Results 121 to 130 of about 66,452 (290)

Computational Aspects of Extending the Shapley Value to Coalitional Games with Externalities

, 2010
Until recently, computational aspects of the Shapley value were only studied under the assumption that there are no externalities from coalition formation, i.e., that the value of any coalition is independent of other coalitions in the system.
Jennings, Nicholas, Marciniak, Dorota, Michalak, Tomasz, Rahwan, Talal, Szamotulski, Marcin   +4 more
core  

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

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value. [PDF]

Entropy (Basel), 2021
Dong H, Sun J, Sun X.
europepmc   +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, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

A Review on Liao’s Dissertation Entitled “The Solutions on Multi-choice Games” and Related Publications [PDF]

In 2007, Liao finished his Ph.d. dissertation[18](Liao 2007) entitled “The Solutions on Multi-choice Games”. Chapter 1 of the dissertation mainly worked on two special cases of the H&R multi-choice Shapley value. One assumes that the weight function w(j)
Hsiao, Chih-Ru
core   +1 more source

Shapley‐value‐based forecast combination

Journal of Forecasting
AbstractThis paper puts forward a new and simple method to combine forecasts, which is particularly useful when the forecasts are strongly correlated. It is based on the Mincer Zarnowitz regression, and a subsequent determination using Shapley values of the weights of the forecasts in a new combination.
Philip Hans Franses, Jiahui Zou, Wendun Wang   +2 more
openaire   +1 more source

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

Advanced Intelligent Systems, EarlyView.
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo   +6 more
wiley   +1 more source

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models

Angewandte Chemie, EarlyView.
AI‐empowered catalysis research via integrated database platform, universal machine learning interatomic potentials (MLIPs), and large language models (LLMs). ABSTRACT The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery.
Di Zhang, Yuanzheng Chen, Chuanyu Liu, Yan Liu, Hongliang Xin, Jiayu Peng, Pengfei Ou, Hao Li   +7 more
wiley   +2 more sources

Metalearning‐Driven Inverse Optimization for Precision Microstructure Fabrication in Digital Light Processing Three‐Dimensional Printing

Advanced Intelligent Systems, EarlyView.
Metalearning‐based inverse optimization enables precise microscale three‐dimensional printing using a DLP system. Distorted structures from conventional printing are analyzed via neural network regression, which predicts optimal exposure time and mask design.
Jae Won Choi, Suhyeong Lim, Jeongsu Lee, Cheol Woo Ha   +3 more
wiley   +1 more source