Results 51 to 60 of about 379 (188)

Textile and colour defect detection using deep learning methods

Coloration Technology, EarlyView.
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui, Abdel‐Fattah Seyam, Renzo Shamey   +2 more
wiley   +1 more source

CRC-Aided Adaptive BP Decoding of PAC Codes. [PDF]

Entropy (Basel), 2022
Zhang X, Jiang M, Zhu M, Liu K, Zhao C.
europepmc   +1 more source

Application of Noise2Inverse and adaptation (Noise2Phase) to single‐mask x‐ray phase contrast micro‐computed tomography

Journal of Microscopy, EarlyView.
Abstract X‐ray phase contrast imaging (XPCI), when implemented in micro‐computed tomography (micro‐CT) mode, offers high‐contrast 3D imaging of weakly‐attenuating material samples. In the so‐called single‐mask edge illumination approach, a mask with periodically spaced transmitting apertures is used to split the x‐ray beam into narrow beamlets; when ...
Khushal Shah, Grammatiki Lioliou, Dai Chen, Alexander Denker, Peter Munro, Marco Endrizzi, Alberto Astolfo, Alessandro Olivo, Charlotte K. Hagen   +8 more
wiley   +1 more source

PlantCTCIP: Chromatin Interaction Prediction Using Convolutional Neural Network and Transformer in Plants

Plant Biotechnology Journal, EarlyView.
ABSTRACT Chromatin interactions establish spatial proximity between distant regulatory elements and their target genes, significantly influencing gene expression, and phenotypic traits. In this study, we present a plant chromatin interaction prediction model called PlantCTCIP based on Convolutional Neural Networks and Transformer.
Zhenye Wang, Siyu Zhou, Ze Guo, Zilan Ning, Jiaqi Cai, Ran Zhao, Ao Xie, Quan Li, Jiangling Zhang, Yongsheng Zhao, Peizhang Li, Haiping Si, Jianbing Yan, Yong Peng, Jianxiao Liu   +14 more
wiley   +1 more source

DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi‐Trait Genomic Prediction and Adaptive Marker Prioritisation

Plant Biotechnology Journal, EarlyView.
ABSTRACT Genomic selection (GS) is critical for accelerating genetic gain in modern plant breeding. Deep learning approaches offer powerful non‐linear representation capabilities for modelling non‐additive effects. However, their application in GS remains restricted, as high‐dimensional, low‐sample and noisy data hinder the identification of ...
Yuexin Ma, Xiang Li, Xiaohao Ji, Chunying Wang, Di Zhang, Tingting Zhai, Haibo Wang, Ping Liu   +7 more
wiley   +1 more source

Deep learning and computer vision for image‐based high‐throughput phenotyping of canning quality traits in dry beans

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Canning color retention is a key quality trait in dry bean (Phaseolus vulgaris L.) breeding, influencing consumer acceptance and commercial value. Public breeding programs maintain canning quality as a selection trait of importance, but existing color evaluation methods such as visual rating are subjective, while instrument colorimetry is ...
Lovepreet Singh, Om Sai Krishna Madhav Lella, Evan M. Wright, Karen Cichy, Valerio Hoyos‐Villegas   +4 more
wiley   +1 more source

Some Extended Results on the Design of Punctured Serially Concatenated Convolutional Codes

, 2017
Massimiliano Laddomada, B. Scanavino
openalex   +1 more source

Automated Tuberculosis Detection in Chest Radiographs: A Hybrid Deep Learning Framework for Clinical Decision Support

Applied AI Letters, Volume 7, Issue 2, June 2026.
A hybrid deep learning framework integrating VGG16, ResNet50, and DenseNet121 is proposed for automated tuberculosis detection from chest X‐ray images. Feature‐level fusion enhances robustness and generalization, achieving 97.4% accuracy across multiple public datasets, supporting reliable clinical decision‐making in resource‐limited healthcare ...
Md. Tahmid Hossain, Hrittik Mural, Monowar Wadud Hridoy   +2 more
wiley   +1 more source

Inter‐Model Feature Fusion for Robust Low‐Resource Speech Recognition

Applied AI Letters, Volume 7, Issue 2, June 2026.
Our Self‐Supervised Feature Fusion (SSF‐FT) method enhances low‐resource speech recognition by adaptively combining features from self‐supervised models trained with Contrastive, Predictive, and Reconstruction objectives. This attention‐weighted ensemble delivers robust performance, particularly in acoustically challenging conditions, extending current
Ussen Kimanuka, Ciira wa Maina, Osman Büyük   +2 more
wiley   +1 more source

Performance Comparison And Analysis Of Serial Concatenated Convolutional Codes

, 2008
Dongwon Lee, Eon Kyeong Joo
openalex   +1 more source