Moreover, by comparing the previous classification systems, the varying nomenclature of previous classifications were explained to better understand the tumor morphology and to predict the intraoperative stratification, and consequently to avoid injury of vital neural tissue during the tumor removal.
In this paper, the comprehensive methodology of the HD for multi-class classification was explained, and the enhanced recognition of Marcellus Shale lithofacies in the Appalachian Basin was also demonstrated.
Third, the proposed rotation invariance method is provided in Section 4, and classification is explained in Section 5. Fourth, the experimental results are presented in Section 6.
The remainder of the paper is organized as follows: Section 2 discusses the existing literature for the considered biometric issues; in Section 3, the framework for age, gender, and race classification is explained in detail while in Section 4, the experimental setup and results are detailed and discussed.
The wide variability in physical activity classification was explained by ostensibly subtle differences between the 12 recommendations for thresholds related to activity volume (time or energy), distribution (e.g., number of days of the week), moderate intensity cut-point (e.g., 3 vs. 4 metabolic equivalents or METs), and duration (including bout length).
Some of the important and widely used classification techniques are explained below.
The proposed feature descriptor and the classification strategy are explained in Section 6.
In Section 3 the image acquisition process and the classification strategy are explained.
Despite the flaws associated with the AKI classification, which are explained below, it seems to have some clear advantages, with at least the recognition that individual baseline creatinine concentration is a much better starting reference point.
In this section, the feature extraction, the feature selection, and the classification methods used in this study are explained briefly.
