Suchergebnisse - deep stack autoencoder~

Application of deep stack autoencoder network in ship weight estimation

Autoren: CHEN Jian,TANG Junyao,ZHU Shengguang,ZHOU Zhaozhao

Quelle: Jisuanji gongcheng, Vol 45, Iss 5, Pp 315-320 (2019)

Schlagwörter: airbag ship launching, deep learning, inverse gradient descent, deep stack autoencoder, layer by layer unsupervised learning, parameters fine-tuning, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/195315.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/0686b609d7de426aa1e6e2d879173cc7

An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP‐Based DRDoS Attack Detection.

Autoren: Sreenivasulu, V. Ravikumar, C. V. Poruran, Sivakumar

Quelle: International Journal of Distributed Sensor Networks. 8/7/2025, Vol. 2025, p1-24. 24p.

Schlagwörter: *DEEP learning, *DENIAL of service attacks, *INTRUSION detection systems (Computer security), *SECURITY management, *ELECTRONIC authentication, *OPTIMIZATION algorithms, *SESSION Initiation Protocol (Computer network protocol)

A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model

Autoren: Zhou, Chuanyan Ma, Jie Li, Fan et al.

Index Begriffe: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2210.14956

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

Autoren: Mousa, Amthal K. Abdullah, Mohammed Najm

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

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

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

Autoren: Yan Chu Syed Muhammad Ali Mingfeng Lu et al.

Quelle: Entropy, Vol 25, Iss 8, p 1194 (2023)

Schlagwörter: heterogeneous features, random subspace method, bearing fault diagnosis, deep stack autoencoder, lasso, Science, Astrophysics, QB460-466, Physics, QC1-999

Relation: https://www.mdpi.com/1099-4300/25/8/1194; https://doaj.org/toc/1099-4300; https://doaj.org/article/3acb3794c8c34b56ab148f36b36be24a

Verfügbarkeit: https://doi.org/10.3390/e25081194
https://doaj.org/article/3acb3794c8c34b56ab148f36b36be24a

Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals.

Autoren: Sahani, Mrutyunjaya Rout, Susanta Kumar Dash, Pradipta Kishor

Quelle: IEEE Transactions on Biomedical Circuits & Systems; Jun2021, Vol. 16 Issue 3, p595-605, 11p

Adaptive hybrid optimization enabled stack autoencoder-based MapReduce framework for big data classification

Autoren: S.Md. Mujeeb K. Madhavi R. Praveen Sam

Quelle: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). :1-5

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002277794259561

Robust broad learning system with parametrized variational mode decomposition for schizophrenia diagnosis.

Autoren: Parija, Sebamai Sahani, Mrutyunjaya Rout, Susanta Kumar

Quelle: Engineering Applications of Artificial Intelligence. Oct2025:Part B, Vol. 158, pN.PAG-N.PAG. 1p.

Schlagwörter: *SCHIZOPHRENIA, *FEATURE extraction, *MATRIX decomposition, *COMPUTER-aided diagnosis, *NEUROPHYSIOLOGY, *DEEP learning, *MACHINE learning

Classification method for imbalanced LiDAR point cloud based on stack autoencoder

Autoren: Peng Ren Qunli Xia

Quelle: Electronic Research Archive, Vol 31, Iss 6, Pp 3453-3470 (2023)

Schlagwörter: lidar point cloud, imbalanced classification, deep neural network, stack autoencoder, oversampling, Mathematics, QA1-939, Applied mathematics. Quantitative methods, T57-57.97

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2688-1594

Zugangs-URL: https://doaj.org/article/9970255490c2435c9d3acc6171ba6d49

Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery

Autoren: Eftychios Protopapadakis Anastasios Doulamis Nikolaos Doulamis et al.

