Search Results - stacked supervised autoencoder (SSAE)

Prognostic prediction of lung adenocarcinoma based on transcriptomic data and stacked supervised autoencoder

Authors: LI Pengpeng CHEN Xicheng HUANG Jinyu et al.

Source: 陆军军医大学学报, Vol 45, Iss 6, Pp 579-585 (2023)

Subject Terms: lung adenocarcinoma, transcriptomics, prognostic prediction, cox regression, autoencoder, Medicine (General), R5-920

File Description: electronic resource

Relation: http://aammt.tmmu.edu.cn/html/202212025.htm; https://doaj.org/toc/2097-0927

Access URL: https://doaj.org/article/4863ed07502a425bac17f4034a36c4bd

An Anthocyanin Prediction Model of Blueberry Pomace Based on Stacked Supervised Autoencoders

Authors: Siqi LIU Guohong FENG Zhongshen LIU et al.

Source: Shipin gongye ke-ji, Vol 44, Iss 10, Pp 304-310 (2023)

Subject Terms: blueberry pomace, anthocyanin, visible near infrared spectroscopy (vnis), stacked supervised autoencoders (ssae), nondestructive testing (ndt), Food processing and manufacture, TP368-456

File Description: electronic resource

Relation: https://doaj.org/toc/1002-0306

Access URL: https://doaj.org/article/cef7c2d15d3541169333bb9fc310c908

A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks

Authors: Jie Lai Xiaodan Wang Qian Xiang et al.

Source: Entropy, Vol 25, Iss 9, p 1274 (2023)

Subject Terms: deep learning, semi-supervised learning, stacked autoencoder, pseudo label, regularization, Science, Astrophysics, QB460-466, Physics, QC1-999

File Description: electronic resource

Relation: https://www.mdpi.com/1099-4300/25/9/1274; https://doaj.org/toc/1099-4300

Access URL: https://doaj.org/article/c1e42ee082384e7aa1c3595b558191f6

Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP.

Authors: Liaw, Lawrence Chuin Ming Tan, Shing Chiang Goh, Pey Yun et al.

Source: Neural Computing & Applications. Aug2024, Vol. 36 Issue 22, p13895-13913. 19p.

Subject Terms: *TUMOR classification, *SUPERVISED learning, *CERVICAL cancer, *MACHINE learning, *EARLY diagnosis, *DEEP learning

Dual‐noise autoencoder combining pseudo‐labels and consistency regularization for process fault classification.

Authors: Guo, Xiaoping Guo, Qingyu Li, Yuan

Source: Canadian Journal of Chemical Engineering; Apr2025, Vol. 103 Issue 4, p1853-1867, 15p

Subject Terms: AUTOENCODERS, FEATURE extraction, MANUFACTURING processes, CLASSIFICATION

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum.

Authors: Jang, Hee-Deok Kwon, Seokjoon Nam, Hyunwoo et al.

Source: Sensors (14248220); Jun2024, Vol. 24 Issue 11, p3601, 12p

Subject Terms: ARTIFICIAL neural networks, CHEMICAL warfare agents, CLASSIFICATION, FEATURE extraction, MATERIALS analysis

A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks.

Authors: Lai, Jie Wang, Xiaodan Xiang, Qian et al.

Source: Entropy; Sep2023, Vol. 25 Issue 9, p1274, 18p

Subject Terms: SUPERVISED learning, PROBLEM solving, CLASSIFICATION, DEEP learning, ALGORITHMS

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model.

Authors: Fouda, Walaa Mirzaliev, Sanjar Abokhoza, Reneh

Source: Journal of Intelligent Systems & Internet of Things; 2025, Vol. 14 Issue 2, p115-126, 12p

Subject Terms: SOCIAL media in education, VIRTUAL classrooms, ONLINE education, OPTIMIZATION algorithms, TEACHING methods

SSAE‐KPLS: A quality‐related process monitoring via integrating stacked sparse autoencoder with kernel partial least squares.

Authors: Ye, Zhenyu Wu, Ping He, Yuchen et al.

Source: Canadian Journal of Chemical Engineering; Oct2023, Vol. 101 Issue 10, p5858-5873, 16p

Subject Terms: LEAST squares, FEATURE extraction, MANUFACTURING processes, PRODUCT quality

Stacked supervised auto-encoder with deep learning framework for nonlinear process monitoring and fault detection.

