Results 61 to 70 of about 13,859 (190)

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Advanced Intelligent Systems, Volume 8, Issue 4, April 2026.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng, Hyobin Kim, Patrick Martin, Junil Kim, Tae‐Hyung Kim, Gi‐Hoon Nam, Dongha Kim, Kyoung Jae Won   +7 more
wiley   +1 more source

Explaining anomalies through semi-supervised Autoencoders

Array
This work tackles the problem of designing explainable by design anomaly detectors, which provide intelligible explanations to abnormal behaviors in input data observations.
Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina, Simona Nisticò   +3 more
doaj   +1 more source

Remaining Useful Life (RUL) Prediction Methods for Machine Health Estimation and Fault Diagnosis: A Comprehensive Review of Latest Techniques and Future Prospects

Engineering Reports, Volume 8, Issue 4, April 2026.
A comprehensive review of model‐based, data‐driven, and hybrid approaches for Remaining Useful Life (RUL) prediction, emphasizing their role in predictive maintenance, fault diagnosis, and enhancing industrial reliability. ABSTRACT This paper aims to provide a state‐of‐the‐art review of the most recent Remaining Useful Life (RUL) prediction methods ...
Arslan Ahmed Amin, Ansa Mubarak, Saba Waseem, Zuhair A. Alqarni, Habib Ullah Manzoor   +4 more
wiley   +1 more source

Column-Wise Autoencoder Representation Learning for Intrusion Detection in Multi-MEC Edge Networks

Applied Sciences
Mobile Edge Computing (MEC) is a key enabler of 5G/6G services, but multi-base-station deployment enlarges the attack surface and motivates edge-native intrusion detection systems (IDSs).
Min-Gyu Kim, Jonghyun Kim
doaj   +1 more source

Weibull‐Neural Network Framework for Wind Turbine Lifetime Monitoring and Disturbance Identification

Wind Energy, Volume 29, Issue 4, April 2026.
ABSTRACT Wind turbines are vital for sustainable energy, yet their reliability under diverse operational and environmental conditions remains a challenge, often leading to costly failures. This study presents a novel Weibull‐Neural Network Framework to enhance wind turbine lifetime monitoring by estimating reliability (R(t)) and mean residual life (MRL)
Fatemeh Kiadaliry, Sadigh Raissi, Alireza Rashidi Komijan   +2 more
wiley   +1 more source

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models

Applied Sciences
This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as ...
Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury   +7 more
doaj   +1 more source

Integration of Physics‐Constrained Learning With Adversarial Autoencoders for Simultaneous Inference of Hydraulic Conductivity and Contaminant Sources

Water Resources Research, Volume 62, Issue 4, April 2026.
Abstract Determining heterogeneous conductivity fields and reconstructing contaminant release histories in subsurface remediation often lead to a high‐dimensional inverse problem. To tackle this issue, researchers typically integrate model outputs with sparse and noisy measurements of hydraulic head and concentration.
Zhenjie Tang, Li He
wiley   +1 more source

An abnormal traffic detection method for chain information management system network based on convolutional neural network

Frontiers in Physics
Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly prominent currently.
Chao Liu, Chunxiang Liu, Changrong Liu
doaj   +1 more source

Temporal Dependency‐Aware Trajectory‐Level Behavioural Metric for Exploration in Reinforcement Learning

CAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 332-348, April 2026.
ABSTRACT Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning. In pursuit of an informative and robust state representation, the behavioural metric groups behaviourally equivalent states together, which share the same single‐step reward and transition distribution.
Anjie Zhu, Yongjun Yang, Guangyi Zhao, Jie Shao   +3 more
wiley   +1 more source

Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning

IEEE Access, 2020
Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering.
Lijuan Wang, Shifei Ding
doaj   +1 more source