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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A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms by Guodong Wu, Diangang Hu, Yongrui Zhang, Guangqing Bao, Ting He

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Identification of line status changes using phasor measurements through deep learning networks by N. E. Gotman, G. P. Shumilova

“…To consider the problem of detecting changes in a power grid topology that occurs as a result of the power line outage / turning on. …”
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APPLICATION OF NON-TEST METHODS FOR CHANEL ESTIMATION by M. L. Maslakov, M. S. Smal

“…Approaches for solving problems of non-test adaptive signals correction  and channel state  estimation in serial data  communication systems using  convolutional encoder  are  proposed. …”
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Invertible Attention-Guided Adaptive Convolution and Dual-Domain Transformer for Pansharpening by Qun Song, Hangyuan Lu, Chang Xu, Rixian Liu, Weiguo Wan, Wei Tu

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Construction of a new class of (2k, k, 1) convolutional codes by Cheng-chang ZHENG, Wan-quan PENG, Bo WEI

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Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video by S. A. Ihnatsyeva, R. P. Bohush

“…The created dataset PolReID1077 contains images of people that were obtained in all seasons, which will improve the correct operation of re-identification systems when the seasons change. …”
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Automatic image segmentation using Region-Based convolutional networks for Melanoma skin cancer detection by Karen Dayana Tovar-Parra, Luis Alexander Calvo-Valverde, Ernesto Montero-Zeledón, Mac Arturo Murillo-Fernández, Jose Esteban Perez-Hidalgo, Dionisio Alberto Gutiérrez-Fallas

“…In both models’ results, variation was very small when the training dataset size changed between 160, 100, and 50 images. In both of the pipelines, the models were capable of running the segmentation correctly, which illustrates that focalization of the zone is possible with very small datasets and the potential use of automatic segmentation to assist in Melanoma detection. …”
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Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy by Tuo Guo, Fengjie Xu, Jinfang Ma, Fahuan Ge

“…Convolutional neural networks (CNNs) are widely used for image recognition and text analysis and have been suggested for application on one-dimensional data as a way to reduce the need for preprocessing steps. …”
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Pseudorandom number generators with self-monitoring of correct operation by Mikhail A. Ivanov, Grigoriy A. Vrazhnov, Andrey V. Starikovsky, Mikhail A. Khoroshaev

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Atrial Fibrillation Type Classification by a Convolutional Neural Network Using Contrast-Enhanced Computed Tomography Images by Hina Kotani, Atsushi Teramoto, Tomoyuki Ohno, Yoshihiro Sobue, Eiichi Watanabe, Hiroshi Fujita

“…Contrast-enhanced CT images of 30 patients with PAF and 30 patients with LSAF were input into six pretrained convolutional neural networks (CNNs) for the binary classification of PAF and LSAF. …”
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How many dollars are in the sea? Estimating sand dollar (Echinarachnius parma) abundance using an iteratively trained convolutional neural network and generalized additive models by Sara Vanaki, Deborah R. Hart, Jui-Han Chang

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Stepwise Corrected Attention Registration Network for Preoperative and Follow-Up Magnetic Resonance Imaging of Glioma Patients by Yuefei Feng, Yao Zheng, Dong Huang, Jie Wei, Tianci Liu, Yinyan Wang, Yang Liu

“…These challenges stem from the considerable deformation of brain tissue and the areas of non-correspondence due to surgical intervention and postoperative changes. We propose a stepwise corrected attention registration network grounded in convolutional neural networks (CNNs). …”
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Correction of crop water deficit indicators based on time-lag effects for improved farmland water status assessment by Yujin Wang, Zhitao Zhang, Yinwen Chen, Shaoshuai Fan, Haiying Chen, Xuqian Bai, Ning Yang, Zijun Tang, Long Qian, Zhengxuan Mao, Siying Zhang, Junying Chen, Youzhen Xiang

“…Results demonstrated that time-lag correction significantly enhanced the correlation between SWC and theoretical CWSI, empirical CWSI, gs, and ET, with increases of 0.15, 0.33, 0.11, and 0.21, respectively; Time-lag mutual information exhibited the highest effectiveness in correcting time-lag effects; The sudden decline in gs and the peak advancement in severe water stress treatments led to abrupt changes in time-lag parameters; The Convolutional Neural Network-Bidirectional Long Short-Term Memory-Adaptive Boosting model achieved the highest accuracy in predicting gs corrected by time-lag mutual information from 8:00–15:00 (R2=0.96). …”
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Enhancing Podocyte Degenerative Changes Identification With Pathologist Collaboration: Implications for Improved Diagnosis in Kidney Diseases by George Oliveira Barros, Jose Nathan Andrade Muller da Silva, Henrique Machado de Sousa Proenca, Stanley Almeida Araujo, David Campos Wanderley, Luciano Reboucas de Oliveira, Washington Luis Conrado Dos-Santos, Angelo Amancio Duarte, Flavio de Barros Vidal

“…The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing a convolutional neural network (CNN) based classifier. …”
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Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders by Chang Seok Bang

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Deep learning assisted analysis of biomarker changes in refractory neovascular AMD after switch to faricimab by Michael Hafner, Franziska Eckardt, Jakob Siedlecki, Benedikt Schworm, Tina R. Herold, Ben Asani, Siegfried G. Priglinger, Johannes B. Schiefelbein

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Machine and Deep Learning–driven Angular Momentum Inference from BHEX Observations of the n = 1 Photon Ring by Joseph Farah, Jordy Davelaar, Daniel Palumbo, Michael Johnson, Jonathan Delgado

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An applied noise model for scintillation-based CCD detectors in transmission electron microscopy by Christian Zietlow, Jörg K. N. Lindner

“…Detectors usually suffer from gain non-linearities and quantum efficiency deviations, which must be corrected for optimal results. All these operations influence the noise and are influenced by it, vice versa. …”
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Prediction of post-Schroth Cobb angle changes in adolescent idiopathic scoliosis patients based on neural networks and surface electromyography by Shuguang Yin, Jiangang Chen, Peng Yan

“…A neural network model integrating Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM) layers, and feature vectors was constructed. …”
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