Changes in globalization and technology have made the competition in the education sector more in-tense. This change in competition has reduced the profit margins of educational organizations. In order to maintain their market share and maintain their assets under these conditions, organizations have had to put greater stress on cost management. Today, cost management has moved beyond simply being a sub-system of accounting and has become one of the main tasks of business management. Higher education organizations need to use scarce resources effectively and efficiently to achieve their goals, develop long-term strategies, and maintain their existence. The extent to which resources are used by whom and for what time should be determined correctly. The outputs obtained during this process should be checked and interpreted correctly. This study was designed as a case study and conducted in a vocational school of a foundation university in Istanbul. The aim of this study is to examine the service production process in foundation vocational school with different costing methods and to compare the results. For this purpose, the costs of the vocational school for the 2016-2017 accounting period were calculated by using both the traditional costing method and the time-driven activity-based cost-ing method. These two methods were then compared to find out their degree of accuracy in calculating the program costs.

Return on equity (ROE) and return on assets (ROA) are important indicators that reveal the sustainability of a company’s profitability performance for both managers and investors. The correct prediction of these indicators will provide a basis for the strategic decisions made by the company managers. The estimation of these signs is a significant factor in supporting the decisions and up-to-date knowledge of potential investors. In this study, return on equity and return on assets were estimated using artificial neural networks (ANNs), multiple linear regression (MLR), and support vector regression (SVR) on the financial data of thirteen companies operating in the iron and steel sector. The success of predicting ROA in the designed model was 86.4% for ANN, 79.9% for SVR, and 74% for MLR. The success of estimating the ROE of the same model was 85.8% for ANN, 80.9% for SVR, and 63.8% for MLR. It is concluded that ANN and SVR can produce successful prediction results for ROA and ROE both accurately and reasonably.

Methane gas emission into the atmosphere is rising due to the use of fossil-based resources in post-industrial energy use, as well as the increase in food demand and organic wastes that comes with an increasing human population. For this reason, methane gas, which is among the greenhouse gases, is seen as an important cause of climate change along with carbon dioxide. The aim of this study was to predict, using machine learning, the emission of methane gas, which has a greater effect on the warming of the atmosphere than other greenhouse gases. Methane gas estimation in Turkey was carried out using machine learning methods. The R2 metric was calculated as logistic regression (LR) 94.9%, artificial neural networks (ANNs) 93.6%, and support vector regression (SVR) 92.3%. All three machine learning methods used in the study were close to ideal statistical criteria. LR had the least error and highest prediction success, followed by ANNs and then SVR. The models provided successful results, which will be useful in the formulation of policies in terms of animal production (especially cattle production) and the disposal of organic human wastes, which are thought to be the main causes of methane gas emission.