Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

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The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer by Leilei Xie, Fenghua Zhu, Zhixue Wang

“…These improvements enable the network to distinguish features at different scales effectively while optimizing spatial information processing and reducing computational costs. …”
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Stability Enhancement of Inverter-Based Microgrids Using Optimized Neural Networks by PANG Kai, TANG Zhiyuan, GAO Hongjun, LIU Youbo, LIU Junyong

“…Subsequently，to minimize the reliance on global system information，a multilabel feature selection algorithm is employed to identify the most relevant local measurements that influence the adjustment of each control parameter. …”
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A method of identification and localization of tea buds based on lightweight improved YOLOV5 by Yuanhong Wang, Yuanhong Wang, Jinzhu Lu, Jinzhu Lu, Qi Wang, Qi Wang, Zongmei Gao

“…The Fuding white tea bud image dataset was established by collecting Fuding white tea images; then the lightweight network ShuffleNetV2 was used to replace the YOLOV5 backbone network; the up-sampling algorithm of YOLOV5 was optimized by using CARAFE modular structure, which increases the sensory field of the network while maintaining the lightweight; then BiFPN was used to achieve more efficient multi-scale feature fusion; and the introduction of the parameter-free attention SimAm to enhance the feature extraction ability of the model while not adding extra computation. …”
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FCDNet: A Lightweight Network for Real-Time Wildfire Core Detection in Drone Thermal Imaging by Linfeng Wang, Oualid Doukhi, Deok Jin Lee

“…Compared to the state-of-the-art YOLOv11n, FCDNet reduces parameters, computation, and model size by 26.9%, 20.6%, and 27.3%, respectively. …”
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Real-Time Transformer Detection of Underwater Objects Based on Lightweight Gated Convolutional Network by Yuhui LI, Huixia CUI, Yaomin LI, Senping JIA

“…To address the challenges in underwater object detection algorithms, including difficult image feature processing, redundant model architectures, and excessive parameter numbers, this paper proposed a real-time Transformer detection method for underwater objects based on a lightweight gated convolutional network. …”
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SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

“…The DSD Module focuses on extracting both deep and shallow features from feature maps using fewer parameters to obtain richer feature representations. …”
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LBT-YOLO: A Lightweight Road Targeting Algorithm Based on Task Aligned Dynamic Detection Heads by Pei Tang, Zhenyu Ding, Minnan Jiang, Weikai Xu, Mao Lv

“…This detection head reduces the number of parameters by sharing the neck network features, and performs task decomposition alignment to achieve high accuracy target detection using dynamic convolution and dynamic feature selection. …”
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Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. …”
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MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMNet. …”
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A Lightweight Dual-Branch Complex-Valued Neural Network for Automatic Modulation Classification of Communication Signals by Zhaojing Xu, Youchen Fan, Shengliang Fang, You Fu, Liu Yi

“…However, existing models face deployment challenges due to excessive parameters and computational complexity. To address these limitations, a lightweight dual-branch complex-valued neural network (LDCVNN) is proposed. …”
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Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments by Zhongyuan Jing, Ruyan Wang

“…Traditional edge intelligence networks usually rely on terrestrial communication base stations as parameter servers to manage communication and computation tasks among devices. …”
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Fall detection method based on spatio-temporal coordinate attention for high-resolution networks by Xiaorui Zhang, Qijian Xie, Wei Sun, Ting Wang

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A Lightweight Greenhouse Tomato Fruit Identification Method Based on Improved YOLOv11n by Xingyu Gao, Fengyu Li, Jun Yan, Qinyou Sun, Xianyong Meng, Pingzeng Liu

“…Meanwhile, the model size is only 3.3 MB, the number of parameters is 1.6 M, and the floating-point computation is 3.9 GFLOPs. …”
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Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model by Xiaoai Dai, Junying Cheng, Shouheng Guo, Chengchen Wang, Ge Qu, Wenxin Liu, Weile Li, Heng Lu, Youlin Wang, Binyang Zeng, Yunjie Peng, Shuneng Liang

“…However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. …”
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DEMNet: A Small Object Detection Method for Tea Leaf Blight in Slightly Blurry UAV Remote Sensing Images by Yating Gu, Yuxin Jing, Hao-Dong Li, Juntao Shi, Haifeng Lin

“…DEMNet introduces a dynamic convolution mechanism into the HGNetV2 backbone to form DynamicHGNetV2, enabling adaptive convolutional weight generation and improving feature extraction for blurry objects. An efficient EMAFPN neck structure further facilitates deep–shallow feature interaction while reducing the computational cost. …”
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A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks by ÙAfsaneh Banitalebi Dehkordi, MohammadReza Soltanaghaei, Farsad Zamani Boroujeni

“…DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. …”
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A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…This approach improves the model’s feature extraction capability while reducing its parameter count. …”
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Three-Stage Channel Split Dense Fusion Network for Single Image Deraining by ZHANG Shuting, WANG Changyue, WANG Changzhong, LENG Qiangkui

“…CSB uses channel split operation to split the rainy image into multiple channels, and applies different rain streaks removal methods according to different levels of features to reduce redundant features and network parameters, and improve the performance ability and computational efficiency of the model. …”
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Expression Recognition Method Based on CBAM-DSC Network by SONG Wen bo, GAO Lu, MIAO Zhuang, LIN Ke zheng

“…The improved Inception module extracts different feature information through different branches while reducing the network parameters and improving the network operation efficiency. …”
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