Výsledky vyhledávání - embedded hybrid features sparse stacked autoencoder

Hybrid Feature Embedded Sparse Stacked Autoencoder and Manifold Dimensionality Reduction Ensemble for Mental Health Speech Recognition

Autoři: Hong Chen Yuan Lin Yongming Li a další

Zdroj: IEEE Access, Vol 9, Pp 28729-28741 (2021)

Témata: 0202 electrical engineering, electronic engineering, information engineering, ensemble learning, feature fusion, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, speech mental health recognition, Embedded hybrid feature sparse stacked autoencoder, L1 regularization, TK1-9971, 3. Good health

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09348883.pdf
https://doaj.org/article/4e1029e37cad4c62884db95a4caa020b
https://dblp.uni-trier.de/db/journals/access/access9.html#ChenLLWWL21
https://ieeexplore.ieee.org/document/9348883

Hybrid Embedded Deep Stacked Sparse Autoencoder with w_LPPD SVM Ensemble

Autoři: Li, Yongming Lei, Yan Wang, Pin a další

Témata: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2002.06761

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques.

Autoři: Kumar, Santosh Kumar, Sunil Shafi, Mehak a další

Zdroj: Multimedia Tools & Applications; Sep2022, Vol. 81 Issue 21, p31075-31106, 32p

Témata: ENDANGERED species, FEATURE extraction, FACE, EXTRACTION techniques

[Psychosis speech recognition algorithm based on deep embedded sparse stacked autoencoder and manifold ensemble]

Autoři: Pin Wang Xiaolin Qin Zuwei Zhang a další

Zdroj: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi. 38(4)

Témata: Databases, Factual, Psychotic Disorders, 0202 electrical engineering, electronic engineering, information engineering, Speech Perception, Humans, Speech, 02 engineering and technology, Algorithms

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/34459164
https://www.ncbi.nlm.nih.gov/pubmed/34459164

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM.

Autoři: Khan, Muhammad Amir Asad, Bilal Vaimann, Toomas a další

Zdroj: Electronics (2079-9292); Apr2024, Vol. 13 Issue 7, p1292, 20p

Témata: INDUCTION motors, RECEIVER operating characteristic curves, FAULT diagnosis, FEATURE extraction

Enhanced Face Recognition in Crowded Environments with 2D/3D Features and Parallel Hybrid CNN-RNN Architecture with Stacked Auto-Encoder.

Autoři: Elloumi, Samir Bahroun, Sahbi Yahia, Sadok Ben a další

Zdroj: Big Data & Cognitive Computing; Aug2025, Vol. 9 Issue 8, p191, 26p

Témata: DEEP learning, FEATURE extraction, IMAGE processing, ELECTRONIC surveillance, FACE perception, PARALLEL processing

Multimodal feature-optimized approaches for cancer classification using microarray gene expression analysis.

Autoři: Jayaseeli, J. D. Dorathi Saranya, S. S. Lakshmi, K. a další

Zdroj: Scientific Reports; 11/12/2025, Vol. 15 Issue 1, p1-22, 22p

Témata: GENE expression, ARTIFICIAL intelligence, CLASSIFICATION, DEEP learning, CANCER prognosis, FEATURE selection, TUMORS

Embedded stacked group sparse autoencoder ensemble with L1 regularization and manifold reduction.

Autoři: Li, Yongming Lei, Yan Wang, Pin a další

Zdroj: Applied Soft Computing. Mar2021, Vol. 101, pN.PAG-N.PAG. 1p.

Témata: Mathematical regularization, Deep learning, Machine learning, Problem solving, Support vector machines, Feature selection, Feature extraction

An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS).

Autoři: Al-Sbou, Ahed Mleih Abd Rahim, Noor Hafhizah

Zdroj: Indonesian Journal of Electrical Engineering & Computer Science; Apr2023, Vol. 30 Issue 1, p481-490, 10p

Témata: RECOMMENDER systems, STANDARD deviations, AUTOENCODERS, INFORMATION overload, INFORMATION resources, DEEP learning

Hybrid deep learning for cutting tool life monitoring and prediction in IN718 milling using limited data.

