SFSIN: A Lightweight Model for Remote Sensing Image Super-Resolution with Strip-like Feature Superpixel Interaction Network by Yanxia Lyu, Yuhang Liu, Qianqian Zhao, Ziwen Hao, Xin Song

“…In addition to traditional methods that rely solely on direct upsampling for reconstruction, our model uses the convolutional block attention module with upsampling convolution (CBAMUpConv), which integrates deep features from spatial and channel dimensions to improve reconstruction performance. …”
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An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction by Josue Lopez-Cabrejos, Thuanne Paixão, Ana Beatriz Alvarez, Diodomiro Baldomero Luque

“…In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. …”
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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…To further optimize performance, a lightweight shared-convolution detection head significantly reduces parameter count and computational cost without compromising detection accuracy. …”
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L-ENet: An Ultralightweight SAR Image Detection Network by Yutong Wang, Min Miao, Shiliang Zhu

“…In the network's neck, the original C3 module has been replaced with the C3EGhost convolution. This convolution module integrates the Ghost and efficient channel attention mechanisms, aiming to mitigate potential accuracy loss during the lightweight process. …”
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SSHFormer: Optimizing Spectral Reconstruction with a Spatial–Spectral Hybrid Transformer by Ang Gao, Yubo Dong, Danhua Liu, Anqi Li, Zhenyuan Lin, Yuyan Li

“…Reconstructing hyperspectral images (HSIs) from RGB images is an effective technique to overcome the high cost of spectrometers. Recently, Transformers have shown potential in capturing long-range dependencies for spectral reconstruction. …”
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A Novel Involution-Based Lightweight Network for Fabric Defect Detection by Zhenxia Ke, Lingjie Yu, Chao Zhi, Tao Xue, Yuming Zhang

“…However, the computation cost of convolution neural networks (CNNs)-based models is very high. …”
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StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training by Ziqi Yang, Yiran Liao, Ziao Chen, Zhenzhen Lin, Wenyuan Huang, Yanxi Liu, Yuling Liu, Yamin Fan, Jie Xu, Lijia Xu, Jiong Mu

“…Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…PConv significantly reduces processing load during convolution operations, enhancing the model's real-time detection performance. …”
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FlexNPU: a dataflow-aware flexible deep learning accelerator for energy-efficient edge devices by Arnab Raha, Deepak A. Mathaikutty, Shamik Kundu, Soumendu K. Ghosh

“…Considering that data movement costs considerably outweigh compute costs from an energy perspective, the flexibility in dataflow allows us to optimize the movement per layer for minimal data transfer and energy consumption, a capability unattainable in fixed dataflow architectures. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…The research aims to develop a deep convolutional neural network algorithm to produce an early detection system for diabetic foot complications with the lowest computational cost (least number of parameters) and maintain high detection capability (highest average value of evaluation parameters). …”
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HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. …”
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A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion by Yan Li, Chunping Li, Tingting Zhu, Shurong Zhang, Li Liu, Zhanpeng Guan

“…The GhostConv module further improves detection accuracy by reducing the number of convolution kernels. Additionally, the BiFPN structure strengthens the model’s ability to detect objects of different sizes by fusing multi-scale feature information. …”
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YOLO-RDM: A high accuracy and efficient algorithm for magnetic tile surface defect detection with practical applications. by Wei Niu, Cheng Lv, Enxu Zhang, Zhongbin Wei

“…By using a lightweight convolution method, we replace the traditional convolution in the original network, thereby improving the feature extraction ability of the model and achieving lightweight processing. …”
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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform. by Mariusz Topolski, Jędrzej Kozal

“…However, plenty of proposed methods are based on convolutional neural networks. This class of models requires a high amount of computational power to train and deploy and large dataset. …”
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Leveraging potential of limpid attention transformer with dynamic tokenization for hyperspectral image classification. by Dhirendra Prasad Yadav, Deepak Kumar, Anand Singh Jalal, Bhisham Sharma, Panos Liatsis

“…Due to this, it has less spatial and high spectral information. Convolutional neural networks (CNNs) emerge as a highly contextual information model for remote sensing applications. …”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

“…Due to the long-term use of the electro-mechanical unit, the traditional maintenance cost is too high. In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. …”
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Evaluation of Various Free Software Options for Catphan 504 Phantom Analysis by Lorena Cunha Fernandes, Maira Ribeiro dos Santos, Leonardo Peres da Silva, Thiago Viana Miranda Lima, Rafael Figueiredo Pohlmann Simões

“…In computed tomography, image quality tests are important to guarantee a correct medical diagnosis and a better cost and benefit for the patient. Purpose: the purpose of this study is to analyse the images reconstructed with different thorax and bone convolution filters using popular free-use software in the field of medical physics, for the Catphan 504 phantom. …”
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Comparisons of different deep learning-based methods on fault diagnosis for geared system by Bing Han, Xiaohui Yang, Yafeng Ren, Wanggui Lan

“…It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. …”
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Ultrasound tomography enhancement by signal feature extraction with modular machine learning method. by Bartłomiej Baran, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Rymarczyk

“…In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Distance-based Weighted Aggregation (FedAvg-DWA) with Random Forest (RF). …”
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