Using random forests models in this large sample of college students we found that four main baseline variables predicted STB at 12-month: suicidal thoughts at baseline, trait anxiety, depression symptoms, and self-esteem. The model including these variables showed good predictive performance (AUC ...
Our deep learning models are capable of predicting household size at personal level, as seen in Figure 1 above. The U.S. Census provides average family size information (Figure 2) for large regions of several hundred homes, known as tracts. The U.S. Census also provides household size distr...
A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action 来自 NCBI 喜欢 0 阅读量: 177 作者:S Abadi,WX Yan,D Amar,I Mayrose 摘要: Author summary The CRISPR-Cas9 system, a microbial adaptive immune system, was recently exploited ...
Two machine learning models were developed to differentiate three types of AAA (Normal AAA:5.4cm). Stage 1 model is trained to detect the presence of AAA, differentiating between normal and abnormal AAA (AAA size<3cm vs≥3cm). Stage 2 model is trained to differentiate between small and large...
A comparative analysis between the Decision Tree and Random Forest machine learning algorithms, evaluated using performance metrics such as F1-score, revealed that the latter is more effective at predicting pallet collapse. This study is pioneering in identifying new critical predictive variables, ...
Energy scenarios, relying on wide-ranging assumptions about the future, do not always adequately reflect the lock-in risks caused by planned power-generation projects and the uncertainty around their chances of realization. In this study we built a machi
The advances in the machine learning approach help overcome the challenges of predicting current traditional thermal indices in a real-time environment. The ... TMS Kumar,CP Kurian - 《Journal of Ambient Intelligence & Humanized Computing》 被引量: 0发表: 2023年 Review on Gaps and Challenges in...
machine learning models and compare the results to those found in prior studies.Results:The accuracy,recall and area under the curve for the random forest model in our study is significantly higher than those of other studies.Machine learning models,particularly the random forest algorithm,can ...
Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning Nowadays, recognizing and predicting students learning achievement introduces a significant challenge, especially in blended learning environments, where o... CG Nespereira,E Elhariri,N El-Bendary,... - ...
Subtle features in people’s everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis i