Comparing the two algorithms
Comparing diagnosis of depression in depressed patients by EEG, based on two algorithms :Artificial Nerve Networks and Neuro-Fuzy Networks
... Background and aims: Depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. So, ...
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Comparing applicability of prevalent Clustering Algorithms for Document Clustering
... Clustering Algorithms are supposed to find groups of similar points in a set of ...present two algorithms, k-means and hierarchical clustering, and compare their applicability for clustering ...
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Comparing Two Grammar Based Generation Algorithms: A Case Study
... However, it is LR locally, when the siblings o f the semantic head literal are selected for expansion on the right-hand side o f a chain rule, or when a non-chain rule is evalu[r] ...
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Comparing Data Integration Algorithms
... 2.2 Data Integration Methods 2.2.1Structural Integration The purpose of structural integration is to try and resolve a variety of conflicts with regards to the structure of the schema. The schema of two data ...
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Comparing the Performance of Frequent Itemsets Mining Algorithms
... III. EXPERIMENT In this section we will compare the above mentioned algorithms on the basis of total time required and the total memory usage. There are two main types of datasets i.e. the synthetic data ...
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Comparing parameter tuning methods for evolutionary algorithms
... Setting EA parameters is commonly divided into two cases, parameter tuning and parameter control [6]. In case of parameter control the parameter values are changing during an EA run. In this case one needs initial ...
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A Workbench for Comparing Collaborative- and Content-Based Algorithms for Recommendations
... recommender algorithms is not an easy task for the very same reasons as described above: there are simply too many objective ...the algorithms output is then compared. There are two basic metrics ...
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Approximate Statistical Tests for Comparing Supervised Classication Learning Algorithms
... the two learning algorithms have very di erent \regions" of poor performance and where the error rates are close to ...learning algorithms are near the decision ...learning algorithms with ...
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Comparing different supervised machine learning algorithms for disease prediction
... learning algorithms, a labelled training dataset is used first to train the underlying algo- ...learning algorithms work to categorise diabetic and non-diabetic ...with two types of problems: ...
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A simulation study comparing aberration detection algorithms for syndromic surveillance
... One feature of Figure 3 deserves mention here. As the duration of the signal increases, sensitivity appears to fol- low either a U-shaped trend (when sensitivity is high in the early days of the outbreak) or to increase ...
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Comparing algorithms for the cow path problem with a non optimal seeker
... visited two times in a certain iteration ...again two times in iteration j + 2, again without finding it if E is ...the algorithms used in this paper, the borders of the visited interval will be ...
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Comparing the Area of Data Mining Algorithms in Network Intrusion Detection
... 3. Related Work In Information security, the machine learning techniques have become more at- tractive to researchers because of their capabilities to process large volume of data and provide classifications without ...
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Comparing data mining algorithms developed for longitudinal observational databases
... ratio, comparing the same population at two different periods of time, reduces the confounding of age, gender and medical state but still has issues as a patient’s medical state may have drastically changed ...
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Methodology for Comparing Coupling Algorithms for Fluid Structure Interaction Problems
... the two-dimensional benchmark test problem of Turek and Hron [2] as an example to examine the relative accuracy of the coupling methods studied; however, the compar- ison technique is equally applicable to more ...
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Comparing marker definition algorithms for watershed segmentation in microscopy images
... compares two different pattern recognition techniques proposed for the automatic detection of markers that allow the application of the Watershed Transform to biomedical images acquired via a ...
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Comparing algorithms, representations and operators for the multi-objective knapsack problem
... 1 Introduction The multi-objective knapsack problem (MKP) is a popu- lar test-bed with researchers developing evolutionary multi- objective algorithms (EMOs). The present paper com- pares the performance of three ...
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Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases
... 4. CONCLUSION In this study, KNN, SVM, C5.0, Logistic Regression and Neural Network were implemented on UCI dataset. Based on investigated methods, decision tree has achieved the best performance. There are different ...
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Comparing genetic algorithms and particle swarm optimisation for an inverse problem
... optimisation algorithms to create an optimum structural dynamics ...the two optimisation algorithms considered here, the particle swarm optimisation algorithm significantly outperformed the genetic ...
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Comparing Two Spreadsheets Excel
... for comparing spreadsheets or text and there. Having two workbooks or two spreadsheets excel so that it sounds like that what can work? Affiliate links and the two spreadsheets excel vlookup ...
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Comparing Means in Two Populations
... • So we see the two groups along the horizontal axis; City = “No” and City = “Yes”. The width of the groups is proportional to the sample size of each group; there are more “No” (non-cities) values so it is drawn ...
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