Results 31 to 40 of about 302,002 (276)

Parametric bootstrap mean squared error of a small area multivariate EBLUP

Communications in statistics. Simulation and computation, 2018
This article deals with mean squared error (MSE) estimation of a multivariate empirical best linear unbiased predictor (MEBLUP) under the unit-level multivariate nested-errors regression model for small area estimation via parametric bootstrap.
A. Moretti, N. Shlomo, J. Sakshaug
semanticscholar   +1 more source

Accurate Solar Radiation Forecasting Through Feature-Enhanced Decision Trees and Wavelet Decomposition [PDF]

EPJ Web of Conferences
This study explores the efficacy of the decision tree algorithm in predicting solar power generation, addressing the inherent variability in photovoltaic (PV) energy production due to weather conditions.
Gaizen Soufiane, Abbou Ahmed, Fadi Ouafia   +2 more
doaj   +1 more source

Efficient Deep Learning Models for Predicting Super-Utilizers in Smart Hospitals

IEEE Access, 2023
In healthcare, a huge amount is paid to meet the requirements of High-Need High-Cost (HNHC) patients, also known as super-utilizers. The major aim of the proposed study is to predict HNHC patients.
Madiha Jaffar, Sundas Shafiq, Nazia Shahzadi, Nabil Alrajeh, Mohsin Jamil, Nadeem Javaid   +5 more
doaj   +1 more source

Predictive machine learning-based error correction in GPS/IMU localization to improve navigation of autonomous vehicles [PDF]

MATEC Web of Conferences
Precise localization is crucial for the safety-critical factor and effective navigation of autonomous vehicles. This applied research examines machine learning models’ use to estimate, predict and correct errors in Global Positioning System (GPS ...
Onyema Uchenna Charles, Shafik Mahmoud
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

Discover Sustainability
This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, and SVR—aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting.
Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam   +7 more
doaj   +1 more source

Precipitation Simulations of the O‐Phase in Ti2AlNb Alloys Processed by Laser Powder Bed Fusion

Advanced Engineering Materials, EarlyView.
Simulated and experimental evolution of the O‐phase volume fraction during postprocessing of a Ti‐21Al‐25Nb (at.%) alloy processed by laser powder bed fusion. With results of sensitivity to input parameters from a thorough and quantified analysis, the interfacial energy matrix/precipitate is the most relevant input parameter for the simulation of the O‐
Silvana Tumminello, Joachim Gussone, Suzana G. Fries, Marcus Rackel, Andreas Stark, Norbert Schell, Jan Haubrich, Guillermo Requena   +7 more
wiley   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Analisis Pengaruh Fungsi Aktivasi, Learning Rate Dan Momentum Dalam Menentukan Mean Square Error (MSE) Pada Jaringan Saraf Restricted Boltzmann Machines (RBM)

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2019
<p>Restricted boltzmann machines (RBM) merupakan algoritma pembelajaran jaringan syaraf tanpa pengawaas (<em>unsupervised learning</em>) yang hanya terdiri dari dua lapisan yang <em>visible layer</em> dan <em>hidden layer</em>.
Susilawati Susilawati, Muhathir Muhathir
openaire   +2 more sources