build an ensemble of classifiers to reach a better performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), on some classes, are mutually exclusive, thus yield in higher accuracy when combined. We reduce KNN overfitting using Principal Component Analysis (PCA),...
Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose...
Automated colon cancer detection helps get rid of the slow and laborious process of manual examination of histopathological tissue specimens using microscope, and provides a reliable second opinion to the histopathologists. Therefore, automated colon cancer detection has been the focus of research community in the past two decades, and researchers have proposed various automatic colon cancer detection...
I-vector space feature has been recently proved to be very efficient in speaker recognition field. In this paper, we assess using the i-vector approach for emotional speaker recognition in order to boost the performance which is deteriorated by emotions. The key idea of the i-vector algorithm is to represent each speaker by a fixed length and low dimensional feature vector. The concatenation of these...
Epilepsy detection using EEG signals is an important clinical practice to study the occurrence of seizures. There is a need to analyze huge volumes of EEG data for finding the epileptic seizures. The manual analysis of EEG records for identifying seizure manifestations is time-consuming and creates an immense workload for the physician. To reduce the EEG analysis time, an autonomous epilepsy detection...
In real applications of one class classification, new features may be added due to some practical or technical reason. While lacking of representative samples for the new features, multi-task learning idea could be used to bring some information from the former learning model. Based on the above assumption, a new multi-task learning approach is proposed to deal with the training of the updated system...
Recognizing the facial expression plays an important role in human computer interaction. Following the recent success of the Convolutional Neural Network (CNN) in image classification and object recognition, this paper proposes a facial expression recognition method that makes full use of CNNs to detect face features globally and locally and that combines global and local generic features for improving...
This paper proposes a speech/music classification system based on i-vector. An analysis of two classification methods, namely cosine distance score (CDS) and support vector machine (SVM) is performed. Two session compensation methods, within-class covariance normalization (WCCN) and linear discriminant analysis (LDA) are also discussed. The performance of proposed systems yields better results compared...
High accuracy fault diagnosis systems are extremely important for effective condition based maintenance (CBM) of rotating machines. In this work, we develop a fault diagnosis system using time and frequency domain statistical features as input to a backend support vector machine (SVM) classifier. We evaluate the performance of the baseline system for speed dependent and speed independent performance...
This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.
The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has seen application across many scientific fields. Graph classification poses two distinct problems: the classification of elements within a graph and the...
The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor...
An innovative and robust image segmentation approach has been proposed for magnetic resonance (MR) brain tumor extraction. We have proposed a novel technique to classify a given MR brain image as benign or malignant. In order to extract the features from given MR brain tumor image, we have first employed wavelet transform which is then followed by Laplacian Eigen maps (LE) so as to curtail the dimensions...
The evacuation of children and the elderly from disaster areas is sometimes difficult. This study aims to use a vibration sensor to estimate situations involving people who remain in a devastated building. This paper proposes a method to estimate the attributes of the people, such as their age or sex, based on the vibration data produced by their footsteps. The vibration data obtained through sensors...
Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter,...
This paper proposed a hybrid feature extraction method to improve the correct recognition rate of a handwritten digit recognition device based on temperature sensor array. The hybrid features are based on the temperature changes of the temperature sensor array during the process of handwriting, and the Principal Component Analysis (PCA) method is used for choosing the principal component of the features...
In this communication we explain how a Support Vector Machine (SVM) can be applied to compute the Euler number or Genus of a 2-D binary image. By taking into account the results provided by a mathematical formulation that is known producing exact results we derive two specialized SVM-based architectures, one useful for the 4-connected case and one useful for the 8-connected case. We validate the applicability...
In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
Parkinson's disease (PD) and essential tremor (ET) are two kinds of tremor disorders which always confusing doctors in clinical diagnosis. Early experiments on structural MRI have already shown that Parkinson's disease can cause pathological changes in the brain region named Caudate_R (a part of Basal ganglia) while essential tremor cannot. Although there are many research work on the classification...
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