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Öğe AUTOMATIC DETECTION OF CYBERBULLYING IN FORMSPRING.ME, MYSPACE AND YOUTUBE SOCIAL NETWORKS(Murat Yakar, 2019) Acı, Çiğdem İnan; Çürük, Eren; Eşsiz, Esra SaraçCyberbullying has become a major problem along with the increase of communication technologies and social media become part of daily life. Cyberbullying is the use of communication tools to harass or harm a person or group. Especially for the adolescent age group, cyberbullying causes damage that is thought to be suicidal and poses a great risk. In this study, a model is developed to identify the cyberbullying actions that took place in social networks. The model investigates the effects of some text mining methods such as pre-processing, feature extraction, feature selection and classification on automatic detection of cyberbullying using datasets obtained from Formspring.me, Myspace and YouTube social network platforms. Different classifiers (i.e. multilayer perceptron (MLP), stochastic gradient descent (SGD), logistic regression and radial basis function) have been developed and the effects of feature selection algorithms (i.e. Chi2, support vector machine-recursive feature elimination (SVM-RFE), minimum redundancy maximum relevance and ReliefF) for cyberbullying detection have also been investigated. The experimental results of the study proved that SGD and MLP classifiers with 500 selected features using SVM-RFE algorithm showed the best results (F_measure value is more than 0.930) by means of classification time and accuracy. © 2019, Murat Yakar. All rights reserved.Öğe Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey(Murat Yakar, 2023) Eşsiz, Esra Saraç; Kılıç, Vahide Nida; Oturakçı, MuratAir pollution in cities is a serious environmental issue. In Turkey, the air quality index values of the measurement stations are calculated according to European Union standards. There are many kinds of measurement parameters (features) and 6 different kinds of air quality classes according to measurement stations in Turkey. Non-valuable features can be eliminated effectively with feature selection methods without any performance loss in classification. This study aims to investigate, analyze and implement a feature selection method using the FireFly Optimization Algorithm (FOA) approach. In the study, data from measurement stations for the Zonguldak region, which is known as the most polluted region in Turkey, are obtained and analyzed. Along with the acquired data, new features have been added such as day type day slots and the Covid19 feature since it is thought that curfew restrictions have an impact on air quality. The results were compared with a filter-based feature selection algorithm namely ReliefF. Experimental results show that FOA based feature selection method outperforms the ReliefF method at classification using the Random Forest classifier for air pollution even if with a fewer number of features. The Macro averaged F-score of the data set is increased from 0.685 to 0.988 using the FOA-based feature selection method. © Author(s) 2023.Öğe Integrated AHP-FMEA risk assessment method to stainless tank production process(Murat Yakar, 2021) Çeliker, Seçkin; Eşsiz, Esra Saraç; Oturakçi, MuratThis study aims to evaluate the hazards of the stainless tank production process in a company by using the Integrated Analytic Hierarchy Process (AHP) and Failure Mode and Effect Analysis (FMEA) methods. First, the hazards in the stainless tank production process were identified. Identified hazards were assessed with the FMEA method to calculate Risk Priority Number (RPN) for each hazard. The same hazards were then weighted by using the AHP method. Finally, AHP and FMEA are integrated to achieve a more objective result by using two subjective methods. According to the integrated method results, risks have been prioritized and an objective ranking has been established for action plans. © Author(s) 2021.Öğe Malware detection: Harmony of Fuzzy and Firefly (FF-MD)(Old City Publishing, 2020) Kiliç, Vahide Nida; Eşsiz, Esra SaraçAndroid Operating System an (OS) is open-source, easy to use, and user-friendly mobile OS. In this way, it is very preferred. As a result, it becomes the target of malicious people. Applications installed on the Android OS from the Google Play Store or by third-party application providers, also known as Android Package Files (APKs), may contain malicious software. So far, a variety of analyzes and detections have been made to detect such malware. While detecting malware, good results have been obtained with various methods, but malicious people have developed methods of hiding themselves against these methods. We propose a new feature selection method based on Firefly Optimization Algorithm (FOA) with the Fuzzy Set-Based (FSB) weighting method. The proposed method performs better than traditional feature selection methods with fewer features. The experimental results of this study proved that FOA is an acceptable optimization algorithm for feature selection to detect malware in terms of classification performance and classification runtime. In addition, experimental evaluation of TFIDF and FSB weighting methods indicates the effectiveness of the FSB weighting with a full feature set. ©2020 Old City Publishing, Inc.Öğe Short-term wind power prediction with harmony search algorithm: Belen region(Murat Yakar, 2022) Eşsiz, Esra SaraçWind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction. © Author(s) 2022.
