Investigation the Biomass in OECD Countries and Turkey: Comparative Analysis with Classification Algorithms

Abstract

In this study, energy and renewable energy issues were investigated. The goal of the study; Investigation of energy production in OECD countries is the identification of the factors affecting it. As per the cluster analysis conclusions; OECD countries are separated into three groups. Also; Classifying models were used to analyze the relationship between biomass and waste generation; energy use, carbon dioxide emissions, and primary energy consumption obtained in OECD countries. Regression analysis, correlation analysis, and decision tree analysis were applied. Carbon dioxide emissions have been the most influential variable on energy production from biomass and waste. The findings of the study propose to design energy policies towards sustainable goals. The study confirms the hypothesis that the economic cycle will help reduce inefficiencies in the energy sector, resulting in higher yields based on key factors identified from biomass. According to the literature, an investigation on various factors related to the field of biomass has been made. Also, this study examined and compared three important variables, which were determined differently from the literature, with classification analyzes that were not performed before. For this reason, it is a support for the literature and an original study.

Industrial White Quartz Stone Classification Using Image Processing and Supervised Learning

“…Activity Recognition (AR) is one of these areas where different sports activities such as walking, pedaling, climbing and swimming are recognized by smart wearable devices [20]. Learning algorithms paired to operate together with classification models such as k-Nearest Neighbors (kNN), Multilayer perceptron (MLP), Decision Tree (DT) and Support Vector Machine (SVM) have been proven effective in a variety of applications ranging from dry bean crops to cherry tomatoes and egg volume estimation [21,[23][24][25]. Most of the studies published in the field of vision-based product classification agree that in object categorization and grading, image resolution, sorting method and color represent the critical parameters of the process [13,[26][27].…”