Results 51 to 60 of about 3,896 (184)

Similarity‐Driven Framework for Efficient Polymer Property Prediction Under Data Scarcity Scenarios

Journal of Polymer Science, EarlyView.
A new approach, based on structural and chemical similarities between polymers, is proposed to improve the performance of artificial neural networks in predicting the glass transition temperature under data‐scarce conditions. ABSTRACT Predicting polymer properties directly from chemical structure is essential for the rational design of advanced ...
Amaia Elizaran Mendarte, Gustavo A. Schwartz   +1 more
wiley   +1 more source

Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks

Applied Sciences, 2023
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques.
Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Dongyang Li, Bin Yan   +5 more
doaj   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection

EURASIP Journal on Audio, Speech, and Music Processing, 2023
Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods.
Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao, Qiaoxi Zhu, Jiantong Tian, Wenwu Wang   +6 more
doaj   +1 more source

A Large Language Model‐Based Approach for Fault Detection and Its Application

Safety Science and Technology, EarlyView.
This work proposes an interpretable fault detection framework utilizing pre‐trained large language models to overcome small sample sizes and label scarcity in industrial datasets. A stepwise tuple‐based validation mitigates hallucinations, ensuring reliable detection.
Yihua Ye, Yin Zhu, Liming Che, Hua Zhou
wiley   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

An Analysis of Image Classification Using Wavelet-Based Autoencoder Architecture and Extreme Learning Machine

Journal of Electrical and Computer Engineering
In recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy.
Salwa Said, Salima Hassairi, Siwar Yahia, Ridha Ejbali, Mourad Zaied   +4 more
doaj   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

A model‐driven robust deep learning wireless transceiver

IET Communications, 2021
Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention.
Sirui Duan, Jingyi Xiang, Xiang Yu
doaj   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, Volume 13, Issue 43, 3 August 2026.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source