Výsledky vyhľadávania - "deep autoencoder network"

A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation Systems

Autori: Sumaia Mohammed Al-Ghuribi Shahrul Azman Mohd Noah Mawal A. Mohammed a ďalší

Zdroj: IEEE Access, Vol 12, Pp 174441-174454 (2024)

Predmety: sentiment analysis, collaborative filtering, Arabic language, user reviews, Electrical engineering. Electronics. Nuclear engineering, deep autoencoder network, TK1-9971

Prístupová URL adresa: https://doaj.org/article/b6affff419e448dca46a96e202c36793

Research on a Feature Enhancement Extraction Method for Underwater Targets Based on Deep Autoencoder Networks

Autori: Fang Ji Guonan Li Shaoqing Lu a ďalší

Zdroj: Applied Sciences, Vol 14, Iss 4, p 1341 (2024)

Predmety: large underwater vehicles, deep autoencoder network, very low signal-to-noise ratio, line spectrum enhancement, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/14/4/1341; https://doaj.org/toc/2076-3417; https://doaj.org/article/2f5ecb6bba1b4f8abaefd7de080c6198

Dostupnosť: https://doi.org/10.3390/app14041341
https://doaj.org/article/2f5ecb6bba1b4f8abaefd7de080c6198

Identification of Traffic Flow Spatio-Temporal Patterns and Their Associated Weather Factors: A Case Study in the Terminal Airspace of Hong Kong

Autori: Weining Zhang Weijun Pan Xinping Zhu a ďalší

Zdroj: Aerospace, Vol 11, Iss 7, p 531 (2024)

Predmety: air traffic flows, flight trajectory, weather factors, deep autoencoder network, clustering, association rules, Motor vehicles. Aeronautics. Astronautics, TL1-4050

Relation: https://www.mdpi.com/2226-4310/11/7/531; https://doaj.org/toc/2226-4310; https://doaj.org/article/2d68512899204e18940e1e1fb99640cc

Dostupnosť: https://doi.org/10.3390/aerospace11070531
https://doaj.org/article/2d68512899204e18940e1e1fb99640cc

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Autori: Xinping Gu Yunpeng Han Junfu Yu

Zdroj: IEEE Access, Vol 8, Pp 9846-9863 (2020)

Predmety: Autonomous vehicle, 0502 economics and business, 05 social sciences, Electrical engineering. Electronics. Nuclear engineering, deep autoencoder network, lane-changing identification, lane-changing decision-making, XGBoost, TK1-9971

Prístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08950329.pdf
https://doaj.org/article/67071ac15d7c48968c49a9255c43bf9e
https://dblp.uni-trier.de/db/journals/access/access8.html#GuHY20
https://ieeexplore.ieee.org/document/8950329/

DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Autori: Yuanchao Su Jun Li Antonio Plaza a ďalší

Prispievatelia: Yuanchao Su Jun Li Antonio Plaza a ďalší

Zdroj: IEEE Transactions on Geoscience and Remote Sensing. 57:4309-4321

Predmety: Deep autoencoder network (DAEN), endmember identification, hyperspectral unmixing, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, deep learning, variational autoencoder (VAE), 02 engineering and technology

Popis súboru: STAMPA

Prístupová URL adresa: https://ieeexplore.ieee.org/document/8628241
https://doi.org/10.1109/TGRS.2018.2890633
https://dblp.uni-trier.de/db/journals/tgrs/tgrs57.html#SuLPMGC19
https://ui.adsabs.harvard.edu/abs/2019ITGRS..57.4309S/abstract