https://hdl.handle.net/20.500.12619/2827 Sat, 11 Apr 2026 15:26:11 GMT 2026-04-11T15:26:11Z https://hdl.handle.net/20.500.12619/6574 An Elective Course Suggestion System Developed in Computer Engineering Department Using Fuzzy Logic Adak, Muhammed Fatih; Yumuşak, Nejat; Taşkın, Harun Besides required courses which are compulsory for each student to be taken, universities also offer elective courses chosen by the students themselves. In their undergraduate study, since students are not guided about the elective courses, they lack information about the description and content of the course and generally fail to take the appropriate ones for their course of study. As a solution, using the knowledge of the previous required courses taken by the student it is possible to guide the student about elective courses appropriate for him/her. In this study, information from the transcripts of students are analyzed, and using this information a relationship is conducted between the required courses and the elective courses taken previously by the student. Rules are extracted by the help of data mining and an elective course suggestion system is developed using fuzzy logic. Successful results are obtained from the tests; it is observed that the students successful from the required courses are also successful in the related elective ones. Fri, 01 Jan 2016 00:00:00 GMT https://hdl.handle.net/20.500.12619/6574 2016-01-01T00:00:00Z https://hdl.handle.net/20.500.12619/6439 An industrial visual inspection system that uses inductive learning Torkul, Orhan; Cedimoğlu, İsmail Hakkı This paper presents an industrial visual inspection system that uses inductive learning. The system employs RULES-3 inductive learning algorithm to extract the necessary set of rules and template matching technique to process an image. Twenty 3 x 3 masks are used to represent an image. Each example consists of 20 frequencies of each mask. The system was tested on five different types of tea or water cups in order to classify the good and bad items. The system was trained using five good cups and then tested for 113 unseen examples. The results obtained showed the high performance of the system: the efficiency of the system for correctly classifying unseen examples was 100%. The system can also decide what type of the cup is being processed. Thu, 01 Jan 2004 00:00:00 GMT https://hdl.handle.net/20.500.12619/6439 2004-01-01T00:00:00Z