A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial... by Davide Consoli, Leandro Parente, Rolf Simoes, Murat Şahin, Xuemeng Tian, Martijn Witjes, Lindsey Sloat, Tomislav Hengl

“…The quality of the result was assessed using a benchmark dataset derived from the aggregated product and comparing different imputation strategies. The resulting reconstructed images can be used as input for machine learning models or to map biophysical indices. …”
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A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms by Dingyi Liao, Jun Sun, Zhiyong Deng, Yudong Zhao, Jiani Zhang, Dinghua Ou

“…However, the spectral noise caused by complex light and shadow interference and water quality differences, combined with the diverse shapes of water bodies and the high computational cost of image processing, severely limits the accuracy of water body recognition in agricultural watersheds. …”
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Automated Loudness Growth Prediction From EEG Signals Using Autoencoder and Multi-Target Regression by D. Rama Harshita, Nitya Tiwari, Himanshu Padole, K. S. Nataraj

“…The extracted features are mapped to psychoacoustic loudness growth estimates using a multi-target regression model based on a convolutional neural network. An ablation study was conducted to analyze the impact of different autoencoder configurations on feature extraction performance. …”
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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

“…Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed. …”
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Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study by Valeria Sorgente, Dante Biagiucci, Mario Cesarelli, Luca Brunese, Antonella Santone, Fabio Martinelli, Francesco Mercaldo

“…Results: We evaluate our approach by exploiting six different datasets. We observe notable results, demonstrating the ability of Deep Convolutional GAN to generate realistic synthetic images for some specific bioimages. …”
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A Dual-Attentive Multimodal Fusion Method for Fault Diagnosis Under Varying Working Conditions by Yan Chu, Leqi Zhu, Mingfeng Lu

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Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

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Utilizing active learning and attention-CNN to classify vegetation based on UAV multispectral data by Sheng Miao, Chuanlong Wang, Guangze Kong, Xiuhe Yuan, Xiang Shen, Chao Liu

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DA-ResNeXt50 method for radio frequency fingerprint identification based on time-frequency and bispectral feature fusion by CHEN Mengdi, ZHANG Wei, SHEN Lei, LEI Fuqiang, ZHANG Jiafei

“…Borrowing from the idea of dense connection, each layer of the four-layer residual unit was directly connected to all previous layers, promoting feature reuse and transmission, which enabled it to better capture subtle differences between classes. Finally, the asymmetric convolution block (ACBlock) was used to replace the 3×3 convolution in the last residual unit of the model. …”
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Effective Algorithm for Biomedical Image Segmentation by Zhao Di, Tang Yi, A. B. Gourinovitch

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Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals by Ahmed Shaheen, Liang Ye, Chrishni Karunaratne, Tapio Seppänen

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Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…Drawing inspiration from the role of cross-frequency coupling in the hippocampal region, which plays a crucial role in advanced cognitive processes such as working memory, this study proposes a Multi-Band Multi-Scale Hybrid Sinc Convolutional Neural Network (MBSincNex). This model integrates multi-frequency and multi-scale Sinc convolution to facilitate time-frequency conversion and extract time-frequency information from multiple rhythms and regions of the EEG data with the aim of effectively model the cross-frequency coupling across different cognitive domains. …”
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Tunnel Crack Segmentation Algorithm Based on Feature Enhancement by Lihua Feng, Aijun Yao, An Huang

“…Lastly, the Adaptive Switchable Atrous Convolution (ASAC) module is introduced, combining the advantages of adaptive convolution and deformable convolution while incorporating Switchable Atrous Convolution (SAC) to enhance multi-scale feature capturing capabilities. …”
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Deep Learning Framework for Oil Shale Pyrolysis State Recognition Using Bionic Electronic Nose by Yuping Yuan, Xiaohui Weng, Yuheng Qiao, Xiaohu Shi, Zhiyong Chang

“…The proposed solution integrates Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) to capture the spatial correlations among different sensors in the electronic nose and the temporal characteristics of the data, respectively. …”
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MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification by Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong

“…Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. …”
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Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning by Ramya Panneerselvam, Sathiyabhama Balasubramaniam

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An air target intention data extension and recognition model based on deep learning by Bo Cao, Qinghua Xing, Longyue Li, Weijie Lin

“…Finally, the temporal block based on dilated causal convolution is built to solve the problem of temporal feature extraction. …”
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SparkNet–A Solar Panel Fault Detection Deep Learning Model by Rohith G, Dikshithula Sai Manish, Rahul Narasimhan A, Anurag Uttam Dhavale, Rohan Reji John

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Machine vision-based automatic fruit quality detection and grading by Amna, Muhammad Waqar AKRAM, Guiqiang LI, Muhammad Zuhaib AKRAM, Muhammad FAHEEM, Muhammad Mubashar OMAR, Muhammad Ghulman HASSAN

“…Image processing algorithms and deep learning frameworks were used for detection of defective fruit. Different image processing algorithms including pre-processing, thresholding, morphological and bitwise operations combined with a deep leaning algorithm, i.e., convolutional neural network (CNN), were applied to fruit images for the detection of defective fruit. …”
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Low-Latency Neural Network for Efficient Hyperspectral Image Classification by Chunchao Li, Jun Li, Mingrui Peng, Behnood Rasti, Puhong Duan, Xuebin Tang, Xiaoguang Ma

“…Based on this, we introduce a split convolution approach that replaces depthwise convolution, resulting in enhanced arithmetic intensity without significant increase in latency. …”
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