Quelle: Remote Sensing, Vol 13, Iss 3, p 371 (2021)

Schlagwörter: semi-supervised learning, deep learning, stack autoencoders, building detection, remote sensing, semantic segmentation, Science

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2072-4292/13/3/371; https://doaj.org/toc/2072-4292

Zugangs-URL: https://doaj.org/article/cd15bfe4145a455bbb00617947fb93f9

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

Autoren: Amthal K. Mousa Mohammed Najm Abdullah

Quelle: Future Internet, Vol 15, Iss 8, p 278 (2023)

Schlagwörter: DDoS detection, distributed denial of service, software defined networking, SDN, network security, Information technology, T58.5-58.64

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1999-5903/15/8/278; https://doaj.org/toc/1999-5903

Zugangs-URL: https://doaj.org/article/7bff68711c434953a108e475f16d50b7

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Autoren: Aulia Fadli Wisnu Ananta Kusuma Annisa et al.

Quelle: Big Data and Cognitive Computing, Vol 5, Iss 4, p 75 (2021)

Schlagwörter: coronavirus disease 2019, drug repurposing, drug-target interaction, health, multilabel classification, stack autoencoder-deep neural network, Technology

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2504-2289/5/4/75; https://doaj.org/toc/2504-2289

Zugangs-URL: https://doaj.org/article/ca744c7ce45245099ae201d6867b4ccb

Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis.

Autoren: Liu, Zhao-Hua Lu, Bi-Liang Wei, Hua-Liang et al.

Quelle: IEEE Transactions on Systems, Man & Cybernetics. Systems. Jul2021, Vol. 51 Issue 7, p4217-4226. 10p.

Schlagwörter: *FAULT diagnosis, *DEEP learning, *ROLLER bearings, *MACHINE learning, *ROTATING machinery, *ALGORITHMS

Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network.

Autoren: Sahani, Mrutyunjaya Choudhury, Sasmita Siddique, Marif Daula et al.

Quelle: Engineering Applications of Artificial Intelligence. Oct2024:Part A, Vol. 136, pN.PAG-N.PAG. 1p.

Schlagwörter: *FIELD programmable gate arrays, *SOLAR temperature, *SOLAR power plants, *STATISTICAL correlation, *ENERGY shortages

Classification method for imbalanced LiDAR point cloud based on stack autoencoder.

Autoren: Ren, Peng Xia, Qunli

Quelle: Electronic Research Archive. 2023, Vol. 31 Issue 6, p1-18. 18p.

Schlagwörter: *LIDAR, *POINT cloud, *BIG data, *COMPUTATIONAL complexity, *DATA analysis

Stack Autoencoder Transfer Learning Algorithm for Bearing Fault Diagnosis Based on Class Separation and Domain Fusion.

Autoren: Sun, Meidi Wang, Hui Liu, Ping et al.

Quelle: IEEE Transactions on Industrial Electronics. Mar2022, Vol. 69 Issue 3, p3047-3058. 12p.

Schlagwörter: *FAULT diagnosis, *MACHINE learning, *DATA distribution, *SUPPLY & demand, *CLASS differences

RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation.

Autoren: Raiguru, Priyadarshini Swain, Bhanja Kishor Rout, Susanta Kumar et al.

Quelle: Applied Soft Computing. Sep2024, Vol. 162, pN.PAG-N.PAG. 1p.

Schlagwörter: Convolutional neural networks, Standard deviations, Supervised learning, Direction of arrival estimation, Field programmable gate arrays, Deep learning

An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder

Autoren: Jinhua Guo Jiaquan Wang Fang Xiao et al.

Quelle: Sensors, Vol 23, Iss 8, p 3908 (2023)

Schlagwörter: lightning, deep learning, feature compression, autoencoder, convolutional neural network, Chemical technology, TP1-1185

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1424-8220/23/8/3908; https://doaj.org/toc/1424-8220

Zugangs-URL: https://doaj.org/article/c3cb0b126366427fb891b370a1f6085d

基于SAE和WGAN 的入侵检测方法研究.

Alternate Title: Research on intrusion detection method based on SAE and WGAN.

Autoren: 刘拥民 许 成 黄 浩 et al.

Quelle: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Feb2025, Vol. 47 Issue 2, p256-264. 9p.

Network Traffic Classification Based on Deep Learning.

Autoren: Junwei Li Zhisong Pan

Quelle: KSII Transactions on Internet & Information Systems. Nov2020, Vol. 14 Issue 11, p4246-4267. 22p.

Schlagwörter: Deep learning, Convolutional neural networks, Long-term memory, Machine learning, Classification, Computer network security