LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion by Zhaomei Qiu, Fei Wang, Tingting Li, Chongjun Liu, Xin Jin, Shunhao Qing, Yi Shi, Yuntao Wu, Congbin Liu

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R-AFPN: a residual asymptotic feature pyramid network for UAV aerial photography of small targets by Zuowen Chen, Yahong Ma, Zi’an Gong, Minghao Cao, Yuyao Yang, Zhiyuan Wang, Tengjie Wang, Jing Li, Yuxi Liu

“…Abstract This study proposes an improved Residual Asymptotic Feature Pyramid Network (R-AFPN) to address challenges in small target detection from the Unmanned Aerial Vehicle (UAV) perspectives, such as scale imbalance, feature extraction difficulty, occlusion, and computational constraints. …”
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Spatial–Spectral Feature Fusion and Spectral Reconstruction of Multispectral LiDAR Point Clouds by Attention Mechanism by Guoqing Zhou, Haoxin Qi, Shuo Shi, Sifu Bi, Xingtao Tang, Wei Gong

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A Rotated Object Detection Model With Feature Redundancy Optimization for Coronary Athero-Sclerotic Plaque Detection by Xue Hao, Haza Nuzly Abdull Hamed, Qichen Su, Xin Dai, Linqiang Deng

“…These redundant features interfere with plaque feature extraction, resulting in decreased performance and increased computational complexity. …”
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A Lightweight Intrusion Detection System with Dynamic Feature Fusion Federated Learning for Vehicular Network Security by Junjun Li, Yanyan Ma, Jiahui Bai, Congming Chen, Tingting Xu, Chi Ding

“…Experimental evaluation on the CAN-Hacking dataset shows that the proposed intrusion detection system achieves more than 99% F1 score with only 1.11 MB of memory and 81,863 trainable parameters, while maintaining low computational overheads and ensuring data privacy, which is very suitable for edge device deployment.…”
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LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection by Shijian Huang, Yunong Tian, Yong Tan, Zize Liang

“…The LCFANet-n model has <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.78</mn><mi>M</mi></mrow></semantics></math></inline-formula> parameters and a computational cost of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.7</mn></mrow></semantics></math></inline-formula> GFLOPs, enabling lightweight deployment. …”
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Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion by Ying Chen, Yuanning Liu, Xiaodong Zhu, Huiling Chen, Fei He, Yutong Pang

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Model Input-Output Configuration Search With Embedded Feature Selection for Sensor Time-Series and Image Classification by Anh Tuan Hoang, Zsolt Janos Viharos

“…Moreover, the algorithm reduced feature dimensionality to just 2&#x2013;5% of the original data, significantly enhancing computational efficiency. …”
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Ship Target Detection in SAR Images Based on Multiple Attention Mechanism and Cross-Scale Feature Fusion by Yuwu Wang, Tieming Wu, Limin Guo, Yuhan Mo

“…This reduces the sensitivity of the CIoU loss function to positional offsets of small targets, with only a slight increase in computational and parameter costs, thereby further improving the detection accuracy of small targets. …”
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Plant Disease Detection Using an Innovative Swin-Axial Transformer by Ao Zhang, Wei Liu

“…By introducing the TokenEmbedder module, the number of tokens is reduced, and multi-scale deep convolution is used to efficiently extract image features, significantly lowering computational costs. …”
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Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening by Guobin Chen, Xianzhong Xie, Shijin Li

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Selection of the Binding Object on the Current Image Formed by the Technical Vision System Using Structural and Geometric Features by Sotnikov O., Sivak V., Pavlov Ya., Нashenko S., Borysenko T., Torianyk D.

“…The most significant result is the identified values of fractal dimension ranges depending on the object content of the image, as well as experimentally established noise parameters to identify the necessary features in histograms of fractal dimensions. …”
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LSTM autoencoder based parallel architecture for deepfake audio detection with dynamic residual encoding and feature fusion by Priyanka Muruganandham, Govardhana Rajan Thangasamy, Sangeetha Jayaraman, Rekha Dharmarajan

“…By integrating diverse speech features-including MFCC, temporal, prosodic, wavelet packet, and glottal parameters the model captures both low- and high-level audio characteristics. …”
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An optimized feature selection using triangle mutation rule and restart strategy in enhanced slime mould algorithm by Ibrahim Musa Conteh, Gibril Njai, Abass Conteh, Qingguo Du

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Feature engineering for fault detection and diagnosis in Power Transmission Lines using a tree-based approach by Hassan N. Noura, Zaid Allal, Ola Salman, Khaled Chahine

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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

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Construction and Application of Feature Recommendation Model for Remote Sensing Interpretation of Rock Strata Based on Knowledge Graph by Liufeng Tao, Qirui Wu, Miao Tian, Zhong Xie, Jianguo Chen, Yueyu Wu, Qinjun Qiu

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HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention by Kaipeng Wang, Guanglin He, Xinmin Li

“…Experiments conducted on a self-built special vehicle dataset containing 2388 images demonstrate that HSF-DETR achieves mAP50 and mAP50-95 of 96.6% and 70.6%, respectively, representing improvements of 3.1% and 4.6% over baseline RT-DETR while maintaining computational efficiency at 59.7 GFLOPs and 18.07 M parameters. …”
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IDENTIFICATION OF THE FEATURES OF THE INFLUENCE OF HETEROGENEOUS-VELOCITY GROUND LAYERS ON LARGE-EARTHQUAKE EFFECTS IN THE MONGOLIAN-SIBERIAN REGION by V. I. Dzhurik, E. V. Bryzhak, S. P. Serebrennikov, A. A. Kakourova

“…The constructed models are characterized by layer thickness, change in longitudinal and transverse wave velocities with depth, volumetric mass, and attenuation decrement.The results of theoretical calculations for the features of the influence of heterogenous-velocity ground layers on the amplitude and frequency composition of the assigned initial signals are presented as the parameters of seismic effects (maximum accelerations, predominant ground motions frequencies and their corresponding amplitude level, resonant frequencies and accompanying ground motions amplification values) for seismic probability models developed based on the calculated accelerograms, spectra, and frequency characteristics.…”
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Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification by Zhiwen Hou, Boyu Wang, Jingrui Liu, Yumeng He, Yuxuan Yao

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