The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines’ performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, andknearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Cyberbullying 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.

Air 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.

This 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.

Öz Cyberbullying is defined as an aggressive, intentional action against a defenseless person by using the Internet, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. In this study we show the effects of feature extraction, feature selection, and classification methods that are used, on the performance of automatic detection of cyberbullying. To perform the experiments FormSpring.me dataset is used and the effects of preprocessing methods; several classifiers like C4.5, Naïve Bayes, kNN, and SVM; and information gain and chi square feature selection methods are investigated. Experimental results indicate that the best classification results are obtained when alphabetic tokenization, no stemming, and no stopwords removal are applied. Using feature selection also improves cyberbully detection performance. When classifiers are compared, C4.5 performs the best for the used dataset.

Vitamin D is among the vitamins necessary for both adults’ and children’s health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.