1,720,967 research outputs found
Design of a hybrid system for the diabetes and heart diseases
No full textData can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites. (C) 2007 Published by Elsevier Ltd
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
No full textIn the present study, emotion recognition from speech signals was performed by using the fuzzy C-means algorithm. Spectral features obtained from speech signals were used as features. The spectral features used were Mel frequency cepstral coefficients and linear prediction coefficients. Certain statistical features were extracted from the spectral features obtained in the study. After the selection of the extracted features, cluster centers were identified by using type-1 fuzzy C-means (FCM) algorithm and used as input to the classifier. Supervised classifiers such as ANN, NB, kNN, and SVM were used for classification. In the study, all seven emotions of the EmoDB database were used. Of the features obtained, FCM clustering was applied to Mel coefficients and obtained clusters centers were used as input for classification. The results showed that using FCM for preprocessing aim increased the success rate. The comparison of the classification methods showed that the maximum success rate was obtained as 92.86% using the SVM classifier.Selcuk University Scientific Research ProjectsSelcuk University; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)The authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects. The authors also thank TUBITAK for their support of this study
Extracting rules for classification problems: AIS based approach
No full textAlthough Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results in most cases may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. In our previous work, a hybrid neural network was presented for classification (Kahramanli & Allahverdi, 2008). In this study a method that uses Artificial Immune Systems (AIS) algorithm has been presented to extract rules from trained hybrid neural network. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Cleveland heart disease and Hepatitis data. The proposed method achieved accuracy values 96.4% and 96.8% for Cleveland heart disease dataset and Hepatitis dataset respectively. It is been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites. (C) 2009 Published by Elsevier Ltd.Selcuk UniversitySelcuk UniversityThis study is supported by the Scientific Research Projects Unit of Selcuk University
EVOLVING RULES FROM NEURAL NETWORKS TRAINED ON BINARY AND CONTINUOUS DATA
No full textAlthough an Artificial Neural Network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from an ANN need to be formed to solve this problem and various methods have been improved to extract these rules. In this study, a new method that uses an Artificial Immune Systems (AIS) algorithm has been presented to extract rules from a trained ANN. The suggested algorithm does not depend on the ANN training algorithms; also, it does not modify the training results. This algorithm takes all input attributes into consideration and extracts rules from a trained neural network efficiently. This study demonstrates the use of AIS algorithms for extracting rules from trained neural networks. The approach consists of three phases: 1. data coding 2. classification of the coded data 3. rule extraction Continuous and noncontinuous values are used together in medical data. Regarding this, two methods are used for data coding and two methods (binary optimisation and real optimisation) are implemented for rule extraction. First, all data are coded binary and the optimal vectors are decoded and used to obtain rules. Then nominal data are coded binary and real data are normalized. After optimization, various intervals for continuous data are obtained and classification accuracy is increased
An Application of Weighted Fuzzy Soft Set Based Decision Making
No full text7th International Conference on Application of Information and Communication Technologies (AICT) -- OCT 23-25, 2013 -- Baku, AZERBAIJANIn recent years decision making methods have become very important to solve some problems in economics, business, finance and etc. In this paper soft sets and fuzzy soft sets were briefly introduced and then they were applied to hotel choosing problem.Minist Educ Azerbaijan, Minist Commun & Informat Technologies, Qafqaz Univ, Baku State Univ, Lomonosov Moscow State Univ, Baku branch, Azerbaijan Tech Univ, ANAS, Inst Informat Technol, SOCAR, IT & Commun Dept, Informat Technol Internationalizat Res Ctr, Inst Elect & Elect Engineers, IEEE Comp Soc Azerbaijan Chapter, State Oil Co Azerbaijan Republ, Azercell Telecom LLC, MiKRO Bilgi Kayit Dagitim A S, Turkish Cooperat & Coordinat AgcyScientific Research Projects Unit of Selcuk UniversitySelcuk UniversityThis study is supported by the Scientific Research Projects Unit of Selcuk University
Emotion Recognition via Agent-Based Modelling
No full text25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYEmotion recognition is one of the most popular research areas in recent times. Emotion recognition is also made from facial expressions and sound signals, as can be done biomedical signals. Especially when face-to face communication is not possible, emotion can he recognized from the sound data. In this study, emotion recognition was performed from the sound data. One of the most important steps in feeling recognition is feature selection. Feature selection can be done in many different ways. In this study, a new agent-based approach to emotion recognition is presented. The agent-based modeling features were then selected by opt-ainet optimization method. The goal is automatic selection of features that give the best classification accuracy.Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Uni
Classification Rule Mining Approach Based on Multiobjective Optimization
No full text2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYIn this paper, a novel approach for classification rule mining is presented. The remarkable relationship between the rule extraction procedure and the concept of multiobjective optimization is emphasized. The range values of features composing the rules are handled as decision variables in the modelled multiobjective optimization problem. The proposed method is applied to three well-known datasets in literature. These are Iris, Haberman's Survival Data and Pima Indians Diabetes Datasets obtained from machine learning repository of University of California at Irvine (UCI). The classification rules are extracted with 100% accuracy for all datasets. These experimental results are the best outcomes found in literature so far.IEEE Turkey Sect, Anatolian Sc
Rule extraction from trained adaptive neural networks using artificial immune systems
No full textAlthough artificial neural network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing it serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. Activation function is critical as the behavior and performance of an ANN model largely depends oil it. So far there have been limited studies with emphasis oil setting a few free parameters in the neuron activation function. ANN's with such activation function Seem to provide better fitting properties than classical architectures with fixed activation function neurons [Xu, S., & Zhang, M. (2005). Data mining - An adaptive neural network model for financial analysis. In Proceedings of the third international conference on information technology and applications]. In this study a new method that uses artificial immune systems (AIS) algorithm has been presented to extract rules from trained adaptive neural network. Two real time problems data were investigated for determining applicability of the proposed method. The data were obtained from University of California at Irvine (UCI) machine learning repository. The datasets were obtained from Breast Cancer disease and ECG data. The proposed method achieved accuracy values 94.59% and 92.3% for ECG and Breast Cancer dataset, respectively. It has been observed that these results arc one of the best results comparing with results obtained from related previous studies and reported in UCI web sites. (c) 2007 Elsevier Ltd. All rights reserved.Selcuk UniversitySelcuk UniversityThis study is supported by the Scientific Research Projects Unit of Selcuk University
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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