Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically co...
Enter the Intel® Extension for Scikit-learn*, part of the Intel® AI Analytics Toolkit. In this webinar, AI technical consulting engineer Rachel Oberman discusses this library, including: How it can speed up scikit-learn in just two lines of code, delivering at least 2x better ...
In this paper, we develop a novel machine learning method for predicting student performance in degree programs that is able to address these key challenges. The proposed method has two major features. First, a bilayered structure comprising multiple base predictors and a cascade of ensemble ...
Today's modern-day machine learning data centers require complex computations and fast, efficient data delivery. The NVIDIA Mellanox Scalable Hierarchical Aggregation and Reduction Protocol, or SHARP, takes advantage of the in-network computing capabilities in the NVIDIA Mellanox Quantum switch, dramaticall...
In addition, both optimal models constructed using synthetic-added data exhibited improvement in recall and precision by more than 33.7% while predicting L-1 and L-2 during the test. Therefore, the application of synthetic data can improve detection performance of machine learning models by solving...
A parallel machine learning-based approach for tsunami waves forecasting using regression trees In order to achieve the results in a short time, the proposed approach relies on the parallelization of the most time consuming tasks and on incremental... E Cesario,D Talia,S Giampa,... - 《Comput...
This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. ...
HMLP (High Performance Machine Learning Primitives) Warning! HMLP and GOFMM are research projects and not in production. For SC'17 and SC'18 artifacts, see /artifact and our our GOFMM papers SC'17 and SC'18 for details. For fast kernels, see our GSKNN paper SC'15 for details. Readme...
Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates 来自 Elsevier 喜欢 0 阅读量: 107 作者:D Vermeulen,AV Niekerk 摘要: Conventional methods of monitoring salt accumulation in irrigation schemes require regular field visits to collect soil ...
Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energ... HA Sakr,MM Fouda,AF Ashour,... - 《Egyptian Inf...