Results 91 to 100 of about 447 (169)

Machine learning for modeling intergranular stress corrosion cracking of stainless steels in light water reactors with uncertainty quantification and explainability

npj Materials Degradation
Intergranular stress corrosion cracking (IGSCC) of austenitic stainless steels remains a critical degradation mechanism in light water reactors (LWRs), driven by coupled mechanical, electrochemical, and microstructural factors.
Dayu Fajrul Falaakh, Chi Bum Bahn
doaj   +1 more source

Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides

Electrochemistry
A machine learning model that can predict the ionic conductivity of lithium-containing oxides using chemical composition and ionic conductivity data was previously developed.
Yudai IWAMIZU, Kota SUZUKI, Michiyo KAMIYA, Naoki MATSUI, Kuniharu NOMOTO, Satoshi HORI, Masaaki HIRAYAMA, Ryoji KANNO   +7 more
doaj   +1 more source

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

Telecom
SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision.
Muhammad Saibtain Raza, Mohammad Nowsin Amin Sheikh, I-Shyan Hwang, Mohammad Syuhaimi Ab-Rahman   +3 more
doaj   +1 more source

Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater

Assessing the cyanobacteria disinfection in sewage and its compliance with international-standards requires determining the concentration and viability, which can be achieve using Imaging Flow Cytometry device called FlowCAM.
Na, Seongyeon, Park, Ki Young, Jaffari, Zeeshan Haider, Cho, Kyung Hwa, Abbas, Ather   +4 more
core   +1 more source

Probabilistic Prediction of Local Scour at Bridge Piers with Interpretable Machine Learning

Local pier scour remains one of the leading causes of bridge failure, calling for predictions that are both accurate and uncertainty-aware. This study develops an interpretable data-driven framework that couples CatBoost (Categorial Gradient Boosting ...
Soonchul Kwon, Jaemyeong Choi, Jongyeong Kim, Taeyoon Kim   +3 more
core   +1 more source

Uncertainty Quantification in Predictive Modelling

Tato práce se zabývá kvantifikací nejistoty v prediktivním modelování se zaměřením na konstrukci a hodnocení predikčních intervalů. Teoretická část vymezuje aleatorní a epistemickou nejistotu a systematicky porovnává čtyři přístupy: lineární regresní ...
Li, Zakhar
core   +2 more sources

Enhancing Boiler Operational Economy via Data-Driven Collaborative Combustion Optimization. [PDF]

ACS Omega
Sui Y, Wang Q, Zhao C, Cai W, Pan H, Lei X, Zhao J, Ding L, Xu M, Shi S.   +9 more
europepmc   +1 more source

Uncertainty in the estimation of aboveground biomass in mangrove forests using multiple-scale sampling data

Most previous works on aboveground biomass (AGB) estimation provide a single estimate of AGB rather than the probability distribution of the predicted values.
Pham Q.-T., Bui Q.-T., Tran V.-T., Nguyen H.D., Ngoc B.T.C., Bretcan P., Son H.N., Van Sang T., Nguyen Q.-H., Pham V.-M., Vu Van, Manh, Nguyen D.-H.   +11 more
core   +1 more source

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics. [PDF]

Metabolites, 2023
Yagin FH, Yasar S, Gormez Y, Yagin B, Pinar A, Alkhateeb A, Ardigò LP.   +6 more
europepmc   +1 more source

Multi-Horizon Probabilistic Wind Power Forecasting for Mountainous Wind Farms Based on Entropy-Weighted Fusion and Permutation Entropy-Guided Decomposition. [PDF]

Entropy (Basel)
Liu C, Shao B, Nie D, Tian N, Dai H, Zeng H, Zhao W, Zhao X, Liu X, Qin C.   +9 more
europepmc   +1 more source