neural-network

Documents about neural-network

• Evaluating Spatiotemporal Changes and Predicting Land-Use Dynamics Using an Artificial Neural Network in Worcester City, Massachusetts#443645Springer Nature OA
• Untrained Position-Encoded Multilayer Perceptron Network for Structured Illumination Microscopy Reconstruction#483474ODU Digital Commons
• Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Nonconservative Forces#261804Figshare
• Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes#296971HAL Science
• Intelligent predictive neural network analysis on two phase bioconvection flow of dusty hybrid nanofluid with Cattaneo Christov flux model and melting phenomena#324017Springer Nature OA
• CocoaDeep: A preliminary study of the performance sensitivity to datasets of Faster RCNN, YOLO and transformer networks for cocoa pod detection#395370HAL (France)
• Neuronal network analyses reveal novel associations between volatile organic compounds and sensory properties of tomato fruits#398914DSpace
• Apparatus and method for monitoring and controlling of a neural network using … — Apex Artificial Intelligence Industries, Inc. (US10802488B1)#607354Google Patents
• Bayesian-regularized neural network analysis of heat and mass transmission in CMC and water hybrid nanofluid with local thermal non equilibrium conditions#130602Springer Nature OA
• ST-CATrust: multidimensional trust fusion evaluation via spatio-temporal graph neural network#147708Springer Nature OA
• A Novel Deep Learning Framework combining optical-SAR Data to Generate Gap-Free 20 M LAI Time Series for Crop Monitoring#291810HAL (France)
• SAGRNet: A novel object-based graph convolutional neural network for diverse vegetation cover classification in remotely-sensed imagery#265749Repositorio Institucional de la Universidad de Burgos (RIUBU)
• Thermal Rating and Prediction of Roadway-Embedded Power Electronics for Dynamic Wireless Power Transfer#287738DigitalCommons@USU
• Determination of aquaponic water macronutrient concentrations based on Lactuca sativa leaf photosynthetic signatures using hybrid gravitational search and recurrent neural network#413970Animo Repository
• … of diagnosing machine components using analog sensor data and neural network — Strong Force Iot Portfolio 2016, Llc (US11755878B2)#607337Google Patents
• Enhanced chaotic sequence population optimization for accelerated deep neural network convergence: a review and case study on palm tree classification in Al-Kharj#252040Springer Nature OA
• Processed grid-level dataset and reproducibility code for sector congestion prediction in air traffic management#339907Figshare
• Comparative analysis of MHD HFE-7100, HFE-7200 and HFE-7500 based nanofluids with Soret and Dufour effects using artificial neural network#355767Springer Nature OA
• Simulation of transient characteristics of thunderstorm out flows through straight-line wind tunnel tests#408834IRIS
• Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers#462424Scholar Commons
• Neural Network Representation Formats — Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. (US20250384299A1)#607345Google Patents
• ACTA-DIRECT Dataset#208003Yoda MOAI Feed
• ACTA-DIRECT Dataset#208010Yoda MOAI Feed
• Design oriented modeling of squeezed MHD convective flow of hybrid nanoliquid over a sensor surface with chemical reaction advanced sensor technology using artificial neural network#299176Springer Nature OA
• Contact-Free Surface Tension Measurements of Microgel-Laden Air–Water Interfaces via Acoustic Levitation#249989Figshare
• Penerapan Quantum Machine Learning Untuk Klasifikasi Ulasan Asli Dan Palsu Pada Amazon#194632Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
• Penerapan Quantum Machine Learning Untuk Klasifikasi Ulasan Asli Dan Palsu Pada Amazon#194853Publikasi Ilmiah STMIK Banjarbaru
• Heteroskedastic Ordered Probit Models with an Artificial Neural Network for Predicting Potential Consumer Ratings on Amazon Software Reviews#385824Springer Nature OA
• Explainable soft-sensing of dissolved oxygen in a multi-station aquatic monitoring network using ensemble machine learning and neural networks#464446IRIS
• “Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks#199729OpenAlex
• Building and Testing a Digital Twin Using Machine Learning Within a Mining Environment in South Africa#413797WIReDSpace
• ACTA-Bw25-RefStd Dataset#208007Yoda MOAI Feed
• Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods#786180Research Online
• Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes with 18F-FDG PET imaging#455567Open Access LMU
• Integrating van’t Hoff Equation with Artificial Neural Network for the Prediction of H₂S Solubility in Ionic Liquids#253453Figshare
• Integrating van’t Hoff Equation with Artificial Neural Network for the Prediction of H₂S Solubility in Ionic Liquids#253454Figshare
• ASSESSMENT OF CNN-ASSISTED SURROGATE MODELS IN PREDICTING MINING-INDUCED STRESS FIELD: A LONGWALL COAL MINING APPLICATION#355187OpenMETU
• Additional file 8 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241900Figshare
• Additional file 7 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241901Figshare
• Additional file 6 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241902Figshare
• Additional file 5 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241903Figshare
• Additional file 4 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241904Figshare
• Additional file 3 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241905Figshare
• Additional file 2 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241907Figshare
• Additional file 1 of Human factors validation study of an artificial neural network‑based preoperative decision‑support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN99301435)#241909Figshare
• Additional file 3 of ECG-based detection of occlusion myocardial infarction: a dedicated deep neural network versus multimodal large language models and physicians — a retrospective diagnostic accuracy study#652515Figshare
• Additional file 2 of ECG-based detection of occlusion myocardial infarction: a dedicated deep neural network versus multimodal large language models and physicians — a retrospective diagnostic accuracy study#652516Figshare
• Additional file 1 of ECG-based detection of occlusion myocardial infarction: a dedicated deep neural network versus multimodal large language models and physicians — a retrospective diagnostic accuracy study#652518Figshare
• Climate controls over ecosystem metabolism: insights from a fifteen-year inductive artificial neural network synthesis for a subalpine forest#614786eScholarship
• Resting‐state magnetoencephalography source magnitude imaging with deep‐learning neural network for classification of symptomatic combat‐related mild traumatic brain injury#619897eScholarship