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False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting...
This paper provides a review of research on the application of data mining techniques for decision making in agriculture. The paper reports the application of a number of data mining techniques including artificial neural networks, Bayesian networks and support vector machines. The review has outlined a number of promising techniques that have been used to understand the relationships of various climate...
Authorship identification is a problem of data mining and classification. There are numerous methods and algorithms have been published to understand its nature. Although, researchers still investigate best and simple solutions due to its heterogeneous and multilingual characteristics. This study introduced new authorship identification process based on artificial neural network (ANN) model using...
Cardiovascular risk prediction is a vital aspect of personalized health care. In this study, retinal vascular function is assessed in asymptomatic participants who are classified into risk groups based on Framingham Risk Score. Feature selection, oversampling and state-of-the-art classification methods are applied to provide a sound individual risk prediction based on Retinal Vessel Analysis (RVA)...
Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition...
In this paper a universal, coarse-grained reconfigurable architecture for hardware acceleration of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs) is proposed. Using proposed architecture, two versions of DTs (Functional DT and Axis-Parallel DT), two versions of SVMs (with polynomial and radial kernels) and two versions of ANNs (Multi Layer Perceptron and...
Intensive Care Unit (ICU) admission is a major factor that affects the healthcare budget. ICU cost is extremely high because its resources are consumed through highly advanced equipment providing quality healthcare service for patients. Thus, the need for a predictive model for the decision to transfer stroke in-patients to the ICU is very important. Also, this predictive model will help to lower...
Opinion Mining is the task of extracting opinions from a sentence on an instance. In this paper, we have collected the quotations from websites of three popular newspaper dailies of India - The Hindu, The Times of India and Deccan Chronicle on an instance. We have proposed a methodology to annotate, label and calculate subjectivity and objectivity of quotations. Using Supervised Learning Classifiers...
In these days, chronic diseases are the imperative reason for death in the world. Therefore, there is a noteworthy increment in consideration being paid to individual wellness as a preventative methodology in healthcare. However, creating and building a prediction model for chronic diseases is an extraordinary change to healthcare technology on the premise of data-analysis and decision-making level...
Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’...
Publications of financial news articles impact the decisions made by investors and, therefore, change the market state. It makes them an important source of data for financial predictions. Forecasting models based on information derived from news have been recently developed and researched. However, the advantages of combining different categories of news articles have not been investigated. This...
Financial fraud detection is an important problem with a number of design aspects to consider. Issues such as algorithm selection and performance analysis will affect the perceived ability of proposed solutions, so for auditors and researchers to be able to sufficiently detect financial fraud it is necessary that these issues be thoroughly explored. In this paper we will revisit the key performance...
In medical institutions, communication between a doctor and a patient or between persons has been regarded as more important. However, it may be difficult for infants, elderly persons or disabled persons to communicate directly their intentions. In this case, an unexpected trouble may occur by the discrepancy of mutual understandings. Therefore, it is thought that it is necessary to prevent discrepancies...
Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
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...
Incremental functional diagnosis is the process of iteratively selecting a test, executing it and based on the collected outcome deciding either to execute one more test or to stop the process since a faulty candidate component can be identified. The aim is to minimise the cost and the duration of the diagnosis process. In this paper we compare six engines based on machine learning techniques for...
The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences...
Posting online reviews and rating their satisfaction on purchased products has become an increasingly popular way to share the information for anonymous candidates who has interest in purchasing the product. In addition, people leave their interests and near-future purchasing plan on the web such as search history and search query volume. From this phenomenon, the prediction of sales performance is...
This work deals with an Intelligent Tutoring System (ITS) for reading comprehension. Such a system could promote reading comprehension skills. An important step towards building a full ITS for reading comprehension is to build an automated ranking system that will assign a hardness level to questions used by the ITS. This is the main concern of this work. For this purpose we, first, had to define...
Earthquakes are what happens when immediate vibrations which shake earth surface, spread as waves as a result of earth crust cracks. Earthquakes depend on variables such as the way of spreading of these waves, calculation of these waves and calculating methods, evaluations of these recorded data sets. Predicting probable earthquakes and minimizing the damages are the important factors. Decision systems...
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