Results 41 to 50 of about 36,209 (267)

What drives the profitability of banking sectors in the European Union? The machine learning approach

International Journal of Management and Economics
The study aims to establish patterns of relations between the profitability of the European Union (EU) banking sectors between 2007 and 2021 and sets of variables appropriate for clusters of countries into which the 27 countries of the EU are divided ...
Bernardelli Michał, Korzeb Zbigniew, Niedziółka Paweł   +2 more
doaj   +1 more source

An interpretable probabilistic prediction algorithm for shield movement performance

Frontiers in Earth Science
Total thrust and torque are two key indicators of shield movement performance. Most existing data-driven machine learning studies focus on developing more accurate models for predicting total thrust and torque but overlook the interpretability of the ...
Yapeng Zhang, Yapeng Zhang, Long Liu, Long Liu, Jian Wu, Jian Wu, Shaoxiang Zeng, Shaoxiang Zeng, Jianquan Hu, Yuanqin Tao, Yuanqin Tao, Yong Huang, Yong Huang, Xuetao Zhou, Xuetao Zhou, Xu Liang   +15 more
doaj   +1 more source

Toward Prostate Cancer Early Warning with a Self‐Powered Wearable Biosensing Platform Integrated with Machine Learning

Advanced Science, EarlyView.
ABSTRACT Current prostate cancer detection methods remain limited in non‐invasiveness and specificity, prompting interest in urinary biomarkers such as sarcosine. Here, we report a urine‐powered wearable platform for non‐invasive sarcosine detection as a proof‐of‐concept for decentralized early warning.
Jing Xu, Hanxiao Chen, Qichen Yuan, Liucun Yin, Yifang Tao, Hong Wang, Yuquan Xue, Delai Fu, Huan Pang, Tie Chong, Li Xue   +10 more
wiley   +1 more source

Flexural performance tests and machine learning analysis of prestressed high strength steel reinforced concrete beams

Journal of Asian Architecture and Building Engineering
This paper reports a study on the flexural performance of prestressed high strength steel reinforced concrete (PHSSRC) beams and proposes machine learning (ML) models for predicting the flexural bearing capacity of steel reinforced concrete (SRC) beams ...
Zhibo Bao, Jun Wang, Yurong Jiao
doaj   +1 more source

A CT-based interpretable machine learning model for preoperative prediction of pancreatic neuroendocrine tumor aggressiveness

Frontiers in Oncology
ObjectivesThis study aimed to develop and validate an interpretable machine learning (ML) model based on structured preoperative CT features for non-invasive prediction of pancreatic neuroendocrine Tumors (PNETs) aggressiveness.MethodsThis retrospective ...
Rong Kong, Shunzu Lu, Yugui Huang, Siyu Tan, Chunxia Zhu, Guowei Chen, Mingrui Yang, Ying Liu, Qixin Wu, Peng Peng   +9 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Explainable Machine Learning Framework for Distributed Denial-of-Service (DDoS) Attack Detection using Comparative Evaluation and SHAP Analysis

Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls
The proliferation of Distributed Denial-of-Service (DDoS) attacks poses critical threats to network infrastructure, while conventional intrusion detection systems struggle to adapt to evolving attack patterns.
Muhammad Fathur Riziq, Ichwan Nul Ichsan
doaj   +1 more source

Faith-Shap: The Faithful Shapley Interaction Index

CoRR, 2022
Shapley values, which were originally designed to assign attributions to individual players in coalition games, have become a commonly used approach in explainable machine learning to provide attributions to input features for black-box machine learning models.
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar   +2 more
openaire   +4 more sources

Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

Advanced Science, EarlyView.
Integrating interpretable machine learning with the fixed‐potential method reveals a novel mechanism: the catalytic activity of the electrochemical nitrogen reduction reaction is governed by partial charge transfer, induced by variations in the intermediate potential of zero charge under constant potential.
Yufei Xue, Dushuo Feng, Yuefei Zhang, Yang Zhang, Yalong Jiao, Aijun Du, Guoping Gao   +6 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source