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Öğe A New Fine-Kinney Method Based on Clustering Approach(World Scientific Publ Co Pte Ltd, 2020) Dagsuyu, Cansu; Oturakci, Murat; Essiz, Esra SaracIn this study, a new approach to Fine-Kinney risk assessment method is developed in order to overcome the limitations of the conventional method with clustering algorithms. New risk level of classes are attempted to determine with K-Means and Hierarchical clustering algorithms with using two different distance functions which are Euclidean and Manhattan distances. According to the results, K-Means algorithms have provided accurate and sensitive cluster of classes. Classes from conventional and K-Means algorithms are applied and compared to the identified risks of a workshop of a medium sized textile company. Results of the study indicate that clustering techniques are new, original and applicable way to define new classes in order to prioritize risks by overcoming the drawbacks of conventional Fine-Kinney method.Öğe A novel approach for predicting global innovation index scores(Inderscience Enterprises Ltd, 2024) Yildirim, Rabia Sultan; Ukelge, Mulayim Ongun; Essiz, Esra Sarac; Oturakci, MuratInnovation has great importance in growth models in today's economy. In the globalising world, countries that renew their product and service range are at the forefront. The way to manage innovation is to measure it. Therefore, to have measurable information, the Global Innovation Index (GII) identifies inputs and outputs that are indicators of innovation. The GII provides a global ranking for countries according to their innovation capacity. In this study, GII scores of 125 countries between the years 2013 and 2020 were estimated using the artificial neural network (ANN). Before the estimation, feature selection was performed from 61 common indicator parameters. 27 parameters that best explain the GII score were selected and used in the ANN. According to the estimated GII scores, the selected 27 parameters are sufficient to calculate the GII score and has been observed that the ANN model is sufficient to determine the approximate GII score of the countries.Öğe Artificial Bee Colony-Based Feature Selection Algorithm for Cyberbullying(Oxford Univ Press, 2021) Essiz, Esra Sarac; Oturakci, MuratAs a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.Öğe The Effects of Attribute Selection in Artificial Neural Network Based Classifiers on Cyberbullying Detection(IEEE, 2018) Curuk, Eren; Aci, Cigdem; Essiz, Esra SaracRecently, as a result of the rapid increase of information communication technologies, the use of smartphones, tablets and laptop computers has become widespread. Especially among young people, social networks have become a part of everyday life and the cyberbullying problem has arisen as a result of hiding the credentials of people in cyberspace and reaching every level. In this study was carried out assays for the detection of cybcrbullying. A total of 3469 reviews from Youtubc were tagged as positive and negative, depending on whether have contained bullying. In the analyzes, the number of features of the data set was reduced to 10, 50, 100, 250 and 500 using the minimum redundancy and maximum relevance (NIRN(R), ReliefF and recursive feature elimination (RFE) algorithms as feature selection algorithms, support vector machines (SVM), stochastic gradient descent (SGD), radial basis function (RBF) and logistic regression (LR) have been preferred as classification algorithms. As a result of the experimental studies, the use of the SGD classifier together with the RFE attribute selection algorithm resulted 0.943 F-measure value. Other quality selection algorithms did not produce as high values as RFE, but F-measure values of about 0.76 to 0.84 were obtained.Öğe Using Fuzzy Sets for Detecting Cyber Terrorism and Extremism in the Text(IEEE, 2018) Uzel, Vahide Nida; Essiz, Esra Sarac; Ozel, Selma AyseThe concept of Cyber Security (CS) has been started to be used with the development of Internet technology. Nowadays, CS has vital importance and Cyber Terror and Extremism (CTE) is one of the CS problems. Terror must be detected before terrorism comes true. In other words, people who commit the crime must be detected automatically before they move on. At this stage, what people say about some issues is very valuable because sayings can be turned into actions. The aim of this study is to use Antisocial Behavior dataset to try to detect CTE in the text contents. To detect CTE, text documents should be converted to numerical vectors which consist of numerical weights of the terms present in the text documents. Vectors are computed by using four different weighting methods in our study. These methods are the well-known binary weighting, term frequency based weighting, term frequency and inverse document frequency based weighting, and our proposed fuzzy set based weighting methods. Naive Bayes Multinomial (NBM) and Support Vector Machines (SVM) are used as classifiers to compare the performances of the weighting methods for CTE detection. Our experimental analysis shows that fuzzy set based weighting method with SVM classifier gives the best classification accuracy which reaches up to 99%.
