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Medical errors and patient safety have been receiving significant attention since the landmark publication by Institute of Medicine in 2000. However, characteristics of physicians implicated in frequent medical errors were not studied systematically. We used National Practitioner Data Bank (NPDB) containing malpractice claims since 1990 to identify characteristics predictive of physicians with frequent...
NB-UVB Phototherapy is one of the most common treatments administrated by dermatologists for psoriasis patients. Although in general, the treatment results in improving the condition, it also can worsen it. If a model can predict the treatment response before hand, the dermatologists can adjust the treatment accordingly. In this paper, we use data mining techniques and conduct four experiments. The...
In order to balance the number of outbound and inbound tasks of each aisle and improve the working efficiency of the multi-tier shuttle system, three principles of storage location assignment were put forward: the principle of minimum correlation degree, the principle of equalization of product items and the principle of equalization of tasks, which were expected to be followed when storing products...
The amount of data circulating on the Internet is increasing day by day. With the increasing use of social media in particular, the importance of analyzing these data is increasing. The use of machine learning approaches to analyze large amounts of data is still popular today. Today, the social network Facebook is the most popular social networking sites. In this study, some data taken on Facebook...
Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons — first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card...
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key approach. This approach will help us to understand the depth of it even before we read...
The purpose of this study is to clarify the applicability of data-driven approach in accounting area. As the first stage, focusing on the model comparison, this paper shows the effectiveness of model selection with data mining technique for the development of earnings prediction model based on financial statement data. In accounting area, researchers have not considered the characteristic of financial...
Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results...
The intelligent logistics distribution of e-commerce is the computer technology and modern hardware equipment, software system and advanced management tools used by the logistics distribution enterprise. Data mining technology is the process of finding the probability distribution of random variables from a large number of source data. Automation of intelligent logistics system can improve labor productivity...
This paper presents a low cost mobile application (app) integrated on an Internet of Things (IoT) ecosystem, which uses varied sensor information collected by mobile devices to track and assist on the logistics of urban goods distribution processes. The proposed approach is leveraged by the trend of decreasing costs for mobile data communication in urban environments. Taking into account basic sensor...
Sentiment analysis (SA) is a process done computationally for detecting opinion as well as determining their polarity. Context dependent opinion words remains as a challenge for SA since their polarity changes according to the context in which they are used. This work proposes a new approach for solving this problem using (aspect, opinion) pairs and logistic regression (LR) model. Syntactic rules...
For the quality of the wine big data identification technology, the introduction of data mining classification algorithm, effectively according to the content of several impact compounds in wine level identification;Are introduced including the Logistic regression and BP neural network and SVM classification algorithm, in view of the three algorithms identify the modeling analysis of wine quality...
In this paper, we present DPWeka, a differentially private prototype based on a widely used data mining software WEKA, for practical data analysis. DPWeka includes a suite of differential privacy preserving computation blocks which support a variety of data analysis tasks including test statistics calculation, regression analysis, and interactive exploratory data analysis. We illustrate the use of...
Last years research gave some preliminary results in approaches to customer online purchase prediction. However, it still remains unclear what exact set of features of data instances should be incorporated in a model and is enough for prediction, what is the best data mining method (algorithm) to use, how stable over time could be such a model, whether a model is transferable from one online store...
Cardiovascular disease is one of the leading causes of death in the United States. It is critical to identify the risk factors associated with cardiovascular diseases and to alert individuals before they experience a heart attack. In this paper, we propose RFMiner, a risk factor discovery and mining framework for identifying significant risk factors using integrated measures. We provide the blueprints...
In this paper, we will propose a digital watermarking for voice signals recorded by a digital voice recorder especially in conferences. We will discuss some requirements for detecting falsifications in the voice signal, because the conference record has the probability that a conclusion is changed for only a falsification in a little time interval. Next, we will show a method of locating an altered...
In this paper, we propose a new version of the LBRW (Learning based Random Walk), LBRW-Co, for predicting users co-occurrence based on mobility homophily and social links. More precisely, we analyze and mine jointly spatio-temporal and social features with the aim to predict and rank users co-occurrences. Experiments are performed on the Foursquare LBSN with accurate and refined measurements. Experimental...
In the present work we build a formal model of an intelligent city analyser which can estimate transport, logistic and socio-economic mobility in the urban system. It can be used for personalized recommendatory control of population. Functional content of the system is based on conditions of reliable mobility. Also we offer solutions to prevent causes of unfavorable social results in urban surroundings.
This paper discusses the application and benefits of data mining techniques to construct prediction models in the field of corporate bankruptcy. It analyzes a dataset of 120 companies using different data mining techniques. Findings show that neural network is recommended as the best model to predict corporate bankruptcy. Findings also show that the proper use and selection of data mining techniques...
The present paper aims to show that neural networks should be constructed so as to increase cooperative potentiality. When the potentiality becomes higher, connections tend to be mutually reinforced and enhanced each other to realize cooperative states in neural networks. Because the cooperative potentiality increase is accompanied by increase in the strength of connection weights, we try to select...
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