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The aim of this paper is to classify the object in hyper spectral images which are high dimensional images and consists of many data channels. Another aim is to use machine learning classification algorithm like support vector machine (SVM) which is good for high dimensional data case. SVM provides a good accuracy of classification. A statistical model is developed to learn and classify hyper spectral...
This paper aims to experimental evaluation of different methodologies to recognize human face based on different facial expression. The face and facial images were captured locally, as the experiment is aimed to be done in India domain. The features were extracted based on two techniques, viz, Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The range of extracted feature is 150,300,600,1200...
Texture classification is a problem that has variousapplications such as remote sensing and forest speciesrecogni- tion. Solutions tend to be custom fit to the datasetused but fails to generalize. The Convolutional NeuralNetwork (CNN) in combination with Support Vector Machine(SVM) form a robust selection between powerful invariantfeature extractor and accurate classifier. The fusion ofexperts provides...
The Classroom Attentiveness Classification Tool (ClassACT) is a system designed to monitor student attentiveness in a variety of instructional phases within the learning environment: lectures, group work, assessments, etc. By collecting information about the user, the user's environment, and the device itself via the various sensors built in to the tablet, processing the data, and then passing it...
This paper presents a novel technique of image classification using BOVW model. The entire process first involves feature detection of images using FAST, the choice made in order to speed up the process of detection. Then comes the stage of feature extraction for which FREAK, a binary feature descriptor is employed. K-means clustering is then applied in order to make the bag of visual words. Every...
Speech impaired people are detached from the mainstream society due to the lacking of proper communication aid. Sign language is the primary means of communication for them which normal people do not understand. In order to facilitate the conversation conversion of sign language to audio is very necessary. This paper aims at conversion of sign language to speech so that disabled people have their...
We study in this paper an authorship attribution in Arabic poetry using text mining classification. Several features such as Characters, Poetry Sentence length; Word length, Rhyme, Meter and First word in the sentence are used as input data for text mining classification algorithms Naïve Bayes NB, Support Vector Machine SVM, and Sequential Minimal Optimization SMO. The data set of experiment was divided...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has...
Support vector machines (SVM), originally introduced as powerful binary classifiers, can also be used for multi-class recognition with the help of creative meta-learning strategies such as commonly used one-vs-rest, one-vs-one and majority voting. In this paper, we explore the potential of creating informed nested dichotomies based on clustering pseudo-labels and probability estimates generated a...
In this paper a new vulnerability detecting method is proposed to detect buffer boundary violations. The main idea is to use the metric of array index manipulation rather than using any heuristic method. We employ a SVM-based classifier to classify the vulnerable functions and innocent functions. Then the vulnerable functions are fed to function call graph guided symbolic execution to precisely determine...
Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance...
Playback attack detection (PAD) is essentially a binary classification task which is used to identify the authentic recordings from the playback recordings. For PAD problem, the difference of the acoustic feature between the authentic and playback recordings mainly comes from the recording channel and the ambient noise. Motivated by the excellent performance of the Gaussian Mixture Model-Universal...
The increasing cardiac diseases of people in recent years demand an early detection of heart diseases using electrocardiogram (ECG) signal processing techniques. In this work we present a semi automatic scheme to discriminate patient-specific ECG beats by using a kernel based feature extraction technique called kernel canonical correlation analysis (KCCA). The heartbeat classification scheme uses...
Petroleum and its products are complex mixture, and how to precise analysis its components is an important part of the oil industry. In this paper, we proposed a components forecasting methods for gasoline octane value prediction based on independent component analysis (ICA) and support vector machine (SVM). By evaluating the accuracy of the models with two feature optimization methods(principal component...
Support vector machine (SVM) is a machine learning method developed in the mid-1990s based on statistical learning theory. SVM classifier is currently more popular classifier. This paper presents a boundary detection technique for retaining the potential support vector. Through seeking to structural risk minimization of the SVM, it improves the learning generalization ability and achieves the minimization...
Nature language processing is an important part in data mining, which counts a lot in the internet age. Feature extraction effects the accuracy of text classification. This paper proposes a method of iterative feature space evolution to optimize the result. Adjusting the extended dictionary and the stop word list, we optimize the feature space time and again to get a better classifier model. The final...
Human detection in images is a fast growing and challenging area of research in computer vision with its main application in video surveillance, robotics, intelligent vehicle, image retrieval, defense, entertainment, behavior analysis, tracking, forensic science, medicalscience and intelligent transportation. This paper presents a robust multi-posture human detection system in images based on local...
Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research....
We present industrial experience on software health monitoring. Our goal was to determine whether we can predict abnormal behavior, based on data captured from software system interfaces. To analyze the system state and predict software health problems, we used Support Vector Machine (SVM) based analysis. To train the SVM, we exploited random testing with feedback and swarm testing with feedback to...
Automatic diagnosis of electrocardiogram (ECG) signal is significant for timely and accurate diagnosis of heart diseases like arrhythmia. Several researchers have proposed different methods in last two decades. In this work we have employed a global ECG beat classification approach based on transformed features like discrete cosine transform (DCT) and discrete wavelet transform (DWT) rather than conventional...
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