Výsledky vyhledávání - deep conventional autoencoder network

A Novel PAPR Reduction Scheme Based on Deep Autoencoder Network for FBMC Systems

Autoři: Xing Cheng Deli Chen Shuaishuai Li a další

Zdroj: IEEE Access, Vol 13, Pp 68948-68958 (2025)

Témata: peak to average power ratio, denoising autoencoder network, deep learning, Filter bank multicarrier, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Přístupová URL adresa: https://doaj.org/article/678c4448230241fc87fe49f3e61edf69

Detection, quantification, malignancy prediction and growth forecasting of pulmonary nodules using deep learning in follow-up CT scans

Autoři: Rafael-Palou, Xavier

Přispěvatelé: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: González Ballester, Miguel Ángel, Piella Fenoy, Gemma, Ribas Ripoll, Vicent Jordi

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: Lung cancer prediction, Nodule detection, Nodule segmentation, Nodule characterization, Nodule malignancy estimation, Nodule follow-up, Nodule re-identification, Nodule growth forecasting, Uncertainty modelling and quantification, Convolutional neural networks, Variational autoencoders, Siamese neural networks, Generative networks, Transfer learning, Computer vision, Deep learning, Machine learning, Predicció del càncer de pulmó, Detecció de nòduls, Segmentació de nòduls, Caracterització de nòduls, Predicció de malignitat de nòduls, Seguiment de nòduls, Re-identificació de nòduls, Creixement de nòduls, Modelatge i quantificació de la malignitat, Xarxes neuronals convolucionals, Autoencoders variacional, Xarxes neuronals siameses, Xarxes generatives, Aprenentatge per transferència, Pipelines, Visió per computador, Aprenentatge profund, Aprenentatge automàtic

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/672964

Deep learning techniques for next generation wireless networks

Autoři: Al Awad, Mohamad Hamdi, Khairi Alsusa, Emad

Přispěvatelé: Al Awad, Mohamad Hamdi, Khairi Alsusa, Emad

Témata: Variational autoencoders, Autoencoder, End-to-end wireless communications system, Machine learning, Deep learning

Přístupová URL adresa: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.886162

Artificial Neural Networks in the Wastewater Industry. From Conventional to Data-based Industrial Control

Autoři: Pisa Dacosta, Ivan

Thesis Advisors: Morell Pérez, Antoni, López Vicario, José, Vilanova i Arbós, Ramon

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: Xarxes neuronals artificials, Redes neuronales artificiales, Artificial neural networks, Control automàtic, Control automático, Automatic control, Estacions depuradores d'aigües residuals, Estaciones depuradoras de aguas residuales, Wastewater treatment plants, Tecnologies

Time: 621.3

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/675219

A Deep Learning-Based Method for Mechanical Equipment Unknown Fault Detection in the Industrial Internet of Things.

Autoři: Liu, Xiaokai Meng, Xiangheng Ning, Lina a další

Zdroj: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p5984, 26p

Témata: DEEP learning, FAULT diagnosis, INDUSTRY 4.0, CONVOLUTIONAL neural networks, AUTOENCODERS, LONG short-term memory, OUTLIER detection, MACHINE learning

Assessment of energy-efficient wireless network using autoencoders with unsupervised deep learning.

Autoři: Abdullah, Mahmood Zaki Hummadi, Fadia Noori

Zdroj: Service Oriented Computing & Applications; Dec2024, Vol. 18 Issue 4, p349-360, 12p

A Deep Learning based Integrated Memory Aware Twin AutoEncoder Network for Anomaly Detection in Video Surveillance on Edge Devices.

Autoři: Suma, S. M., Ramakrishna

Zdroj: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 1, p1162-1172, 11p

Témata: ANOMALY detection (Computer security), AUTOENCODERS, DEEP learning, PUBLIC safety, VIDEO surveillance, SENSOR networks

Performing Hybrid Spectrum Sensing with an Adaptive and Attentive Multi-stacked Deep Learning Network in a Cognitive Radio Network.

Autoři: Koteswara Rao, R. Sahaai, Madona B. Sharanya, C.

Zdroj: Journal of Information & Knowledge Management. Aug2025, Vol. 24 Issue 4, p1-36. 36p.

Témata: *Real-time computing, Cognitive radio, Deep learning, Mathematical optimization, Convolutional neural networks, Signal detection, Long short-term memory

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

Autoři: Tao R Li H Lu J a další

Zdroj: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Oct; Vol. 62 (10), pp. 3089-3106. Date of Electronic Publication: 2024 May 22.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE

Výrazy ze slovníku MeSH: Diabetic Retinopathy*/diagnosis , Blood Glucose*/analysis , Neural Networks, Computer*, Humans ; Diabetes Mellitus, Type 2/diagnosis ; Diabetes Mellitus, Type 2/blood ; Blood Glucose Self-Monitoring/methods ; Blood Glucose Self-Monitoring/instrumentation ; Wearable Electronic Devices ; Deep Learning ; Male ; Female ; Algorithms

Multi-scale Residual Segmentation Network for Histopathological Image.

