Machine Learning‐Accelerated Reconstruction of Periodic Nanostructures with X‐ray Fluorescence Spectroscopy Methods by Vinh‐Binh Truong, Analía Fernández Herrero, Kas Andrle, Victor Soltwisch, Philipp Hönicke

“…Abstract With advancements in the semiconductor industry, the complexity of three‐dimensional (3D) nanostructures becomes higher with continuously decreasing feature sizes. In order to monitor the processing steps, it is crucial to accurately determine the critical dimensions and composition of these nanostructures. …”
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Emotion recognition with a Randomized CNN-multihead-attention hybrid model optimized by evolutionary intelligence algorithm by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Filippo Sanfilippo

“…To address these challenges, we propose an innovative emotion recognition framework that integrates a Randomised Convolutional Neural Network (RCNN) with a Multi-Head Attention model, further optimized by the Football Team Training Algorithm (FTTA) metaheuristic to enhance network parameters effectively. The RCNN, characterized by fixed random weights in its convolutional layers, efficiently extracts features from facial landmarks, enabling robust and diverse feature extraction while reducing computational load. …”
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RDCRNet: RGB-T Object Detection Network Based on Cross-Modal Representation Model by Yubin Li, Weida Zhan, Yichun Jiang, Jinxin Guo

“…The proposed network features a Cross-Modal Feature Remapping Module that aligns modality distributions through statistical normalization and learnable correction parameters, significantly reducing feature discrepancies between modalities. …”
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A Lightweight Method for Road Defect Detection in UAV Remote Sensing Images with Complex Backgrounds and Cross-Scale Fusion by Wenya Zhang, Xiang Li, Lina Wang, Danfei Zhang, Pengfei Lu, Lei Wang, Chuanxiang Cheng

“…Moreover, the CAA attention mechanism is employed to strengthen the model’s global feature extraction abilities; (2) a cross-scale feature fusion strategy known as GFPN is developed to tackle the problem of diverse target scales in road damage detection; (3) to reduce computational resource consumption, a lightweight detection head called EP-Detect has been specifically designed to decrease the model’s computational complexity and the number of parameters; and (4) the model’s localization capability for road damage targets is enhanced by integrating an optimized regression loss function called WiseIoUv3. …”
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U-Net-based VGG19 model for improved facial expression recognition by Xiaohu ZHAO, Jingyi ZHANG, Mingzhi JIAO, Lixun XIE, Lanfei WANG, Weiqing SUN, Di ZHANG

“…The improved model not only boosts performance in terms of feature extraction and fusion but is also adept in solving the pressing problems of parameter size and computational efficiency. …”
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Silkworm segmentation based on encoder-decoder structure by Huimin Zhuang, Zicheng Liu, Jianping Dong, Dequan Guo, Guoquan Yuan, Bo Liu, Yu Liu

“…Compared with the U-Net model, the computational complexity is reduced by 84.25 % (0.855 GFLOPs vs. 5.427 GFLOPs), and the number of parameters is reduced by 95.32 % (0.67 M vs. 14.33 M).…”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…Nevertheless, current models frequently face challenges related to feature selection, interpretability, and computational demands. …”
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Spatial and Spectral Structure-Aware Mamba Network for Hyperspectral Image Classification by Jie Zhang, Ming Sun, Sheng Chang

“…Extensive experiments on four benchmark HSI datasets demonstrate that DADFMamba outperforms state-of-the-art deep learning models in classification accuracy while maintaining low computational costs and parameter efficiency. Notably, it achieves superior performance with only 30 training samples per class, highlighting its data efficiency. …”
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Edge-Optimized Lightweight YOLO for Real-Time SAR Object Detection by Caiguang Zhang, Ruofeng Yu, Shuwen Wang, Fatong Zhang, Shaojia Ge, Shuangshuang Li, Xuezhou Zhao

