Results 41 to 50 of about 3,896 (184)

Hyperspectral anomaly detection via memory‐augmented autoencoders

CAAI Transactions on Intelligence Technology, 2023
Recently, the autoencoder (AE) based method plays a critical role in the hyperspectral anomaly detection domain. However, due to the strong generalised capacity of AE, the abnormal samples are usually reconstructed well along with the normal background ...
Zhe Zhao, Bangyong Sun
doaj   +1 more source

Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley   +1 more source

Resource‐Aware Contrastive Scattering Meta‐Learning for Efficient Few‐Shot Acoustic Anomaly Detection

Advanced Intelligent Systems, EarlyView.
This paper introduces a resource‐aware Contrastive Scattering Meta‐Learning (CSML) framework for acoustic anomaly detection. By leveraging training‐free wavelet scattering and metric‐based meta‐learning, the model achieves competitive performance with only 50 K learnable parameters—a 98% reduction compared to state‐of‐the‐art frameworks—enabling ...
Rami Zewail, Bassem Mokhtar
wiley   +1 more source

Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

Energies, 2023
Buildings’ heating, ventilation, and air-conditioning (HVAC) systems account for significant global energy use. Proper maintenance can minimize their environmental footprint and enhance the quality of the indoor environment.
Ruiqi Tian, Santiago Gomez-Rosero, Miriam A. M. Capretz   +2 more
doaj   +1 more source

Ultralow‐Power Floating‐Gate InGaZnO Content‐Addressable Memory for Wearable Electrocardiogram Anomaly Detection

Advanced Intelligent Systems, EarlyView.
This study introduces a subthreshold in‐memory anomaly detection architecture for wearable ECG monitoring using floating‐gate IGZO content‐addressable memories on a flexible substrate. Area‐engineered coupling provides a high subthreshold slope, while subthreshold operation realizes intrinsic exponential distance evaluation and linear voltage sensing ...
Hyung‐Jun Noh, Sunyeol Bae, Yumin Yun, Junhyeong Park, Soo‐Yeon Lee   +4 more
wiley   +1 more source

A translational multimodal machine‐learning prototype predicting valproate response in epilepsy treatment

Epilepsia, EarlyView.
Abstract Objective Epilepsy affects ~1% of the global population and often requires lifelong antiseizure medication (ASM) therapy. Valproic acid (VPA) is a commonly prescribed first‐line ASM, yet only approximately half of patients achieve sustained seizure freedom. Treatment selection remains largely empirical.
Simeon Platte, Afsheen Kumar, Giorgia Guerini, Massimo Pandolfo, Denise Haslinger, Colin B. Josephson, Guillermo Delgado‐García, Navprabhjot Kaur, Michaela‐Pauline Lux, Heiko Stempfle, Chantal Depondt, Reetta Kälviäinen, Felix Rosenow, Sophie von Brauchitsch, Karl Martin Klein, Andreas G. Chiocchetti   +15 more
wiley   +1 more source

A tutorial on multi-view autoencoders using the multi-view-AE library

CoRR
There has been a growing interest in recent years in modelling multiple modalities (or views) of data to for example, understand the relationship between modalities or to generate missing data. Multi-view autoencoders have gained significant traction for their adaptability and versatility in modelling multi-modal data, demonstrating an ability to ...
Ana Lawry Aguila, André Altmann
openaire   +2 more sources

Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder

Sensors, 2022
As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields.
Bokun Wang, Caiqian Yang
doaj   +1 more source

Artificial Intelligence for Identifying Tumor‐Reactive CD8+ T Cells: Biological Principles, Computational Advances, and Future Directions

Med Research, EarlyView.
This review details a three‐stage paradigm shift for tumor‐reactive CD8+ T‐cell identification: decoding transcriptomic states, deciphering clonal functional efficacy, and molecular‐level therapeutic TCR design. Addressing translational hurdles and generative AI “scientific blind spots”—such as missing catch bonds—we present a visionary roadmap.
Chao Yang, Yingying Sun, Yan Zhang, Yifen Shi, Zhiwen Shi   +4 more
wiley   +1 more source

A Deep Nonlinear Subspace Modeling and Reconstruction for Diffusion‐Weighted Imaging Using Denoising Auto‐Encoder

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To present a novel, nonlinear subspace modeling and joint k–q‐space reconstruction technique for high‐resolution, multi‐band, multi‐shell diffusion‐weighted imaging (DWI). Methods High b‐value (> 1000 s/mm2), high resolution DWI has the drawback of generally low signal‐to‐noise ratios (SNRs).
Julius Glaser, Zhengguo Tan, Annika Hofmann, Frederik Bernd Laun, Florian Knoll   +4 more
wiley   +1 more source