AIMC Topic: Autoencoder

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Deterministic Autoencoder using Wasserstein loss for tabular data generation.

Neural networks : the official journal of the International Neural Network Society Jan 29, 2025
Tabular data generation is a complex task due to its distinctive characteristics and inherent complexities. While Variational Autoencoders have been adapted from the computer vision domain for tabular data synthesis, their reliance on non-determinist...
Autoencoder Neural Networks, Computer Humans Algorithms Deep Learning
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A Fusion Network With Stacked Denoise Autoencoder and Meta Learning for Lateral Walking Gait Phase Recognition and Multi-Step-Ahead Prediction.

IEEE journal of biomedical and health informatics Jan 7, 2025
Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. We presented a fusion network with stacked denoise autoencoder and meta learning (SDA-NN-ML) to recognize gait p...
Humans Neural Networks, Computer Support Vector Machine Young Adult Machine Learning Gait Signal Processing, Computer-Assisted Adult Walking Female Autoencoder Male
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Spatiotemporal multi-feature fusion vehicle trajectory anomaly detection for intelligent transportation: An improved method combining autoencoders and dynamic Bayesian networks.

Accident; analysis and prevention Jan 3, 2025
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technolo...
Accidents, Traffic Autoencoder Time Factors Autonomous Vehicles China Models, Statistical Bayes Theorem
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Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis.

Computers in biology and medicine Jan 1, 2025
Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning me...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Heart Algorithms Deep Learning Humans Autoencoder Neural Networks, Computer
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Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings.

Neural networks : the official journal of the International Neural Network Society Dec 4, 2024
This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering...
Bayes Theorem Algorithms Normal Distribution Deep Learning Humans Cluster Analysis Autoencoder Neural Networks, Computer
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Efficient few-shot medical image segmentation via self-supervised variational autoencoder.

Medical image analysis Aug 1, 2025
Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with unlabeled images to form initial masks, which are then refined by the segmentation model. However, ...
Humans Algorithms Supervised Machine Learning Image Interpretation, Computer-Assisted Autoencoder Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Speech signals-based Parkinson's disease diagnosis using hybrid autoencoder-LSTM models.

Computers in biology and medicine Jul 1, 2025
Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages ha...
Middle Aged Speech Signal Processing, Computer-Assisted Autoencoder Parkinson Disease Female Aged Male Humans Neural Networks, Computer
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GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

Computers in biology and medicine Jun 1, 2025
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.
Biomarkers Female Aged Humans Alzheimer Disease Deep Learning Male Autoencoder Single-Cell Analysis Leukocytes, Mononuclear Sequence Analysis, RNA
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Medication Recommender System for ICU Patients Using Autoencoders.

Studies in health technology and informatics May 15, 2025
Patients admitted to the intensive care unit (ICU) are often treated with multiple high-risk medications. Over- and underprescribing of indicated medications, and inappropriate choice of medications frequently occur in the ICU. This risk has to be mi...
Medical Order Entry Systems Decision Support Systems, Clinical Autoencoder Intensive Care Units Humans Electronic Health Records
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GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.

Briefings in bioinformatics May 1, 2025
Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on gra...
Algorithms Gene Regulatory Networks Computational Biology Humans Neural Networks, Computer Autoencoder
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