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Lung sound is one of the important information in the diagnosis of respiratory disease. Many researchers have developed various algorithms to diagnose lung disease through the lung sounds. One of the parameters used as the feature of lung sound is entropy, a measure of the signal complexity in which the normal biological signal and the pathological biological signal have different complexities. Entropy...
In radar tracking, the relationship between target motion and radar measurement is nonlinear, which is described by conversion function from polar to Cartesian coordinate. In this paper, based on information entropy, a new method is presented to measure the amount of target Cartesian position information acquired from radar polar coordinate. Furthermore, by applying linear approximation and without...
Skin melanoma is one of the common and most important cancer among human beings. In recent years, a numerous procedures have been proposed to detect and analyze skin cancer. The initial screening of the skin cancer is carried out visually by a doctor. Later, the suspicious regions are recorded using a digital dermatoscope. In the proposed research work, extraction of the cancerous region from the...
Sentiment Analysis is the process of figuring out the emotions from a piece of writing that whether it is positive, negative or neutral and is used to tell the speaker's attitude. The trend, today, is to consider the opinions of a variety of individuals around the globe before purchasing an item using micro-blogging data. Customers tend to go over a lot of reviews about a particular item before buying...
Machine Learning plays very important role in processing of large amounts of structured and unstructured data. A set of algorithms can be used to get meaningful insights into the data that are helpful in making effective business decisions. Document clustering is one of the popular machine learning technique used to group unstructured data (text documents) based on its content and further analyze...
Skull stripping is an useful technique for segmenting the brain tissue which is used for analysis of neuroimaging data. Thus accurate segmentation of brain tissue by removal of non-brain tissues like skull, muscle/skin, and cerebrospinal fluid is an important task for diagnosis a disease and pre-planning for a surgery. In this paper we present a technique for segmenting the brain from skull in a synthetic...
This paper excavated the review theme of clothing products by method of association rules, and built a maximum entropy model for the reviews classification. Then this paper did experimental verification to large-scale clothing product reviews classification, which verified the practical effect that maximum entropy model had in the comment text classification problems. In the process of classification,...
Random Forests and their many variations developed to one of the most successful instruments to automatically analyse image data. One of the most crucial parts is the definition and selection of node tests within the individual trees, which among other things allow for trade-offs between accuracy and computational load. This paper discusses several different approaches to test creation and compares...
Two algorithms for building classification trees, based on Tsallis and Rényi entropy, are proposed and applied to customer churn problem. The dataset for modeling represents highly unbalanced proportion of two classes, which is often found in real world applications, and may cause negative effects on classification performance of the algorithms. The quality measures for obtained trees are compared...
In this paper we present a comparison between various statistical descriptors and analyze their goodness in classifying textural images. The chosen statistical descriptors have been proposed by Tamura, Battiato and Haralick. In this work we also test a combination of the three descriptors for texture analysis. The databases used in our study are the well-known Brodatz's album and DDSM (Heath et al...
Ensemble techniques have been widely used for improving the classification performance, and recent studies show that ensembling classifiers through multi-modal perturbation can further improve the classification performance. In this paper, we propose a selective ensemble algorithm based on multi-modal perturbation (called SE_MP). In SE_MP, we devise a multi-modal perturbation method based on sampling...
In cloud computing environment, an application is always composed of several service components. A collection of service components is called a service family, and we name the cloud service components as service family members. In this paper, we propose a solution named Icebreaker to assemble service components belonging to the same application without sniffing tenants' privacy. Icebreaker characterizes...
Parkinson's disease (PD) is a disorder of the central nervous system and about 89% of the people with PD suffering from speech and voice disorders. In this paper, we adopted a dynamic feature selection based on fuzzy entropy measures for speech pattern classification of Parkinson's diseases. To investigate the effect of feature selection, Linear Discriminant Analysis (LDA) was applied to distinguish...
Traffic identification technique is used for classification of different network protocols and applications even with detection of users' network activities. In this paper, we conduct our study on some typical users' network activities and present a traffic identification method to describe the feature about users' behaviors. We convert users' network activities information into different sequences...
In-house monitoring of elders and automatic fall detection using intelligent sensors is a very desirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. The efforts of building such systems have been spanning for decades, but there still is a lot of room for improvement. This paper proposes a novel approach to make a successful monitoring...
In this paper we describe a technique for joint estimation of head pose and multiple soft biometrics from faces (Age, Gender and Ethnicity). Our proposed Multi-Objective Random Forests (MORF) framework is a unified model for the joint estimation of multiple characteristics that automatically adapts the measure of information gain used for evaluating the quality of weak learners. Since facial characteristics...
Diabetes mellitus is a major disease spread all across the globe. Long-time diabetes mellitus causes the complication in the retina called Diabetic Retinopathy (DR), which results in visual loss and sometimes blindness. In this paper, we discuss a simple and effective algorithm for segmentation of the optic disk (OD) and bright lesions such as hard exudates from color retinal images. Color fundus...
Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities. Most anomalies in brain functioning manifest with their signature characteristics in EEG pattern. Epileptic seizure, which is a brain abnormality well-studied through EEG analysis, is an abnormal harmonious neural activity in the brain characterized by the presence of spikes in EEG. An automated...
Support Vector Machine (SVM) is a popular machine learning technique for classification. SVM is computationally infeasible with large dataset due to its large training time. In this paper we compare three different methods for training time reduction of SVM. Different combination of Decision Tree (DT), Fisher Linear Discriminant (FLD), QR Decomposition (QRD) and Modified Fisher Linear Discriminant...
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques,...
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