“…However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, making them impractical for real-time deployment on edge computing platforms. …”
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Detection and Recognition of Underwater Acoustic Communication Signal Under Ocean Background Noise by Mengyi Li, Jilong Li, Haihong Feng

“…Both methods exhibit strong generalization ability, feature fewer model parameters, and have lower computational complexity.…”
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Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation by Thai la, Linh Tao, Dai Watanabe

“…The integration of the 2D cost aggregation and multi-stage feature extraction results in an efficient architecture for cost aggregation, simplifying the model and ensuring computational efficiency without sacrificing accuracy. …”
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FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR by Renzheng Xue, Shijie Hua, Haiqiang Xu

“…Initially, we introduce a lightweight RFConv-Block module that enhances spatial feature extraction capabilities while reducing computational redundancy. …”
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ROSE-BOX: A Lightweight and Efficient Intrusion Detection Framework for Resource-Constrained IIoT Environments by Silin Peng, Yu Han, Ruonan Li, Lichen Liu, Jie Liu, Zhaoquan Gu

“…Furthermore, to reduce computing resource requirements and latency while improving detection performance, Bayesian optimization is applied to fine-tune the parameters of XGBoost (BO-XGBoost) to obtain the best detection results. …”
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Research on Early Diagnosis Methods for Broiler Chicken Diseases Based on Swarm Intelligence Optimization Algorithms and Random Forest by X Peng, C Chen, L Yu, X Kong, B Sun

“…Comparative analysis revealed that traditional PCA methods risk losing essential pathological features by disregarding nonlinear data relationships, whereas deep learning requires substantial computational resources and high-quality datasets. …”
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LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving by Yunchuan Yang, Shubin Yang, Qiqing Chan

“…To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. …”
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Geometry-Constrained Learning-Based Visual Servoing with Projective Homography-Derived Error Vector by Yueyuan Zhang, Arpan Ghosh, Yechan An, Kyeongjin Joo, SangMin Kim, Taeyong Kuc

“…The fixed-dimension error function ensures a constant Jacobian size regardless of the number of feature points, thereby reducing computational complexity. …”
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A Massive Image Recognition Algorithm Based on Attribute Modelling and Knowledge Acquisition by Guohua Li, An Liu, Huajie Shen

“…Then, an efficient deep neural network mapping algorithm is designed and implemented for the microprocessing architecture and software programming framework of this edge processor, and a layout scheme is proposed to place the input feature maps outside the kernel DDR and the reordered convolutional kernel matrices inside the kernel storage body and to design corresponding efficient vectorization algorithms for the multidimensional matrix convolution computation, multidimensional pooling computation, local linear normalization, etc., which exist in the deep convolutional neural network model. …”
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Multiscale Convolutional Fusion Network for Efficient Monaural Speech Separation by Rui Yang, Shanliang Pan

“…Finally, cross-scale modulation upsampling efficiently reconstructs the acoustic features. Experiments on three datasets demonstrate that our method achieves state-of-the-art performance among lightweight speech separation models, with low computational complexity and fast inference. …”
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Research on Correction Method of pin-by-pin Homogenization Environmental Effect of PWR Based on Machine Learning by LI Tianya1, 2, , LUO Qi3, , YAO Dong2, HE Caiyun1, CHAI Xiaoming2, CAI Yun2, ZHANG Bin2, ZHANG Hongbo2, LIAO Hongkuan1, DUAN Yongqiang1

“…By analyzing the machine learning model’s feature sensitivity, the combination features of pin material, location, surrounding assembly, energy spectrum, and leakage-related information were determined. …”

SAG-YOLO: A Lightweight Real-Time One-Day-Old Chick Gender Detection Method by Yulong Chang, Rongqian Sun, Zheng Yang, Shijun Li, Qiaohua Wang

“…Firstly, the model reduces both parameter size and computational complexity by replacing the original feature extraction with the StarNet lightweight Backbone. …”
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