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In this paper, we propose a kernel low-rank multitask learning (KL-MTL) method to handle multiple features from the variational mode decomposition (VMD) domain for hyperspectral (HSI) classification. Core ideas of the proposed method are twofold: 1) a non-recursive VMD method is applied to extract various features (i.e. intrinsic mode functions (IMFs)) of the original data concurrently; 2) KL-MTL...
These days, a lot number of elderly people need health care which may cause huge financial costs, especially in formal case. Machine Learning and the profound achievements in sensing technology provide the opportunities to monitor people living independently at home and can detect a distress situation affordably. Although there are some approaches to do recognize activities for this purpose, but there...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Yield estimation is becoming a challenging task for circuits that are replicated in millions of instances on a large design (High Replication Circuits, HRC) such as SRAMs and flip flops. This is because a rare event in a circuit cell may have a large impact on the system yield. To achieve high yield in HRC, the failure probability of the individual cell is requested to be very small. Thus the number...
In this work, a simple method for separation between normal and abnormal heart sounds (Phonocardiogram) is presented. Mel-Frequency Cepstral Coefficients (MFCC) are extracted from two different datasets of heartbeats. Several Classifiers, such as, Support Vectors Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Classification Tree (CT) and discriminative analysis (DA), are used. Simulation...
A novel discriminative graph-based fusion (DGF) method is proposed for urban area classification to fuse heterogeneous features from two data sources, i.e., hyperspectral image (HSI) and light detecting and ranging (LiDAR) data. The features include spectral characteristics in HSI, height in LiDAR data, and geometry in image processing technologies like morphological profiles (MPs). Our proposed DGF...
The use of technology has grown extensively in many fields, including that of the medical sciences. However, there has not been a lot of research done in the field regarding the use of basic stance parameters to classify the presence or lack thereof of locomotive disorders. In this paper, we shall be presenting the most optimum classification algorithm for the binary classification of a variety of...
Density estimation is a fundamental part of statistical analysis and data mining. In high-dimensional domains, parametric methods and the commonly used non-parametric methods like histograms or Kernel estimators fail to perform properly. In this paper, we present computationally efficient data structures for efficient implementation of the Bayesian sequential partitioning (BSP), as a framework for...
An improved batch process fault identification approach with kernel exponential discriminant analysis (KEDA) is proposed, in which performance index based on difference degree is given to identify fault classification. This method takes the advantages of both the kernel technology and the exponential discriminant analysis technique. The proposed KEDA method shows powerful ability in dealing with nonlinear,...
Chinese traditional visual culture symbols (CT-VCSs) is formed in the tradition and has the characteristic of Chinese unique ideological and cultural connotation. It is a visual cultural heritage of Chinese culture. So the research on CT-VCSs has important practical significance. In this paper, it is mainly about the recognition and classification of CT-VCSs based on machine learning. We make use...
This paper presents an extension of a comparative study of classifier architectures for automatic fault diagnosis, with a special emphasis on the Extreme Learning Machine (ELM), with and without kernel mapping. Besides the explanation of the ELM model, an attempt is made to find theoretical hints of the excellent generalization capabilities of this model, based on the findings of Cover about dichotomies...
Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, image processing and computer vision. One of the most frequently used deep learning methods in image processing is the convolutional neural networks. Compared to the traditional artificial neural networks, convolutional neural networks do not use the predefined kernels,...
Because sparse matrix-vector multiplication (SpMV) is an important and widely used computational kernel in many real-world applications, it behooves us to accelerate SpMV on modern multi- and many-core architectures. While many storage formats have been developed to facilitate SpMV operations, the compressed sparse row (CSR) format is still the most popular and general storage format. However, parallelizing...
This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified...
Extreme learning machine, which has recently lead to gain popularity of single hidden layer feed-forward neural networks, provides a key solution for non-linear problems with least norm and least square solutions at a very low run time. In this work, it is intended to increase the success of hyperspectral image classification with using kernel extreme learning machine. For this purpose, a hybrid kernel...
Fibromyalgia (FM) is a widespread painful disease that has a 2–8% prevalence. Its diagnosis is generally performed by American College of Rheumatology (ACR) criteria. However, these criteria are subjective and their reliability is controversial. In this study, painful stimulation and Transcutaneous Electrical Nerve Stimulation (TENS) were applied to both hands of healthy controls and FM patients and...
Classification is one of the most researched issues in Machine Learning. In this study, the Lorentzian Support Vector Machine (LSVM) method is proposed that performs classification in Lorentzian space. This proposed new classifier forms a hyperplane separating the classes based on the Lorentzian metric and maximize margins between nearest points to the hyperplane according to the Lorentzian distance...
By passing of time, the size of data such as fMRI scans, speech signals and digital photographs becomes very high and it takes large amount of time for data processing. To overcome this problem, the dimensionality of data should be reduced. Whereas graph embedding introduces a successful framework for dimensionality reduction, we use it as the base of our proposed method. In this framework, similarity...
In this work, a new method for discrimination between normal and heart murmurs sound is presented. Statistical parameters, such as standard deviation (SD), are extracted from two datasets of heartbeats. Several classification technics, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), discriminative analysis, and classification tree, are used. Simulation results obtained...
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