Výsledky vyhledávání - "Multilayer neural networks"

LEU3: An Attention Augmented-Based Model for Acute Lymphoblastic Leukemia Classification

Autoři: Dutta, M. Uddin Mojumdar, M. Alamgir Kabir, M. a další

Zdroj: IEEE Access. 13:31630-31642

Témata: attention mechanism, Blood cell cancer, convolutional neural networks, deep learning, leukemia disease, Deep neural networks, Diagnosis, Diseases, Lung cancer, Multilayer neural networks, Oncology, Patient treatment, Personalized medicine, Acute lymphoblastic leukaemias, Attention mechanisms, Blood cells, Bone marrow, Convolutional neural network, Peripheral blood smears, Treatment plans

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-70685
https://doi.org/10.1109/ACCESS.2025.3542609

Image regression-based digital qualification for simulation-driven design processes, case study on curtain airbag

Autoři: Arjomandi Rad, Mohamma Cenanovic, Mirza Salomonsson, Kent

Zdroj: Journal of engineering design (Print). 34(1):1-22

Témata: Convolution, Convolutional neural networks, Multilayer neural networks, Product development, Case-studies, Convolutional neural network, Data-driven design, Design alternatives, Design-process, Dynamic relaxation, Image regression, Modelling techniques, Simulation-driven designs, Surrogate modeling, Product design

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-59726
https://doi.org/10.1080/09544828.2022.2164440

An Overview of Deep Learning Methods Used in Vibration-Based Damage Detection in Civil Engineering

Autoři: Avci O. Abdeljaber O. Kiranyaz, Mustafa Serkan

Zdroj: Dynamics of Civil Structures, Volume 2 ISBN: 9788743803805

Témata: Structural health monitoring, Damage localization, Vibration-based damage detection, Civil infrastructures, Multilayer neural networks, Backpropagation, Deep learning, 02 engineering and technology, Civil engineering structures, Damage detection, 0201 civil engineering, Learning tool, Learning methods, Damage Identification, 0202 electrical engineering, electronic engineering, information engineering, Structural dynamics, Convolutional neural networks, Machine-learning, Infrastructure health

Přístupová URL adresa: https://link.springer.com/chapter/10.1007/978-3-030-77143-0_10
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118993145&doi=10.1007/978-3-030-77143-0_10&partnerID=40&md5=e10e21bd04ab01e18363c3e17c23c925
https://hdl.handle.net/10576/30581

A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

Autoři: Yacob, Filmon Semere, Daniel

Zdroj: Journal of Intelligent Manufacturing. 32(4):1173-1187

Témata: Skin Model Shapes, Variation propagation, Virtual machining, Backpropagation, Forecasting, Machining, Machining centers, Manufacture, Multilayers, Geometric characteristics, Geometrical deviations, Machining simulation, Manufacturing errors, Multi-stage machining process, Multistage manufacturing, Variation source identification, Multilayer neural networks

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285337
https://doi.org/10.1007/s10845-020-01649-z

SETTLE: SEquential Training-based Transfer LEarning in heterogeneous wireless sensor networks

Autoři: Mertens J. S. Galluccio L. Morabito G.

Zdroj: 2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :256-262

Témata: Adversarial machine learning, Contrastive Learning, Federated learning, Multilayer neural networks, Transfer learning

Popis souboru: application/pdf

Prediction of ship maneuvering motion in regular wave with gray-box modelling

Autoři: Yang HAN Lizhu HAO Chao SHI a další

Zdroj: Zhongguo Jianchuan Yanjiu, Vol 20, Iss 1, Pp 47-57 (2025)

Témata: ships, maneuverability, maneuvering motion, gray-box modelling, multilayer neural networks, fourier transforms, onrt, Naval architecture. Shipbuilding. Marine engineering, VM1-989

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1673-3185

Přístupová URL adresa: https://doaj.org/article/b633bb40e2d243338d78efa7a9a8b922

Preprocessing of recto-verso printed documents based on neural networks for text analysis

