Spherical multigrid neural operator for improving autoregressive global weather forecasting by Yifan Hu, Fukang Yin, Weimin Zhang, Kaijun Ren, Junqiang Song, Kefeng Deng, Di Zhang

“…Here, we introduce a spherical multigrid neural operator (SMgNO) that integrates spherical harmonic convolution and low resolution SFNO in the multigrid framework, effectively alleviating data distortions while requiring few computational resources. …”
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Multi-Branch Residual Multiscale CNN Based Power Transformer Fault Diagnosis on Vibration Signal by Binwen Zhang, Fei Jiao, Jie Tong, Yuanpeng Tan, Zhonghao Zhang, Shuang Lin

“…In this paper, an end-to-end multi-branch-attention-multiscale CNN (MAMCNN) framework is proposed based on a one-dimensional convolutional neural network, in which multi-branch inputs, multiscale residual learning, and attention mechanism-guided multi-branch fusion techniques are integrated to identify states of the 220 kV transformer. …”
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Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation by Zeguo Zhang, Zeguo Zhang, Zeguo Zhang, Liang Cao, Liang Cao, Liang Cao, Jianchuan Yin, Jianchuan Yin, Jianchuan Yin

“…The framework is optimized for real-time onboard deployment under communication constraints. …”
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GLN-LRF: global learning network based on large receptive fields for hyperspectral image classification by Mengyun Dai, Tianzhe Liu, Youzhuang Lin, Zhengyu Wang, Yaohai Lin, Changcai Yang, Riqing Chen

“…However, most current hyperspectral image classification networks follow a patch-based learning framework, which divides the entire image into multiple overlapping patches and uses each patch as input to the network. …”
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Accurate recognition of UAVs on multi-scenario perception with YOLOv9-CAG by Jincan Zhu, Jian Rong, Weili Kou, Qingyang Zhou, Peichun Suo

“…This work pioneers a multimodal UAVs detection framework that significantly improves identification accuracy in challenging conditions, pushing the boundaries of UAVs identification technology.…”
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Novel Multi-Scale Attention Generative Adversarial Network for Photovoltaic Solar Cell Defect Inspection Using Electroluminescence Images by Yuanjun Guan, Yang Liu, Jiayi Wang, Tao Wang, Qianchuan Yi, Wenxin Jiang, Xiaopu Gu, Yichen Zhang, Li Zhang, Tianyan Han, Binbing Huang, Lilei Hu

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Can machine learning distinguish between elite and non-elite rowers? by Orten Kristine Fjellkårstad, Helgesen Sander Elias Magnussen, Chen Bihui, Baselizadeh Adel, Torresen Jim, Herrebrøden Henrik

“…In the current study, we employed various deep learning frameworks, including Gated Recurrent Unit networks (GRUs), Convolutional Neural Networks (CNNs), and Multi-Layer Perceptrons (MLPs), to search for differences between elite and non-elite rowers using a rowing ergometer. …”
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Improving CNN predictive accuracy in COVID-19 health analytics by Tae-hoon Kim, Asadi Srinivasulu, Ravikumar Chinthaginjala, Dhakshayani J, Xin Zhao, Safia Obaidur Rab, Sivarama Prasad Tera

“…Abstract The COVID-19 pandemic has underscored the critical necessity for robust and accurate predictive frameworks to bolster global healthcare infrastructures. …”
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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. …”
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Short-Term Water Demand Forecasting Using Machine Learning Approaches in a Case Study of a Water Distribution Network Located in Italy by Qidong Que, Jinliang Gao, Wenyan Wu, Huizhe Cao, Kunyi Li, Hanshu Zhang, Yi He, Rui Shen

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Origin-destination prediction from road average speed data using GraphResLSTM model by Guangtong Hu, Jun Zhang

“…This article presents a novel integrated framework, effectively merging the distinctive capabilities of graph convolutional network (GCN), residual neural network (ResNet), and long short-term memory network (LSTM), hereby designated as GraphResLSTM. …”
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Enhancing Arabic handwritten word recognition: a CNN-BiLSTM-CTC architecture with attention mechanism and adaptive augmentation by Bounour Imane, Ammour Alae, Khaissidi Ghizlane, Mostafa Mrabti

“…This work introduces an enhanced Arabic handwritten word recognition architecture that integrates the attention mechanism (AM) into an end-to-end framework combining convolutional neural networks (CNN), Bidirectional long short-term memory (BiLSTM), and connectionist temporal classification (CTC), while utilizing word beam search (WBS) for decoding. …”
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A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction by Muhammad Farooq Siddique, Faisal Saleem, Muhammad Umar, Cheol Hong Kim, Jong-Myon Kim

“…This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analysis using CWT with a classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and a 1D convolutional residual network (1D conv ResNet). …”
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UniLF: A novel short-term load forecasting model uniformly considering various features from multivariate load data by Shiyang Zhou, Qingyong Zhang, Peng Xiao, Bingrong Xu, Geshuai Luo

“…To address the above problems, we design a novel STLF model called UniLF based on Transformer framework, which contains the proposed convolutional enhancement-fusion embedding method to capture the correlations between load and covariates for embedding, the proposed feature reconstruction-decomposition block to distill multiscale features as well as more detailed local-global variations from 2D space and the core mask-guided multiscale interactive self-attention mechanism to further realize the enhanced interactions of scale features and temporal features. …”
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Advanced deep learning architectures for enhanced mammography classification: a comparative study of CNNs and ViT by Shubhi Sharma, Yeshwant Singh, Tanupriya Choudhury

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Nature-Inspired Multi-Level Thresholding Integrated with CNN for Accurate COVID-19 and Lung Disease Classification in Chest X-Ray Images by Wafa Gtifa, Ayoub Mhaouch, Nasser Alsharif, Turke Althobaiti, Anis Sakly

“…This study addresses the diagnostic gap by introducing a novel hybrid framework for precise segmentation and classification of lung conditions. …”
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Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding by Yelan Wu, Pugang Cao, Meng Xu, Yue Zhang, Xiaoqin Lian, Chongchong Yu

“…To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. …”
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AuxTransUNet: Enhancing Remote Sensing Image Segmentation of Open-Pit Mining Areas in Qinghai–Tibet Plateau by Fangzhou Hong, Guojin He, Guizhou Wang, Zhaoming Zhang, Yan Peng

“…To address these challenges, we propose AuxTransUNet, a hybrid deep learning framework that integrates CNNs with transformers to enhance both local detail extraction and global contextual understanding. …”
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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

“…Six prevalent machine learning models were adopted, and a unified learning framework leveraged a Random Forest (RF) as a second-layer model to enhance the predictive performance of these base models. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

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