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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Baby Cry Classification Using Structure-Tuned Artificial Neural Networks with Data Augmentation and MFCC Features by Tayyip Ozcan, Hafize Gungor

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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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Clinical features and outcomes of pseudolithiasis induced by ceftriaxone in Chinese children: a single-center observational study by Chen Jin, Chen Jin, Wen-Li Xiu, Wen-Li Xiu, Yao Liu, Yao Liu, Xi-Wei Hao, Qian Dong, Qian Dong

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MorphoCellSorter is an Andrews plot-based sorting approach to rank microglia according to their morphological features by Sarah Benkeder, Son-Michel Dinh, Paul Marchal, Priscille De Gea, Muriel Thoby-Brisson, Violaine Hubert, Ines Hristovska, Gabriel Pitollat, Kassandre Combet, Laura Cardoit, Bruno Pillot, Christelle Leon, Marlene Wiart, Serge Marthy, Jérôme Honnorat, Olivier Pascual, Jean-Christophe Comte

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LMSFA-YOLO: A lightweight target detection network in Remote sensing images based on Multiscale feature fusion by Yuanbo Chu, Jiahao Wang, Longhui Ma, Chenxing Wu

“…These methods optimize convolutional computation cost and enhance multiscale information extraction, significantly reducing computational cost and parameters, while improving feature representation and fusion without sacrificing accuracy. …”
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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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A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…Second, we propose the feature reuse module (FRM). The FRM significantly reduces the parameters and computational costs of the model, making the model more lightweight. …”
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Slim-YOLO: An Improved Sugarcane Tail Tip Recognition Algorithm Based on YOLO11n for Complex Field Environments by Chunming Wen, Yang Cheng, Shangping Li, Leilei Liu, Qingquan Liang, Kaihua Li, Youzong Huang

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Fusion of non-iterative deep neural network feature extraction with kernel extreme learning machine for plant disease classification by Kirti Kirti, Navin Rajpal, Virendra P. Vishwakarma, Pramod Kumar Soni

“…The method extracts deep, discriminative features via ResNet-50 and feeds them into a lightweight KELM for final classification. …”
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Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical charac... by Jimin Hao, Man Liu, Zhigang Zhou, Chunling Zhao, Liping Dai, Songyun Ouyang

“…This study presents a predictive model integrating clinical parameters, computed tomography (CT) characteristics, and serum tumor markers to forecast EGFR mutation status in NSCLC patients. …”
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A Lightweight Multi-Scale Context Detail Network for Efficient Target Detection in Resource-Constrained Environments by Kaipeng Wang, Guanglin He, Xinmin Li

“…Extensive evaluations highlight the effectiveness of MSCDNet, which achieves 40.1% mAP50-95, 86.1% precision, and 68.1% recall while maintaining a low computational load with only 2.22 M parameters and 6.0 G FLOPs. …”
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LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion by Hang Yu, Bingzong Liu, Lei Wang, Teng Li

“…This model designs three effective modules, including the following: (1) a wavelet transform method for image compression and the frequency domain feature extraction; (2) a lightweight partial convolutional module for channel feature extraction; and (3) an improved multidimensional attention module to realize the weight assignment of different dimensional features. …”
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HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading by Muhammad Hassaan Ashraf, Hamed Alghamdi

“…This approach can lead to information loss in the initial stages due to limited feature utilization across adjacent layers. To address this limitation, we propose a Hierarchical Features Fusion Convolutional Neural Network (HFF-Net) within a Diabetic Retinopathy Screening and Grading (DRSG) framework. …”
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LitePipeNet: Research on a Lightweight and Efficient Segmentation Model for UAV Pipeline Inspection in Mining Areas by Huang Yong, Xiao Shengwang, Xia Xing, Huang Qiu

“…LitePipeNet integrates Multi-Path Weight Convolution (MPWConv) for enhanced perception, a Lightweight Bidirectional Feature Pyramid Network (LBiFPN) for efficient feature fusion, and a Multi-Level Decoupled Segmentation Head (MLHead) to optimize segmentation. …”
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Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of the Volterra Type: Mathematical Methodology and Illustrative Applica... by Dan Gabriel Cacuci

“…Using a single large-scale (adjoint) computation, the 1st-FASAM-NIE-V enables the most efficient computation of the exact expressions of all first-order sensitivities of the decoder response to the feature functions and also with respect to the optimal values of the NIE-net’s parameters/weights after the respective NIE-Volterra-net was optimized to represent the underlying physical system. …”
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A lightweight hyperspectral image multi-layer feature fusion classification method based on spatial and channel reconstruction. by Yuping Yin, Haodong Zhu, Lin Wei

“…Firstly, this method reduces redundant computations of spatial and spectral features by introducing Spatial and Channel Reconstruction Convolutions (SCConv), a novel convolutional compression method. …”
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A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition by Sunil Kumar Prabhakar, Dong-Ok Won

“…Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. …”
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Automatic Recognition of Tunnel Water Leakage Based on Adaptive Information Extraction Network and Multiscale Feature Enhancement Module by Dandan Wang, Gongyu Hou, Qinhuang Chen, Weiyi Li, Haoxiang Li, Yaohua Shao, Xunan Yu

“…An adaptive information extraction network, integrating spatial and channel squeeze-and-excitation mechanisms, is adopted in the encoder to enhance critical feature representation and accelerate inference. Additionally, a multiscale and lightweight feature enhancement module is introduced to capture global contextual information while reducing the number of parameters. …”
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A Lightweight Multi-Frequency Feature Fusion Network with Efficient Attention for Breast Tumor Classification in Pathology Images by Hailong Chen, Qingqing Song, Guantong Chen

“…At the same time, the incorporation of a linear attention (LA) mechanism lowers the model’s computational complexity and further enhances its global feature extraction capability. …”
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