Results 151 to 160 of about 44,463 (307)
A traceability model for upper corner gas in fully mechanized mining faces based on XGBoost-SHAP
Gong-kuang zidonghuaTo address the weak interpretability caused by the "black-box" structure of current gas concentration prediction models in the upper corner of fully mechanized mining faces, a gas traceability model based on XGBoost-SHAP was proposed for the upper corner
SHENG Wu, WANG Lingzi
doaj +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Exploring commuter stress dynamics through machine learning and double optimization
Mehran University Research Journal of Engineering and TechnologyTravel dynamics significantly impact commuter stress, influenced by traffic behavior, road conditions, travel modes, distance, and socio-demographic characteristics.
Ashar Ahmed +2 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed +15 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo +3 more
wiley +1 more source
Explaining ML predictions with SHAP
As machine learning models become increasingly accurate and complex, explainability has become essential to ensure trust, transparency, and informed decision-making. SHapley Additive exPlanations (SHAP) provide a rigorous and intuitive approach for interpreting model predictions, delivering consistent and theoretically grounded feature attributions ...
openaire +2 more sources
Interpretable Diagnostics with SHAP-Rule: Fuzzy Linguistic Explanations from SHAP Values
MathematicsThis study introduces SHAP-Rule, a novel explainable artificial intelligence method that integrates Shapley additive explanations with fuzzy logic to automatically generate interpretable linguistic IF-THEN rules for diagnostic tasks. Unlike purely numeric SHAP vectors, which are difficult for decision-makers to interpret, SHAP-Rule translates feature ...
Alexandra I. Khalyasmaa +2 more
openaire +1 more source
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni +11 more
wiley +1 more source