Suchergebnisse - "Convolutional neural network"

MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification

Autoren: Rayed, Md. Eshmam Jim, Jamin Rahman Islam, Md Juniadul et al.

Quelle: IEEE Access. 13:93977-94008

Schlagwörter: Accuracy, Plant diseases, Computational modeling, Deep learning, Computer architecture, Crops, Mathematical models, Robustness, Real-time systems, Image classification, Image processing, convolutional neural network, mango leaf disease, explainable AI

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-72165
https://doi.org/10.1109/ACCESS.2025.3571450

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

Autoren: Dutta, M. Uddin Mojumdar, M. Alamgir Kabir, M. et al.

Quelle: IEEE Access. 13:31630-31642

Schlagwörter: 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

Dateibeschreibung: print

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

Deep Learning for Detection of Harmful Events in Real-World, Noisy Optical Fiber Deployments

Autoren: Sadighi, Leyla Karlsson, Stefan Natalino Da Silva, Carlos et al.

Quelle: Journal of Lightwave Technology. 43(13):6092-6101

Schlagwörter: fully-connected layers, One-Dimension (1D) Convolutional Neural Network (CNN), harmful vibration, nonharmful vibration, Machine Learning (ML), State of Polarization (SOP) variations, eavesdropping, Deep Learning (DL)

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/545956
https://research.chalmers.se/publication/545956/file/545956_Fulltext.pdf

Self-explaining analysis of facility environments on 2-lane rural roads with an improved lightweight CNN considering drivers’ visual perception

Autoren: Ren, Weixi Yu, Bo Chen, Yuren et al.

Quelle: International Journal of Transportation Science and Technology. 19:99-113

Schlagwörter: Improved lightweight convolutional neural network, Road category perception, Self-explaining analysis, Speeding, Drivers’ visual perception characteristics

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/542530
https://research.chalmers.se/publication/542530/file/542530_Fulltext.pdf

Detection-Guided Attention for Selective Target Classification Using Radar Micro-Doppler Spectrograms

Autoren: Gusland, Daniel Rolfsjord, Sigmund Ahlberg, Jörgen

Quelle: IEEE Sensors Journal. 25(8):14370-14378

Schlagwörter: Spectrogram, Radar tracking, Target tracking, Doppler effect, Training, Autonomous aerial vehicles, Sensor phenomena and characterization, Convolutional neural networks, Signal to noise ratio, Propellers, Attention, classification, convolutional neural network (CNN), micro-Doppler, multitarget, radar

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-213450
https://doi.org/10.1109/JSEN.2025.3545378

MaksChroms: An open-source CNN-based tool for dose prediction based on dicentric chromosome assay

Autoren: Głowacki, Maksymilian Bucher, Martin Endesfelder, David et al.

Quelle: Radiation Physics and Chemistry. 239

Schlagwörter: Artificial intelligence, Biological dosimetry, Convolutional neural network, Dicentric chromosomes, Gamma radiation, MaksChroms

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-247836
https://doi.org/10.1016/j.radphyschem.2025.113329

An efficient hybrid Hopfield convolutional neural network for detecting spam bots in Twitter platform

Autoren: A.V. Santhosh Kumar N. Suresh Kumar R. Kanniga Devi

Quelle: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 569-587 (2025)

Schlagwörter: Stop word removal, stemming, Binary golden search optimization algorithm, Electronic computers. Computer science, Science, Spam bot detection, Hybrid Hopfield convolutional neural network, QA75.5-76.95, Online social networks

Zugangs-URL: https://doaj.org/article/3a0112cc05c347a68ba0384a76c6ff3a

Machine-Learning-Based Probe Skew Correction for High-Frequency BH Loop Measurements

Autoren: Yakun Wang Song Liu Jun Wang et al.

Quelle: Wang, Y, Liu, S, Wang, J, Cui, B & Yang, J 2025, 'Machine Learning Based Probe Skew Correction for High-frequency BH Loop Measurements', IEEE Transactions on Power Electronics, vol. 40, no. 10, pp. 14356-14362. https://doi.org/10.1109/TPEL.2025.3564663

Schlagwörter: BH loop measurement, Signal Processing (eess.SP), machine learning, power magnetics, High-frequency, Convolutional neural network (CNN), Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2501.12209
https://research-information.bris.ac.uk/ws/files/454238525/FINAL_VERSION_ver2.PDF
https://hdl.handle.net/1983/9949866f-a02d-466b-9057-bb6974f39ee6

An Enhanced Noise Removal-based SAR Image Recognition Using DnCNN and Wavelet Transform

Autoren: Choi, Y. Kim, G. Kim, B. et al.

Quelle: Radioengineering. 34:429-437

Schlagwörter: noise, Navy SAR, Convolutional Neural Network (CNN), Denoising Convolutional Neural Network (DnCNN), wavelet transform

Dateibeschreibung: text; application/pdf

Zugangs-URL: https://hdl.handle.net/11012/255213

Reinforcement learning-driven feature selection enhanced by an evolutionary approach tuning for criminal suspect identification

Autoren: Gao, Zhenming Jian, Zhang Seyed Jalaleddin, Mousavirad

Quelle: Scientific Reports. 15(1)

Schlagwörter: Convolutional neural network, Crime, Criminal suspects, Differential evolution, Proximal policy optimization

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-56087
https://doi.org/10.1038/s41598-025-25920-6

An ensemble model based on transfer learning for the early detection of Alzheimer’s disease

Autoren: Asghari Varzaneh, Zahra Mousavi, Seyyed Mohammad Khoshkangini, Reza et al.

