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Publication Index: WoSSubject: Data mining

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Now showing 1 - 3 of 3
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    Article
    Citation - WoS: 20
    Citation - Scopus: 26
    Increasing Accuracy of Two-Class Pattern Recognition With Enhanced Fuzzy Functions
    (Pergamon-elsevier Science Ltd, 2009) Celikyilmaz, Asli; Tuerksen, I. Burhan; Aktas, Ramazan; Doganay, M. Mete; Ceylan, N. Basak
    In building an approximate fuzzy classifier system, significant effort is laid oil estimation and fine tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy Clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based oil it dual optimization method, which yields simultaneous estimates of the parameters of (c-classification functions together with fuzzy c partitioning of dataset based oil a distance measure. The merit of novel IFCF is that the information oil natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results Of the new modeling approach indicate that the new IFCF is it promising method for two-class pattern recognition problems. (c) 2007 Elsevier Ltd. All rights reserved.
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    Article
    Citation - WoS: 12
    Citation - Scopus: 16
    A 30-Year Journey of Hospitality and Tourism Research: a Comprehensive Topic Modeling Analysis
    (Emerald Group Publishing Ltd, 2024) Kurt, Senol; Assefa, Feven Zewdie; Tuzlukaya, Sule Erdem; Karatepe, Osman M.
    PurposeThe purpose of this study is to provide an overview of the research conducted on hospitality and tourism articles published in Q1 category journals from 1990 to 2023. This study also aims to measure the topic prevalence in selected journals throughout the years, their change over time and similarities of journals.Design/methodology/approachLatent dirichlet allocation algorithm is used as a topic modeling method to identify and analyze topics in hospitality and tourism research over the past 30 years.FindingsThe results of the study indicate that hospitality and tourism research has recently focused on topics such as employee behavior, customer satisfaction, online reviews, medical tourism and tourist experience. However, the results also indicate a negative trend in topics such as hotel management, sustainability, profession, economic growth and tourist destination.Practical implicationsThis study can be used to examine the evolution of research patterns over time, find hot and cold themes and uncover untapped or understudied areas. This can aid academics in their investigations and practitioners in making sound strategic decisions.Originality/valueThis study contributes to the existing literature by providing a new approach and comprehensive analysis of hospitality and tourism research topics. It delineates an overview of the progression of hospitality and tourism research over the past 30 years, identifies the trending topics and explores the potential impacts that these identified topics may have on future studies.
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    Conference Object
    A Comparison of Neural Network Approaches for Network Intrusion Detection
    (Springer international Publishing Ag, 2020) Oney, Mehmet Ugur; Peker, Serhat
    Nowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.