DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation by Elizar Elizar, Rusdha Muharar, Mohd Asyraf Zulkifley

“…The SPP layer is designed to pool features from densely convolutional layers at different scales or sizes, which will be combined to maintain a set of spatial information. …”
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MDS-Net: An Image-Text Enhanced Multimodal Dual-Branch Siamese Network for Remote Sensing Change Detection by Tao Wang, Tiecheng Bai, Chao Xu, Erlei Zhang, Bin Liu, Xining Zhao, Hongming Zhang

“…Remote sensing change detection (RSCD), which aims to identify differences between bitemporal images, has made great progress through the application of deep learning methods. …”
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Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography by Luke Oakden-Rayner, Catherine M Jones, John Lambert, Jarrel Seah, Cyril Tang, Quinlan D Buchlak, Michael Robert Milne, Xavier Holt, Hassan Ahmad, Nazanin Esmaili, Peter Brotchie

“…Objectives To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. …”
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Accurate geometric correction for NOAA/AVHRR data by HUANG Jing-feng, XU Hong-wei, WANG Ren-chao, JIANG Heng-xian

“…The combined RMSs were calculated for 59 NOAA/AVHRR data of Zhejiang province, The result of variance ahalysis and multiple comparisons showed that the RMSs differences of RST, 1st degree polynomial and 2ed polynomial warping methods were significant. …”
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Contrasted Trends in Chlorophyll‐a Satellite Products by Etienne Pauthenet, Elodie Martinez, Thomas Gorgues, Joana Roussillon, Lucas Drumetz, Ronan Fablet, Maïlys Roux

“…Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. …”
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Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection by Yue Wang, Xinhong Wang, Shi Qiu, Xianghui Chen, Zhaoyan Liu, Chuncheng Zhou, Weiyuan Yao, Hongjia Cheng, Yu Zhang, Feihong Wang, Zhan Shu

“…The inclusion of a boundary difference loss further optimizes the edge details of targets. …”
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A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data by Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli

“…To reach this goal, this paper proposes a fully data-driven, deep learning approach based on a convolutional neural network. The network takes as input the radar image spectrum and outputs the sea wave directional spectrum. …”
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An Improved Deep Learning Model for Online Tool Condition Monitoring Using Output Power Signals by Lang Dai, Tianyu Liu, Zhongyong Liu, Lisa Jackson, Paul Goodall, Changqing Shen, Lei Mao

“…In this study, a novel method is proposed to assess the cutting tool condition, which consists of a convolutional neural network with wider first-layer kernels (W-CONV), and long short-term memory (LSTM). …”
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Deep learning‐based dose prediction for low‐energy electron beam superficial radiotherapy by Jialin Huang, Zhitao Dai, Shuai Hu, Yuanchun Ye, Yuling Chen, Ming Li, Tianye Niu, Jinfen Zheng, Yongsheng Huang, Yuanjie Bi

“…In the evaluation of dose distributions across six anatomical regions, C3D consistently outperformed other deep learning models (3D U‐Net, Deep Convolutional Neural Network, and HD U‐Net) in both accuracy and robustness. …”
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FAHM: Frequency-Aware Hierarchical Mamba for Hyperspectral Image Classification by Peixian Zhuang, Xiaochen Zhang, Hao Wang, Tianxiang Zhang, Leiming Liu, Jiangyun Li

“…Convolutional neural networks and Transformers have garnered substantial attention in hyperspectral image (HSI) classification, and recently Mamba has made significant progress in modeling long sequences. …”
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Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems by Min Wei, Min Wei, Yongqing Yu, Xing Du, Yupeng Song, Lifeng Dong, Qikun Zhou, Linfeng Wang, Longying Zhang, Yamei Wang

“…However, there is a notable gap in research regarding the comparative performance of different models and the impact of data expansion. …”
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Analysis of Single-Pilot Intention Modeling in Commercial Aviation by Lei Dong, Hongbing Chen, Changxiao Zhao, Peng Wang

“…This was done to avoid safety hazards caused by different intents and inconsistent operations of the single pilot and the cockpit automation system. …”
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Segmentation-Guided Deep Learning for Glioma Survival Risk Prediction with Multimodal MRI by Jianhong Cheng, Hulin Kuang, Songhan Yang, Hailin Yue, Jin Liu, Jianxin Wang

“…Specifically, the task interrelation is addressed using a hybrid convolutional neural network-Transformer (CNN-Transformer) encoder to represent the shared high-level semantic features by co-training a decoder for glioma segmentation and a Cox model for survival prediction. …”
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Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic by Manideep Narra, Muhammad Umer, Saima Sadiq, Ala' Abdulmajid Eshmawi, Hanen Karamti, Abdullah Mohamed, Imran Ashraf

“…Additionally, the influence of different preprocessing steps is also analyzed regarding fake news detection. …”
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Multi-anchor adaptive fusion and bi-focus attention for enhanced gait-based emotion recognition by Jincheng Li, Xuejing Dai, Ruiao Yan, Chengqing Tang, Yunpeng Li

“…To address these issues, we propose a novel temporal graph convolutional network (MDT-GCN) that integrates multi-anchor (MAAF) and bi-focus attention (BFA) mechanisms. …”
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A Novel Approach for Identifying and Eliminating the Degradations in Real Time Images by S. Rajkumar, L. M. Jenila Livingston, Vansh Juneja, Shashwat Sharv, Pratik Dattatray Mahajan

“…The CNN model is trained on a dataset of images with various noises, learning to discriminate between different noise patterns. After classifying the type of noise, we apply a enhancement approach utilizes deep learning techniques by leveraging U-Net based CNN model to reduce noise levels in an image. …”
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Systematic Review on Automation of Central Tire Inflation System Based on Terrain Conditions by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Marites B. Tabanao, Emmanuel A. Salcedo, Emmy Grace T. Requillo, John Paul T. Cruz

“…Incorrect vehicle tire pressure affects vehicle dynamics, fuel efficiency, and driver safety across different terrain conditions. The current central tire inflation system (CTIS) alleviates this issue by adjusting the tire pressure to a predetermined reference level. …”
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End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN by Yong Chen, Guangxin Li, Anhe Li, Bolin He

“…The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. …”
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EvapoDeep: A Dual Deep Learning Framework Utilizing GNSS Data for Evapotranspiration Modeling and Predictive Analysis by Saeed Ebrahimi, Saeid Haji-Aghajany, Yazdan Amerian, Melika Tasan

“…This DET₀ is then modeled using an advanced Convolutional Neural Network-based deep learning method. …”
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Can AI-Based ChatGPT Models Accurately Analyze Hand–Wrist Radiographs? A Comparative Study by Ahmet Yıldırım, Orhan Cicek, Yavuz Selim Genç

“…No statistically significant difference was observed in the mean absolute error (MAE) among the models (<i>p</i> > 0.05). …”
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