Search Results - Embedded hybrid feature 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

[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

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

Authors: Elloumi, Samir Bahroun, Sahbi Yahia, Sadok Ben et al.

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

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

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

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

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

Authors: Jayaseeli, J. D. Dorathi Saranya, S. S. Lakshmi, K. et al.

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

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

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

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

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

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

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

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

Authors: Rao, Jingzhi Ji, Cheng Wang, Jingde et al.

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

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

Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models.

Authors: Siam, Al Mahmud Bhowmik, Pankaj Uddin, Md Palash

Source: PLoS ONE; 7/16/2025, Vol. 20 Issue 7, p1-34, 34p

Subject Terms: MACHINE learning, CREDIT card fraud, DECISION support systems, RANDOM forest algorithms, PEARSON correlation (Statistics), FEATURE selection

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

Authors: Hu, Caixia Zhao, Tingting Duan, Yingying et al.

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

Subject Terms: 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.

Authors: Pitchaiyan, Shanthi Savarimuthu, Nickolas

Source: 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.

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

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

Subject Terms: OPTIMIZATION algorithms, COMPUTER input design, ARTIFICIAL intelligence, CYBERTERRORISM, DATABASES

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

Authors: Khan, Waqar Ahmed Masoud, Mahmoud Eltoukhy, Abdelrahman E. E. et al.

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

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

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

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

Authors: Tariq, Usman Ahanger, Tariq Ahamed

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

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

A video anomaly detection framework based on hybrid feature-enhanced memory reconstruction and jigsaw puzzle.

Authors: Liu, Hongfei He, Ning Huang, Xunrui et al.

Source: Signal, Image & Video Processing; Jan2025, Vol. 19 Issue 1, p1-15, 15p