MHOE-DETR: A Ship Detection Method for Small and Fuzzy Targets Based on Satellite Remote Sensing Image Data by Zhuhua Hu, Xiyu Fan, Yaochi Zhao, Wei Wu, Jie Liu

“…We propose an online convolutional reparameterization efficient layer aggregation networks cross-stage fusion network. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…The innovative use of a convolutional layer with 64 7 × 7 filters, followed by batch normalization and Rel U activation, allows for the extraction and representation of intricate patterns from the input data. …”
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A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery by Ilyas Nurmemet, Yang Xiang, Aihepa Aihaiti, Yu Qin, Yilizhati Aili, Hengrui Tang, Ling Li

“…Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. …”
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Nystromformer based cross-modality transformer for visible-infrared person re-identification by Ranjit Kumar Mishra, Arijit Mondal, Jimson Mathew

“…Abstract Person re-identification (Re-ID) aims to accurately match individuals across different camera views, a critical task for surveillance and security applications, often under varying conditions such as illumination, pose, and background. …”
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Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study by Salah Bouktif, Akib Mohi Ud Din Khanday, Ali Ouni

“…We proposed a hybrid deep learning–based neural network approach (Bidirectional Encoder Representations from Transformers [BERT]+convolutional neural network [CNN]+long short-term memory [LSTM]) to classify suicidal and nonsuicidal posts. …”
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STDNet: Improved lip reading via short-term temporal dependency modeling by Xiaoer Wu, Zhenhua Tan, Ziwei Cheng, Yuran Ru

“…In particular, we designed a local–temporal block, which aggregates interframe differences, strengthening the relationship between various local lip regions through multiscale convolution. …”
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Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

“…In this paper, we propose an advanced hybrid model for Convolutional and Long Short-Term Memory (CNN-LSTM), which exploits the main advantages of convoluted neural networks and LSTM networks. …”
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NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning by Jianliang Wang, Chen Chen, Jiacheng Wang, Zhaosheng Yao, Ying Wang, Yuanyuan Zhao, Yi Sun, Fei Wu, Dongwei Han, Guanshuo Yang, Xinyu Liu, Chengming Sun, Tao Liu

“…The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. …”
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Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing by Andre Dalla Bernardina Garcia, MD Samiul Islam, Victor Hugo Rohden Prudente, Ieda Del’Arco Sanches, Irene Cheng

“…We divide the growth cycle into different rice phenological stages: beginning, middle and end of season, as well as the season transition periods. …”
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Quantifying the non-isomorphism of global urban road networks using GNNs and graph kernels by Linfang Tian, Weixiong Rao, Kai Zhao, Huy T. Vo

“…Abstract A novel concept of quantifying graph non-isomorphism is introduced to measure structural differences between graphs, and thus overcoming the strict limitations of traditional graph isomorphism tests. …”
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RESEARCH ON CARBIDES IN M50 BEARING STEEL BASED ON MASK R-CNN DEEP LEARNING MODEL by SUN Ruiming, LI Shuxin, LU Siyuan, JIN Yongsheng, XIAO Huahai

“…Under the scanning electron microscopy (SEM), they exhibit significant differences in the shape, size, and distribution. Some carbides have larger sizes and uneven distribution. …”
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Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study by Simon Stankoski, Ivana Kiprijanovska, Martin Gjoreski, Filip Panchevski, Borjan Sazdov, Bojan Sofronievski, Andrew Cleal, Mohsen Fatoorechi, Charles Nduka, Hristijan Gjoreski

“…These results demonstrate the sensitivity of the sensor data. Furthermore, the convolutional long short-term memory model, which is a combination of convolutional and long short-term memory neural networks, emerged as the best-performing DL model for chewing detection. …”
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Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis by Hisaki Makimoto, Takayuki Okatani, Masanori Suganuma, Tomoyuki Kabutoya, Takahide Kohro, Yukiko Agata, Yukiyo Ogata, Kenji Harada, Redi Llubani, Alexandru Bejinariu, Obaida R. Rana, Asuka Makimoto, Elisabetha Gharib, Anita Meissner, Malte Kelm, Kazuomi Kario

“…We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. …”
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A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite by S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, S. Zhao, S. Zhao, X. Wang, X. Wang, D. J. Varon

“…We evaluate the ability of the algorithm to discover new methane sources with a suite of transfer tasks, in which training and evaluation data come from different regions. Results show that DSAN (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.86) outperforms four convolutional neural networks (CNNs), MethaNet (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.70), ResNet-50 (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.77), VGG16 (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.73), and EfficientNet-V2L (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.78), in transfer tasks. …”
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Dual attention mechanisms with patch-level significance embedding for ischemic stroke classification in brain CT images by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Nithin Raj, J. Pooja, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…Our approach integrates a dual attention mechanism dynamic and cross attention with hybrid convolutional kernels to analyze the relative importance of brain regions in stroke diagnosis. …”
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Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram by Ho-Jung Song, Ju-Hyuck Han, Sung-Pil Cho, Sung-Il Im, Yong-Suk Kim, Jong-Uk Park

“…A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. …”
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A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media by Yuhe Gao, Jishen Jia, Lei Cai, Meng Zhou, Haojie Chai, Jinze Jia

“…To address these issues, this paper proposes a repair network to correct object image distortion in water-air cross-media. Firstly, convolutional combination performs feature extraction on water-air cross-media images, which retains the same features at the same scale and marks feature points with large differences. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are popular deep learning architectures currently used for rapid flood simulations. …”
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Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy by Yuming Zhong, Ying Dan, Yin Cai, Jiamin Lin, Xiaoyao Huang, Omnia Mahmoud, Eric S. Hald, Akshay Kumar, Qiang Fang, Seedahmed S. Mahmoud

“…This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. …”
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DAMI-YOLOv8l: A multi-scale detection framework for light-trapping insect pest monitoring by Xiao Chen, Xinting Yang, Huan Hu, Tianjun Li, Zijie Zhou, Wenyong Li

“…The DMC module improves multi-scale feature extraction to enable the effective capture and merging of features across different detection scales while reducing network parameters. …”
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