Results 41 to 50 of about 2,430,587 (138)

Clinlabomics‐Enabled Blending Ensemble Learning for Low‐Cost Pan‐Cancer Detection and Classification Using Routine Clinical Laboratory Data

Advanced Intelligent Systems, Volume 7, Issue 12, December 2025.
Researchers develop clinlabomics assisted for cancer identification, an artificial intelligence‐powered system using routine clinical lab data to detect and identify 10 cancer types. Tested on 19 199 individuals, it achieves 90.39% sensitivity and 82.41% specificity in cancer detection, with 72.57% accuracy in identifying specific cancer types ...
Bowen Zhang, Lin Li, Long Cheng, Yujie Liu, Zhaoqing Liu, Jiangpeng Wu, Pengyi Zhang, Jiarui Wang, Dekui Zhang, Shuyan Li   +9 more
wiley   +1 more source

Low‐Dimensional Controls on Oceanic Basalt Geochemistry Revealed by Regression‐Based Machine Learning Models

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 4, December 2025.
Abstract The low dimensionality of basalt geochemical data has long been inferred but has not been quantitatively evaluated from the relationships among elemental contents. We developed regression‐based machine learning models and examined the relationships among elemental contents for basalts.
Satoshi Matsuno, Masaoki Uno, Atsushi Okamoto   +2 more
wiley   +1 more source

Parameter Estimation in Land Surface Models: Challenges and Opportunities With Data Assimilation and Machine Learning

Journal of Advances in Modeling Earth Systems, Volume 17, Issue 11, November 2025.
Abstract Accurately predicting terrestrial ecosystem responses to climate change over long‐timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially ...
Nina Raoult, Natalie Douglas, Natasha MacBean, Jana Kolassa, Tristan Quaife, Andrew G. Roberts, Rosie Fisher, Istem Fer, Cédric Bacour, Katherine Dagon, Linnia Hawkins, Nuno Carvalhais, Elizabeth Cooper, Michael C. Dietze, Pierre Gentine, Thomas Kaminski, Daniel Kennedy, Hannah M. Liddy, David J. P. Moore, Philippe Peylin, Ewan Pinnington, Benjamin Sanderson, Marko Scholze, Christian Seiler, T. Luke Smallman, Noemi Vergopolan, Toni Viskari, Mathew Williams, John Zobitz   +28 more
wiley   +1 more source

tkzeng/ngboost: NGBoost for GeneBayes v1.0

<p>NGBoost modified for GeneBayes v1.0</p ...
mlko53, Eric Ma, Giulia A.D. Savorgnan, Tommy, Zain Patel, Christopher Smith, Anand Avati, Tony Zeng, Daiki Katsuragawa, Angel Droth, Lucca Zenóbio, cd, Carlos Aguilar, Jose Manuel Llorens, kenmatsu4, Zhuyi Xue, Alejandro Schuler, Zhirui (Jerry) Wang, Ross Johnstone, Sams, Joshua Adelman, Blake Atkinson, YutaYamazaki, yu9824, dguzelkokar, Daisy Ding, Sai Anil, Tony Duan, Ryan Wolbeck   +28 more
core   +1 more source

Real Power Loss Reduction by Enhanced Imperialist Competitive Algorithm [PDF]

, 2017
In this paper, an Enhanced Imperialist Competitive (EIC) Algorithm is proposed for solving reactive power problem. Imperialist Competitive Algorithm (ICA) which was recently introduced has shown its decent performance in optimization problems.
Kanagasabai, Lenin, Lenin Kanagasabai
core   +1 more source

Predictive Approaches for 3D‐Printing: Methods and Approaches for Polymeric Materials

WIREs Computational Molecular Science, Volume 15, Issue 5, September/October 2025.
3D printing shows great promise, but suffers from a restricted range of materials. We show how computational methods, from physics‐based to data‐driven methods, can streamline the development of new polymer materials. Developing integrated approaches offers future opportunities in autonomous discovery and design, harnessing 3D printing's full potential.
Isabel Cooley, Weiling Wang, Vladimir Kozyrev, Ricky D. Wildman, Blair F. Johnston, Anna K. Croft   +5 more
wiley   +1 more source

Machine learning-based probabilistic predictions for Concrete Filled Steel Tube (CFST) column axial capacity [PDF]

This study presents a novel probabilistic machine learning (ML) approach using Natural Gradient Boosting (NGBoost) to predict the axial compressive capacity of Concrete Filled Steel Tube (CFST) columns.
Lai D., Contento A., Xue J., Wei J., Demartino C., Albanesi T., Briseghella B.   +6 more
core   +1 more source

Analysis of the Utilization of Machine Learning to Map Flood Susceptibility

Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.
ABSTRACT This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial‐and‐error approaches, this work aims at establishing a ...
Ali Pourzangbar, Peter Oberle, Andreas Kron, Mário J. Franca   +3 more
wiley   +1 more source

Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME

Engineering Reports, Volume 7, Issue 1, January 2025.
The significance of this study is centered around the creation of a precise and resilient diabetes classification framework that is inherently transparent. The results of the framework perform well consistently and it suggests that the framework may be more likely to generalize effectively to new, unseen data.
Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin   +5 more
wiley   +1 more source

DKhan123/Optimisation-Algorithm-2.0: Optimisation Algorithm

, 2021
Optimisation Algorithm 2.0 ...
DKhan123
core   +1 more source