Autoři: BOZDAĞ KARAKEÇİ, Zehra TALU, Muhammed Fatih

Zdroj: Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi; eyl2024, Vol. 15 Issue 3, p623-632, 10p

Témata: CONVOLUTIONAL neural networks, DEEP learning, IMAGE segmentation, OBJECT recognition (Computer vision), AUTOENCODERS, PATHOLOGY, ARTIFICIAL neural networks, IMAGE processing, ARTIFICIAL intelligence, MARKOV random fields, TRAINING needs

Multilinear hyperspectral unmixing based on autoencoder and recurrent neural network.

Autoři: Jin, Zehui Yi, Xiaorui Liu, Yue a další

Zdroj: Applied Soft Computing. Dec2025:Part B, Vol. 185, pN.PAG-N.PAG. 1p.

Témata: Autoencoders, Recurrent neural networks, Remote sensing, Deep learning, Nonlinear statistical models, Light scattering

A deep multiple self-supervised clustering model based on autoencoder networks.

Autoři: Zhu, Ling Liu, Zijin Liu, Guangyu

Zdroj: Scientific Reports; 5/26/2025, Vol. 15 Issue 1, p1-14, 14p

Témata: CLUSTERING algorithms, ARTIFICIAL intelligence, AUTOENCODERS, IMAGE processing, COMPUTER workstation clusters

KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network

Autoři: Aditya Khamparia Gurinder Saini Babita Pandey a další

Zdroj: Multimedia Tools and Applications. 79:35425-35440

Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Přístupová URL adresa: http://dblp.uni-trier.de/db/journals/mta/mta79.html#KhampariaSPT0K20
https://dblp.uni-trier.de/db/journals/mta/mta79.html#KhampariaSPT0K20
https://doi.org/10.1007/s11042-019-07839-z
https://link.springer.com/article/10.1007/s11042-019-07839-z

A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks.

Autoři: Kumar, Anubhav Radhakrishnan, Rajamani Sumithra, Mani a další

Zdroj: Future Internet. May2025, Vol. 17 Issue 5, p221. 18p.

Témata: *Machine learning, Extreme learning machines, Autoencoder, Blended learning, Infrastructure (Economics)

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning

Autoři: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. a další

Zdroj: Sensors. 25(16)

Témata: Autoencoders, Control Of Dynamical Systems, Cyber-physical Systems, Deep Reinforcement Learning, Long Short-term Memory, Sensor-driven Modeling, Circular Cylinders, Computer Control Systems, Convolution, Dynamical Systems, Dynamics, Embedded Systems, Flow Control, Fuel Additives, Nonlinear Dynamical Systems, Real Time Control, Robust Control, Auto Encoders, Control Of Dynamical System, Cybe-physical Systems, High-dimensional, Higher-dimensional, Reinforcement Learning Agent, Reinforcement Learnings, Short Term Memory

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-73172
https://doi.org/10.3390/s25165149

Hybrid Greylag Goose deep learning with layered sparse network for women nutrition recommendation during menstrual cycle.

Autoři: Logapriya, E. Rajendran, Surendran Zakariah, Mohammad

Zdroj: Scientific Reports; 2/18/2025, Vol. 15 Issue 1, p1-22, 22p

Témata: OPTIMIZATION algorithms, DEEP learning, AUTOENCODERS, NUTRITIONAL requirements, FEATURE extraction, MENSTRUATION, MENSTRUAL cycle

A hybrid 1DCNN-GRU deep learning framework for classifying caprine granulosa cell fertility potential using single-cell transcriptomics.

Autoři: Sananmuang, Thanida Puthier, Denis Chokeshaiusaha, Kaj

Zdroj: Veterinary World. Jul2025, Vol. 18 Issue 7, p1922-1935. 14p.

Témata: Granulosa cells, Fertility, Convolutional neural networks, Transcriptomes, Deep learning, Recurrent neural networks, Goats, Biomarkers

A Hybrid Deep Physics Neural Network Model for Unsupervised Autoencoder to Perform Data Conditioning

Autoři: Srinath Madasu Keshava Prasad Rangarajan

Zdroj: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). :1-7

Témata: 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Přístupová URL adresa: https://dblp.uni-trier.de/db/conf/isspit/isspit2019.html#MadasuR19

LSTM-Autoencoder Network for the Detection of Seismic Electric Signals.

Autoři: Xue, Jiyan Huang, Qinghua Wu, Sihong a další

Zdroj: IEEE Transactions on Geoscience & Remote Sensing. Jun2022, Vol. 60, p1-12. 12p.

Témata: *DEEP learning, *SEISMIC networks, *EARTHQUAKE prediction, *HAZARD mitigation, *ELECTRONIC data processing, *CONVOLUTIONAL neural networks, *MAGNETOTELLURICS

Deep Spectral Clustering Using Dual Autoencoder Network

Autoři: Xu Yang Junchi Yan Feng Zheng a další

Zdroj: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :4061-4070

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), 02 engineering and technology, Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/pdf/1904.13113
http://arxiv.org/abs/1904.13113
https://dblp.uni-trier.de/db/conf/cvpr/cvpr2019.html#YangDZYL19
http://dblp.uni-trier.de/db/conf/cvpr/cvpr2019.html#YangDZYL19
https://ieeexplore.ieee.org/document/8953592/
https://www.arxiv-vanity.com/papers/1904.13113/
https://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Deep_Spectral_Clustering_Using_Dual_Autoencoder_Network_CVPR_2019_paper.html
https://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Deep_Spectral_Clustering_Using_Dual_Autoencoder_Network_CVPR_2019_paper.pdf