The Evaluation of Risk of Substance Abuse Among The Youth through Bayesian Classification

In recent years, the rising use of addictive drugs and substances has become one of the biggest social problems around the world. The illicit use of a variety of drugs appears to be increasing among elementary and high schools students in Turkey. Therefore, it can be said that there is a big rising risk for the youth: substance abuse and addiction. There are many reasons leading students to be an addicted user. At first an adolescent cannot see the bad sides and realize the harmful effects of the substances. After being a drug abuser, this person struggles with the addiction and his/her life gets worse. Scientific studies show that it becomes very difficult for aperson to get rid of this habit after being a user. Hence, preventing students from being addicted becomes an important issue. The aim of this study is to determine a young person's probability of becoming a drug user in the future by means of Bayesian classification algorithm. The study is focused on informing the educators and families about the students who entertain high risk, and taking precautions and counter measures before it is too late. As data collection method, a questionnaire is asked the elementary and high school students in Buyukcekmece district of Istanbul and to the patients of substance abuse and addiction in the hospitals. The data collected from the questionnaires are used to indicate the percentage of risk probability for each student with the aid of Bayesian classification algorithm

Efficient Underground Object Detection for Ground Penetrating Radar Signals

Ground penetrating radar (GPR) is one of the common sensor system for underground inspection. GPR emits electromagnetic waves which can pass through objects. The reflecting waves are recorded and digitised, and then, the B-scan images are formed. According to the properties of scanning object, GPR creates higher or lower intensity values on the object regions. Thus, these changes in signal represent the properties of scanning object. This paper proposes a 3-step method to detect and discriminate landmines: n-row average-subtraction (NRAS); Min-max normalisation; and image scaling. Proposed method has been tested using 3 common algorithms from the literature. According to the results, it has increased object detection ratio and positive object discrimination (POD) significantly. For artificial neural networks (ANN), POD has increased from 77.4 per cent to 87.7 per cent. And, it has increased from 37.8 per cent to 80.2 per cent, for support vector machines (SVM)