Preparation For Classification Research Articles

Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, we describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, we identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

Biodiagnostic and Nomenclature Anthropogenic Soils of Azerbaijan

Morphogenetic Diagnostics, Nomenclature and Classification of Azerbaijan Soils has been prepared on the basis of the newest ideas concerning the available printing and fund materials of the field, chamber, laboratorial researches in the Institute of Soil Science and Agrochemistry of ANAS. Protection and preservation of the available soils require the soil diagnostics formation which is up to the contemporary requirements. The materials concerning the soil diagnostics, nomenclature and classification constantly expand and improve. The local soil condition and individual characters are assumed as a basis by using of the ideas and principles of WRB, FAO-UNESCO schools, Russian soil science in the given Azerbaijan soil classification preparation. The soils morphogenetic structure is assumed as a main principle in Azerbaijan soil classification. The soil is classified naturally and its morphogenetic diagnostics is given. For the first time great soil taxons (soil class, section, type) and low taxons (soil subtype, sort, type, diversity, line) which are up to the international and national requirements separated and a large diagnostics has been given. Formation of the soils contemporary diagnostics and classification concerning the International standards will be basis for the preparation of the Red Book of Soils.