GLNet: global-local feature network for wheat leaf disease image classification by Shangze Li, Shen Liu, Mingyu Ji, Yuhao Cao, Bai Yun, Bai Yun

“…GLNet, which adopts a unique global-local convolutional neural network architecture, realizes the comprehensive capturing of multi-scale features in an image by processing the global feature block and local feature block in parallel and integrating the information of both of them with the help of a feature fusion block. …”
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Synergistic use of handcrafted and deep learning features for tomato leaf disease classification by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…It utilizes enhancement filters and segmentation algorithms to isolate with Regions-of-Interests (ROI) in images tomato leaves. These features based arranged in ABCD rule (Asymmetry, Borders, Colors, and Diameter) are integrated with outputs from a Convolutional Neural Network (CNN) pretrained on ImageNet. …”
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A Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compression by Q. Z. Fang, S. B. Gu, J. G. Wang, L. L. Zhang

“…Finally, the Multi-Kernel Convolutional Attention Model (MCAM) integrates global branching to extract frequency domain context and enhance local feature representation through multi-scale convolutions. …”
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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…After selecting keyframes based on point feature counts and line feature overlap angles, we integrate convolutional neural networks (CNNs) and graph neural networks (GNNs) to enhance sparse matching, thereby improving both accuracy and computational efficiency. …”
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Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features by Xin Cheng, Jintao Wang, Xinjun Chen, Fan Zhang

“…Finally, the feature vector was fed into an ensemble model of a two-dimensional bidirectional long short-term memory network and a convolutional neural network with an attention mechanism for training, and the prediction results were obtained through a fully connected layer. …”
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Vision Transformers (ViTs) for Feature Extraction and Classification of AI-Generated Visual Designs by Qing Yun

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A Unified Approach to Voice Classification: Leveraging Spectrograms, Mel Spectrograms, and Statistical Features by Muhammad Talha, Huma Ghafoor, Seung Yeob Nam

“…This study presents a multi-input neural network architecture for voice classification that integrates two parallel convolutional neural networks (CNNs) for spectrogram and Mel spectrogram images, along with a fully connected dense network for six handpicked numerical statistical features from time domain signal. …”
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Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis by Belal A. Hamed, Heba Mamdouh Farghaly, Ahmed Omar, Tarek Abd El-Hafeez

“…We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in AD. …”
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Multimodal scene recognition using semantic segmentation and deep learning integration by Aysha Naseer, Mohammed Alnusayri, Haifa F. Alhasson, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Jeongmin Park

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Enhanced Skin Lesion Classification Using Deep Learning, Integrating with Sequential Data Analysis: A Multiclass Approach by Azmath Mubeen, Uma N. Dulhare

“…This study introduces a novel method for classifying skin lesions, including nodules, by combining a unified attention (UA) network with deep convolutional neural networks (DCNNs) for feature extraction. …”
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Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction by Xin He, Yichen Ma, Jiancang Xie, Gang Zhang, Tuo Xie

“…This study proposes a wind power prediction approach based on graph convolutional networks, incorporating ramp feature recognition and error correction mechanisms. …”
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Rolling Based on Multi-Source Time–Frequency Feature Fusion with a Wavelet-Convolution, Channel-Attention-Residual Network-Bearing Fault Diagnosis Method by Tongshuhao Feng, Zhuoran Wang, Lipeng Qiu, Hongkun Li, Zhen Wang

“…Meanwhile, an efficient and lightweight deep learning model (WaveCAResNet) is constructed based on residual networks by integrating multi-scale analysis via a wavelet convolutional layer (WTConv) with the dynamic feature optimization properties of channel-attention-weighted residuals (CAWRs) and the efficient temporal modeling capabilities of weighted residual efficient multi-scale attention (WREMA). …”
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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

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Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion by Jinyin Pan, Shidong Wang, Huachao Li, Zhanliang Yuan, Binbin Yuan, Jinyan Peng, Yuanyuan Liu

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Software Defect Prediction Based on Effective Fusion of Multiple Features by Chaozheng Zhang, Junhua Wu

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A text classification model for dynamic fusion of global and local features by ZHENG Wenjun, ZHANG Shunxiang

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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

“…Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. By seamlessly integrating deep convolutional networks, DeepBGS independently extracts salient features, indicating that hyperspectral imaging technology can be used to effectively identify barnyard grass in the early stages, and pave the way for the development of advanced early detection systems.…”
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Peatland pixel-level classification via multispectral, multiresolution and multisensor data using convolutional neural network by Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P. Pitkänen, Sakari Tuominen, Paavo Nevalainen, Jonne Pohjankukka, Jukka Heikkonen

“…These diverse data sources, characterized by different spatial resolutions, are fused to preserve their spatial integrity, enabling richer feature extraction for classification tasks. …”
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ConvGRU: A Lightweight Intrusion Detection System for Vehicle Networks Based on Shallow CNN and GRU by Shaoqiang Wang, Jiahui Cheng, Yizhe Wang, Shutong Li, Lei Kang, Yinfei Dai

“…To address these challenges, this study proposes ConvGRU, a lightweight vehicular network intrusion detection model that integrates a shallow Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU). …”
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spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation by Tianjiao Zhang, Hongfei Zhang, Zhongqian Zhao, Saihong Shao, Yucai Jiang, Xiang Zhang, Guohua Wang

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