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E Learning courses are much in demand in recent times. The need to study student's performance and predicting their performance is increasing along with it. With the growing popularity of educational technology, various data mining algorithms suitable for predicting student performance have been reviewed. The best algorithm depends on the nature of prediction the faculty wants to make. As the amount...
Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including:...
The article describes the possibilities of parallelization algorithms of decision trees and their implementation in programming languages that use the features of the functional programming paradigm. They allow simplifying transformation of sequential algorithm to its parallel form. As example we consider the C4.5 algorithm and comparison of its implementation in Java 7 and Java 8 programming languages...
Breast cancer is one of the leading cause of death for women today and it is the most common cancer in developed countries. The cause and degree of the breast cancer are very much associated with the malfunctions of its tissues and cells. It is very hard and rigorous task for the doctors to observe the clinical records for many affected patients and regulate the therapy manually. Therefore, it is...
In software engineering, information retrieval which is also referred as data mining has attracted many researcher's attention. By the virtue of its definition, data mining is responsible for extracting relevant data from large volume of database or dataset. In this context, several techniques have been proposed in literature. Through this paper, an attempt to comparative analysis of various classification...
Breast cancer is a major threat for middle aged women throughout the world and currently this is the second most threatening cause of cancer death in women. But early detection and prevention can significantly reduce the chances of death. An important fact regarding breast cancer prognosis is to optimize the probability of cancer recurrence. This paper aims at finding breast cancer recurrence probability...
The massive amount of data collected by healthcare sector can be effective for analysis, diagnosis and decision making if it is mined properly. Hidden information extracted from the voluminous data can provide help and remedy to handle critical healthcare situations. Chronic kidney disease is a fatal illness of kidney which can be prevented with early correct predictions and proper precautions. Data...
Cardiovascular disease is a worldwide health problem and according to American Heart Association (AHA), it also causes an approximate death of 17.3 million each year. Therefore early detection and treatment of asymptomatic cardiovascular disease which can significantly reduce the chances of death. An important fact regarding such life-threatening disease prognosis is to identify the patient's physical...
A standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which...
In response to globalization, International Financial Reporting Standards (IFRS) has become the norm of the global capital markets. Companies preparing financial statements using IFRS may make the financial situation fully disclosed. Nevertheless, an overestimated accrual expense of a balance sheet may not only underestimate the earnings data, but also increase the cash outflows of the statement of...
Decision tree algorithms are very popular in the field of data mining. This paper proposes a distributed decision tree algorithm and shows examples of its implementation on big data platforms. The major contribution of this paper is the novel KS-Tree algorithm which builds a decision tree in a distributed environment. KS-Tree is applied to some real world data mining problems and compared with state-of-the-art...
This paper based on the analysis of the basic meaning in data mining and the structure of decision tree uses the decision tree algorithm — C4.5 to establish a soil quality grade prediction model and combines the soil composition in Lishu to be a training sample. C4.5 algorithm also expresses the acquired knowledge by means of quantitative rules. The experiment results manifest that the expression...
Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims...
Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of...
In recent years, the fast development of mobile, wireless communication and sensor technologies has provided new possibilities for supporting learning activities. Ubiquitous learning, which is learning that can take place anywhere and anytime, is the best example. In order to provide learners with adequate learning experience, factors such learner's characteristics and context should be considered...
Data mining is the procedure of breaking down data from unlike perspectives and resuming it into useful information. It is very important in the field of classification of the objects. It has been fruitfully applied in expert systems to get knowledge. We can determine appropriate classification of unknown objects according to decision tree rules by applying inductive methods to the given values of...
An optimization classification algorithm for MRI images of premature brain injury is introduced. Based on the shortcomings of the classical ID3 algorithm in dealing with the continuous attributes of medical image, the new algorithm selects the testing feature by comparing the information gain ratio and adds the handling methods for filling null values. Then it discrete the continuous attributes by...
Extraction of relevant Information from data Is a challenging task. Many times an analyst may end up with an erroneous classifier because of huge, redundant, unreliable and noisy data. It may also be due to misinterpretation of results and usage of inappropriate techniques for a specific situation. In our study, we have investigated the two main approaches in data mining which are Decision Tree (J48...
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
In this paper we introduce three main features extracted from Moodle logs in order to be uses a possible means to predict future student grades. We discuss the statistical analysis on these features and show how they cannot be applied isolatedly to model our data. We then apply them as a whole and use principal component analysis to derive a decision tree based on the features. With derived tree we...
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