One of the most important aspects of knowledge management (KM) is to create a system that is capable of providing mechanisms and methodologies allowing the right knowledge to be at the right place and at the right person as well as at the right time within an enterprise. There have been several models developed for this purpose. The main objectives of these models are to organize the knowledge activities to increase competitive advantage and turn the market share into a continuous and permanent superiority through utilizing the intellectual capital of the enterprise for better products and services. When existing models are carefully analyzed, it can be clearly seen that every model addresses different aspects of KM. While some of these models have been intensifying on the use of technology and production of knowledge, the others rather focus on the utilization of knowledge itself. Although these models point out the importance of managerial participation, they are mainly designed to be facilitated at operational levels. It is now obviously seen that there is a need for a new model that will deal with KM at strategic, tactic, and operational levels in an integrated manner. In this study, an enterprise knowledge management model (EKMM) is developed to facilitate this. The model is also called the "Knowledge Tower" due to its dynamics and tower-like infrastructure. EKMM does not only deal with utilizing the knowledge but also create KM strategies, knowledge culture as well as respective leveraging activities. It provides an extensive assessment capability to make sure that the KM practices are carried out as effectively as possible. This surely helps identifying the lack of implementations and areas requiring improvements.

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.

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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.

The automotive sector, today, is a key sector for developed and developing countries. A powerful automotive sector is one of the common characteristics of industrialised countries. Two significant problems of a genuine production environment are unknown demand and unbalanced production times. These two parameters impact the semi-finished and finished product inventory levels which cause an increase in the total cost of production systems. Forecasting the possible demand for automobiles has gained importance in this sense in recent years. In one of Turkey's leading automobile companies operating in the provice of Sakarya, the number of orders for future months is estimated over the number of orders for past months while determining the number of automobile sales. In this study, it was aimed to determine this company's automobile sales by using demand forecasting methods. However, the company's managers do not want to depend on a single method while deciding on any issue. To this end, time series analysis, causal methods and artificial neural networks were used to chieve demand forecasting. The method that makes the best estimation will be used for this company by comparing these methods. Considering the forecasts to be made using this method, it was aimed to establish a firm base for the annual budgets and main production plan of the company. By using this method, the company will be able to better predict some of its policies and production plans about the automotive sector by predicting the numbers regarding sales in advance.