Results 81 to 90 of about 447 (169)

A Comparative Evaluation of Deep Learning and Machine Learning Models for River Suspended Sediment Concentration Forecasting [PDF]

Suspended sediment concentration (SSC) in rivers is a crucial parameter required in hydrological studies, water resources management, and many other relevant applications.
Gharehbaghi, Amin, Heddam, Salim, Kim, Sungwon, Mehdizadeh, Saeid, Gharehbaghı, Amın   +4 more
core   +1 more source

Mathematical and machine learning analysis of high-density polyethylene thermal degradation

Scientific Reports
Thermogravimetric analysis (TGA) is widely used to characterize polymer degradation, but repeated experiments and conventional single-step kinetic models can become inefficient or inadequate when degradation is nonlinear, multistage, atmosphere-dependent,
Sonia Sakthi S, Arulmozhi M
doaj   +1 more source

Probabilistic Forecasting and Robust Optimization for Managing Uncertainty in Smart Beta Portfolio Optimization [PDF]

تحقیقات مالی
ObjectiveThis study employs a probabilistic forecasting approach and robust optimization to address parameter uncertainty in portfolio optimization models within the Iranian capital market.
Farid Tondnevis, Hossein Valamehr
doaj   +1 more source

An interpretable XGBoost-SHAP machine learning model for reliable prediction of mechanical properties in waste foundry sand-based eco-friendly concrete

Results in Engineering
Construction and development projects worldwide heavily rely on concrete as their primary building material, making it an essential component of global infrastructure and growth. The various key ingredients that compose concrete contribute differently to
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Aliakbar Gholampour, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim   +6 more
doaj   +1 more source

An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm

CoRR
The recent global spread of monkeypox, particularly in regions where it has not historically been prevalent, has raised significant public health concerns. Early and accurate diagnosis is critical for effective disease management and control. In response, this study proposes a novel deep learning-based framework for the automated detection of monkeypox
Ahmadreza Shateri, Negar Nourani, Morteza Dorrigiv, Hamid Nasiri   +3 more
openaire   +2 more sources

AI-Driven Prediction of Surface Roughness and Cutting Force in Milling Aluminum Alloy Under Data-Scarce Conditions

Machines
Accurate prediction of surface roughness and cutting forces in milling aluminum alloys remains challenging under data-scarce conditions, where limited experimental data restricts the application of conventional machine learning models.
Mohammad Hossein Ebrahimi, Seyed Ali Niknam   +1 more
doaj   +1 more source

Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions

Astronomy
Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves.
Sankalp Gilda
doaj   +1 more source

Formation resistivity inversion and uncertainty analysis based on hybrid machine learning model

四川大学学报. 自然科学版
In the process of extended reach wells and horizontal wells， it is of great significance for geosteering work to quickly and accurately extract stratigraphic information from the logging data of LWD tools by establishing an effective inversion model ...
QIAN Yu-Qing, HE Zhi-Li
doaj  

NGBoost algorithm-based prediction of mechanical properties of a hot-rolled strip and its interpretability research with ANOVA values

AIMS Mathematics
<p>Hot-rolled strip steel is an essential material extensively used in various industrial fields, with its mechanical properties being critical to product quality and engineering design. This article presents a method for predicting the mechanical properties of hot-rolled strip steel using the NGBoost (natural gradient boosting) algorithm.
Hongyi Wu, Jinwen Jin, Zhiwei Li
openaire   +2 more sources

Data-Driven PM_2.5 Exposure Prediction in Wildfire-Prone Regions and Respiratory Disease Mortality Risk Assessment

Fire
Wildfires generate substantial smoke containing fine particulate matter (PM2.5) that adversely impacts health. This study develops machine learning models integrating pre-wildfire factors like weather and fuel conditions with post-wildfire health impacts
Sadegh Khanmohammadi, Mehrdad Arashpour, Milad Bazli, Parisa Farzanehfar   +3 more
doaj   +1 more source