Results 1 to 10 of about 84,677 (290)

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

Frontiers in Earth Science, 2023
The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is ...
Tianze Zhang, Hui Chai, Hongjun Wang, Tongcui Guo, Liangjie Zhang, Wenqi Zhang   +5 more
doaj   +1 more source

Combining conventional ultrasound and ultrasound elastography to predict HER2 status in patients with breast cancer

Frontiers in Physiology, 2023
Introduction: Identifying the HER2 status of breast cancer patients is important for treatment options. Previous studies have shown that ultrasound features are closely related to the subtype of breast cancer.Methods: In this study, we used features of ...
Xiaoying Zhuo, Xiaoying Zhuo, Ji Lv, Ji Lv, Binjie Chen, Jia Liu, Yujie Luo, Jie Liu, Xiaowei Xie, Jiao Lu, Ningjun Zhao, Ningjun Zhao   +11 more
doaj   +1 more source

A Model-agnostic XAI Approach for Developing Low-cost IoT Intrusion Detection Dataset

Journal of Information Security and Cybercrimes Research, 2023
This study tackles the significant challenge of generating low-cost intrusion detection datasets for Internet of Things (IoT) camera devices, particularly for financially limited organizations.
Enoch Opanin Gyamfi, Zhiguang Qin, Daniel Adu-Gyamfi, Juliana Mantebea Danso, Judith Ayekai Browne, Dominic Kwasi Adom, Francis Effirim Botchey, Nelson Opoku-Mensah   +7 more
doaj   +1 more source

Prediction and analysis model for ground peak acceleration based on XGBoost and SHAP

Yantu gongcheng xuebao, 2023
In order to establish a prediction method for the ground peak acceleration (PGA) that does not depend on the soil constitutive model but only on the ground motion and site characteristics, six characteristic parameters are chosen through the feature ...
QI Wanwan 1, 2, SUN Rui 1, 2, ZHENG Tong 1, 2, QI Jinlei 1, 2
doaj   +1 more source

Conditional Expectation Network for SHAP

SSRN Electronic Journal, 2023
A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP).
Richman, Ronald, Wüthrich, Mario V.
openaire   +2 more sources

On the Performance of Machine Learning Based Flight Delay Prediction – Investigating the Impact of Short-Term Features

Promet (Zagreb), 2022
People and companies today are connected around the world, which has led to a growing importance of the aviation industry. As flight delays are a big challenge in aviation, machine learning algorithms can be used to forecast those.
Delia Schösser, Jörn Schönberger
doaj   +1 more source

Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease

Frontiers in Cardiovascular Medicine, 2023
IntroductionCardiovascular disease (CVD) is a group of diseases involving the heart or blood vessels and represents a leading cause of death and disability worldwide.
Chengling Bin, Qin Li, Jing Tang, Chaorong Dai, Ting Jiang, Xiufang Xie, Min Qiu, Lumiao Chen, Shaorong Yang   +8 more
doaj   +1 more source

Ensembles of Random SHAPs

Algorithms, 2022
The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a large number of features.
Lev Utkin, Andrei Konstantinov
openaire   +3 more sources

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

Machine learning based ground motion site amplification prediction

Frontiers in Earth Science, 2023
Site condition impact on seismic ground motion has been a complex but important subject in earthquake hazard analysis. Traditional studies on site amplification effect are either based on site response via wave propagation simulation or regression ...
Xiangqi Wang, Zifa Wang, Zifa Wang, Jianming Wang, Pengyu Miao, Haotian Dang, Zhaoyan Li   +6 more
doaj   +1 more source