Results 21 to 30 of about 84,677 (290)

Research on a short-term photovoltaic power prediction method based on CatBoost

Zhejiang dianli, 2023
The intermittent and fluctuating generation power of PV power plants has an increasingly prominent impact on the safe， stable， and economical operation of power grids.
CHEN Haihong, YI Yongli, HUANG Shen, HAN Jingyi   +3 more
doaj   +1 more source

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach

, 2021
We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the ...
Fernández-Loría, Carlos, Han, Xintian, Provost, Foster   +2 more
core  

Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records

Reviews in Cardiovascular Medicine, 2023
Background: Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction.
Chien-Yu Chi, Hadi Moghadas-Dastjerdi, Adrian Winkler, Shuang Ao, Yen-Pin Chen, Liang-Wei Wang, Pei-I Su, Wei-Shu Lin, Min-Shan Tsai, Chien-Hua Huang   +9 more
doaj   +1 more source

SHAP Enrollment and Eligibility Activities: Implications for Process and System Modernization Under National Health Reform [PDF]

, 2012
Shares five states' experiences and best practices in using State Health Access Program grants to expand public health coverage through community-based outreach and improved eligibility and enrollment processes, as well as implications for federal ...
Brigette Courtot, Teresa A. Coughlin
core  

Soliton content with quadratic nonlinearities [PDF]

, 1999
Summary form only given. Quadratic solitons, that form through cascading in materials with second-order nonlinearities, are a topic of current intense investigation.
Artigas García, David, Mazilu, Dumitru, Mihalache, Dumitru, Pérez Torres, Juan, Torner Sabata, Lluís   +4 more
core   +3 more sources

Precise Control of Drug Release in Machine Learning‐Designed Antibody‐Eluting Implants for Postoperative Scarring Inhibition in Glaucoma

Advanced Healthcare Materials, EarlyView.
We developed a micro‐sized, biocompatible implant for postoperative sustained delivery of anti‐fibrotic antibodies in glaucoma surgery. Machine learning‐guided optimization of polymer composition, implant geometry, and porosity enabled precise control of drug release.
Mengqi Qin, Wenbing Jiang, Kai Xin Thong, Zeynep Ulker, Brihitejas Patel, Cynthia Yu‐Wai‐Man   +5 more
wiley   +1 more source

The economic explainability of machine learning and standard econometric models-an application to the U.S. mortgage default risk

International Journal of Strategic Property Management, 2021
This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements.
Dong-sup Kim, Seungwoo Shin
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Design and prediction of high optical density photovoltaic polymers using machine learning-DFT studies

Open Physics
This research provides a machine learning (ML) method to predict the optical density of photovoltaic (PV) polymers. The results show that extra gradient boosting regressor and random forest regressor are the best-performing models among all the tested ML
Aljaafreh Mamduh J., Hassan Abrar U.
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source