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Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew members. The fateful incident still compel the researchers and analysts to understand what can have led to the survival of some passengers and demise of the others. With the use of machine learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set, the research...
Machine learning and Data mining techniques are rapidly establishing themselves in medical and health care fields. This paper addresses a similar issue where the fitness of an individual can be predicted by analyzing few attributes associated with that individual. A hybrid classifier algorithm is developed by merging Decision Tree and Naïve Bayes algorithms which will classify the Fitness data set...
Data mining is an advanced technology, which is the process of discovering actionable information from large set of data, which is used to analyze large volumes of data and extracts patterns that can be converted to useful knowledge. Medical data mining has a great potential for exploring the hidden patterns in the data sets of medical domain. These patterns can be utilized to do clinical diagnosis...
In this paper, we propose learning analytic tasks to understand the learning process in a smart classroom. Learning analytics can extract knowledge from a course to better understand students and their learning processes. The learning analytic tasks must evaluate different aspects in the course: the teaching and learning process, the student performance, and the pedagogical practices, among other...
Innovation in the public-sector refers to the development of important improvements in the public administration and their corresponding services. One of such public services is the social security, of which central process has been the information security of their offered services. The aim of the present study has been the analysis of the trends and the discovery of behavioural patterns in the attacks...
Mechanical and electrical equipments are widely used in industry. Existing electro-hydraulic mixing equipments mainly use expert systems for fault diagnasis. However, in order to increase the accuracy of diagnasis, the expert systems have to acquire more knowledge. And diagnosis system will bring great uncertainty due to limited knowledge. Furthermore, existing fault diagnosis system has the disadvantages...
In recent years, type II diabetes has become a serious disease that threaten the health and mind of human. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes a type II diabetes prediction model based on random forest which aims at analyzing some readily available indicators (age, weight, waist, hip, etc.) effects on diabetes and discovering some...
Hospital length of stay (LOS) of patients is an important factor for planning and managing the resource utilization of a hospital. There has been considerable interest in controlling hospital cost and increasing service efficiency, particularly in stroke and cardiac units where the resources are severely limited. This study introduces an approach for early prediction of LOS of stroke patients arriving...
This paper aims to predict the factors and build prediction models for the persuasive message changing student's attitude by applying classification techniques. We used a questionnaire to collect data such as gender, age and their satisfaction with persuasive messages, obtained from students at Khon Kaen University. The classification rule generation process is based on the decision tree as a classification...
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...
Big data applications are developed and being explored by the computer science organization, which is classified and accepted by huge data sets collected from sensor networks, online networks, medical agencies, etc. To deal with the difficulty in analysis of data, we conduct research on the novel algorithms for data mining and knowledge discovery through network property. At first, we introduce necessary...
The aim of this article is to describe the design, implementation and evaluation of the educational application to support learning of data mining algorithms. The role of the application is to help students to better understand the algorithms such as Naive Bayes classifier, decision trees and association rules. The application also includes a test area that allows students to generate and solve different...
Machine learning is a subdivision of Artificial Intelligence (AI) that is concerned with the design and development of intelligent algorithms that enables machines to learn from data without being programmed. Machine learning mainly focus on how to automatically recognize complex patterns among data and make intelligent decisions. In this paper, intelligent machine learning algorithms are used to...
In this work a decision support system (DSS) for the conversion of Unified Parkinson's Disease Rating Scale (UPDRS) motor symptoms into a Hoehn & Yahr stage representation is proposed. Accurate estimation of a Parkinson's Disease patient's Hoehn & Yahr stage is of great importance since this single value is enough to represent condition, severity of symptoms and localization and disease progression...
This decision tree is normally applicable in data mining in order to produce a framework that predicts the value of object or its dependent variable, established on the various input or independent variable. CART algorithms are mainly used in Medical, Statistics etc. For heart disease patients it is complex for medical practitioners to predict the heart attack as it is a complex task that requires...
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...
Data Preprocessing is an essential and primary step in the process of knowledge discovery; because the data obtained from the logs may be incomplete, noisy or inconsistent. The quality of the training data plays a vital role in the success of the data mining algorithms thus; Data Preprocessing should not be an exception in the process of knowledge discovery. The most promising attributes of the quality...
Data Mining is an emerging field used in educational purposes to improve the perceptive and learning method of students. It focuses on recognizing, extracting and calculating data associated to the learning method and improving student's performance. Mining in a learning field is known as educational information mining which is fretful with exploring latest techniques to find out knowledge from educational...
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...
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