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Cross section area (CSA) of spinal canal has been an important indicator for lumbar spinal stenosis (LSS), which remains the leading preoperative diagnosis for adults older than 65 years. Until recently, the machine learning algorithms had been investigated in [5–7] for an automatic classification system. The automatic classification system exploited the luminance of cerebrospinal fluid (CSF) as the...
Correct incipient fault diagnosis is crucial to the health management of the analog circuits, though remaining challenging. This paper presents a novel fault diagnosis method to diagnose ordinary soft fault and incipient soft fault for linear analog circuit. Due to the presence of analog circuit noise stress and the tolerance of the components, the linear circuit responses are considered as a stochastic...
External insulation characteristic is the key issues in the design and operation process of high voltage transportation and transfer facilities, whose fundamental base is the breakdown characteristic of air gap. Applying artificial intelligence algorithm using machine learning to design external insulation of transmission line is one of the new directions in the study of external insulation. Therefore,...
Marking duplicate bugs from bug report data has the significance to reduce effort and costs of software development, maintenance and evolution. Prior work has used machine learning techniques to mark duplicate bugs but has employed incomplete knowledge which can be not very effective with the explosive growth in data volume and complexity. To redress this situation, in this paper we discover knowledge...
In this paper, we focused on the problem of automatic modulation classification of digital signals. Several useful characteristic parameters which can be used for modulation analysis are extracted from spectral correlation, for different types of modulated signals have different power spectral density functions. A density estimation approach based on Support Vector Machine (SVM) is developed. Also,...
Supervised classification techniques use labeled samples in order to train the classifier. In a hyperspectral image, usually the number of such samples is limited, and as the number of bands available increases, this limitation becomes more severe. Such consequences suggest the need for reducing the dimensionality via a preprocessing method. This reduction should enable the estimation of feature extraction...
In this paper, we propose to learn object representations with inference from temporal correlation in videos to achieve effective visual tracking. Unlike traditional methods which perform feature learning either at image level or based on intuitive temporal constraint, we employ the recurrent network with Long Short Term Memory (LSTM) units to directly learn temporally correlated representations of...
Automated classification of HEp-2 cell images is crucial for fast and accurate detection of autoimmune diseases. Recent competitions resulted in high classification rates on publicly available datasets. However, performance on low-resolution HEp-2 images typically lagged behind that of high-resolution images due to the blurring and sub-sampling of fine cellular details. Direct interpolation of low-resolution...
In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non-convex optimization to solve the latent variable. However, as the optimization objective is image-level instead of sample-level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same...
The synchronized spontaneous low frequency fluctuations of the BOLD signal, as captured by functional MRI measurements, is known to represent the functional connections of different brain areas. The aforementioned MRI measurements result in high-dimensional time series, the dimensions of which correspond to the activity of different brain regions. Recently we have shown that Dynamic Time Warping (DTW)...
Energy expenditure (EE) estimation from accelerometer-based wearable sensors is important to generate accurate assessment of physical activity (PA) in individuals. Approaches hitherto have mainly focused on using accelerometer data and features extracted from these data to learn a regression model to predict EE directly. In this paper, we propose a novel framework for EE estimation based on statistical...
Alzheimer's Disease (AD) can take different courses: some patients remain relatively stable while others decline rapidly within a given period of time. Losing more than 3 Mini-Mental State Examination (MMSE) points in one year is classified as rapid cognitive decline (RCD). This study used neuropsychological test scores and quantitative EEG (QEEG) markers obtained at a baseline examination to identify...
Excessive lung water occurs when too much water accumulates in the lung, causing breathing difficulty. Current diagnosis methods include X-rays and CT-scans. However, because of their bulk and the need for trained professionals to operate, physicians rely on auscultation for preliminary diagnosis. Recent attempts have been made to automate the auscultation process and some degree of success has been...
An enormous increase in the number of internet users which will tend to rise further, cluster-based web servers (CBWS) are experiencing a dramatic increase in web traffic. Round-robin load-balancing algorithm (RLBA), is one of the most widely used for distributing loads among the web servers due to its simplicity. However, in the case of non-uniform web traffic, RLBA load distribution is inefficient...
According to the operation of the automaton transient impact, nonlinear, non-stationary signal, a method which is based on the time-frequency characteristics and PCA-SVM automaton fault diagnosis is proposed. Firstly, this paper uses statistical analysis and overall empirical mode decomposition method to construct high dimensional mixed domain initial feature vector from the characteristics of different...
There is growing interest in social image classification because of its importance in web-based image application. Though there are many approaches on image classification, it is a great problem to integrate multi-modal content of social images simultaneously for social image classification, since the textual content and visual content are represented in two heterogeneous feature spaces. In this study,...
In this work, the core objective is to implement an automatic and reliable system for the classification of plants into two plant categories named as monocotyledonous and dicotyledonous using the microscopic images of plant stem cross sections. The system can be used for the classification of plants when a large number of new plant species are discovered and it can be applied in plant disease detection...
One purpose of using computational models in cognitive related researches is to use them as a tool to understand human's learning processes, especially focusing on understanding the processes of language development. Although connectionist computational models can simulate the learning process in a black-box way, understanding the inside rational relations is as important as the correlations between...
The University of the Philippines places a high regard on its undergraduate programs. A significant budget is allocated by the Philippine government for the university's undergraduate programs because of its impact to the country's research and development, education, governance, and other aspects. However, a number of undergraduate students drop out of the campus or shift out from one program to...
In this paper, a novel weighted multi-task joint sparse representation method is proposed for hyperspectral image classification. It is assumed that the importance of atoms in a dictionary can be weighted when they are used in sparse representation according to the similarities between tasks and classes. We utilize tasks instead of classes in pre-classification to group all samples into several clusters,...
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