AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…The ADown module dynamically adapts its downsampling strategy according to the feature characteristics, effectively reducing the number of parameters and computational complexity, while enhancing the model's ability to capture crack edges and fine textural details. …”
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A Method for Recognizing Dead Sea Bass Based on Improved YOLOv8n by Lizhen Zhang, Chong Xu, Sai Jiang, Mengxiang Zhu, Di Wu

“…Second, the C2f-faster–EMA (efficient multi-scale attention) convolutional module was designed to replace the C2f module in the backbone network of YOLOv8n, reducing redundant calculations and memory access, thereby more effectively extracting spatial features. Then, a weighted bidirectional feature pyramid network (BiFPN) was introduced to achieve a more thorough integration of deep and shallow features. …”
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Low-light image enhancement method for underground mines based on an improved Zero-DCE model by WANG Yiwei, LI Xiaoyu, WENG Zhi, BAI Fengshan

“…A Cascaded Convolution Kernel (CCK) was employed in the deep network to reduce the number of model parameters and computational cost, thereby shortening the training time. …”
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An improved framework to prioritize sub-watersheds: Construction of Watershed Morphometric Composite Index (WMCI) for mountain soil conservation by Shravan Kumar Ghimire, Yafeng Lu, Yukuan Wang

“…The study has introduced a feature scaling approach to the morphometric measurement based on the relationship of parameters with soil erodibility and constructed a composite index of parameters. …”
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A Lightweight Barcode Detection Algorithm Based on Deep Learning by Jingchao Chen, Ning Dai, Xudong Hu, Yanhong Yuan

“…The EfficientViT block based on a linear self-attention mechanism is introduced into the backbone of the original model to enhance the model’s attention to barcode features. In the model’s neck, linear mapping and grouped convolution are used to improve the C2f module, and the ADown convolution block is utilized to modify the model’s downsampling, which reduces the model’s parameters and computational cost while improving the efficiency of model feature fusion. …”
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Prediction of heart failure risk factors from retinal optical imaging via explainable machine learning by Sona M. Al Younis, Samit Kumar Ghosh, Hina Raja, Feryal A. Alskafi, Siamak Yousefi, Siamak Yousefi, Ahsan H. Khandoker

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AnomLite: Efficient binary and multiclass video anomaly detection by Anna K. Zvereva, Mariam Kaprielova, Andrey Grabovoy

“…AnomLite is competitive due to its computational efficiency, requiring only 11 million parameters, and its robustness, achieving a ROC AUC of 0.99, Average Precision of 0.99 and F1-Score (Weighted) of 0.92 and outperforming comparable models in anomaly detection tasks. …”
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DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments by Giovanni Pintore, Marco Agus, Alberto Signoroni, Enrico Gobbetti

“…This lightweight architecture requires fewer tunable parameters and computational resources than competing methods. …”
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Spatial-Temporal Cooperative In-Vehicle Network Intrusion Detection Method Based on Federated Learning by Liu Tao, Zhao Xiyang

“…This paper proposes a spatial-temporal collaborative intrusion detection method for IVN based on federated learning (FL), aiming to address the limitations of traditional intrusion detection methods in data privacy protection, temporal modeling, and computational efficiency. The method employs an autoencoder (AE) to achieve feature compression, reducing data dimensionality and extracting core spatial features. …”
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Orga-Dete: An Improved Lightweight Deep Learning Model for Lung Organoid Detection and Classification by Xuan Huang, Qin Gao, Hanwen Zhang, Fuhong Min, Dong Li, Gangyin Luo

“…However, their morphological analysis remains hindered by manual detection inefficiencies and the high computational cost of existing algorithms. To overcome these challenges, this study proposes Orga-Dete—a lightweight, high-precision detection model based on YOLOv11n—which first employs data augmentation to mitigate the small-scale dataset and class imbalance issues, then optimizes via a triple co-optimization strategy: a bi-directional feature pyramid network for enhanced multi-scale feature fusion, MPCA for stronger micro-organoid feature response, and EMASlideLoss to address class imbalance. …”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…Channel pruning further reduces the decoder’s parameters, decreasing model size and computational requirements while maintaining precision. …”
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Hybrid Reinforcement Learning-Based Collision Avoidance Algorithm for Autonomous Vehicle Clusters by Chubing Guo, Jianshe Wu, Panzheng Luo, Zhigang Wang, Kai Zhang, Ziyi Yang, Zengfa Dou, Kan Song

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Explainable post hoc portfolio management financial policy of a Deep Reinforcement Learning agent. by Alejandra de-la-Rica-Escudero, Eduardo C Garrido-Merchán, María Coronado-Vaca

“…We empirically illustrate it by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time. …”
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A Lightweight Multi-Scale Model for Speech Emotion Recognition by Haoming Li, Daqi Zhao, Jingwen Wang, Deqiang Wang

“…A_Inception combines the merits of Inception module and attention-based rectified linear units (AReLU) and thus can learn multi-scale features adaptively with low computational cost. Meanwhile, to extract most important emotional information, we propose a new multiscale cepstral attention and temporal-cepstral attention (MCA-TCA) module. …”
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Fluid Equation-Based and Data-Driven Simulation of Special Effects Animation by Yujuan Deng

“…For continuous image sequences, a linear dynamic model algorithm based on pyramidal optical flow is used to track the feature centers of the objects, and the spatial coordinates and motion parameters of the feature points are obtained by reconstructing the motion trajectories. …”
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Adaptive Global Dense Nested Reasoning Network into Small Target Detection in Large-Scale Hyperspectral Remote Sensing Image by Siyu Zhan, Yuxuan Yang, Muge Zhong, Guoming Lu, Xinyu Zhou

“…Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. …”
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Research on foreign object intrusion detection in railway tracks based on MSL-YOLO by Hongxia Niu, Dingchao Feng, Tao Hou

“…Specifically, a Multi-scale Shared Convolution Module (MSCM) is designed to replace SPPF, enhancing feature extraction while reducing parameters and computational cost. …”
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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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Multi-Step Dynamic Ensemble Selection to Estimate Software Effort by Akshay Jadhav, Shishir Kumar Shandilya, Ivan Izonin, Roman Muzyka

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Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration by Xuejun Zhang, Aobo Xu, Ganxin Ouyang, Zhengrong Xu, Shaofei Shen, Wenkang Chen, Mingxian Liang, Guiqi Zhang, Jiashun Wei, Xiangrong Zhou, Dongbo Wu

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