Mobile based optical form evaluation system

Optical forms that contain multiple-choice answers are widely used both for electing students and evaluating student achievements in education systems in our country and worldwide. Optical forms are evaluated by employing optical mark recognition techniques through optical readers. High cost of these machines, limited access to them, long waiting time for evaluation results make the process hard for educationists working in cities or countries. In this study, a mobile application was developed for the educationists who own mobile phones or tablets for the purpose of evaluating students' answer sheets quickly and independent of location and optical readers. Optical form recognition, reading and evaluation processes are done on the image of student's answer sheet that is taken with the mobile phone or tablet of educationist. The Android based mobile application that we developed has a user-friendly interface, high success rate and is the first of our knowledge application that operates on mobile platforms in this field.</span

A real-time social network-based knowledge discovery system for decision making

The increasing amount of data in social networks has complicated data processing and interpretation. Therefore, intelligent decision-support mechanisms that have the ability to automatically extract meaning from data and interpret the opinions of people in real time have become inevitable. In this study, an intelligent multilingual decision support system was implemented, and a new algorithm that employs text mining and sentiment analysis techniques was developed to automatically interpret the opinions of social network users about the places they plan to visit. The system can be used as a baseline for sentiment analysis in social networks and can be adapted to build new systems. In this study, we set our main focus on Turkish language and show the applicability of our approach for other languages through the experiments for English language. The dataset required for the implementation of text mining techniques was created based on the venue recommendations shared on Foursquare social media platform. As a result, a contribution was made to the way the social network users make decisions without reading thousands of recommendations. Our results show that the developed system achieves classification accuracy of 84.49% for Turkish and 95% for English. Finally, the most liked or disliked foods/beverages are correctly identified for 107 out of 128 venues

Implementation of a web-based service for mobile application risk assessment

The Android operating system has increased in popularity and has been increasing its shares in the smart phone market. Users can carry out their daily work such as paying bills, being social, and sharing photos through mobile applications. These applications have access to sensitive information about the user, such as location, contacts, call logs, and SMS messages. However, the users have no knowledge of the applications or the personal information these applications have access to. Even if an application is not malware or does not have malicious behavior, it can compromise the security and privacy of the user by accessing the permissions and gathering sensitive personal information. In this study, we have designed and implemented a prototype of a novel fuzzy risk inference system that serves as a web based service. The system analyzes the risks related to Android-based mobile applications and performs risk scoring by taking several features into account. The system presents the user with the risks of exposure before the installation of applications on the user's device and serves as an intelligent decision support system