A machine learning-driven framework accurately predicts MPA exposure and supports individualized dosing in childhood-onset LN.
Accurate assessment of soil salinity is critical for sustainable agriculture and food security, yet remains technically challenging at fine spatial scales.
A study published in The Journal of Engineering Research at Sultan Qaboos University presents an advanced intrusion detection system (IDS) designed to improve the accuracy and efficiency of ...
Abstract: Vertical federated learning (VFL) enables a paradigm for vertically partitioned data across clients to collaboratively train machine learning models. Feature selection (FS) plays a crucial ...
A new study published in the journal Minerals sheds light on this sweeping shift. Titled Big Data and AI in Geoscience: From ...
Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
This challenge is examined in Application of AI in Cyberattack Detection: A Review, published in the journal Sensors, where researchers explore how artificial intelligence techniques, from ensemble ...
Figure 1: LASSO feature ranking and SHAP explanatory for Cases 1, 2, and 3 feature selection models. A positive SHAP value indicates a positive impact on prediction, leading the model to predict 1 ...
Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results