Autoři: Savino P Tonazzini A

Zdroj: 3rd Conference on Digital Preservation and processing technology of Written Heritage, in conjunction with the 7th IEEE International Congress on Information Science and Technology (IEEE CiSt'23), Agadir-Essaouira, Morocco, 16-22/12/2023
info:cnr-pdr/source/autori:Savino P.; Tonazzini A./congresso_nome:3rd Conference on Digital Preservation and processing technology of Written Heritage, in conjunction with the 7th IEEE International Congress on Information Science and Technology (IEEE CiSt'23)/congresso_luogo:Agadir-Essaouira, Morocco/congresso_data:16-22%2F12%2F2023/anno:2023/pagina_da:/pagina_a:/intervallo_pagine

Témata: Shallow multilayer neural networks, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Ancient document text analysis, Recto-verso documents, 02 engineering and technology, Degraded document binarization, Optical character recognition

Popis souboru: application/pdf

Přístupová URL adresa: http://www.cnr.it/prodotto/i/490208
https://publications.cnr.it/doc/490208

Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation

Autoři: Shuang Wang Bahaeddin Eravci Rustam Guliyev a další

Zdroj: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. :2626-2636

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Iterative methods, Large-scales, Multilayer neural networks, Backpropagation, Quadratic programming, scalable machine learning, Model size, Machine Learning (cs.LG), Deep neural networks, Large-scale graph management, large-scale graph management, Quantisation, Oversmoothing in GNN, graph neural networks, oversmoothing in GNNs, Quantizers, Message propagation, Graph neural networks, 68T07, Scalable machine learning, Message passing, Number of layers, quantization, I.m

Přístupová URL adresa: http://arxiv.org/abs/2308.14949

Training a shallow NN to erase ink seepage in historical manuscripts based on a degradation model

Autoři: Savino P Tonazzini A

Zdroj: Neural computing & applications
(2024). doi:10.1007/s00521-023-09354-7
info:cnr-pdr/source/autori:Savino P.; Tonazzini A./titolo:Training a shallow NN to erase ink seepage in historical manuscripts based on a degradation model/doi:10.1007%2Fs00521-023-09354-7/rivista:Neural computing & applications (Print)/anno:2024/pagina_da:/pagina_a:/intervallo_pagine:/volume

Témata: Shallow multilayer neural networks, Registration of recto-verso documents, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Degraded document binarization, Ancient manuscript virtual restoration

Popis souboru: application/pdf

Přístupová URL adresa: https://link.springer.com/article/10.1007/s00521-023-09354-7
https://hdl.handle.net/20.500.14243/452079
https://doi.org/10.1007/s00521-023-09354-7

Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification

Autoři: Mikhail Kulyabin Paul A. Constable Aleksei Zhdanov a další

Zdroj: IEEE Access, Vol 12, Pp 52352-52362 (2024)
IEEE Access

Témata: MULTILAYER NEURAL NETWORKS, ELECTRORETINOGRAM, electroretinogram, AUTISM SPECTRUM DISORDERS, gated MLP, TRANSFORMER, ASD, 03 medical and health sciences, CLASSIFICATION (OF INFORMATION), 0302 clinical medicine, GATED MLP, MULTILAYERS, NONINVASIVE MEDICAL PROCEDURES, WAVEFORM, RETINA, DEEP LEARNING, SIGNAL ANALYSIS, deep learning, RECORDING, WAVELET ANALYSIS, TK1-9971, ELECTRORETINOGRAMS, ERG, transformer, WAVELET-ANALYSIS, WAVEFORMS, Electrical engineering. Electronics. Nuclear engineering, NEURODEGENERATIVE DISEASES, MULTILAYERS PERCEPTRONS

Popis souboru: application/pdf

Přístupová URL adresa: https://doaj.org/article/44521ebdae604c5688d995fedecd9eed
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190174297&doi=10.1109/ACCESS.2024.3386638&partnerID=40&md5=b7f4e5d724077ff5dd8330efdd3fb37e
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10495056.pdf
http://elar.urfu.ru/handle/10995/141543

Benefits of Mirror Weight Symmetry for 3D Mesh Segmentation in Biomedical Applications

Autoři: Dordiuk, V. Dzhigil, M. Ushenin, K.

