Results 121 to 130 of about 25,341,143 (317)

Explainable AI for survival analysis: a median-SHAP approach

With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their interpretation strongly depends on both the summary statistic and the estimator for it, which in turn define ...
Ter-Minassian, Lucile, Ghalebikesabi, Sahra, Diaz-Ordaz, Karla, Holmes, Chris   +3 more
openaire   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

What Are the Preferences of Chinese Farmers for Drones (UAVs): Machine Learning in Technology Adoption Behavior

Drones
With the continuous advancement of sustainable agriculture, drone technology has become a focus of attention. Current research primarily relies on classical models for questionnaire surveys and analyses within specific regions, rather than implementing ...
Fanhao Yang, Jianya Zhao, Jinteng Liu, Zijia Luo, Xingchen Gu, Shu Wang   +5 more
doaj   +1 more source

Precision Diagnosis of Depression Levels via Distributed Classification and SHAP Analysis

Information Theories and Applications
This article presents a novel classification service designed for medical datasets with limited sample sizes, specifically focusing on depression assessment based on blood pressure oscillograms. The service employs multiple binary classification components, each trained to differentiate specific class separations. A key feature of the method is the use
Vladislav Kaverinskiy, Kyrylo Malakhov
openaire   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Interpretable chronic obstructive pulmonary disease identification using chest X-ray radiomics: a multicenter study

Insights into Imaging
Objectives To construct and validate a combined model integrating chest X-ray (CXR)-based radiomic features and clinical characteristics for chronic obstructive pulmonary disease (COPD) identification, while enhancing model interpretability.
Qian Zhou, Weihao Zhai, Taohu Zhou, Yi Wang, Xiuxiu Zhou, Xiaoqing Lin, Jie Li, Huawei Wu, Qi Dai, Yanqing Ma, Fangyi Xu, Hong Zhang, Yanming Ge, Li Fan   +13 more
doaj   +1 more source

XIMED: A Dual-Loop Evaluation Framework Integrating Predictive Model and Human-Centered Approaches for Explainable AI in Medical Imaging

Machine Learning and Knowledge Extraction
In this study, a structured and methodological evaluation approach for eXplainable Artificial Intelligence (XAI) methods in medical image classification is proposed and implemented using LIME and SHAP explanations for chest X-ray interpretations.
Gizem Karagoz, Tanir Ozcelebi, Nirvana Meratnia   +2 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

Building a SHAP interpretable prediction model for stroke-associated pneumonia [stroke-associated pneumonia (SAP)] in patients with aneurysmal subarachnoid hemorrhage (aSAH)

Frontiers in Neurology
BackgroundAneurysmal subarachnoid hemorrhage (aSAH) is a neurological emergency characterized by intracranial aneurysm rupture, leading to blood influx into the subarachnoid space and imposing high mortality and disability rates.
Yuqi Zhang, Yuqi Zhang, Mingjian Lin, Yun-Xiang Zhou, Xiaoyong Lin, Yingcong Wei, Bo Li, Jintang Li, Honghai Luo, Yifan Deng, Zhongzong Qin, Gang Zhu, Gang Zhu   +12 more
doaj   +1 more source