As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.

As a result of the researches, it has been revealed that heart attack is the number one cause of death in the world. This problem will continue to increase, especially today and in the future. In this study, a heart attack was predicted by considering the factors affecting heart attack. Due to the uncertain conditions in heart attack, the fuzzy logic method, which is frequently used in healthcare, was used and expert opinions were taken into account in the model created. 576 rules were defined using the Mamdani fuzzy inference method. The study was tested with 10 patient data and the results were compared with the actual values. In addition, multiple regression analysis was performed, variables that had a significant effect on heart attack were determined, and the relationship between dependent and independent variables was examined. It was shown in the study that dependent variables explained the independent variable by 41.9% thanks to the multiple regression analysis. The regression equation obtained in line with these results significantly predicted the heart attack and the effect levels of the independent variables were determined.

Increasing customer satisfaction by producing quality products at a low cost is an important issue for businesses. For this reason, companies have to choose suppliers that will best meet their and their customers’ requests. Besides, many factors such as increasing the importance given to the environment, increasing the recycling rate of the products and ensuring sustainability are in an important position in the selection of suppliers. This has created the concept of green supplier selection. Businesses now prefer suppliers that carry out production considering green factors. In this study, green supplier selection was performed for a company producing steel doors. Alternative suppliers of the steel sheet raw material used in the company and the green criteria taken into account when choosing these suppliers were determined. Afterward, the company’s managers were interviewed and an evaluation and comparison of criteria and suppliers were carried out. The fuzzy analytic hierarchy process method was used to determine the weights of the determined criteria. The fuzzy multi-objective optimization by ratio analysis method was used in the evaluation and selection of suppliers. As a result of the study, the suppliers were listed as T1 > T2 > T4 > T5 > T6 > T3. It was decided that the best supplier for the company is the T1 supplier.

The rapid increase of the population and the number of motor vehicles brought about the transportation problem today. It has brought the efforts of the operators to determine the headway of the vehicles during the day in order to minimize the waiting times of the passengers at the stops and increase the satisfaction of the passengers, taking into account the passenger demand. Nowadays, especially during the current pandemic period (COVID-19), passenger demand forecasting becomes much more significant, so that measures can be taken and headway planning can be made to adjust the social distance by identifying the number of passengers in advance. In this study, the significance of demand forecasting in the railway sector is considered, and the study tackles the issue in two stages: on line and station basis that make the study different from others. In the first stage of the study, passenger demand forecasting is made on line basis with statistical techniques such as regression analysis and simple average, the mean absolute percentage error values are calculated and compared. Regression analysis is conducted with SPSS Statistics 21.0 programme. In the second stage of the study, passenger demand forecasting is made with artificial neural network and machine learning (ML) algorithms technique on station basis and the error values (mean absolute error, BIAS, mean squared error, mean absolute percentage error, and root mean squared error) are compared. As a result of the study, while the best demand forecasting method is simple average on line basis, it is seen that the most successful and reliable results for demand forecasting on station basis are obtained through decision tree, which is one of the ML algorithms.