One of the great threats of earthquakes is soil liquefaction which can put many lives in danger and damage important structures. The evaluation of soil liquefaction potential is of great importance for geotechnical engineers in order to avoid future costs. The most common approach for such an evaluation has been using historical records of soil liquefaction, gathered from different locations. Most of the historical records include SPT and CPT data. This paper focuses on the evaluation of liquefaction potential using shear wave velocity (SVW) database, including 415 site observations. Among many models for liquefaction potential assessment, ANN and SVM are popular and used by different researchers to better predict the future hazards of liquefaction. In this paper, we introduce Random Forest as a method for liquefaction assessment to classify soils with respect to their potential of liquefaction when exposed to certain earthquakes. The details of feature selection and model development is stated. It is shown that Random Forest, tested on 83 unseen cases (20% of database), is able to predict the liquefaction potential in soils with 92.77% accuracy. Also, Random Forest can provide the relative importance of the predictors, among which the normalized shear wave velocity (Vs1) obtained the highest significance score for liquefaction potential prediction.

Even though there is a growing reliance on the Internet for the promotion and sale of tourism products and services, companies' utilization of web sites is still not widespread. Consequently, web site evaluation frameworks are relatively limited. The objective of this paper is to analyze the content of the web sites of Turkish travel agencies in order to determine their adoption level of the Web as a tool of e-business. For this purpose, the authors have developed a framework based on different models available in the literature. The data was collected from 150 travel agencies by using an on-line questionnaire. The results of the survey show that most of the travel agencies that have a web site do not take full advantage of the e-business capabilities of the Internet. Almost all the respondents use their web sites to provide basic corporate information and basic product/service information, which do not require interactivity.

This study aimed to examine the level of employee satisfaction in companies where e-learning is used as a corporate training tool. For this purpose, first two levels, reaction and learning, of Kirkpatrick's classic 4-level evaluation model are used. End-user surveys are taken into consideration for the reaction level and results of the posttests are used for the learning level. Regression analyses are made to determine the effects of gender, age, work experience, education level, job level and e-learning interactivity level of the employees on the reaction-based and learning-based satisfaction of the e-learners.

The growth and popularity of social media platforms have generated a new social interaction environment thus a new collaboration and communication network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments since people create, share or exchange their information, ideas, pictures or video using them. One of these popular platforms is Twitter, which via its voluntary information sharing structure, provides researchers data potential of benefit for their studies. Based on Twitter data, in this study a multilingual sentiment detection framework is proposed to compute European Gross National Happiness (GNH). This framework consists of a novel data collection, filtering and sampling method, and a newly constructed multilingual sentiment detection algorithm for social media big data, and tested with nine European countries (United Kingdom, Germany, Sweden, Turkey, Portugal, The Netherlands, Italy, France and Spain) and their national languages over a six year period. The reliability of the data is checked with peak/troughs comparison for special days from Wikipedia news lists. The validity is checked with a group of correlation analyses with OECD Life Satisfaction survey reports’, Euro-Dollar and other currency exchanges, and national stock market time series data. After validity and reliability confirmations, the European GNH map is drawn for six years. The main problem addressed is to propose a novel multilingual social media sentiment analysis framework for calculating GNH for countries and change the way of OECD type organizations’ survey and interview methodology. Also, it is believed that this framework can serve more detailed results (e.g., daily or hourly sentiments of society in different languages).

As organizations’ investments on information systems/information technology (IS/IT) increase, the assessment methods used during IS/IT investment decision-making process holds more and more importance. Since successful IS/IT projects are key to the sustainability of an organization, identifying the factors which have effects on project success carries useful insights. In this study, 18 assessment methods are identified based on the literature. A novel classification method is proposed and assessment methods are classified into financial, strategic, and organizational categories. A novel rule-based method for determining the size of IS/IT projects is also proposed. Detailed information on project characteristics, employed IS/IT assessment methods, and project success is collected for 110 real-world IS/IT projects. The collected data is utilized in ANOVA and Regression tests to examine the factors which affect project success. Use of organization-related assessment methods, which is proposed in this study, is found to increase the success rate of the projects. Obligation towards the project and use of multi-criteria methodology have significant relationships with project success whereas project size, use of gut feeling during evaluation, and employed system developmentmethodology do not have statistically significant impacts on project success.