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Wireless capsule endoscopy (WCE) is a revolutionary imaging technique that enables direct inspection of the gastrointestinal tract in a non-invasive way. However, viewing the large amounts of images is a very time-consuming and labor intensive task for clinicians. In this paper, we propose an automatic bleeding detection method in the WCE images. We propose a two-stage saliency map extraction method...
Dropout rates for students in correspondence and open courses are on increase. There is a need of analysis of factors causing increase in dropout rate. The discovery of hidden knowledge from the educational data system by the effective process of data mining technology to analyze factors affecting student drop out can lead to a better academic planning and management to reduce students drop out from...
Researches indicate that electroencephalography (EEG) can be used to classify data of imagined speech. It can be further utilized to develop speech prosthesis and synthetic telepathy systems. The objective of this paper is to improve the classification performance in imagined speech by selecting the features that extract maximum discriminatory information from the data. The features extracted are...
Data Mining is the process of discovering interesting patterns and knowledge from large amounts of data. One of the most important techniques of Data Mining is classification which is used for prediction purposes. In this paper, we present a novel classifier for classification in the field of Medical Data Mining. The idea is to apply the Adaptive Classifier on the sample medical dataset, and compare...
Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper...
This study investigates the predictive power of feature sets extracted from different brain structures for lateralization of the epileptogenic focus in mesial temporal lobe epilepsy (mTLE) patients based on imaging features. To this end, volumes of multiple brain structures are extracted from preoperative images of 68 unilateral mTLE patients. Our data set consists of 54 patients with visually observable...
In big data universities, an understanding of how the individual learning style and preferences interacts with the instructional medium presented is needed. In this study we examined the VARK learning style inventory using the variable-centered, person-centered and social approaches. We worked on a big “data set” which encompasses two data sources the first was LMS while the second was social media...
Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
Online Peer-to-Peer (P2P) lending has achieved explosive development recently, which could be beneficial to both sides of individual lending. In this study, a data mining (DM) approach to predict the performance of P2P loan before funded is proposed. Using data from the Lending Club, we explore the characteristics of loan and its applicant and use random forest to do the feature selection in the modeling...
The online retail industry is one of the world's largest and fastest growing industries having huge amount of online sales data. This sales data includes information about customer buying history, goods or services offered for the customers. Hidden relationships in sales data can be discovered from the application of data mining techniques. Data mining is an inter disciplinary promising field that...
Contexts and aspects have been distinguished as the significant factors in fabricating recommender systems. Most recommender systems aim at utilizing either non-contextual preferences or contextual preferences distinctly, while very few endeavors have been made to identify the significance of both. Hence an attempt has been made to study the influence of both, users' context dependent and context...
Intrusion Detection System (IDS) is used to preserve the data integrity and confidentiality from attacks. In order to identify the type of attack in IDS, different methodologies like various data mining techniques exist. But some are very time consuming and laborious. Therefore we have proposed the usage of SVM (Support Vector Machine) for classification of attack from large amount of raw intrusion...
Crime analysis is one of the most important activities of the majority of the intelligent and law enforcement organizations all over the world. Generally they collect domestic and foreign crime related data (intelligence) to prevent future attacks and utilize a limited number of law enforcement resources in an optimum manner. A major challenge faced by most of the law enforcement and intelligence...
Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction...
A large number of extreme floods were closely related to heavy precipitation which lasted for several days or weeks. Long-lead prediction of extreme precipitation, i.e., prediction of 6–15 days ahead of time, is important for understanding the prognostic forecasting potential of many natural disasters, such as floods. Yet, long-lead flood forecasting is a challenging task due to the cascaded uncertainty...
Aiming at the problem of Chinese thesaurus construction, we propose a method of using HMM to extract new terms from academic literature to expand automatically entry-words for Chinese thesaurus. This method converts the new terms extraction problem to a sequence labelling problem. It uses HMM fully integrated lexical information and syntactic information of new terms, as well as local context information,...
Decision tree has become one of the most accepted tools for mining data streams after Hoeffding tree was anticipated in the literature. The most vital point of constructing the decision tree is to find out the best attribute to split the considered node. Numerous methods to resolve this problem were presented so far, however, there are some shortcomings such that they are either mathematically not...
KNN is amongst the simplest top ten classification algorithm of data mining. Being effective and efficient it has some drawbacks which cannot be overlooked. Moreover, real world data is fuzzy in nature. To overcome this drawback fuzzy KNN was introduced which was based on fuzzy membership. But, it had large time complexity as the membership is calculated at the classification period. To improve this,...
Over recent years, the world has experienced a huge growth in the volume of shared web texts. Its users generate daily a huge volume of comments and reviews related to different aspects of their lives. In general, opinion mining/sentiment analysis refers to the task of identifying positive and negative opinions, emotions and evaluations related to an article, news, products, services, etc [1]. Arabic...
Recent developments in geotechnologies provide resources to propose innovative strategies for urban and environmental management, including remote sensing data and computational resources for processing them. With the main objective of identifying urban areas of illegal occupation, this work uses WorldView-2-sensor images and the InterIMAGE, an image interpretation software, based on knowledge, under...
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