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Diabetic retinopathy (DR) is an eye abnormality caused by long term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR, yielding a large body of diagnostic work focused on automatic detection of MA. However, automated detection of MAs is difficult because (1) the small size...
Supervised learning approaches are widely used for driving style classification; however, they often require a large amount of labeled training data, which is usually scarce in a real-world setting. Moreover, it is time-consuming to manually label huge amounts of driving data due to uncertainties of driver behavior and variances among the data analysts. To address this problem, a semisupervised approach,...
The small companies become increasingly important in bank's lending business. But the challenge is how bank's credit assessment is made in a small amount of time and money. Compare with the big companies, the small companies often need a small amount of cash flow. They may not provide the complete certificates or documents, so that the bank has to collect information of the companies and evaluate...
This paper presents a novelty classification method based on multivariate Bernoulli naive Bayes with Dirichlet prior and hyper parameter optimization. We test the proposed method on 15-Scenes and Msrc-v2 data set by comparing with basic multivariate Bernoulli naive Bayes and SVM (Support Vector Machine). The experiments show that our method has advantages both in running time and classification precision.
Anomaly detection based on communication behavior is one of difficult problems of industrial control systems for intrusion detection. A normal communication behavior control model is established by using improved one-class SVM and a PSO-OCSVM algorithm based on particle swarm algorithm is designed to optimize parameters in this paper. This method established an intrusion detection model to identify...
Currently microblog search engines have the function to find related users according to input topic keywords. Traditional approaches rank users by their authentication information or their self descriptions (introductions or labels).However, many users may not publish the posts closely related to their certification profile. In this paper, we study the problem of identifying domain-dependent influential...
Diabetes is a worldwide public health challenge with a yearly increasing incidence. Many approaches using different machine learning classifiers have been developed for automatic diagnosis of diabetes. However, they mostly rely on a single classifier or a hybrid model to make the diagnosis decision, which might be weaker than a voted decision of multiple classifiers. In this study, we present an approach...
Early detection of melanoma, the deadliest form of skin cancer, has the potential to reduce morbidity and mortality. However, clinical diagnosis of melanoma is not trivial even for experienced dermatologists, as it is complex and subjective. Thus, it is necessary to develop an automated computer-aided diagnosis (CAD) system for melanoma, which makes objective judgments based on quantitative measures...
The identification of phyllosilicates by NASA's CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) strongly suggests the presence of water-related geological processes. A variety of water-bearing phyllosilicate minerals have already been identified by several research groups utilizing spectral enrichment techniques and matching phyllosilicate-rich regions on the Martian surface to known...
Signal sorting of low probability of interception (LPI) radar signals is extremely important in electronic support measurement (ESM) systems. Usually, there are multipath interferences in the intercepted signals, which result in the decrease of signal sorting performance of conventional methods. In this paper, we present a local ambiguity function (AF)-based signal sorting method for multipath fading...
An intelligent gas sensor is developed which used for gas concentration measurement. This device applies ARM7 kernel as the Microcontroller to measure the thin film's resistance. After calibrating the gas concentration with component's resistance, the sensor displays the gas concentration. To reduce adjunctive error which result from temperature drift, and improve the precise of the sensor, a compensational...
Network is more and more popular in the present society. Least squares support vector machine is a kind modified support vector machine for classification, which can solve a convex quadratic programming problem. Least squares support vector machine is presented to network intrusion detection. We apply KDDCUP99 experimental data of MIT Lincoln Laboratory to research the classification performance of...
In efficiency analysis of weapon system, in order to capture and represent the decision maker's preferences and then to select the most desirable alternative, sensitivity analysis method of operational effectiveness based on LS-SVM is proposed. Firstly, the principle of effectiveness evaluation method based on LS-SVM is discussed. Secondly, to extract learning samples from the MADM problem, an approach...
Subtle changes in brain tissue that reflect the pathological processes of disorders such as mild cognitive impairment (MCI) are much more difficult to observe on a patient's magnetic resonance imaging (MRI) scan than those obvious abnormalities such as large strokes or tumors. Thus, it is necessary to develop an automated computer-aided diagnosis (CAD) system which will be more efficient and accurate...
In this study, we present a systematic method for early detection of mild cognitive impairment (MCI) from magnetic resonance images (MRI) using image differences and clinical features. Early detection of MCI has pivotal importance to delay or prevent the onset of Alzheimer's disease (AD). Subjects were selected from the Open Access Series of Imaging Studies (OASIS)database and included 89 MCI subjects...
Radar emitter recognition is of great importance in modern ELINT and ESM systems. The conventional methods for emitter recognition usually use one classifier. For specific emitter recognition, there are slight differences between the feature vectors from radars with the same type. So the recognition result of single classifier is unreliable and instable. In this paper we propose a new combining method...
Radar emitter recognition plays an important role in military automated command and control system. It is a composite task that involves radar signal interception, modulation recognition, features extraction and classification. In this paper, a wavelet packet transform is used for feature extraction of unintentional modulation on pulse (UMOP). Then specific radar emitter recognition is achieved by...
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