Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images by Yuanjun Guan, Yang Liu, Jiayi Wang, Tao Wang, Qianchuan Yi, Wenxin Jiang, Xiaopu Gu, Yichen Zhang, Li Zhang, Tianyan Han, Binbing Huang, Lilei Hu

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Spatiotemporal Patterns of Intermittent Snow Cover From PlanetScope Imagery Using Deep Learning by Zhaocheng Wang, Jaya Venkatesh Jaya Baskar, Maneesh Sarma Sistla Naga Sai, Bohumil Svoma, Enrique R. Vivoni

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Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women by Yi-Xin Li, Yu Lu, Zhe-Ming Song, Yu-Ting Shen, Wen Lu, Min Ren

“…Abstract Background Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation, which suffer from low specificity and high interobserver variability. This study aimed to develop and validate an artificial intelligence (AI)-driven diagnostic model to improve diagnostic accuracy and reduce variability. …”
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Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip by Yang Li, Leo Yan Li-Han, Hua Tian

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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…This study explores a bias correction approach based on convolutional neural networks (CNNs) to improve the accuracy of Expert Team on Climate Change Detection and Indices (ETCCDI) extreme precipitation indices calculated from the Coupled Model Intercomparison Project Phase Six (CMIP6) daily predictions. …”
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Multi-Pathway 3D CNN With Conditional Random Field for Automated Segmentation of Multiple Sclerosis Lesions in MRI by Reeda Saeed, Shahab U. Ansari, Muhammad Hanif, Kamran Javed, Usman Haider, Iffat Maab, Saeed Mian Qaisar, Pawel Plawiak

“…One of the challenges in automatic MS lesion segmentation is the high variability of the lesion’s size and shape. In this work, a novel hybridization of the multi-scale features extraction, multi-pathway 3D convolutional neural network (CNN), and Conditional Random Field (CRF) is employed for an automated MS lesion detection and segmentation. …”
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Renal Biopsy Pathological Tissue Segmentation: A Comprehensive Review and Experimental Analysis by Yan Liu, Taiping Wang, Xinxin He, Wei Wang, Qiang He, Juan Jin

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Analysis and Prediction of Deformation of Shield Tunnel Under the Influence of Random Damages Based on Deep Learning by Xiaokai Niu, Yuqiang Pan, Wei Li, Zhitian Xie, Wei Song, Chengping Zhang

“…Furthermore, this study introduces a convolutional neural network (CNN) surrogate model to enable the rapid prediction of shield tunnel deformation under random damage conditions. …”
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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…Ultimately, this representation is used within a directed graph convolutional architecture and investigated with explainable artificial intelligence (AI) tools, offering a more detailed understanding of how stroke alters communication within the brain. …”
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An intelligent recognition method for electrical work permits based on seed growth strategy and deep neural networks by LIAO Meiying, ZHOU Junhuang, ZHANG Yongjun

“…Then， during text recognition， the method combines DenseNet’s deep feature extraction capabilities with the CTC technique’s mechanism for aligning variable-length sequences， enhancing the recognition performance of character sequences. …”
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A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework by Mengji Yang, Haiqing Zhang, Xi Yu, Aicha Sekhari Seklouli, Abdelaziz Bouras, Yacine Ouzrout

“…This framework enhances the ability to capture long-term dependencies through the combined effects of efficient convolution parameter optimization and variable-oriented multivariate modeling. …”
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The potential role of synthetic computed tomography in spinal surgery: generation, applications, and implications for future clinical practice by Shreya Sankar, Jake Michael McDonnell, Stacey Darwish, Joseph Simon Butler

“…This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. …”
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Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM by LI Yunfeng, LAN Xiaosheng, SHEN Hongchang, XU Tongle

“…The fault diagnosis classification model outputs the highest classification accuracy of 100% after training, and the accuracy is better than the other five fault diagnosis models in the anti-noise experiment and the variable load experiment. The results show that the combination of convolutional neural network to extract fault features and parameters to optimize the classification model structure of support vector machine can not only improve the diagnostic accuracy, but also have strong generalization performance.…”
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COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings by Jordi Laguarta, Ferran Hueto, Brian Subirana

“…<italic>Methods:</italic> We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. …”
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Enhanced Offline Writer Recognition System Employing Blended Multi-Input CNN and Bi-LSTM Model on Diverse Handwritten Texts by Naresh Purohit, Subhash Panwar

“…Due to the variety of text visuals, especially handwriting images, author recognition is challenging. Convolution Neural Network (CNN) excels in many fields. …”
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SAR Small Ship Detection Based on Enhanced YOLO Network by Tianyue Guan, Sheng Chang, Chunle Wang, Xiaoxue Jia

“…The SR module employs re-parameterized convolution along with channel shuffle operations to improve feature extraction capabilities. …”
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Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach by Muhammad Khalid Hamid, Said Khalid Shah, Ghassan Husnain, Yazeed Yasin Ghadi, Shahab Ahmad Al Maaytah, Ayman Qahmash

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AngleCam: Predicting the temporal variation of leaf angle distributions from image series with deep learning by Teja Kattenborn, Ronny Richter, Claudia Guimarães‐Steinicke, Hannes Feilhauer, Christian Wirth

“…AngleCam is based on pattern recognition with convolutional neural networks and trained with leaf angle distributions obtained from visual interpretation of more than 2500 plant photographs across different species and scene conditions. …”
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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…The CNN architecture includes two convolution layers, global max-pooling, and two fully connected layers, with 11 input variables and a single output for CS prediction. …”
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Improved deep learning for automatic localisation and segmentation of rectal cancer on T2‐weighted MRI by Zaixian Zhang, Junqi Han, Weina Ji, Henan Lou, Zhiming Li, Yabin Hu, Mingjia Wang, Baozhu Qi, Shunli Liu

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