Tranquilizer misuse is a behavior associated with substance use disorder. As of now there is only one published article that includes a predictive model on misuse of subscription tranquilizers. The aim of this study is to predict ongoing tranquilizer misuse whilst comparing three different methods of classification; (1) regularized logistic regression, (2) adaptive boosting and (3) support vector machines. Data from the National Survey of Drug Use and Health (NSDUH) from 2019 is used to predict misuse among the individuals in the sample from 2020. The regularized logistic regression and the support vector machines models both yield an AUC of 0.88, which is slightly higher than the adaptive boosting model. However, the support vector machine model yields a higher level of sensitivity, meaning that it is better at detecting individuals who misuse. Although the difference in performance between the methods is relatively small and is most likely caused by the fact that different methods perform differently depending on the characteristics of the data.

Road extraction from satellite or aerial imagery is a popular topic in remote sensing, and there are many road extraction algorithms suggested by various researches. However, the need of reliable remotely sensed road information still persists as there is no sufficiently robust road extraction algorithm yet. In this study, we explore the road extraction problem taking advantage of the multi-resolution analysis and adaptive boosting based classifiers. That is, we propose a new road extraction algorithm exploiting both spectral and structural features of the high resolution multi-spectral satellite images. The proposed model is composed of three major components / feature extraction, classification and road detection. Well-known spectral band ratios are utilized to represent reflectance properties of the data whereas a segmentation operation followed by an elongatedness scoring technique renders structural evaluation of the road parts within the multi-resolution analysis framework. The extracted features are fed into Adaptive Boosting (Adaboost) learning procedure, and the learning method iteratively combines decision trees to acquire a classifier with a high accuracy. The road network is identified from the probability map constructed by the classifier suggested by Adaboost. The algorithm is designed to be modular in the sense of its extensibility, that is / new road descriptor features can be easily integrated into the existing model. The empirical evaluation of the proposed algorithm suggests that the algorithm is capable of extracting majority of the road network, and it poses promising performance results.