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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LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images by Gul e Sehar Shahid, Jameel Ahmad, Chaudary Atif Raza Warraich, Amel Ksibi, Shrooq Alsenan, Arfan Arshad, Rehan Raza, Zaffar Ahmed Shaikh

“…LIU-Net balances model complexity and computational load to provide consistent performance and uses Inception blocks to capture features at different scales, which makes it relatively lightweight. …”
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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

“…Suicidal ideation captures the new suicidal statements used during the COVID-19 pandemic that represents a different context of expressions. ObjectiveIn this study, our aim was to detect suicidal ideation by mining textual content extracted from social media by leveraging state-of-the-art natural language processing (NLP) techniques. …”
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Multiscale implicit frequency selective network for single-image dehazing by Zhibo Wang, Jia Jia, Jeongik Min

“…As hazy and clear images considerably differ in high-frequency components, we introduce an implicit frequency selection module to amplify high-frequency components of features and generate candidate feature maps. …”
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Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
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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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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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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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Rice Leaf Disease Image Enhancement Based on Improved CycleGAN by YAN Congkuan, ZHU Dequan, MENG Fankai, YANG Yuqing, TANG Qixing, ZHANG Aifang, LIAO Juan

“…However, rice disease image recognition faces challenges such as limited availability of datasets, insufficient sample sizes, and imbalanced sample distributions across different disease categories. To address these challenges, a data augmentation method for rice leaf disease images was proposed based on an improved CycleGAN model in this reseach which aimed to expand disease image datasets by generating disease features, thereby alleviating the burden of collecting real disease data and providing more comprehensive and diverse data to support automatic rice disease recognition.MethodsThe proposed approach built upon the CycleGAN framework, with a key modification being the integration of a convolutional block attention module (CBAM) into the generator's residual module. …”
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Reduction of Electromagnetic Reflections in 3D Airborne Transient Electromagnetic Modeling: Application of the CFS-PML in Source-Free Media by Yanju Ji, Xuejiao Zhao, Jiayue Gu, Dongsheng Li, Shanshan Guan

“…To solve the problem of electromagnetic reflections caused by the termination of finite-difference time-domain (FDTD) grids, we apply the complex frequency-shifted perfectly matched layer (CFS-PML) to airborne transient electromagnetic (ATEM) modeling in a source-free medium. …”
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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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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

“…The framework is evaluated on a dataset of 2023 CT of four different classes (i.e., acute: 361, chronic: 267, subacute: 382, and normal: 1013 images), employing both four and nine non-overlapping patch configurations. …”
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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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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…FMs were recorded from 51 typically developing participants. We compared three different sensor modalities (pressure, inertial, and visual sensors). …”
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Advanced deep learning techniques for automated extraction of non-debris-covered areas of glaciers in High-Mountain Asia using time-series remote sensing data by Gexia Qin, Ninglian Wang, Bo Jiang, Yuwei Wu, Yanchao Yin, Zhijie Li

“…Deep learning approaches have gained prominence for automatic glacier boundary extraction due to their localized nature of convolutional operations, potentially leading to incomplete or fragmented glacier pixel representations. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. …”
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CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model by Shanwei Liu, Shuaipeng Wang, Wei Zhang, Tao Zhang, Mingming Xu, Muhammad Yasir, Shiqing Wei

“…Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. …”
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Beyond averaging: A transformer approach to decoding event related brain potentials by Philipp Zelger, Manuel Arnold, Sonja Rossi, Josef Seebacher, Franz Muigg, Simone Graf, Antonio Rodríguez-Sánchez

“…In the study, 29 normal-hearing participants between 18 and 30 years were presented with acoustic stimuli at five different sound levels between 65 and 95 dB and provided their subjective loudness rating, which was categorized as ”too loud” and ”not too loud”. …”
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