[PDF] Top 20 Classification of Disease Data Using Bijective Soft Set
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Classification of Disease Data Using Bijective Soft Set
... world data play a important role for understanding and manipulating any ...that data is too large to keep. If we classify the data means the data size can be reduce like uncertain data ... See full document
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COMPARISON OF CLASSIFICATION TECHNIQUES ON HEART DISEASE DATA SET
... from data, Data Mining is an analytic process designed to explore ...data. Classification analysis is one of the main techniques used in Data ...The classification method ... See full document
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Classification Of Outliers For Predicting The Heart Disease Using Distributed Data Mining With Ai
... medical data management to drug discovery programs for complex human diseases including cancer and neurodegenerative ...Big data analytics will march towards a predictive ...existing data (such as ... See full document
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Heart Disease Prediction Using Data Mining Classification
... Heart disease for male patients using data mining ...preprocessed data set consists of 210 records, which have all the available 8 fields from the ...three data mining ... See full document
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Prediction of Heart Disease using Soft Computing and Data Mining
... solve data mining applications. Neural Network is a set of processing units when assembled in a closely interconnected network, offers rich structure exhibiting some features of the biological neural ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... Since the equation (6) does not result in closed form solution, the estimator of Β and Ψ can be determined by the iteratively process. Liu and Rubin [3] used the EM algorithm to obtain the maximum likelihood estimator ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... the classification performance of any single ...highest classification accuracy, ensemble methods have suffered significantly from their large volume of base ...novel soft set based method to ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... Most of Content Based Image Retrieval (CBIR) system use global texture features for representing and retrieving images. If local texture features are ignored during the initial stage of image processing, the performance ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... a set of various practices related con agile testing, each of them with their own features, knowledge that motivates to identify the existing scientific literature in specialized databases about this subject in ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... problems. Data mining is the process of extracting knowledge from database with statistics and optimal retrieval ...of data mining is enhancing day by day, therefore mining information from a huge database ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... EPO is an ontology designed to support the semantic annotation of epidemiology resources, data integration, information retrieval and knowledge discovery activities in epidemiology domain. It follows the OBO ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... determined using the method of Fuzzy Multi Attribute Decision Making (FMADM) to generate the best alternative boarding house and know the criteria which became the characteristics and behavior of those searchers ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... of data transfer from medical simulators by various ...unified data system. This target is based on developed and documented binary data format, where data are presented in the form of coupled ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... training data, but it never be tested entirely on DDI2013 ...the classification step, we developed a new method that uses 4 binary classifiers in sequence to exploit the lexical field particularity of each ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... Figure 6 shows the Total Network Load for Baadachi Approach with attack, OLSR and SOLS with attack. With the increasing simulation time, all three test cases showed an increased Total Network Load when the simulation ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... 4.2 Results of Statistical Analysis 4.2.1 Reliability and validity test Validity is an indication of an instrument’s ability to measure what it claims to and reliability is the reliability and consistency of a survey. In ... See full document
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ENSEMBLE SELECTION AND OPTIMIZATION BASED ON SOFT SET THEORY FOR CUSTOMER CHURN CLASSIFICATION
... The study was conducted by using search engine like Scopus, IEEEXplore, ACM Digital Library, Science Direct, Google Scholar, Springer, Wiley InterScience and Citeseer Library database. The search string used were ... See full document
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Mammogram images classification based on fuzzy soft set
... of soft set and preliminary concept of fuzzy soft set theory; afterwards, it looks at how digital mammography and early breast cancer detection led to the growth of the computer aided ... See full document
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Classification Experiments for Number Plate Recognition Data Set Using Weka
... WEKA the open source software provided under the General Public License. Where the System was developed at University of Waikato(New Zealand). WEKA mainly stands for the Waikato Environment for the purpose of Knowledge ... See full document
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Classification of tb disease diagnosis using anfis
... Based on approach of error correction learning, Back propagation is a systematic method for training, provides a computationally efficient method for changing the synaptic weights in the neural network, with ... See full document
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