Suchergebnisse - Restricted stacked autoencoder*

Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder—DeepBreath

Autoren: Soleymani, Farzan Paquet, Eric

Quelle: Expert Systems with Applications. 156:113456

Schlagwörter: blockchain, deep reinforcement learning, Artificial Intelligence, General Engineering, 0202 electrical engineering, electronic engineering, information engineering, portfolio management, online leaning, 02 engineering and technology, settlement risk, Knowmad Institut, Computer Science Applications, restricted stacked autoencoder

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.sciencedirect.com/science/article/pii/S0957417420302803
https://www.sciencedirect.com/science/article/abs/pii/S0957417420302803
https://dblp.uni-trier.de/db/journals/eswa/eswa156.html#SoleymaniP20
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002289207461966
https://doi.org/10.1016/j.eswa.2020.113456
https://www.openaccessrepository.it/record/84775

Traceability and analysis method for measurement laboratory testing data based on intelligent Internet of Things and deep belief network

Autoren: He PeiDong Li XiaoJun Shen WenQi et al.

Quelle: Journal of Intelligent Systems, Vol 33, Iss 1, Pp 6821-30 (2024)

Schlagwörter: intelligent iot, deep belief network, metrology laboratory, data traceability, abnormal analysis, stacked denoising autoencoder model, cosine similarity, gaussian bernoulli restricted boltzmann machine, cloud-management-edge-end, Science, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2191-026X

Zugangs-URL: https://doaj.org/article/b2a8183e0b4746a7aaeba8a4f7b0893c

Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization.

Autoren: Zeng, Chi Li, Jialing Habibi, Abbas

Quelle: Scientific Reports; 7/18/2025, Vol. 15 Issue 1, p1-16, 16p

Schlagwörter: EMOTION recognition, PARTICLE swarm optimization, COMPUTER performance, OPTIMIZATION algorithms, DEEP learning, FEATURE extraction, AUTOENCODERS

SAE-GAN: integrating stacked attention autoencoder and generative adversarial networks for multivariate time series anomaly detection.

Autoren: Sun, Shimin Liu, Xiangyun Han, Li et al.

Quelle: Applied Intelligence; Nov2025, Vol. 55 Issue 16, p1-18, 18p

A hybrid stacked sparse autoencoder and LightGBM framework for high-performance intrusion detection in IoT networks.

Autoren: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

Quelle: Iran Journal of Computer Science; Dec2025, Vol. 8 Issue 4, p2175-2198, 24p

Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models.

Autoren: Abdussamad Daud, Hanita Sokkalingam, Rajalingam et al.

Quelle: Engineering Reports; Sep2025, Vol. 7 Issue 9, p1-17, 17p

Schlagwörter: TYPE 2 diabetes, MACHINE learning, CLINICAL decision support systems, METADATA, FORECASTING, FEATURE extraction

Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder.

Autoren: Jorge, T. Magalhães, J. Silva, R. et al.

Quelle: Vehicle System Dynamics; Feb2025, Vol. 63 Issue 2, p232-257, 26p

Schlagwörter: FEATURE extraction, RAILROAD trains, AUTOENCODERS, ARTIFICIAL intelligence, MULTISENSOR data fusion, ROLLING stock

Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder.

Autoren: Wang, Ying Lu, Hong Xiao, Xianyong et al.

Quelle: IET Generation, Transmission & Distribution (Wiley-Blackwell). Apr2020, Vol. 14 Issue 8, p1242-1250. 9p.

Enhanced particle swarm optimization-based hyperparameter optimized stacked autoencoder for credit card fraud detection.

Autoren: Abadlia, Houda Smairi, Nadia

Quelle: International Journal of Data Science & Analytics; Aug2025, Vol. 20 Issue 2, p1239-1253, 15p

Machine learning-based analysis of multi-region bone fracture detection and classification using biomedical images

Autoren: Alla M. Elsheikh Azhari A. Elhag

Quelle: Alexandria Engineering Journal, Vol 128, Iss , Pp 186-199 (2025)

Schlagwörter: Bone Fracture Detection, Wild Gibbon Optimization, Weiner Filtering, DenseNet201, Stacked Autoencoder, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1110016825007033; https://doaj.org/toc/1110-0168

Zugangs-URL: https://doaj.org/article/c76d016c267f428c9768e54f322e1e24

Classification of autistic subjects employing modified volume local binary pattern (MVLBP) and stacked Autoencoder (SAE) on functional magnetic resonance imaging (fMRI).

