Results 251 to 260 of about 150,441 (331)

Application of Machine Learning in Heat Treatment Process Design of Carburized Steel

Materials Genome Engineering Advances, EarlyView.
To accelerate heat treatment design, we constructed a closed‐loop machine learning strategy involving multi‐source datasets and feature screening. The optimized model accurately predicts hardness and friction coefficients, successfully guiding the process optimization for two typical carburized steels with high experimental consistency.
Di Jiang, Yihao Zheng, Chunyang Luo, Xuefei Wang, Chi Zhang, Zhaodong Wang   +5 more
wiley   +1 more source

A Machine Learning Model Integrating Systemic Immune-Inflammation Index for Predicting 28-Day Mortality in Geriatric Sepsis Secondary to Community-Acquired Bacterial Pneumonia. [PDF]

Clin Interv Aging
Xu Y, Chen W.
europepmc   +1 more source

Machine Learning‐Driven Predictive Modeling and Multi‐Objective Exploration of Oxaliplatin‐Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency

Materials Genome Engineering Advances, EarlyView.
Integrating ML and multi‐objective optimization enabled efficient, accurate design of nanocarriers with optimized loading and encapsulation efficiencies. ABSTRACT Oxaliplatin (OXA), a critical third‐generation platinum chemotherapeutic, is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle‐based delivery ...
Abbas Rahdar, Maryam Shirzad, Sonia Fathi‐Karkan, M. Ali Aboudzadeh   +3 more
wiley   +1 more source

Transferability of aboveground biomass estimation using Sentinel-1/2 and GEDI data in subtropical forests of complex terrain, China. [PDF]

iScience
Wang G, Dong W, Zhang H, Ji Y, Zhang W, Zhao H, Wang M, Ma Y, Song C, Jiang Y, Bai C, Wu J.   +11 more
europepmc   +1 more source

Experimental Modal Analysis‐Driven Gaussian Process Regression Modeling of Rectangular Steel Beams With Various Hole Geometries

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Understanding the dynamic behavior of structural components is crucial for optimizing performance and ensuring structural integrity. This study presents a new method that combines a systematic experimental investigation of four distinct hole geometries (circular, square, compact rectangular, and long rectangular) with varying hole counts, all ...
Amir Hossein Rabiee, Majid Rajaei, Mohammad Hossein Mahdavi Nasab, Parsa Shafiei   +3 more
wiley   +1 more source

Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator. [PDF]

Front Oncol
Ding L, Peng L, Xu Z, Cui Z, Wang Z.
europepmc   +1 more source

Lessons Learned From Using Simple Supervised Learning Tools on Small‐Ensemble Data—Applicability to Tunnel Design and Monitoring

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT Integrating interdisciplinary strategies with artificial intelligence (AI), particularly machine learning (ML), is an effective way of addressing urgent engineering challenges. Therefore, a thorough evaluation of existing methodologies is essential, taking into account their respective strengths, limitations and opportunities.
Lina‐María Guayacán‐Carrillo, Jean‐Michel Pereira, Jean Sulem   +2 more
wiley   +1 more source

Machine learning-based risk classification of depressive symptoms among patients with hearing loss: evidence from the Health and Retirement Study (HRS). [PDF]

Compr Psychoneuroendocrinol
Wang Y, Li C, Shen X.
europepmc   +1 more source

Opening the Black Box of Nonprofit Reputation and Volunteer Attraction With Supervised Machine Learning

Nonprofit Management and Leadership, EarlyView.
ABSTRACT With the aim to explore the potential of machine learning for nonprofit research, this article contrasts traditional linear regression with four contemporary supervised machine learning approaches. Concretely, we predict (1) reputation ratings and (2) the total number of volunteers for 4021 non‐profit organizations in the U.S.
Moritz Schmid, Hannes Lampe, Jurgen Willems   +2 more
wiley   +1 more source

A spatially interpretable machine learning framework for urban waterlogging risk mapping in Beijing. [PDF]

PeerJ
Tang Y.
europepmc   +1 more source