Search Results - Semi-supervised autoencoder

Exploring Semi-Supervised Autoencoders: An Approach to Classify Psychotic Disorders through Proteomic Imbalanced Data

Explorando Autoencoders Semi-Supervisionados: Uma Abordagem para Classificar Distúrbios Psicóticos através de Dados Proteómicos não Balenceados

Authors: Ribeiro, Mariana Montenegro

Subject Terms: Psychotic Disorders, Semi-supervised Autoencoder, Deep Learning, Proteomic Data, Mass Spectrometry, Perturbações Psicóticas, Autoencoder Semi-supervisionado, Dados Proteómicos, Espectrometria de Massa

Availability: https://hdl.handle.net/10316/117998

DVCAE: Semi-Supervised Dual Variational Cascade Autoencoders for Information Popularity Prediction

Authors: Jiaxing Shang Xueqi Jia Xiaoquan Li et al.

Source: IEEE Transactions on Knowledge and Data Engineering. 37:5838-5851

Enabling Out-of-Sample Extension in Semi-Supervised Manifold Alignment through Twin Autoencoders

Authors: Jake S. Rhodes Adam G. Rustad Marshall S. Nielsen

Source: 2025 International Conference on Sampling Theory and Applications (SampTA). :1-5

Mental workload recognition from EEG signals via semi-supervised autoencoders

Authors: Qi Liu Xu Jiang Huanjie Wang et al.

Source: Computer Methods in Biomechanics and Biomedical Engineering. :1-14

A Variational Autoencoder with Semi-Supervised Latent Space for Source Term Estimation

Authors: Abdullah Abdulaziz Mike E. Davies Steve McLaughlin et al.

Source: 2025 IEEE Statistical Signal Processing Workshop (SSP). :1-5

A Semi-supervised Convolutional Autoencoder for Multiple Physical Parameters Measurement in Laser Absorption Tomography

Authors: Anyang Liang Yi Jin Zhai Chao et al.

Source: 2025 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). :1-6

Novel Semi-Supervised Multi-Scale Graph Convolution Autoencoders for Industrial Fault Diagnosis

Authors: Jiaqi Shen Le Yao Bingbing Shen et al.

Source: 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). :1089-1094

An AutoEncoder-Based Semi-Supervised Learning for Efficient Blood Pressure Estimation with Minimal Labeled Data

Authors: Huimin Rong Hao Wu Ruifang Liu

Source: 2025 IEEE 7th International Conference on Artificial Intelligence Circuits and Systems (AICAS). :1-5

A Semi-Supervised Variational Autoencoder for Fault Detection of Low-Severity Inter-Turn Short-Circuit in PMSMs

Authors: Mingda Zhu Du Nguyen Peihua Han et al.

Source: 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). :1-6

XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition

Authors: M. Mohana P. Subashini George Ghinea

Source: Signal, Image and Video Processing. 19

Subject Terms: autoencoder, semi-supervised learning, convolutional sparse autoencoder, facial expression recognition, feature representation, unsupervised learning

Access URL: https://bura.brunel.ac.uk/handle/2438/31141

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM

Authors: Peipei Zeng Shuimiao Kang Fan Fan et al.

Source: Front Neuroinform
Frontiers in Neuroinformatics, Vol 19 (2025)

Subject Terms: semi-supervised anomaly detection, one classification support vector machine, Neurosciences. Biological psychiatry. Neuropsychiatry, heart sound detection, sample imbalance, convolutional autoencoder, RC321-571, Neuroscience

Access URL: https://pubmed.ncbi.nlm.nih.gov/39944753
https://doaj.org/article/59e646350ce746aba235b5d7fd35df0d

Semi-supervised Fusion Masked Autoencoder for Robust Tool Breakage Monitoring in Noisy Conditions

Authors: Shenping Mei Xuandong Mo Yajing Liu et al.

Source: IFAC-PapersOnLine. 59:2427-2432

Self-Supervised Learning Meets Custom Autoencoder Classifier: A Semi-Supervised Approach for Encrypted Traffic Anomaly Detection

Authors: A. Ramzi Bahlali Abdelmalik Bachir Abdeldjalil Labed

Source: IEEE Access. 13:139141-139154

Synthetic Aperture Local Conformal Autoencoder for Semi-Supervised Speaker’s DOA Tracking

Authors: Idan Cohen Sharon Gannot Ofir Lindenbaum

Source: IEEE Transactions on Audio, Speech and Language Processing. 33:2918-2931

An Approach to One-Shot Semi-Supervised Federated Learning for Text Classification with Pretrained Sentence Bottleneck Autoencoders

Authors: Soyoung Jeon Wooju Kim

Source: Journal of Intelligence and Information Systems. 30:273-292

Comparative Evaluation of Autoencoders for Semi-Supervised Anomaly Detection on Univariate Time Series Data

Authors: Tijana Markovic Sara Moricz Miguel Leon

Source: 2024 International Conference on Machine Learning and Applications (ICMLA). :1321-1328

Stacked semi-supervised autoencoder-regularized RVFLNs for reliable prediction of molten iron quality in blast furnace

Authors: Ping Zhou Peng Zhao Zihui Ou et al.

Source: Neural Computing and Applications. 37:10425-10438

Semi-Supervised Channel Equalization Using Variational Autoencoders

Authors: David Burshtein Eli Bery

Source: IEEE Transactions on Wireless Communications. 23:19681-19695

Semi-Supervised Blockchain Anomaly Transaction Detection Based on Deep AutoEncoder and Multi-Layer Perceptron

Authors: Chundong Wang Weijie Yang

Source: 2024 4th International Conference on Digital Society and Intelligent Systems (DSInS). :115-119

SDAC-DA: Semi-Supervised Deep Attributed Clustering Using Dual Autoencoder

Authors: Kamal Berahmand Sondos Bahadori Maryam Nooraei Abadeh et al.

Source: IEEE Transactions on Knowledge and Data Engineering. 36:6989-7002