Results 51 to 60 of about 4,909,083 (351)

An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP

Journal of Hydroinformatics, 2023
In contrast to the traditional black box machine learning model, the white box model can achieve higher prediction accuracy and accurately evaluate and explain the prediction results.
Tiexiang Mo, Shanshan Li, Guodong Li
doaj   +1 more source

Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods

Frontiers in Earth Science, 2023
With the sustainable development of the construction industry, recycled aggregate (RA) has been widely used in concrete preparation to reduce the environmental impact of construction waste.
Xinyi Zhang, Chengyuan Dai, Weiyu Li, Y. Chen   +3 more
semanticscholar   +1 more source

Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Information, 2019
Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun, Chang Chen, Lu Wang, Hongwei Kang, Yong Shen, Qingyi Chen   +5 more
doaj   +1 more source

Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform

ACS Central Science, 2022
Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the synthesis of organic compounds, but the limited availability of context-specific data currently necessitates experimental ...
Anirudh M. K. Nambiar, Christopher P Breen, Travis Hart, Timothy Kulesza, T. Jamison, Klavs F. Jensen   +5 more
semanticscholar   +1 more source

Fair Bayesian Optimization [PDF]

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML models. However, most of these techniques are specialized to cater to a single family of ML models and a specific definition of fairness, limiting their adaptibility in practice ...
Perrone, Valerio, Donini, Michele, Zafar, Muhammad Bilal, Schmucker, Robin, Kenthapadi, Krishnaram, Archambeau, Cédric   +5 more
openaire   +2 more sources

Deep-Learning-Guided High-Throughput Evaluation of Ligands for Selective Sr/Cs Coordination

He huaxue yu fangshe huaxue, 2023
From a coordination chemistry perspective, we aimed to advance the knowledge of Sr/Cs separation in the scheme of spent nuclear fuel reprocessing. Based on data mining of crystal structures and deep learning architecture, we summarized and analyzed ...
ZHANG Zhi-yuan, DONG Yue, QIU Yu-qing, BI Ke-xin, HU Kong-qiu, DAI Yi-yang, ZHOU Li, LIU Chong, JI Xu, SHI Wei-qun   +9 more
doaj   +1 more source

Accurate and reliable estimation of kinetic parameters for environmental engineering applications: A global, multi objective, Bayesian optimization approach

MethodsX, 2019
Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed
Derek C. Manheim, Russell L. Detwiler
doaj   +1 more source

Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction

, 2019
Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication ...
Burger, Sven, Hammerschmidt, Martin, Rockstuhl, Carsten, Santiago, Xavier Garcia, Schneider, Philipp-Immanuel, Soltwisch, Victor   +5 more
core   +1 more source

MIOpt: optimization framework for backward problems on the basis of the concept of materials integration

Science and Technology of Advanced Materials: Methods, 2023
In materials design, it is very difficult to accurately design forward problems owing to the variety of scales and phenomena to be considered and the increasing number of input and output variables.
Satoshi Minamoto, Koyo Daimaru, Masahiko Demura   +2 more
doaj   +1 more source

Bayesian confidence in optimal decisions

Psychological Review, 2020
The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favour of the options. The drift diffusion model (DDM) implements this approach, and provides an excellent account of decisions and response times.
Joshua Calder-Travis, Lucie Charles, Rafal Bogacz, Nick Yeung   +3 more
openaire   +5 more sources