Autoren: M., Kaviya Elakkiya Dejey

Quelle: Multimedia Tools & Applications; Jun2025, Vol. 84 Issue 20, p22161-22186, 26p

Schlagwörter: FUNCTIONAL magnetic resonance imaging, INDEPENDENT component analysis, AUTISM spectrum disorders, IMAGE processing, DEEP learning

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model

Autoren: Xiaoai Dai Junying Cheng Shouheng Guo et al.

Quelle: Discrete Dynamics in Nature and Society, Vol 2023 (2023)

Schlagwörter: Mathematics, QA1-939

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1607-887X

Zugangs-URL: https://doaj.org/article/71c0bb7c7e5e435bbe38d9f526122872

Type 2 Diabetes Data Classification Using an Attention-based Stacked Autoencoder and TabNet Hybrid Model.

Autoren: Aleesa, Rula Sami Al-bermani, Noora kadhim Ismael, Hayder A.

Quelle: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 3, p761-772, 12p

Schlagwörter: CLINICAL decision support systems, TYPE 2 diabetes, MACHINE learning, DISEASE risk factors, RECEIVER operating characteristic curves, FEATURE extraction

A study on the similarities of Deep Belief Networks and Stacked Autoencoders

Autoren: de Giorgio, Andrea

Schlagwörter: Deep Learning, Restricted Boltzmann Machine, Stacked Autoencoder, Autoencoder, Depp Belief Network, Machine Learning, Computer Science, F-Mapping, Electrical Engineering, Electronic Engineering, Information Engineering, Elektroteknik och elektronik

Dateibeschreibung: application/pdf

Verfügbarkeit: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174341

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model.

Autoren: Dai, Xiaoai Cheng, Junying Guo, Shouheng et al.

Quelle: Discrete Dynamics in Nature & Society; 4/28/2023, p1-20, 20p

Schlagwörter: DEEP learning, IMAGE recognition (Computer vision), REMOTE-sensing images, BOLTZMANN machine, REMOTE sensing, FEATURE extraction, ELECTRONIC data processing

Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.

Autoren: Alzakari, Sarah A. Hassairi, Salima Hussan, Amel Ali Al. et al.

Quelle: Scientific Reports; 7/19/2025, Vol. 15 Issue 1, p1-11, 11p

Schlagwörter: BREAST cancer, CLASSIFICATION, COMPUTER-assisted image analysis (Medicine), AUTOENCODERS, DEEP learning, MACHINE learning, WAVELETS (Mathematics), IMAGE processing

Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks.

Autoren: Prasad, Kashi Sai Lydia, E. Laxmi Rajesh, M. V. et al.

Quelle: Scientific Reports; 12/28/2024, Vol. 14 Issue 1, p1-23, 23p

Schlagwörter: OPTIMIZATION algorithms, COMPUTER input design, ARTIFICIAL intelligence, CYBERTERRORISM, DATABASES

Demodulating Optical Wireless Communication of FBG Sensing with Turbulence-Caused Noise by Stacked Denoising Autoencoders and the Deep Belief Network.

Autoren: Bogale, Shegaw Demessie Yao, Cheng-Kai Manie, Yibeltal Chanie et al.

Quelle: Electronics (2079-9292); Oct2024, Vol. 13 Issue 20, p4127, 11p

Schlagwörter: FIBER Bragg gratings, FREE-space optical technology, SIGNAL denoising, LIGHT scattering, OPTICAL communications

Correlation-guided Masked Autoencoder with Multimodal Contrastive Interaction on Point Clouds.

Autoren: Ren, Peng Li, Xiaoheng Bai, Yunfeng et al.

Quelle: ACM Transactions on Multimedia Computing, Communications & Applications; Dec2025, Vol. 21 Issue 12, p1-23, 23p

Performance enhancement in hybrid SDN using advanced deep learning with multi‐objective optimization frameworks under heterogeneous environments.

Autoren: Bishla, Deepak Kumar, Brijesh

Quelle: International Journal of Communication Systems; 2/1/2025, Vol. 38 Issue 3, p1-18, 18p

Schlagwörter: SOFTWARE-defined networking, AUTOENCODERS, ZEBRAS, TOPOLOGY