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Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis

The aim of this study is to show that the decision rules generated from Rough Sets Theory can be used for a new relational analysis. Rough Sets Theory generally works with small datasets more than big data. If we can deal with the decision rules and its complexities, it is still possible to analyze big data with Rough Set Theory. That is why in this study the authors offer a statistical method to overdue problems which belongs to big data. According statistical methods, a lots of decision rules generated from rough sets theory become useful information. Using a real case data on the traffic accident which were taken place in USA in 2013, this paper finds the relationships between accident causation factors which may be referred to decision makers in the field of traffic.

A Novel Qualitative Identification and Control of Two Degree of Freedom Inverted Pendulum based on Ant colony system

In this study, Genetic Algorithm method is effectively applied to find a solution for exam-invigilator timetabling problem (ETP) categorized as NP-Complete. Problem consists of two phases starting from assignment of the courses to the appropriate time slots and rooms. After that invigilators are assigned to the exams. Objective of the study is to find a solution which does not have overlaps in exams nor invigilators, while invigilators requests are satisfied as much as possible. Genetic Algorithms are widely used in many areas such as optimization, design, control, scheduling, and artificial intelligence with their great robustness. Factual data from a public university in Turkey is utilized in this case study. According to results, proposed genetic algorithm model has shown a good performance in terms of assigning invigilators to the time slots by fulfilling constraints.