Autoři: Sarat Babu, Mulpur

Zdroj: Forschung im Ingenieurwesen; 9/11/2025, Vol. 89 Issue 1, p1-19, 19p

A Deep Non-Stationary Feature Extraction and Feature Fusion Framework for Real Industrial Process Monitoring.

Autoři: Rao, Jingzhi Ji, Cheng Wang, Jingde a další

Zdroj: Processes; Nov2025, Vol. 13 Issue 11, p3538, 19p

Témata: FEATURE extraction, FAULT diagnosis, MACHINE learning, DEEP learning, TIME series analysis, QUALITY control, ELECTRONIC data processing

Visible-near infrared hyperspectral imaging for non-destructive estimation of leaf nitrogen content under water-saving irrigation in protected tomato cultivation.

Autoři: Hu, Caixia Zhao, Tingting Duan, Yingying a další

Zdroj: Frontiers in Plant Science; 2025, p1-15, 15p

Témata: NITROGEN content of plants, MACHINE learning, NONDESTRUCTIVE testing, HYPERSPECTRAL imaging systems, DEEP learning, IRRIGATION water, TOMATO farming

Deep Stacked Autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor.

Autoři: Pitchaiyan, Shanthi Savarimuthu, Nickolas

Zdroj: Journal of Information Technology Research; 2022, Vol. 15 Issue 1, p1-26, 26p

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

Autoři: Prasad, Kashi Sai Lydia, E. Laxmi Rajesh, M. V. a další

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

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

Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time.

Autoři: Khan, Waqar Ahmed Masoud, Mahmoud Eltoukhy, Abdelrahman E. E. a další

Zdroj: Journal of Intelligent Manufacturing; Feb2025, Vol. 36 Issue 2, p1313-1339, 27p

Témata: EXTREME learning machines, AUTOENCODERS, ERROR functions, PRODUCTION planning, NETWORK performance

Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure.

Autoři: Tariq, Usman Ahanger, Tariq Ahamed

Zdroj: PeerJ Computer Science; Apr2025, p1-43, 43p

Témata: BOTNETS, SMART cities, INTERNET of things, ARTIFICIAL neural networks, DEEP learning, DATA structures, INTRUSION detection systems (Computer security)

Deep sparse autoencoder integrated with three‐stage framework for glaucoma diagnosis.

Autoři: Wang, Wenle Zhou, Wei Ji, Jianhang a další

Zdroj: International Journal of Intelligent Systems; Oct2022, Vol. 37 Issue 10, p7944-7967, 24p

Témata: GLAUCOMA, SUPPORT vector machines, DEEP learning, OPTIC disc, BLENDED learning, TONOMETERS, DIAGNOSIS

Comprehensive Learning Salp Swarm Algorithm with Ensemble Deep Learning-based ECG Signal Classification on Internet of Things Environment.

Autoři: Tounsi, Mohamed Ali, Haider Azar, Ahmad Taher a další

Zdroj: Engineering, Technology & Applied Science Research; Feb2025, Vol. 15 Issue 1, p19492-19500, 9p

Témata: LONG short-term memory, SIGNAL classification, DEEP learning, AUTOENCODERS, FEATURE extraction

Feature selection using autoencoders with Bayesian methods to high-dimensional data.

Autoři: Shu, Lei Huang, Kun Jiang, Wenhao a další

Zdroj: Journal of Intelligent & Fuzzy Systems. 2021, Vol. 41 Issue 6, p7397-7406. 10p.

Témata: *FEATURE selection, *MACHINE learning

A Method for Improving the Robustness of Intrusion Detection Systems Based on Auxiliary Adversarial Training Wasserstein Generative Adversarial Networks.

Autoři: Wang, Guohua Yan, Qifan

Zdroj: Electronics (2079-9292); Jun2025, Vol. 14 Issue 11, p2171, 24p

Témata: GENERATIVE adversarial networks, CONVOLUTIONAL neural networks, LONG short-term memory, AUTOENCODERS, DEEP learning, GENERALIZATION, INTRUSION detection systems (Computer security)