Search Results - embedded hybrid features sparse stacked autoencoder*

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

Authors: Hong Chen Yuan Lin Yongming Li et al.

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

Subject Terms: 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

Access URL: 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

Authors: Li, Yongming Lei, Yan Wang, Pin et al.

Index Terms: 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.

Authors: Kumar, Santosh Kumar, Sunil Shafi, Mehak et al.

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

Subject Terms: ENDANGERED species, FEATURE extraction, FACE, EXTRACTION techniques

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

Authors: Pin Wang Xiaolin Qin Zuwei Zhang et al.

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

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

Access URL: 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.

Authors: Khan, Muhammad Amir Asad, Bilal Vaimann, Toomas et al.

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

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

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

Authors: Li, Yongming Lei, Yan Wang, Pin et al.

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

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

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

Authors: Sarat Babu, Mulpur

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

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

Authors: Wang, Wenle Zhou, Wei Ji, Jianhang et al.

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

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

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

Authors: Shu, Lei Huang, Kun Jiang, Wenhao et al.

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

Subject Terms: *FEATURE selection, *MACHINE learning

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Authors: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Arabic and Latin Scene Text Recognition by Combining Handcrafted and Deep-Learned Features.

Authors: Tounsi, Maroua Moalla, Ikram Pal, Umapada et al.

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Aug2022, Vol. 47 Issue 8, p9727-9740, 14p

Subject Terms: TEXT recognition, HIDDEN Markov models

In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach.

Authors: Sun, Hao Zhao, Shengqiang Peng, Fangyu et al.

Source: Journal of Intelligent Manufacturing; Jan2024, Vol. 35 Issue 1, p387-411, 25p

Subject Terms: MACHINING, PRINCIPAL components analysis, MACHINE parts, ENGINEERING, SIMPLE machines, QUALITY of service

UAV propeller fault diagnosis using deep learning of non-traditional χ ² -selected Taguchi method-tested Lempel-Ziv complexity and Teager-Kaiser energy features.

Authors: Al-Haddad LA Giernacki W Basem A et al.

Source: Scientific reports [Sci Rep] 2024 Aug 10; Vol. 14 (1), pp. 18599. Date of Electronic Publication: 2024 Aug 10.

Publication Type: Journal Article

Journal Info: 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: PubMed not MEDLINE; MEDLINE

Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm.

Authors: Tamim, Nasser Elshrkawey, Mohamed Nassar, Hamed

Source: Applied Sciences (2076-3417); Jul2021, Vol. 11 Issue 13, p6178, 25p

Subject Terms: DIABETIC retinopathy, RETINAL imaging, GENETIC algorithms, FISHER discriminant analysis, COMPUTER-aided diagnosis, DECISION trees

LLRHNet: Multiple Lesions Segmentation Using Local-Long Range Features.

Authors: Liu, Liangliang Wang, Ying Chang, Jing et al.

Source: Frontiers in Neuroinformatics; 5/5/2022, Vol. 16, p1-15, 15p

Subject Terms: CONVOLUTIONAL neural networks, DIAGNOSTIC imaging, WHITE matter (Nerve tissue), IMAGE segmentation, MEDICAL technology

A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization.

Authors: Duceac, Mădălina Buzea, Călin Gheorghe Pleșea-Condratovici, Alina et al.

Source: Diagnostics (2075-4418); Jul2025, Vol. 15 Issue 13, p1628, 40p

Subject Terms: ARTIFICIAL neural networks, ENSEMBLE learning, CLINICAL decision support systems, AUTOENCODERS, BLENDED learning

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification.

Authors: Johnsen, Timothy K. Bi, Xiangyu Chou, Chunwei et al.

Source: Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-23, 23p

Subject Terms: EVAPOTRANSPIRATION, AUTOENCODERS, STOCHASTIC analysis, MACHINE learning, HYDROLOGICAL forecasting, NOISE control, TELEMETRY

A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection.

Authors: Zhu, Honglei Wei, Pengjuan Xu, Zhigang

Source: IET Computer Vision (Wiley-Blackwell); Apr2024, Vol. 18 Issue 3, p405-419, 15p

Subject Terms: TRANSFORMER models, INTRUSION detection systems (Computer security), HUMAN skeleton, COMPUTER vision, VIDEO surveillance, FEATURE extraction, ALGORITHMS

The classification of absence seizures using power-to-power cross-frequency coupling analysis with a deep learning network.

Authors: Medvedev, A.V. Lehmann, B.

Source: Frontiers in Neuroinformatics; 2025, p1-11, 11p

Subject Terms: BRAIN waves, AUTOENCODERS, ARTIFICIAL intelligence, DEEP learning, DATABASES, UNIVERSITY hospitals

RAVE-HD: A Novel Sequential Deep Learning Approach for Heart Disease Risk Prediction in e-Healthcare.

Authors: Khan, Muhammad Jaffar Raza, Basit Faheem, Muhammad

Source: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2866, 50p

Subject Terms: HEART diseases, DEEP learning, MEDICAL screening, FEATURE selection, MACHINE learning, PREDICTION models, DIGITAL health, STATISTICAL bias