Results 111 to 120 of about 294 (197)

MEN‐IML: A Multi‐Scale Edge‐Aware Network for Image Manipulation Localisation

IET Image Processing, Volume 20, Issue 1, January/December 2026.
We present MEN‐IML, a multi‐scale edge‐aware network for image manipulation localisation. The proposed framework incorporates a multi‐scale edge‐aware feature fusion module to capture multi‐scale contextual information of manipulated regions, along with an edge feature‐aware mechanism that significantly enhances edge detail representation.
Lei Zhang, Xiaodong Lu, Qinglong Jia, Minhui Chang   +3 more
wiley   +1 more source

Radar Emitter Identification Based on Typical Parameter Sequence: HBNP Clustering, Hierarchical Denoising and LSTM Classification

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
To address “high‐density interference” and “multi‐mode parameter agility” in Radar Emitter Identification (REI), as well as existing models' flaws (pulse sequence models' weak anti‐noise ability, statistical feature models' multi‐mode confusion), this paper proposes a TPS‐based REI method–constructing a three‐level signal model and PRI probability ...
Chen‐Qian Zhao, Hong‐Lei Qin, Rong‐Ling Lang   +2 more
wiley   +1 more source

ESORecon‐Net: A Novel Framework for Enhanced Brain MRI Image Reconstruction Using Echo State Networks and Osprey Optimization

IET Software, Volume 2026, Issue 1, 2026.
This study uses advanced approaches on the enlarged BRATS dataset to increase brain magnetic resonance imaging (MRI) image reconstruction accuracy and reliability. This study addresses MRI image processing issues such as noise, artifacts, and high‐quality reconstruction. These traits are essential for brain tumor detection and analysis.
N. Sashi Prabha, N. Rama Rao, Nadeem Sarwar   +2 more
wiley   +1 more source

HDR‐SA: A Hybrid Deep Learning and RoBERTa‐Based Framework for Sentiment and Aspect Analysis

IET Software, Volume 2026, Issue 1, 2026.
The ability to comprehend complex viewpoints in text is critical for sentiment analysis (SA), particularly at the aspect level, yet existing models struggle with accurately identifying sentiment polarities and aspect‐specific expressions due to their reliance on large, manually annotated, domain‐specific datasets.
Laxmi Pamulaparthy, C. H. Sumalakshmi, Nadeem Sarwar   +2 more
wiley   +1 more source

A Multi‐Branch Attention‐Enhanced Architecture for OSA Detection Using ECG Signals

IET Signal Processing, Volume 2026, Issue 1, 2026.
Obstructive Sleep Apnea (OSA) is a prevalent and underdiagnosed sleep disorder that can lead to serious cardiovascular and cognitive complications if left untreated. This study presents a novel deep learning architecture based on convolution, LSTM, short‐time Fourier transform (STFT), attention, and transformer modules for automated OSA detection using
Alireza Cheshmberah, Majid Ziaratban, Stavros Ntalampiras   +2 more
wiley   +1 more source

Scene‐Driven Semantic Alignment for Generative Communication in Underwater Images

IET Signal Processing, Volume 2026, Issue 1, 2026.
Underwater images serve as one of the most intuitive media for human perception of underwater environments. However, the limited bandwidth and susceptibility to noise in underwater acoustic (UWA) channels pose significant challenges for traditional image encoding and transmission methods, thereby hindering high‐quality image reconstruction.
Yuetong Li, Zhenyu Jia, Yuyang Peng, Yi Zhu, Fei Yuan, Li Chai   +5 more
wiley   +1 more source

Rapid Nasal Breathing as a Biometric Trigger: High‐Accuracy Electroencephalogram‐Based Authentication for Clinical Applications

The Clinical Respiratory Journal, Volume 20, Issue 1, January 2026.
Our study demonstrates rapid nasal breathing‐evoked EEG signals enable high‐accuracy (98.3%) biometric authentication via a hybrid ResNet‐Swin Transformer model. This noninvasive approach eliminates external stimuli, offering secure patient identification for critical respiratory care settings, such as ventilator access and continuous monitoring ...
Cai Chen, Xianghong Kong, Danyang Lv, Xiangwei Meng, Chongxuan Tian, Zhi Li, Fengxia Wu, Ningling Zhang, Dedong Ma   +8 more
wiley   +1 more source

DiCleavePlus: A Transformer‐Based Model to Detect Human Dicer Cleavage Sites Within Cleavage Patterns

Genes to Cells, Volume 31, Issue 1, January 2026.
DiCleavePlus integrates pre‐miRNA sequence and secondary structure information through Transformer‐based feature extraction to predict Dicer cleavage site positions with high accuracy. The model converts Dicer cleavage prediction into a multi‐class task, enabling precise and robust identification of cleavage intervals across human pre‐miRNAs.
Lixuan Mu, Tatsuya Akutsu
wiley   +1 more source

Deep Learning–Based Automated Crack Detection for Post‐Earthquake Damage Assessment in Reinforced Concrete Structures

Advances in Civil Engineering, Volume 2026, Issue 1, 2026.
This study explores the integration of deep learning technologies, specifically U‐Net based segmentation methods, for evaluating earthquake‐induced damages. The study leverages a dataset derived from the Kahramanmaraş earthquake to train and test deep learning models capable of identifying and quantifying structural damages such as concrete cracks. The
Kemal Hacıefendioğlu, Serhat Demir, Vagelis Plevris   +2 more
wiley   +1 more source

Prediction of Severe Accident Progression Using Machine Learning With Data‐Driven Surrogate Modeling as Operator Support Tool

International Journal of Energy Research, Volume 2026, Issue 1, 2026.
Severe accidents in nuclear power plants (NPPs) pose critical challenges due to heightened environmental harshness that can impair instrumentation functionality. This impairment leads to “blind conditions,” where operators lack essential thermal‐hydraulic data, hindering decision‐making during pivotal moments, as exemplified by the Fukushima Daiichi ...
Joon Young Bae, Chang Hyun Song, JinHo Song, Jeong Ik Lee, Miro Seo, Sung Joong Kim, Jize Zhang   +6 more
wiley   +1 more source