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The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends...
The importance of data security and confidentiality increases day by day, since for most companies and organizations data remains as the most important asset. Standard database security measures like access control mechanisms, authentication and encryption technologies are of little help when it comes to preventing data theft from insiders. By incorporating intrusion detection mechanisms, we can improve...
Autonomic nervous system (ANS) is a control system that acts largely unconsciously and regulates bodily functions. An autonomic malfunction can lead to serious problems related to blood pressure, heart, swallowing, breathing and others. A set of dynamic tests are therefore adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. These tests generate big amount of data...
For the quality of the wine big data identification technology, the introduction of data mining classification algorithm, effectively according to the content of several impact compounds in wine level identification;Are introduced including the Logistic regression and BP neural network and SVM classification algorithm, in view of the three algorithms identify the modeling analysis of wine quality...
The present paper presents a novel approach for semi-supervised classification of remote sensing imagery using {K-Means+(GMM-EM)} clustering cascade followed by selection of an amount of clustered pixels to be added to the training set according to their GMM responsibilities. The proposed method has the following steps: (a) clustering of the multispectral pixels using the cascade composed by K-means...
Image classification mainly uses the classifier to classify the extracted image features. In the traditional image feature extraction, it is difficult to set the appropriate feature patterns for the complex images. Simultaneously, the training algorithm of the classifier also affects the accuracy of image classification. In order to solve these problems, the combination of deep belief networks and...
With the explosion of Web 2.0, customers are able to share their opinions and sentiments online. This has led to new opportunities for companies and organizations to understand people's opinions towards their products or services and can serve to improve their products or market strategy more effectively. However, the data on the Web is huge and unstructured, which makes it difficult to analyze automatically...
The loyalty and retention of students in educational institutions has become one of the greatest challenges for the management area of these institutions. A promising solution to achieve this goal is the use of educational data mining to identify patterns that aid in decision making. This paper presents a proposal for the creation of temporal attributes with the purpose of helping to predict the avoidance...
Traditional methods for hyperspectral image classification typically use raw spectral signatures without considering spatial characteristics. In this work, a classification algorithm based on Gabor features and decision fusion is proposed. First, the adjacent and high correlated spectral bands are intelligently grouped by coefficient correlation matrix. Following that, Gabor features in each group...
Algorithms used in data mining techniques are of great importance in the field of health care, especially in the case of getting patterns or models that are undiscovered in databases. In the area of health care, leukemia affects the blood status and can be discovered by using the Blood Cell Counter (CBC). This study aims to predict the leukemia existence by determining the relationships of blood properties...
We present the key steps in the dynamogram classification algorithm development. These are data processing, procedures of generation and selection of features, constructing of a neural network classifier and estimation of its work quality. To estimate the possibility to single out complex defects (subclasses), we analyzed the structure of the input pattern sample with the aid of clusterization algorithms...
Nowadays, along with the development of information technologies, storage and analysis of biomedical datasets are easy in health sector. In this area, Machine Learning methods provide a great contribution for evaluation and interpretation of data. In this paper, in addition to Support Vector Machines, Decision Tree, K-Nearest Neighbors, Naive Bayes and Dictionary Learning methods, Random Feature Subspaces...
With the rapid growth of web technology there is a huge amount of data present in the web for internet users. Such data is mainly from the social media such as Facebook [4], twitter, etc., where millions of people express their views in their daily interaction which can be their sentiments or opinions about a particular thing. Large amount of data also present in the forms of reviews and ratings in...
Users of search engines interact with the system using different size and type of queries. Current search engines perform well with keyword queries but are not for verbose queries which are too long, detailed, or are expressed in more words than are needed. The detection of verbose queries may help search engines to get pertinent results. To accomplish this goal it is important to make some appropriate...
Chronic pelvic pain is a common clinical condition with negative consequences in many aspects of women's life. The clinical presentation is heterogeneous and the involvement of several body systems impairs the identification of the exact etiology of the problem. At the same time, a clinical treatment of good quality depends on the professional and the learning process is slow. The goal of this paper...
Medical image analysis is a pioneer research domain due to the challenges posed by different kinds of images and the complexities in attaining the accurate prediction of abnormalities presence. Brain MRI classification into normal and abnormal has received increasing attention because of the high level of difficulty in handling those huge numbers of images. Recently, many computational techniques...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
As there is a exponential growth of social networks and due to large usage of social media, there is a increasing demand for data in the web for the users which leads to recent trends and ideas in the field of research. The users will be eagerly using these data for the future purpose and get information about the opinions of others thus there is a need of automatic summarization of opinion of the...
Classification is one of the important tasks in Data Mining or Knowledge Discovery with prolific applications. Satisfactory classification depends on characteristics of the dataset too. Numerical and nominal attributes are commonly occurred in the dataset. However, classification performance may be aided by discretization of numerical attributes. At present, several discretization methods and numerous...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
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