Attention-based multi-scale convolution and conformer for EEG-based depression detection by Ze Yan, Ze Yan, Ze Yan, Yumei Wan, Xin Pu, Xiaolin Han, Mingming Zhao, Haiyan Wu, Wentao Li, Xueying He, Yunshao Zheng

“…Depression is a common mental health issue, and early detection is crucial for timely intervention. …”
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Research on the Method of Crop Pest and Disease Recognition Based on the Improved YOLOv7-U-Net Combined Network by Wenchao Xiang, Zitao Du, Xinran Liu, Zehui Lu, Yuna Yin

“…This paper proposes an improved YOLOv7-U-Net combined network for crop pest and disease recognition, aiming to address the issue of insufficient accuracy in existing methods. …”
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Global context-aware attention model for weakly-supervised temporal action localization by Weina Fu, Wenxiang Zhang, Jing Long, Gautam Srivastava, Shuai Liu

“…This module solves the issue that the model overemphasizes the localized features due to the addition of the MAM. …”
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A Pump‐Free, Hydraulic‐Amplification Oscillatory Microfluidic Device for Continuous Particle and Cell Manipulation by Yong Liu, Mingyi Liang, Shanshan Xu, Sheng Yan

“…The PHOMF device has a hydraulic‐amplification module for pressure transfer and a soft microchannel module for the generation of oscillatory flows. …”
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PRNet: 3D Object Detection Network-Based on Point-Region Fusion by Yufei Fu, Yuhao Guo, Hui Hu

“…The designed Image Feature Fusion module (IF-Fusion) fuses image feature maps of varying sizes in a pairwise manner. …”
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A brain tumor segmentation method based on attention mechanism by Juan Cao, Jinjia Liu, Jiaran Chen

“…Additionally, a spatial attention module (SPA) is introduced to establish global feature correlations, critical for accurate tumor segmentation. …”
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Knowledge graph convolutional networks with user preferences for course recommendation by Zhong Hua, Jianbai Yang, Weidong Ji

“…The KGCN-UP model consists of two key modules. First, the user preference propagation module refines user preferences by exploring relational chains in the knowledge graph and dynamically adjusting attention to improve user representation. …”
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MSCSCC-Net: multi-scale contextual spatial-channel correlation network for forgery detection and localization of JPEG-compressed image by Wuyang Shan, Jingchuan Yue, Steven X. Ding, Junying Qiu

“…We design a contextual spatial correlation module (CSCM) and a contextual channel correlation module (CCCM) to generate contextual spatial-channel features at different scales, with the aim of distinguishing between JPEG artifacts, forgery artifacts, and authentic regions. …”
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Enhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions. by Lingbo Li, Runpu Wang, Miaojie Zou, Fusen Guo, Yuheng Ren

“…At the same time, the module filters out redundant information from multi-scale features, reducing the number of model parameters. …”
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Refining features for underwater object detection at the frequency level by Wenling Wang, Zhibin Yu, Zhibin Yu, Mengxing Huang

“…The blurring problem caused by water scattering on underwater images makes the high-frequency texture edge less obvious, affecting the detection effect of objects in the image. To address this issue, we design a multi-scale high-frequency information enhancement module to enhance the high frequency features extracted by the backbone network and improve the detection effect of the network on underwater objects. …”
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Sustainable Self-Training Pig Detection System with Augmented Single Labeled Target Data for Solving Domain Shift Problem by Junhee Lee, Heechan Chae, Seungwook Son, Jongwoong Seo, Yooil Suh, Jonguk Lee, Yongwha Chung, Daihee Park

“…The proposed system consists of the following three modules: (1) data collection module; (2) preprocessing module that selects key frames and extracts SLOT data; and (3) domain-adaptive pig detection module that applies DAS to SLOT data to generate optimized augmented data, which are used to train the base model. …”
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An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift. by Yansong Liu, Shuang Wang, He Sui, Li Zhu

“…First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. …”
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IACGAN-ST: A Fault Diagnosis Method for Scraper Conveyor Bearings Under Imbalanced Data Conditions by Hongwei Wang, Shengyu Guan, Xuesong Wang, Lijie Wang, Qun Zhang, Kaiman Liu, Zhiwen Fang, Hongli Wang

“…Subsequently, a Convolution and Attention Fusion Module (CAFM) is incorporated into the ACGAN generator to enhance the quality and diversity of generated samples. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…Finally, the basic unit of deep feature extraction is constructed based on the standard multi-head self-attention mechanism and residual connection, with the obtained feature information and shallow features jointly input into the reconstruction module via skip connections to complete super-resolution reconstruction of coalmine images. …”
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An interpretable physics-informed deep learning model for estimating multiple air pollutants by Binjie Chen, Jiacong Hu, Yumiao Wang, Tian Feng, Weiwei Sun, Zunlei Feng, Gang Yang, Huiqiong Wang

“…Moreover, widely applied machine learning models have demonstrated a lack of interpretability and a weak physical basis. To address these issues, this study developed an interpretable physics-informed deep neural network to estimate multiple air pollutants simultaneously (IPMDNN). …”
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DiffuSAR: Frequency Domain-Aware Diffusion Model for SAR Image Generation by Zilu Ying, Wenyu Ke, Yikui Zhai, Zhihao Long, Jianhong Zhou, Hufei Zhu, C. L. Philip Chen

“…A practical approach to address this issue involves the use of artificially generated training samples to supplement the dataset. …”
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Cross-Scale Feature Blending Model for Surface Defect Identification in Machine Tool Elements Resilient to Contaminant Interference by Dong Wu, Chunhua Guo, Renpu Li, Zhigang Ma

“…This framework, inspired by the fundamental U-Net architecture, is developed with three crucial modules: the Nonlinear Activation-Free (NAF) block, the Feature Blending Block(FBB), and the Focused Linear Attention (FLA) module. …”
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MDS-Net: An Image-Text Enhanced Multimodal Dual-Branch Siamese Network for Remote Sensing Change Detection by Tao Wang, Tiecheng Bai, Chao Xu, Erlei Zhang, Bin Liu, Xining Zhao, Hongming Zhang

“…Consequently, this leads to issues such as inaccurate identification of nonsemantic changes, incomplete boundary extraction due to the degradation of local feature details, and the loss of small targets. …”
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Optimization Design and Test Analysis of Rice Electric Binder Knotter Based on ADAMS by Difa Bao, Jufei Wang, Zhi Liang, Chongcheng Chen, Wuxiong Weng, Shuhe Zheng, Jinbo Ren

“…At present, rice binders still face the issues of a low bundling efficiency and quality, which affect the progress of rice harvesting during the harvest season. …”
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Agent-Based Intelligent Fuzzy Traffic Signal Control System for Multiple Road Intersection Systems by Tamrat D. Chala, László T. Kóczy

“…This emergent feature suggests that fuzzy controllers have the potential to evolve and adapt to traffic complexity issues in urban environments when operating in an autonomous agent-based mode. …”
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