Zdroj: 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)
2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023-Proceedings

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, J.3, HARD CONSTRAINTS, CONVOLUTION, MULTILAYER NEURAL NETWORKS, 3D MESHES, Computer Vision and Pattern Recognition (cs.CV), MEDICAL APPLICATIONS, SYMMETRY IN NEURAL NETWORK, INVERSION INVARIANT, SYMMETRY IN NEURAL NETWORKS, Computer Science - Computer Vision and Pattern Recognition, WEIGHT SYMMETRY, ROTATION INVARIANT, Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, MESH SEGMENTATION, 65D18, 68U10, MESH GENERATION, I.4.6, Image and Video Processing (eess.IV), Electrical Engineering and Systems Science - Image and Video Processing, CONVOLUTIONAL NEURAL NETWORKS, 3D MESH SEGMENTATION, BIOMEDICAL SEGMENTATION, HEART, NEURAL-NETWORKS

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2309.17076
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180377048&doi=10.1109/CSGB60362.2023.10329838&partnerID=40&md5=66ad56216256930400f994c8fa9007e0
http://elar.urfu.ru/handle/10995/131073
https://arxiv.org/pdf/2309.17076

A Machine Learning Architecture Replacing Heavy Instrumented Laboratory Tests: In Application to the Pullout Capacity of Geosynthetic Reinforced Soils

Autoři: Ali, T. Haider, W. Ali, Nazakat a další

Zdroj: Sensors. 22(22)

Témata: ANN, Bayesian regularization, geosynthetic reinforced soil, machine learning, pullout capacity, weathered granite soil, Fuzzy neural networks, Granite, Learning algorithms, Mean square error, Multilayer neural networks, Network architecture, Quality assurance, Soil testing, Soils, Statistical tests, Geosynthetic reinforced soils, Granite soil, Laboratory test, Learning architectures, Machine-learning, Weathered granites, Laboratories

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-61156
https://doi.org/10.3390/s22228699

A novel discrete deep learning-based intelligent methodology for energy consumption classification

Autoři: Mehdi Khashei Fatemeh Chahkoutahi Negar Bakhtiarvand

Zdroj: Energy Reports, Vol 9, Iss, Pp 4861-4871 (2023)

Témata: Energy consumption, 11. Sustainability, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Intelligent modeling and classification, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Deep multilayer neural networks (DMNs), Discrete and continuous learning algorithms, 7. Clean energy, TK1-9971

Přístupová URL adresa: https://doaj.org/article/5dffb1c5a3544d57bb32d2b2fc2a2121

Exponential bipartite synchronization of distributed-order multilayer neural networks with antagonistic interactions via aperiodic intermittent control

Autoři: Dongsheng Yang Hu Wang Guojian Ren a další

Zdroj: Communications in Nonlinear Science and Numerical Simulation. 147:108820

Témata: distributed order calculus, intermittent control, General theory for ordinary differential equations, multilayer neural networks, bipartite synchronization, Model systems in control theory, Stability theory for ordinary differential equations

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/8035185
https://doi.org/10.1016/j.cnsns.2025.108820

Neural Grain implemented in monolithic 3D technology

Autoři: Pletea, I. Alecsandrescu, I. Nicolae, L. a další

Zdroj: International Symposium on Signals, Circuits and Systems

Témata: bioinformatics, computer architecture, Computing power, edge computing, Green computing, Multilayer neural networks, Network architecture, neurons, Three dimensional computer graphics, Topology

Popis souboru: application/pdf

Relation: https://ibn.idsi.md/vizualizare_articol/236268

Dostupnost: https://ibn.idsi.md/vizualizare_articol/236268
https://doi.org/10.1109/ISSCS66034.2025.11105656

Multilayer Neurolearning of Measurement-Information-Poor Hydraulic Robotic Manipulators with Disturbance Compensation

Autoři: Guichao Yang Zhiying Shi

Zdroj: Mathematics ; Volume 13 ; Issue 4 ; Pages: 683

Témata: hydraulic robotic manipulator, hydraulic robot arm, multilayer neural networks, extended state observer, disturbance compensation, measurement noise, input saturation

Popis souboru: application/pdf

Relation: https://dx.doi.org/10.3390/math13040683

Dostupnost: https://doi.org/10.3390/math13040683

Computational anatomy atlas using multilayer perceptron with Lipschitz regularization

Autoři: Ushenin, K. Dordiuk, V. Dzhigil, M.

