Pure data correction enhancing remote sensing image classification with a lightweight ensemble model by Huaxiang Song, Hanglu Xie, Yingying Duan, Xinyi Xie, Fang Gan, Wei Wang, Jinling Liu

“…Abstract The classification of remote sensing images is inherently challenging due to the complexity, diversity, and sparsity of the data across different image samples. Existing advanced methods often require substantial modifications to model architectures to achieve optimal performance, resulting in complex frameworks that are difficult to adapt. …”
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Self-Correlation Network With Triple Contrastive Learning for Hyperspectral Image Classification With Noisy Labels by Kwabena Sarpong, Mohammad Awrangjeb, Md. Saiful Islam, Islam Helmy

“…However, current deep learning approaches typically employ conventional convolution methods that treat all spatial frequency components uniformly, neglecting the exploration of feature-dependent knowledge, significantly affecting learning with noisy labels. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. …”
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Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations by Kai-Cheng Chuang, Ping-Sung Cheng, Yu-Hung Tsai, Meng-Hsiun Tsai

“…After preprocessing, data was trained with a novel model combining gated recurrent units (GRU) and graph convolutional network (GCN) to predict the level of miRNA-disease associations. …”
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Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador by Mohammad Amin Khalili, Silvio Coda, Domenico Calcaterra, Diego Di Martire

“…To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. …”
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SSMSFuse: A Spectral and Spatial Multiscale Coupling Fusion Model for Hyperspectral and Multispectral Image by Siyuan Liu, Yingchao Fan, Qi Hu, Bing Li, Yudong Zhang, Shuaiqi Liu

“…Spa-Net is constructed using a multiscale convolutional neural network to better mine multilevel spatial features from MSI. …”
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Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

“…High-resolution images of galaxies can identify fine structures within galaxies, which are essential for identifying and distinguishing different substructures within merging systems. However, due to observational and instrumental limitations, galaxy data is often collected at low resolution. …”
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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…This study exploits experimental data from chatter and chatter-free cuts during machining operations with commercial COSAR-60 low-carbon steel under two different grain sizes (as received and ultra-fined). …”
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Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model by Kenneth DeVoe, Gary Takahashi, Ebrahim Tarshizi, Allan Sacker

“…This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. …”
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Models and methods for analyzing complex networks and social network structures by Ju. P. Perova, V. P. Grigoriev, D. O. Zhukov

“…Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. …”
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Multi-scale window transformer for cervical cytopathology image recognition by Jiaxiang Yi, Xiuli Liu, Shenghua Cheng, Li Chen, Shaoqun Zeng

“…Our design enables long-range feature integration but avoids whole image self-attention (SA) in ViT or twice local window SA in Swin Transformer. We find convolutional feed-forward networks (CFFN) are more efficient than original MLP-based FFN for representing cytopathology images. …”
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Lightweight GAN-Assisted Class Imbalance Mitigation for Apple Flower Bud Detection by Wenan Yuan, Peng Li

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Multi-Layer Modeling and Visualization of Functional Network Connectivity Shows High Performance for the Classification of Schizophrenia and Cognitive Performance via Resting fMRI by Duc My Vo, Anees Abrol, Zening Fu, Vince D. Calhoun

“…In the first, a deep convolutional neural network (DCNN) is trained to produce heatmaps from multiple convolution layers. …”
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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…The ensemble feature vector is optimized using three different meta-heuristic algorithms that are Binary Dragonfly algorithm (BDA), Ant Colony Optimization algorithm and Moth Flame Optimization algorithm (MFO). …”
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Predicting microbe-disease associations via graph neural network and contrastive learning by Cong Jiang, Cong Jiang, Junxuan Feng, Junxuan Feng, Bingshen Shan, Bingshen Shan, Qiyue Chen, Jian Yang, Jian Yang, Gang Wang, Gang Wang, Xiaogang Peng, Xiaozheng Li, Xiaozheng Li

“…Then, we design a feature encoder that combines graph convolutional network and graph attention mechanism to learn the node features of networks, and propose a feature dual-fusion module to effectively integrate node features from each layer's output. …”
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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification by Md. Eshmam Rayed, Jamin Rahman Jim, Md Juniadul Islam, M. F. Mridha, Md Mohsin Kabir, Md. Jakir Hossen

“…Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. …”
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Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN by Simanto Saha, Mathias Baumert, Alistair Mcewan

“…This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. …”
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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular Summation Fields (GASFs) and Gramian Angular Difference Fields (GADFs). This fusion enriches the structural encoding of temporal dynamics. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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A Lightweight Fault Diagnosis Framework for Hydro-Turbine Main Shaft Bearing Under Noise Interference by Hongwei Zhang, Zhao Liu, Hansong Si, Kaipeng Yu, Shuaifang Li, Zhenwu Yan

“…This framework integrates and enhances the local feature extraction capabilities of convolutional neural networks with the global feature extraction capabilities of Transformers. …”
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