Results 171 to 180 of about 66,452 (290)

A road data assets revenue allocation model based on a modified Shapley value approach considering contribution evaluation. [PDF]

Sci Rep
Li S, Chu L, Wang J, Zhang Y.
europepmc   +1 more source

An application of the Shapley value to the analysis of co-expression networks. [PDF]

Appl Netw Sci, 2018
Cesari G, Algaba E, Moretti S, Nepomuceno JA.   +3 more
europepmc   +1 more source

Neural Network‐Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy

Drug Testing and Analysis, EarlyView.
We construct a neural network for the classification of bromazolam and fluorofentanyl in illicit opioid samples. The model outperforms a random forest classifier and shows elevated performance for low concentration samples. ABSTRACT Community‐based drug checking services are challenged in their ability to reliably detect low concentration adulterants ...
Joshua Jai, Lea Gozdzialski, Bruce Wallace, Chris G. Gill, Dennis Hore   +4 more
wiley   +1 more source

Real‐time monitoring of tunnel structures using digital twin and artificial intelligence: A short overview

Deep Underground Science and Engineering, EarlyView.
How artificial intelligence (AI) and digital twin (DT) technologies are revolutionizing tunnel surveillance, offering proactive maintenance strategies and enhanced safety protocols. It explores AI's analytical power and DT's virtual replicas of infrastructure, emphasizing their role in optimizing maintenance and safety in tunnel management.
Mohammad Afrazi, Danial Jahed Armaghani, Hossein Afrazi, Hadi Fattahi, Pijush Samui   +4 more
wiley   +1 more source

An Improved Shapley Value Method for a Green Supply Chain Income Distribution Mechanism. [PDF]

Int J Environ Res Public Health, 2018
Xu Z, Peng Z, Yang L, Chen X.
europepmc   +1 more source

AI‐Driven Precision Annealing for High Performance Fe‐Based Amorphous Alloys

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The four stages of the research process are as follows: First, data is collected and a database is constructed. This is followed by feature selection and analysis, then the establishment of machine learning models, and finally formulation design and preparation.
Yichuan Tang, Shaopeng Liu, Silong Li, Ruonan Ma, Peinan Li, Zheng Wang, Kun Wang, Kaiyan Cao, Sidan Ding, Chao Zhou, Fanghua Tian, Sen Yang, Minxia Fang, Yin Zhang   +13 more
wiley   +1 more source

Machine‐Learning‐Enabled Wood with Nanopump Functionalization for Solar Interfacial Evaporation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed machine learning to design an iron‐cobalt‐carbon‐wood photothermal material, achieving high‐efficiency evaporation at 2.807 kg m−2 h−1 and excellent salt resistance. The integrated system increased the daily water production efficiency of solar distillation by 1.5 times, providing an innovative solution for sustainable seawater ...
Chaohai Wang, Rui Li, Chang He, Xinfeng Zhu, Junning Wang, Rongfu Peng, Ho Ngoc Nam, Yanli Mao, Wei Liu, Mingshan Zhu, Yusuke Yamauchi   +10 more
wiley   +1 more source

Machine learning‐based prediction of elevated N terminal pro brain natriuretic peptide among US general population

ESC Heart Failure, Volume 12, Issue 2, Page 859-868, April 2025.
Abstract Aims Natriuretic peptide‐based pre‐heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre‐heart failure, has not been well established.
Yuichiro Mori, Shingo Fukuma, Kyohei Yamaji, Atsushi Mizuno, Naoki Kondo, Kosuke Inoue   +5 more
wiley   +1 more source

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography

Epilepsia, EarlyView.
Abstract Objective This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predictors such as interictal epileptiform discharges (IEDs).
Émile Lemoine, An Qi Xu, Mezen Jemel, Frédéric Lesage, Dang K. Nguyen, Elie Bou Assi   +5 more
wiley   +1 more source

Spectral entropy variability of intraoperative electrocorticography predicts outcome after epilepsy surgery in people with focal cortical dysplasia

Epilepsia, EarlyView.
Abstract Objective Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is difficult to predict. We explored whether spectral entropy, a fast computable electroencephalographic (EEG) feature, could estimate epileptic activity in intraoperative electrocorticography (ioECoG) and
Eline V. Schaft, Sem Hoogteijling, Dongqing Sun, Cyrille H. Ferrier, Pieter van Eijsden, Wim M. Otte, Galia Anguelova, Nick F. Ramsey, Maryse A. van't Klooster, Maeike Zijlmans, on behalf of the RESPect database study group, Charlotte van Asch, Lydia van de Berg, Dorien van Blooijs, Kim Boshuisen, Mireille Bourez, Kees Braun, Jan Willem Dankbaar, Matteo Demuru, Pieter van Eijsden, Cyrille Ferrier, Tineke Gebbink, Fernando Gomez‐Acebo Ruiz, Peter Gosselaar, Kimberley Grasmeijer, Renate van Griethuysen, Jiaojiao Guo, Irene Heijink, Monique Hobbelink, Friso Hoefnagels, Sem Hoogteijling, Geertjan Huiskamp, Sarah Jacobs, Floor Jansen, Susanne Jelsma, Nicole van Klink, Merel Koopmans, Frans Leijten, Wouter Maathuis, Mariska Mantione, Angelika Muhlebner, Brechje Olgers, Janine van Ophorst, Miranda Postma, Sandra van der Salm, Eline Schaft, Anniek van Schaik, Monique van Schooneveld, Dongqing Sun, Ziyi Wang, Martine van Zandvoort, Eva Zuidhoek, Willemiek Zweiphenning, Jack Zwemmer   +53 more
wiley   +1 more source