Various software development life cycle models are adopted in the software domain. The selection of appropriate models is the main concern of this research since many factors affect the selection of these models. Characteristics of models, their advantages and disadvantages, in which projects they will be used, and how important the characteristics of the models have been studied in numerous research. In the scope of this research, first, the most preferred software life cycle development models, their features, and the factors that affect their selection were determined. Second, the study aims to compare the influential factors in selecting these models to determine the critical factors and rank them according to their importance level. An Analytical Hierarchy Process study was adopted and performed to achieve this comparison. Lastly, each software life cycle development model was compared to each other, and the important models were revealed in terms of each factor. According to the research results, the most critical factor in the model selection was "Project size."

With the advances in technology and its effects on our lives, people could shop easily via e-commerce websites from any place and at any time. Since online shopping environments, where sellers and buyers come together, are considered equal to traditional shopping stores, interfaces are designed from a customer-driven approach. During the design phase of such websites, to reduce the confusion level of customers, how they are affected by the colors or components should be considered. Within the scope of this study, it is aimed to understand whether there is a change in users' emotional states during the shopping process on e-commerce sites including activities such as exploring, product search, and finding and purchasing. Also, the problems experienced during the online shopping process are aimed to be identified and to propose solutions for them. In this context, the participants with different demographic characteristics experienced a real shopping process. Pulse and galvanic skin conductivity (GSR) levels were measured to determine the participants' emotional states during the shopping process. The significance of the study is that the data is collected from different channels and sample groups with different characteristics are included in the experiment. In the light of the findings, it was determined that the GSR and Pulse levels of the customers changed in tasks such as account creation and alternative product selection. On the other hand, there is no significant difference between education levels when demographic factors are evaluated. But there is a significant change in emotions between generation and gender groups in terms of GSR and Pulse levels.

Emotions affect our decisions in our daily lives in many aspects. Shopping can be an example in which emotions are effective in our decisions. Also, during the shopping process, consumers' emotions can be affected by several factors including light and layout. Consumers' behaviors are researched for marketing strategies and it is not easy to identify consumers' behaviors and emotions. In an e-commerce setting, it is required to improve product design, page design, categorization, and ease of use according to consumers' emotions and thoughts. Various methods are available to analyze users' emotions on e-commerce web pages. The current study has examined emotional changes during online shopping process in e-commerce web interface with Electroencephalography (EEG) and Facial Coding System (FACS). The data was collected by a camera and EEG sensors in an experimental setup with 32 participants having different demographic characteristics. Emotional changes were observed in participants' FACS data according to gender and tasks. On the other hand, changes in EEG data was gathered according to gender, education, and generation levels. The results can be used by the directors of the e-commerce companies to increase customer loyalty.

The aim of this study is to automatically differentiate railroads from roads obtained by a general road extraction algorithm. In high resolution aerial images, the sleepers between railroad tracks depict a distinctive texture from other elongated objects. By employing this additional cue, a new Fourier based feature descriptor is developed to distinguish railroad objects in aerial images. After local Fourier features calculation, the periodic railroad texture is locally detected in the frequency domain.