Quelle: Scientific Reports. 15(1)

Schlagwörter: Alzheimer’s disease, Convolutional neural network, Ensemble learning, Medical imaging, Transfer learning

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-80005
https://doi.org/10.1038/s41598-025-22025-y

Fly pollinator foraging in boreal forests is shaped by climate, forest structure and flower resources

Autoren: Díaz-Calafat, Joan Öckinger, Erik Felton, Adam et al.

Quelle: Oikos.

Schlagwörter: Canopy openness, climate change, climatic refugia, Convolutional Neural Network, deep learning, diptera, forest composition, pollinator conservation, YOLOv5

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-247979
https://doi.org/10.1002/oik.11405

Beam Offset Detection in Laser Stake Welding of T-Joints Using a Convolutional Neural Network

Autoren: Nilsen, Morgan Mi, Yongcui Sikström, Fredrik

Quelle: IOP Conference Series. 1332(1)

Schlagwörter: beam offset detection, laser stake welding, T-joint, convolutional neural network, Production Technology, Produktionsteknik

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-24333
https://doi.org/10.1088/1757-899x/1332/1/012035

Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods

Autoren: Chaari, Nada Winski, Greg Hallbäck, Magnus et al.

Quelle: Journal of clinical monitoring and computing.

Schlagwörter: Arterial blood pressure, Deep convolutional neural network, Hypotension events, Intraoperative hypotension, MAP-derived methods, Model generalizability

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-370402
https://doi.org/10.1007/s10877-025-01357-0

A convolutional neural network-based joint detection and localization spatiotemporal scheme for process control through speckle pattern imaging

Autoren: Sabahno, Hamed Khodadad, Davood

Quelle: Computers & industrial engineering. 210

Schlagwörter: Convolutional neural network, Process control, Control charts, Image data, Speckle pattern analysis, Monte Carlo simulation, measurement, deformation, engineering science with specialization in microsystems technology, teknisk fysik med inriktningen mikrosystemteknik

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-244533
https://doi.org/10.1016/j.cie.2025.111538

Koopman theory-inspired method for learning time advancement operators in unstable flame front evolution

Autoren: Yu, Rixin Herbert, Marco Klein, Markus et al.

Quelle: Physics of fluids. 37(2)

Schlagwörter: Nonlinear equations, Premixed flames, Chaotic behaviour, Convolutional neural network, Flame front, Fourier, High-dimensional, Higher-dimensional, Learn+, Learning time, Partial differential, Unstable flames, Convolutional neural networks

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-78046
https://doi.org/10.1063/5.0252716

Automatic detection of grains in partially recrystallized microstructures using deep learning

Autoren: Lin, Fengxiang Fang, Haixing Liu, Hong et al.

Quelle: Materials Characterization. 219

Schlagwörter: Deep neural networks, Image segmentation, Recrystallization (metallurgy), Automatic Detection, Convolutional neural network, Deep learning, EBSD, Instance segmentation, Quantitative information, Recrystallisation, Recrystallization process, Recrystallized microstructures, User friendly, Convolutional neural networks, Maskinteknik, Mechanical Engineering, Materialteknik, Materials Engineering

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-102531
https://doi.org/10.1016/j.matchar.2024.114576

Deep insights into gastrointestinal health: A comprehensive analysis of GastroVision dataset using convolutional neural networks and explainable AI

Autoren: Fahad, Muhammad Mobeen, Noor E Imran, Ali Shariq et al.

Quelle: Biomedical Signal Processing and Control. 102

Schlagwörter: GastroVision, Deep learning, Model soups, Model explainability, Generative AI, Explainable AI (XAI), Generative adversarial network (GAN), Convolutional neural network (CNN), Health Informatics, Hälsoinformatik

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-133748
https://doi.org/10.1016/j.bspc.2024.107260

From data scarcity to diagnostic precision: A novel data augmentation and fault diagnosis framework for district heating substations

Autoren: van Dreven, Jonne Cheddad, Abbas Alawadi, Sadi et al.

Quelle: Engineering applications of artificial intelligence. 151

Schlagwörter: Convolutional Neural Network, Data augmentation, District heating, Fault diagnosis, Long Short-Term Memory, Transfer learning, Benchmarking, Premixed flames, Rectifier substations, Automated fault detection, Data faults, Data scarcity, Fault localization, Faults diagnosis, Heating substations, Short term memory, Convolutional neural networks

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-27717
https://doi.org/10.1016/j.engappai.2025.110662

An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty

Autoren: Basnin, Nanziba Mahmud, Tanjim Islam, Raihan Ul et al.

Quelle: Diagnostics. 15(1)

Schlagwörter: Alzheimer’s disease, convolutional neural network (CNN), federated learning, belief rule base, FedAvg, FedProx, genetic algorithm, Cyber Security, Cybersäkerhet

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-111329
https://doi.org/10.3390/diagnostics15010080