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Educational data mining has received much attention worldwide due to its significance in the education domain. Among a large number of the educational data mining tasks, early in-trouble student prediction is a popular one. This task focuses on identifying the students who are at risk in their study as soon as possible before the end of the permitted period of study time. For early detection, data...
Many real-world problems involve learning models for rare classes in situations where there are no gold standard labels for training samples but imperfect labels are available for all instances. In this paper, we present RAPT, a three step predictive modeling framework for classifying rare class in such problem settings. The first step of the proposed framework learns a classifier that jointly optimizes...
Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently, diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a...
Neural network technique has been recently preferred in textile sector for the prediction task because the traditional mathematical and statistical methods can be inadequate to derive complex relations within textile datasets. Meanwhile ensemble learning has become a popular machine learning approach in recent years due to the high prediction performance it provides. Therefore, this study proposes...
This paper aims to build data mining model to predict the performance of candidate teachers who apply for employment in education of high schools of Gaza Strip. We apply three classification algorithms on our dataset which are Decision Tree, Naïve Bays and KNN. Our dataset contains 8000 teacher records collected from ministry of education in Gaza Strip. Although there are a lot of researchers...
Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations,...
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...
Word prediction is an applicable task for medical purposes and it can be done by analyzing brain's activities. Functional Magnetic Resonance Imaging (fMRI) is a technique for obtaining 3D images, related to the neural activity of brain through time. By subtracting fMRI images, which are captured consecutively, brain's operation can be detected. In this paper, a novel approach, based on machine learning...
With the development of the aviation industry and the improvement of people's living standard, more and more people choose aircraft as their way of travel, but the airline adjusts the price according to the revenue management in real time. The purpose of this paper is to design different decision-making tools from the customer's perspective, and to provide customers with the relevant information needed...
A wide range of text-based artifacts contribute to software projects (e.g., source code, test cases, use cases, project requirements, interaction diagrams, etc.). Traceability Link Recovery (TLR) is the software task in which relevant documents in these various sets are linked to one another, uncovering information about the project that is not available when considering only the documents themselves...
The arrival times of buses are often hard to predict due to variation of real time traffic conditions, deployment schedules and traffic incidents. The provision of timely arrival time information is thus vital for passengers to minimize their waiting time and improve riders' confidence in the public transportation system, directly promoting more ridership. Multiple buses are commonly observed to arrive...
Tagging provides a convenient means to assign tokens of identification to research papers which facilitate recommendation, search and disposition process of research papers. This paper contributes a document centered approach for auto-tagging of research papers. The auto-tagging method mainly comprises of two processes:- classification and tag selection. The classification process involves automatic...
The design of effective financial early warning algorithm is of great significance to the financial management of the company. The weak classification algorithm can be improved to a high classification algorithm with high recognition rate through the ensemble learning. The algorithm can overcome the drawback of low classification accuracy of single classifier. Therefore, this paper combines decision...
Data about terrorist attacks in India was analysed. Several machine learning algorithms were trained on the Indian subset of the Global Terrorism Database to learn to predict the perpetrator of a terrorist attack, given data about the types of attack, target and weapon in addition to the location, year and other attributes of the event. It was found that Support Vector Machine technique gave accuracy...
The purpose of using Predictive Modeling for presumptive diagnosis of Type 2 Diabetes Mellitus based on symptomatic analysis is the optimization of the diagnosis phase of the disease through the process of evaluating symptomatic characteristics and daily habits, allowing the forecasting of T2DM without the need of medical exams through predictive analysis. The tool used was SAP Predictive Analytics...
A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction...
A large number of text data are regularly published in social networks and the media. Processing and analysis of such information is an highly required direction. This paper focuses on the way to use the entropy measure when dealing with big volumes of text data in classification. The used entropy measure stands for algorithm quality criteria when defining a class in a set of data. The work also features...
Decision tree model is one of data mining method for builds classification models in the form of a tree structure. These methods are produced various ways of splitting a data set into branch like segments that call nodes. Today, forecasting method is very importance for every side especially agriculture. Because some farmers who want to predict their crops for each semester. This paper describes about...
This paper proposes a novel ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and an application of adaptive selection of the most suitable model for each data instance. Ensemble methods, an important type of machine learning technique, have drawn a lot of attention in both academic research and practical applications,...
Software-Defined Networking (SDN) is an emerging network architecture that decouples the control plane and the data plane to provide unprecedented programmability, automation, and network control. The SDN controller exercises centralized control over network software, and in doing so, it can monitor and respond to malicious traffic for network protection. This paper proposes a threat-aware system...
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