-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative …

integrated framework comprises four widely used supervised machine learning (ML) models of Random Forest (RF) classifier and RF … few studies have utilized machine learning to improve efficacy and effectiveness in supplier evaluation and selection. …

diagnosis is one of the best defenses against the spread of breast cancer. Machine learning (ML) tools are now available for … cancer detection and prediction. This study presents a comparative assessment of machine learning models for diagnosing …

heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors …

machine learning methods have previously been used for the early detection of heart diseases. However, existing data …-driven machine learning (ML) approaches fall short of providing efficient and accurate heart disease detection. This misdiagnosis … in the existing data-driven machine learning approaches, (2) the paper utilizes a comprehensive private dataset from the …

use machine learning approaches and big data to predict high-cost users among people with cardio‑ vascular disease (CVD … expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1 … regression models. Results The machine learning models had far better accuracy in predicting high health-cost users compared with …

use machine learning approaches and big data to predict high-cost users among people with cardio‑ vascular disease (CVD … expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1 … regression models. Results The machine learning models had far better accuracy in predicting high health-cost users compared with …

consumption and GHG emissions data were obtained from "Our World in Data" for the period 1965-2021. Then, two machine learning … emissions, highlighting the importance of reducing coal consumption. This study's originality lies in the use of machine … learning techniques to identify the key energy consumption sources driving GHG emissions in the three major economies, as well …

standard method for weight assignments This can hinder the quntification of evaluation accuracy. Machine learning (ML) can …