Zdroj: 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)
2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022

Témata: IMPLICIT REPRESENTATION, LIPSCHITZ REGULARIZATION, LIPSCHITZ, MULTILAYER NEURAL NETWORKS, Quantitative Biology - Tissues and Organs, 02 engineering and technology, REGULARISATION, COMPUTER VISION, COMPUTATIONAL ANATOMY ATLAS, FOS: Biological sciences, 0202 electrical engineering, electronic engineering, information engineering, MULTILAYERS, ML-ENGINEERING, ATLAS GENERATION, LIPSCHITZ CONTINUITY, Tissues and Organs (q-bio.TO), COMPUTATIONAL ANATOMY, MULTILAYERS PERCEPTRONS

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2211.03122
http://elar.urfu.ru/handle/10995/131168
https://arxiv.org/pdf/2211.03122

Data‐driven modeling of band‐pass filter for sub‐5G applications

Autoři: Aysu Belen Mehmet A. Belen İskenderun Meslek Yüksekokulu -- Hibrid ve Elektrikli Taşıtlar Teknolojisi Bölümü a další

Přispěvatelé: Aysu Belen Mehmet A. Belen İskenderun Meslek Yüksekokulu -- Hibrid ve Elektrikli Taşıtlar Teknolojisi Bölümü a další

Zdroj: Microwave and Optical Technology Letters. 65:2210-2216

Témata: Artificial intelligence, Wireless communication system, Surrogate modeling, Speed control, Performance, Design optimization, Multilayer neural networks, Microwave filter, Design solutions, Data driven, Microstrip filters, 5G mobile communication systems, Electromagnetic simulation, Microwave filters, 5g, Signal, Microwave Filters, Support vector machines, Optics, Radiofrequencies, Line, Bandpass filters, Data-driven model, Data driven modeling, Regression, Convolution, Simulation Driven Design, Micro-strips, Antenna, Antennas, Convolutional neural networks, Regression analysis, 5G

Popis souboru: application/pdf

Přístupová URL adresa: https://hdl.handle.net/20.500.12508/2820

Estimation of joint torques using an artificial neural network model based on kinematic and anthropometric data

Autoři: Kasim Serbest Murat Tolga Ozkan Murat Cilli a další

Přispěvatelé: Kasim Serbest Murat Tolga Ozkan Murat Cilli a další

Zdroj: Scopus
Web of Science

Témata: Inverse problems, Anthropometry, Sit-to-stand, Inverse dynamics, Multilayer neural networks, Back Propagation, Torque estimation, Backpropagation, Kinematic data, Joint torques, Network layers, Artificial neural network modeling, Sit to stand, Torque, Joints (anatomy), Model-based OPC, Input datas, Back-propagation, Human limbs

Přístupová URL adresa: https://avesis.gazi.edu.tr/publication/details/a509506f-ffb1-40bf-a28d-17a501e1b11f/oai

Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness

Autoři: Tuna, Ömer Faruk Çatak, Ferhat Özgür Eskil, Mustafa Taner

Zdroj: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). :1098-1103

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Output layer, IOU, Network-based, White box, Object detection, Computer Science - Artificial Intelligence, Multilayer neural networks, Activation functions, Deep learning, 02 engineering and technology, Adversarial machine learning, Chemical activation, Trustworthy AI, Machine Learning (cs.LG), Hidden layers, Artificial Intelligence (cs.AI), Deep neural networks, Loss functions, 0202 electrical engineering, electronic engineering, information engineering, Robustness, Machine-learning

Popis souboru: application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2202.07342
https://hdl.handle.net/11729/5093