Siamese text classification network (SiamTCN) for multi-class multi-label information extraction of typhoon disasters from social media data by Zhi He, Chengle Zhou, Liwei Zou, Suhong Zhou, Xueqiang Zhao

“…Social media data plays a vital role in disaster management, while deep learning-based methods gain more attention in typhoon disaster research. However, most existing classification methods for typhoon disasters are limited to multi-class but single-label levels, contradicting the reality that a social media text may correspond to multiple types of disaster damage. …”
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Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction by Sudip Chakraborty, Maloy Kumar Devnath, Atefeh Jabeli, Chhaya Kulkarni, Gehan Boteju, Jianwu Wang, Vandana P. Janeja

“…This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. …”
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Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight by Xueying Cao, Hongmin Gao, Ting Qin, Min Zhu, Ping Zhang, Ping Zhang, Peipei Xu, Peipei Xu

“…Finally, we propose a multi-scale spatial boundary feature extraction block to guide the model in emphasizing the most important spatial locations and boundary regions.ResultWe evaluate BE-Net on medical microscopic hyperspectral pathological image datasets of gastric intraepithelial neoplasia and gastric mucosal intestinal metaplasia. …”
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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Considering the temporal continuity and spatial correlation of NDVI time-series data, we combine long short-term memory with a convolution (LSTM-Conv) structure and utilize residual learning and dense connection strategies to mine the spatiotemporal features in depth. …”
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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images by Yuhang Zhang, Wuxia Zhang, Songtao Ding, Siyuan Wu, Xiaoqiang Lu

“…The “from-to” information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. …”
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Feature Interaction and Adaptive Fusion Network With Spectral Modulation for Pansharpening by Lihua Jian, Jiabo Liu, Lihui Chen, Di Zhang, Gemine Vivone, Xichuan Zhou

“…In addition, a residual structure-based self-guided spatial-channel adaptive convolution is introduced to accommodate diverse features within FASA adaptively. …”
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Satellite Image Time-Series Classification with Inception-Enhanced Temporal Attention Encoder by Zheng Zhang, Weixiong Zhang, Yu Meng, Zhitao Zhao, Ping Tang, Hongyi Li

“…Thirdly, the proposed IncepTAE is more lightweight due to the use of group convolutions. IncepTAE achieves 95.65% and 97.84% overall accuracy on two challenging datasets, TimeSen2Crop and Ghana. …”
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Re-Parameterization After Pruning: Lightweight Algorithm Based on UAV Remote Sensing Target Detection by Yang Yang, Pinde Song, Yongchao Wang, Lijia Cao

“…However, UAV remote sensing requires target detection algorithms to have higher inference speeds and greater accuracy in detection. At present, most lightweight object detection algorithms have achieved fast inference speed, but their detection precision is not satisfactory. …”
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Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder–Decoder Network by Salih Taha Alperen Özçelik, Hüseyin Üzen, Abdulkadir Şengür, Hüseyin Fırat, Muammer Türkoğlu, Adalet Çelebi, Sema Gül, Nebras M. Sobahi

“…Background: Dental disorders are one of the most important health problems, affecting billions of people all over the world. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…Four AI algorithms- multilayer perceptron (MLP), linear regression, support vector regression model, decision tree, convolution neural network, and U-Net were used for dose estimation. …”
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A Deep Learning Approach for Crop Disease and Pest Classification Using Swin Transformer and Dual-Attention Multi-Scale Fusion Network by R. Karthik, Armaano Ajay, Akshaj Singh Bisht, T. Illakiya, K. Suganthi

“…Current diagnostic methods are mostly manual, which is time-consuming and requires domain expertise. …”
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GGLA-NeXtE2NET: A Dual-Branch Ensemble Network With Gated Global-Local Attention for Enhanced Brain Tumor Recognition by Adnan Saeed, Khurram Shehzad, Shahzad Sarwar Bhatti, Saim Ahmed, Ahmad Taher Azar

“…Simultaneously, local information is captured through multiple convolutions with a gating layer. The gating mechanism within the GGLA dynamically balances the contributions of global and local information, enabling the model to adaptively focus on the most relevant features for accurate classification. …”
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Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module by Wei Ji, Kelong Zhai, Bo Xu, Jiawen Wu

“…Experimental results show that compared with the original DETR model, the proposed algorithm has improved in AP, AP50, and AP75 indicators, especially in the AP50 indicator, which has the most obvious improvement reaching a detection accuracy of 97.12%. …”
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Investigation of ensembles of deep learning models for improved chronic kidney diseases detection in CT scan images by I.I. Ayogu, C.F. Daniel, B.A. Ayogu, J.N. Odii, C.L. Okpalla, E.C. Nwokorie

“…In general, nonetheless, kidney stone was the most difficult disease to detect for all the models investigated. …”
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EcoDetect-YOLOv2: A High-Performance Model for Multi-Scale Waste Detection in Complex Surveillance Environments by Jing Su, Ruihan Chen, Mingzhi Li, Shenlin Liu, Guobao Xu, Zanhong Zheng

“…Conventional waste monitoring relies heavily on manual inspection, while most detection models are trained on close-range, simplified datasets, limiting their applicability for real-world surveillance. …”
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Analytical simulation of meander morphology from equilibrium to long-term evolution: Impacts of channel geometry and vegetation-induced coarsening by Yanjie Sun, Xiaolong Song, Zhi Li, Haijue Xu, Yuchuan Bai

“…Vegetation effects are most pronounced in channels with moderate width-to-depth ratios, where they can significantly influence migration rates and bed topography. …”
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Learning Domain Generalized Remote Sensing Image Segmentation by Multiscale Instance Disentanglement by Jie Luo, Tianwen Luo, Maoyang Wang, Linyi Li, Wen Zhang, Lingkui Meng

“…Rapid development has been made in the past decade owing to the deep learning techniques. Most of the existing methods assume that the training and inference remote sensing images hold the identical and independent distribution. …”
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GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network With High Renewables by Dinesh Kumar Mahto, Mahipal Bukya, Rajesh Kumar, Akhilesh Mathur, Vikash Kumar Saini

“…Implementing traditional OPF algorithms can be challenging for large-scale networks with complex topologies and constraints. The most recent advancement in learning-based models has shifted the paradigm towards data-driven approaches. …”
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MFFCI–YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information by Sheng Xu, Lin Song, Junru Yin, Qiqiang Chen, Tianming Zhan, Wei Huang

“…Most current researches primarily focus on improving experimental accuracy using large models, often neglecting the deployment challenges. …”
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Combined influence of quantum iterative reconstruction level and kernel sharpness on image quality in photon counting CT angiography of the upper leg by Kristina Krompaß, Florian Andreas Goldbrunner, Viktor Hartung, Süleyman Ergün, Dominik Peter, Robin Hendel, Henner Huflage, Theresa Sophie Patzer, Jan-Lucca Hennes, Thorsten Alexander Bley, Jan-Peter Grunz, Philipp Gruschwitz

“…Sharp kernels benefited most from noise reduction of higher QIR levels in lower extremity PCD-CTA. …”
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