Search Results - "Shapley Additive Explanations"

Interpretable SHAP Analysis of Key Operating Parameters in Methane Dry Reforming.

Authors: Devasahayam, Sheila^1,2 (AUTHOR)

Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2618. 22p.

Subject Terms: *Temperature, *Shapley Additive Explanations, *Synthesis gas, *Nickel, *Machine learning

Operation Mode Identification for High Renewable Energy Penetration Power Systems Based on Planning–Operation Coupling and SHAP Interpretability.

Authors: Yang, Haotian Zhu, Shiyu Fu, Mengyu et al.

Source: International Transactions on Electrical Energy Systems. 6/11/2026, Vol. 2026, p1-16. 16p.

Subject Terms: *Renewable energy sources, *Shapley Additive Explanations, *Electric power system management, *Cluster analysis (Statistics), *Electric power systems, *Decision trees, *Energy storage

Interpretable Tree‐Based Models for Predicting Short‐Term Rockburst Risk Considering Multiple Factors.

Authors: Chen, Shuai Dou, Linming Li, Atao et al.

Source: Energy Science & Engineering. Jun2026, Vol. 14 Issue 6, p2941-2960. 20p.

Subject Terms: *Boosting algorithms, *Shapley Additive Explanations, *Risk assessment, *Machine learning, *Coal mining safety, *Random forest algorithms

Enhancing Reservoir Simulation History Matching Using SHAP Value for Parameter Range Selection.

Authors: Eremyan, Grachik Magdeev, Adel M. Al-Shargabi, Mohammed et al.

Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2285. 34p.

Subject Terms: *Shapley Additive Explanations, *Parameter estimation, *Optimization algorithms, *Sensitivity analysis, *Particle swarm optimization, *Machine learning, *Oil reservoir engineering

An explainable deep learning framework for marine gas oil price forecasting: integrating LSTM with SHAP-based feature importance.

Authors: Okasha, Samer Hassan Zaher, Hegazi

Source: Journal of Shipping & Trade. 5/2/2026, Vol. 11 Issue 1, p1-28. 28p.

Multi-source information fusion for credit risk assessment of local government financing platforms using PSO-XGBoost and SHAP.

Authors: Xu, Liang Fang, Debin

Source: Financial Innovation. 5/19/2026, Vol. 12 Issue 1, p1-37. 37p.

Transparent insights: explainable AI with machine learning classifiers for early stage of depression classification.

Authors: Islam, S. M. Rakibul Yunus, Shaykh Shakil, Rashiduzzaman et al.

Source: Telkomnika. Jun2026, Vol. 24 Issue 3, p915-925. 11p.

Subjects: Machine learning, Shapley Additive Explanations, College students, Artificial intelligence, Mental health, Classification of mental disorders, Early diagnosis

Explainable AI for Railway Track Maintenance: Integrating SMOTE and SHAP in Class‐Imbalanced Predictive Models.

Authors: Sayed, Muhammad Masoom Mustafa Rind, Touqeer Ali Ahmed, Shiraz et al.

Source: Journal of Engineering (2314-4912). 6/25/2026, Vol. 2026, p1-18. 18p.

Subjects: Railroad track maintenance & repair, Shapley Additive Explanations, Artificial intelligence, Resampling (Statistics), Prediction models, Machine learning

Projection for Ecological Carrying Capacity Based on the Interpretable CAXO Model: The Case of China.

Authors: Tang, Xiaoyan Liu, Funan Feng, Jingyu

Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1690. 27p.

Subjects: Ecological carrying capacity, Shapley Additive Explanations, Ecological forecasting, Sustainable development

Geographic Terms: China, Northwest China

A Prior Knowledge-Guided Remote Sensing Framework for Maize Yield Estimation and Spatiotemporal Interpretability Analysis.

Authors: Qi, Beisong Zhang, Xinle Chen, Lu et al.

Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1455. 29p.

Subjects: Remote sensing, Shapley Additive Explanations, Boosting algorithms, Precision farming, Machine learning, Agricultural forecasts, Spatiotemporal processes, Corn harvesting

Geographic Terms: China

Explainable machine learning for injury severity prediction in two-wheeler and light motor vehicle (LMV) single-vehicle crashes.

Authors: Sorum, N. G. Sorum, M. G.

Source: Advances in Transportation Studies. Jul2026, Vol. 69, p153-176. 24p.

Subjects: Shapley Additive Explanations, Machine learning, Motorcycling accidents, Road safety measures, Traffic accidents, Trauma severity indices

Geographic Terms: Imphāl (India), India

Explaining the impact of design choices on model quality in predictive process monitoring.

Authors: Kim, Sungkyu Comuzzi, Marco Di Francescomarino, Chiara

Source: Journal of Intelligent Information Systems. Jun2026, Vol. 64 Issue 3, p1031-1056. 26p.

Subjects: Prediction models, Shapley Additive Explanations, Artificial intelligence, Encoding, Machine learning

Data-Driven Prediction and Inverse Design of Fluoride Glasses via Explainable GA-BP Neural Networks.

Authors: Zhou, Runze Yuan, Xinqiang Zhang, Longfei et al.

Source: Materials (1996-1944). May2026, Vol. 19 Issue 9, p1685. 23p.

Subjects: Fluoride glasses, Shapley Additive Explanations, Back propagation, Design techniques, Genetic algorithms, Optical materials, Prediction models

Collision Type Prediction Involving Elderly Drivers: A Machine Learning Framework With Explainability Using Kolmogorov–Arnold Networks.

Authors: Balyagati, Philip F. Masanja, Ntemi Methusela Ngereza, Abdul S. et al.

Source: Journal of Advanced Transportation. 7/13/2026, Vol. 2026, p1-15. 15p.

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

Authors: Ye, Qian Fang, Guilin Li, Liping et al.

Source: PLoS ONE. 7/2/2026, Vol. 21 Issue 7, p1-14. 14p.

Rapid prototyping of optical lenses using the Isolation forest model for anomaly detection.

Authors: Korga, Sylwester Tomasz¹, s.korga@pollub.pl

Source: Advances in Science & Technology Research Journal; 2026, Vol. 20 Issue 5, p1-17, 17p

A high-efficiency and explainable multi-feature fusion model for tailings ash content prediction of coal flotation.

Authors: Wen, Zhiping Zhou, Maiqiang Yang, Sijie et al.

Source: International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 7, p2309-2329. 21p.

Quantitative Diagnosis of Multiple Actuator Faults in Quadrotor UAVs Using CNN-LSTM and SHAP.

Authors: Du, Ping Deng, Chao Zhong, Haihui et al.

Source: Unmanned Systems. Jun2026, p1-19. 19p.

An Explainable Artificial Intelligence Framework for the Classification of Pumpkin Seed Varieties (Cucurbita pepo L.) Using Morphological Features.

Authors: Sabzi, Sajad Daliran, Omid Pourdarbani, Raziyeh et al.

Source: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p5958, 22p

Calibrated and Explainable Gradient Boosting for Road Traffic Crash Severity Prediction: SHAP Audit and Cross-Jurisdiction Transfer Evaluation.

Authors: Alhawarat, Mohammad Alkhatib, Ahmad Nijem, Qasem

Source: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p5876, 34p