Results 111 to 120 of about 25,600 (268)

Enhanced Monocular Depth Estimation Using ResNet Backbone and Multiscale Feature Decoding for Precise Object Distance Measurement

Advanced Intelligent Systems, EarlyView.
A lightweight monocular perception framework generates high‐fidelity depth maps and integrates YOLOv8 detection to estimate object‐wise distances from a single RGB image. Evaluated on KITTI and the proposed D‐Far250 dataset, the system demonstrates accurate long‐range perception up to 250 m while maintaining real‐time performance, enabling scalable ...
Faseeh Muhammad, Misbah Bibi, Murad Ali Khan, Syed Shehryar Ali Naqvi, Adnan Ejaz Muhammad, Do-Hyeun Kim   +5 more
wiley   +1 more source

Scene‐Customized Learning for Multi‐Depth 3D Phase‐Only Hologram Generation

Advanced Intelligent Systems, EarlyView.
 Scene‐customized geometric modeling constructs GM‐4K, a controllable 4K RGB‐D dataset for learning‐based multi‐depth hologram generation. By tuning intensity spectra and depth‐region sampling, the dataset reveals how training‐data statistics affect phase‐only hologram encoding and supports a spectral test framework for evaluating model generalization ...
Yanan Zhang, Jiaqi Li, Tao Jing, Yang Yu, Yang Zhang, Xingpeng Yan   +5 more
wiley   +1 more source

Artificial Neural Network Driven Computational Modeling of Naegleria fowleri Epidemics Using Variational Optimization

Advanced Physics Research, EarlyView.
ABSTRACT This study presents a mathematical framework to analyze the transmission dynamics of an amoeba‐induced central nervous system infection. The population is divided into compartments including susceptible, exposed, infected, quarantined, hospitalized, recovered, protected, and deceased.
Wakeel Ahmed, Areej Yaseen, Ghulamullah Saeedi, Shakeel Ahmed   +3 more
wiley   +1 more source

A direct normal irradiation forecasting model based on artificial neural networks

Revue des Énergies Renouvelables, 2016
We investigate the forecasting of the hourly Direct Normal Irradiation (DNI) using Artificial Neural Networks (ANN). The data used are hourly satellite data for the region of Ouarzazate in the South West Mediterranean basin region.
I. Belhaj, O. El Fatni, S. Barhmi, E.H. Saidi   +3 more
doaj  

Prosumers and Sustainable Market Governance: Development of the Community‐Oriented Marketing Approach Scale

Business Strategy and the Environment, EarlyView.
ABSTRACT This research develops and empirically validates the Community‐Oriented Marketing Approach (COMA), a 20‐item multidimensional scale designed to measure prosumer perceptions within participatory market systems. COMA conceptualizes prosumers as active co‐value creators and institutional agents, driving sustainable market governance.
Alpaslan Kelleci, Oguzhan Essiz
wiley   +1 more source

Sex Prediction From the Clavicle Using Computerized Tomography Images via Traditional and Hybrid Deep Learning Models

Clinical Anatomy, EarlyView.
ABSTRACT The aim of this study is to perform high accuracy sex prediction from clavicle images using proposed hybrid deep learning models and traditional deep learning models. The clavicle of 807 female and 805 male individuals obtained from Computed Tomography were segmented in 3D format and saved in jpeg format as superior–inferior and right–left ...
Yusuf Secgin, Muhammet Cakmak, Deniz Senol, Seren Kaya, Oğuzhan Oztürk, Oğuzhan Harmandaoğlu, Hatice Yenigül, Eslem Yaren Bıyık, Omer Onbaş   +8 more
wiley   +1 more source

Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition

Cereal Chemistry, EarlyView.
ABSTRACT Background and Objectives The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees ...
Colten N. Nickerson, Sarah Hogarth, Andrea K. Stone, Gene C. Arganosa, Pankaj Bhowmik, Tom D. Warkentin, Jordan Ubbens, Michael T. Nickerson   +7 more
wiley   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

DETERMINATION OF THE NEXT STOPPING FLOOR IN ELEVATOR TRAFFIC CONTROL BY MEANS OF NEURAL NETWORKS

Electrica, 2005
When a group of lifts serve together it is important coordinate the movements of the individual lifts in such a way that the lift group should operate efficiently.
C.Erdem İMRAK, Mustafa ÖZKIRIM
doaj  

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source