Výsledky vyhledávání - hybrid deep convolutional autoencoder~

Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006

Autoři: Djeddou, Messaoud Hellal, Aouatef Hameed, Ibrahim A. a další

Témata: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2408.14626

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Autoři: Yan, Shen Shao, Haidong Xiao, Yiming a další

Zdroj: Robotics and Computer-Integrated Manufacturing. 79

Témata: Machine tools, Deep learning, Unsupervised anomaly detection, Hybrid robust convolutional autoencoder, Noises, Drift och underhållsteknik, Operation and Maintenance

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-92934

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

An Improved Deep Learning Model for DDoS Detection Based on Hybrid Stacked Autoencoder and Checkpoint Network.

Autoři: Mousa, Amthal K. Abdullah, Mohammed Najm

Zdroj: Future Internet. Aug2023, Vol. 15 Issue 8, p278. 16p.

Témata: *Machine learning, Deep learning, Convolutional neural networks, Denial of service attacks, Botnets, Software-defined networking

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

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Autoři: Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San a další

Zdroj: Cognitive Neurodynamics; 5/19/2025, Vol. 19 Issue 1, p1-13, 13p

TriDeepRec: a hybrid deep learning approach to content- and behavior-based recommendation systems.

Autoři: Ghadami, Amirhossein Tran, Thomas

Zdroj: User Modeling & User-Adapted Interaction. Nov2024, Vol. 34 Issue 5, p2085-2114. 30p.

Témata: *STANDARD deviations, *CUSTOMER relations, *RECOMMENDER systems, DEEP learning, BLENDED learning

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps.

Autoři: Altantawy, Doaa A. Yakout, Mohamed A.

Zdroj: Journal of Intelligent Manufacturing. Jun2025, Vol. 36 Issue 5, p3359-3397. 39p.

Témata: *MANUFACTURING processes, *INFORMATION processing, CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, AUTOENCODERS

Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data

Autoři: Geglio, Anthony Hedayati, Eisa Tascillo, Mark a další

Témata: Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, C.3, I.2.1, I.2.6, I.5.1, text

URL: http://arxiv.org/abs/2202.07592

RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS

Autoři: Neha Agarwal Rajendra Pandey P. Smitha Rajagopal

Zdroj: Proceedings on Engineering Sciences, Vol 5, Iss S1, Pp 63-68 (2023)

Témata: tcp/ip packet, 03 medical and health sciences, internet of things (iot), 0302 clinical medicine, intrusion detection system (ids), industrial systems, and splinted decision tree (hdca-sdt), hybrid deep convolutional autoencoder, security, TA1-2040, Engineering (General). Civil engineering (General)

Přístupová URL adresa: https://doaj.org/article/1c2ac034097640d995a3e4e2bb99191b

Detecting Brain Tumor Stages Using Convolutional AutoEncoder (CAE) with Hybrid Deep Learning Method

Autoři: D Agalya S. Kamalakkannan

Zdroj: 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS). :796-803

Breast Cancer Detection using Convolutional Autoencoder with Hybrid Deep Learning Model

Autoři: null S. Ranjana null A. Meenakshi

Zdroj: International Journal of Computational and Experimental Science and Engineering. 11

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images

Autoři: B, Nageswara Rao U Rajendra, Acharya Ru-San, Tan a další

Zdroj: Cognitive Neurodynamics. 19

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40401248

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach

Autoři: Roseline Oluwaseun Ogundokun Pius Adewale Owolawi Chunling Tu a další

Zdroj: Tomography. 11:91

Prediction of wing buffet pressure loads using a convolutional and recurrent neural network framework

Autoři: R. Zahn A. Weiner C. Breitsamter

Zdroj: CEAS Aeronautical Journal. 15:61-77

Témata: Original Paper, Deep learning, Convolutional autoencoder, Long short-term memory neural network, Wing buffet aerodynamics, Airbus XRF-1 configuration, 7. Clean energy, ddc

Popis souboru: application/pdf

Přístupová URL adresa: https://mediatum.ub.tum.de/1763734

Health index estimation through integration of general knowledge with unsupervised learning

Autoři: Bajarunas, Kristupas Baptista, Marcia L. Goebel, Kai a další

Zdroj: Reliability Engineering & System Safety. 251

Témata: Convolutional autoencoder, Health index, Hybrid model, Prognostics, Unsupervised learning, Operation and Maintenance Engineering, Drift och underhållsteknik

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-108434
https://doi.org/10.1016/j.ress.2024.110352

AI-Powered Radiomics for Breast Cancer Prognosis and Aggressiveness Assessment via Ultrasound Imaging

Autoři: Issam Ismail Zaidkilani, Nadeem

Thesis Advisors: García García, Miguel Angel, Puig Valls, Domènec Savi

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: Càncer de Mama - Imatges per Ultrasò, Anàlisi d'Imatges Mèdiques, Diagnòstic Assistit per Ordinador (CAD), Cáncer de Mama - Imágenes por Ultrasonido, Análisis de Imágenes Médicas, Diagnóstico Asistido por Computadora (CAD), Breast Cancer-Ultrasound, Medical Image Analysis, Computer-Aided Diagnosis (CAD), Ciències

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/695647

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets.

Autoři: Salehiyan, Ahmad Moghaddam, Pardis Sadatian Kaveh, Masoud

Zdroj: Future Internet. Jul2025, Vol. 17 Issue 7, p279. 34p.

Témata: Intrusion detection systems (Computer security), Edge computing, Autoencoder, Internet security, Cyber physical systems, Deep learning, Generative adversarial networks

Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors.

Autoři: Chandramouli NA Natarajan S Alharbi AH a další

Zdroj: Scientific reports [Sci Rep] 2024 Dec 28; Vol. 14 (1), pp. 30979. Date of Electronic Publication: 2024 Dec 28.

Způsob vydávání: Journal Article; Retracted Publication

Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Výrazy ze slovníku MeSH: Wearable Electronic Devices* , Human Activities*, Humans ; Neural Networks, Computer ; Deep Learning ; Emergencies ; Telemedicine/instrumentation ; Movement

Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification

Autoři: R. Vijay Anand G. Magesh I. Alagiri a další

Zdroj: Transactions on Emerging Telecommunications Technologies. 36