Authors: Ying, Lee Jia Zainal, Ahmad Norazwan, Md Nor

Source: AIP Conference Proceedings; 2023, Vol. 2785 Issue 1, p1-5, 5p

Subject Terms: DEEP learning, CONVOLUTIONAL neural networks, SYSTEMS engineering, SUPPORT vector machines

Utilizing chaos game representation for enhanced classification of SARS-CoV-2 variants with stacked sparse autoencoders.

Authors: Coutinho, Maria G. F. Câmara, Gabriel B. M. Barbosa, Raquel de M. et al.

Source: Neural Computing & Applications; Nov2024, Vol. 36 Issue 31, p19823-19837, 15p

Subject Terms: ARTIFICIAL neural networks, WHOLE genome sequencing, SARS-CoV-2, DEEP learning, VIRAL genomes

Mask-based Self-supervised Network Intrusion Detection System.

Authors: Lu, Xiaoya Liu, Yifan Feng, Fan et al.

Source: Applied Soft Computing. Sep2025, Vol. 181, pN.PAG-N.PAG. 1p.

Subject Terms: Intrusion detection systems (Computer security), Anomaly detection (Computer security), Machine learning, Computer performance, Data corruption, Artificial neural networks

Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems.

Authors: Alotaibi, Saud S. Alghamdi, Turki Ali

Source: Scientific Reports; 8/19/2025, Vol. 15 Issue 1, p1-17, 17p

Subject Terms: DEEP learning, INTRUSION detection systems (Computer security), AUTOENCODERS, ALGORITHMS, CYBER physical systems, INTERNET security, CYBERTERRORISM, FEATURE selection

Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder.

Authors: Aouedi, Ons Piamrat, Kandaraj Bagadthey, Dhruvjyoti

Source: Computer Networks. Apr2022, Vol. 207, pN.PAG-N.PAG. 1p.

Subject Terms: *DECISION trees, DEEP learning, SUPERVISED learning, SUPPORT vector machines, MACHINE learning, RANDOM forest algorithms

HYBRID DEEP LEARNING NETWORK-BASED APPROACH FOR AIR COMBAT MANEUVER DECISION-MAKING.

Authors: SHEEBA D. LINGESHWARI G. ANORELIN A.

Source: i-Manager's Journal on Computer Science; Sep2025, Vol. 13 Issue 2, p83-93, 11p

Subject Terms: LONG short-term memory, DEEP learning, DECISION making, METADATA, AIR warfare, ENSEMBLE learning, AUTONOMOUS robots

Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network.

Authors: Truchi, Marin Lacoux, Caroline Gille, Cyprien et al.

Source: Frontiers in Bioinformatics; 2024, p1-17, 17p

An intelligent fusion-based transfer learning model with artificial protozoa optimiser for enhancing gesture recognition to aid visually impaired people.

Authors: Mengash, Hanan Abdullah Alqadi, Basma S. Marzouk, Radwa

Source: Scientific Reports; 11/21/2025, Vol. 15 Issue 1, p1-20, 20p

Subject Terms: PEOPLE with visual disabilities, ARTIFICIAL intelligence, HUMAN-computer interaction, IMAGE processing, SIGN language, DEEP learning, TRANSFER of training, HUMAN activity recognition

Stacked sparse autoencoders monitoring model based on fault-related variable selection.

Authors: Yin, Jie Yan, Xuefeng

Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Mar2021, Vol. 25 Issue 5, p3531-3543, 13p

Subject Terms: ELECTRONIC data processing, INFORMATION processing, CHEMICAL industry, ACQUISITION of data, DATA modeling, EUCLIDEAN distance

Latent Relationship Guided Stacked Sparse Autoencoder for Hyperspectral Imagery Classification.

Authors: Liu, Li Wang, Yuebin Peng, Junhuan et al.

Source: IEEE Transactions on Geoscience & Remote Sensing; May2020, Vol. 58 Issue 5, p3711-3725, 15p

Subject Terms: CLASSIFICATION, LEARNING ability, MATHEMATICAL regularization, SPARSE approximations

Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET.

Authors: Polat, Huseyin Turkoglu, Muammer Polat, Onur

Source: IET Communications (Wiley-Blackwell); Dec2020, Vol. 14 Issue 22, p4089-4100, 12p