MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention by Zhao Liangjun, Ning Feng, Xi Yubin, Liang Gang, He Zhongliang, Zhang Yuanyang

“…Furthermore, the Wise-IoU loss function is adopted, and a dynamic non-monotonic focusing mechanism is employed for bounding box loss, significantly enhancing the model’s ability to handle low-quality images. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Finally, the Wise-IoU loss function is adopted to replace the traditional bounding box loss. …”
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Research on frost resistance and life prediction of fly ash manufactured sand concrete under negative temperature curing by Li Gong, Tao Ran, Yanzhong Bu, Tianle Xu, Xuehao Zhao

“…Throughout freeze-thaw cycles, the frost resistance of NC concrete is weaker than SC concrete, and the mass loss ratio of concrete decreases and then increases, while the RDEM exhibits a declining trend; NMR and SEM analyses indicated that an appropriate quantity of FA could optimize the pore structure of the concrete and reduce the proportion of large pores and cracks, porosity decreased and then increased with rising FA dosage, under two curing temperatures, the highest frost resistance of concrete is achieved when the FA content is 15 %; the specimens T-5-FA15 and T20-FA15 had the lowest porosities of 2.23 % and 1.97 %. …”
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Unsupervised Anomaly Detection with Continuous-Time Model for Pig Farm Environmental Data by Heng Zhou, Seyeon Chung, Malik Muhammad Waqar, Muhammad Ibrahim Zain Ul Abideen, Arsalan Ahmad, Muhammad Ans Ilyas, Hyongsuk Kim, Sangcheol Kim

“…The model is trained in a self-supervised manner and optimized using a reconstruction-based loss function. …”
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VBP-YOLO-prune: Robust apple detection under variable weather via feature-adaptive fusion and efficient YOLO pruning by Haohai You, Hao Wang, Zhanchen Wei, Chunguang Bi, Lijuan Zhang, Xuefang Li, Yingying Yin

“…The model incorporates a V7 downsampling module, BiFPN feature fusion, and an improved PIOUv2 loss function, aiming to improve multi-scale representation and bounding box regression. …”
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Serum levels of various vitamins in periodontal health and disease- a cross sectional study by Mayank Hans, Praveen Kumar Malik, Veenu Madaan Hans, Ashi Chug, Mayank Kumar

“…They play an essential role in the metabolism and maintenance of tissue function. This investigation aimed to assess if a deficiency of certain essential vitamins is a risk factor for developing periodontitis. …”
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A Self-Supervised Specific Emitter Identification Method Based on Contrastive Asymmetric Masked Learning by Dong Wang, Yonghui Huang, Tianshu Cui, Yan Zhu

“…To further enhance feature discriminability, a learnable non-linear mapping is introduced to compress high-dimensional encoded features into a compact low-dimensional space, accompanied by a contrastive loss function that simultaneously achieves feature aggregation of positive samples and feature separation of negative samples. …”
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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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Improved Field Obstacle Detection Algorithm Based on YOLOv8 by Xinying Zhou, Wenming Chen, Xinhua Wei

“…Third, WIoU v3 (Wise Intersection over Union v3) optimized the target boundary loss function, assigning greater focus to medium-quality anchor boxes and enhancing the detector’s overall performance. …”
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A Novel Method for Traceability of Crude Oil Leakage on Offshore Platforms Based on Improved YOLOv5 Model Multi-Target Identification and Correlation Analysis by Zhenghua Wang, Shihai Zhang, Chongnian Qu, Zongyi Zhang, Feng Sun

“…Additionally, the CBAM attention module is incorporated to enhance the model’s ability to learn target features. The EIOU loss function is used to improve the model’s convergence speed and its ability to identify small or partially occluded targets. …”
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BCSM-YOLO: An Improved Product Package Recognition Algorithm for Automated Retail Stores Based on YOLOv11 by Pingqing Hou, Shaoze Huang

“…Finally, based on the Minimum Point Distance-IoU (MPD-IoU) loss function, the alignment relationship between the bounding box output from BiFPN is precisely optimized to improve bounding box regression accuracy, and the key innovation of BCSM-YOLO is its unique combination of these advanced technologies that work together to address the specific challenges of the supermarket environment. …”
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An efficient algorithm for pedestrian fall detection in various image degradation scenarios based on YOLOv8n by Jianhui Xun, Xuefeng Wang, Xiufang Wang, Xiaoliang Fan, Peishuai Yang, Zhifei Zhang

“…Additionally, the Cross Stage Partial Bottleneck with 2 Convolution Block (C2f) was optimized to reduce computational load and parameter count without compromising performance, while the Inner Extended Intersection over Union (Inner-EIoU) loss function was employed to improve bounding box regression accuracy and speed. …”
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Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system. by Gourab Saha, Fariha Shahrin, Farhan Hasin Khan, Mashook Mohammad Meshkat, Akm Abdul Malek Azad

“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. The LSTM model is used for healthy vegetation area forecasting highlighting the changes of the vegetation area over time. …”
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NDAWL-PINN: a new non-dimensionalization and multi-task learning approach for efficient training of physics-informed neural networks to solve the shallow water equations by Xin Qi, Dawei Zhang, Fan Wang, Wuxia Bi, Mingda Lu

“…This paper introduces a novel training approach that integrates non-dimensionalization with a multi-task learning technique, termed Automatic Weighted Loss (AWL), to autonomously achieve an optimal balance for each loss function. …”
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A Deep Learning-Based Solution to the Class Imbalance Problem in High-Resolution Land Cover Classification by Pengdi Chen, Yong Liu, Yuanrui Ren, Baoan Zhang, Yuan Zhao

“…Additionally, we introduce a synthetic loss function that combines cross-entropy loss, Dice loss, and auxiliary loss to improve optimization stability. …”
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Corrosion Predictive Model in Hot-Dip Galvanized Steel Buried in Soil by Lorena-De Arriba-Rodríguez, Francisco Ortega-Fernández, Joaquín M. Villanueva-Balsera, Vicente Rodríguez-Montequín

“…The objective of this research is the development of a multivariate quantitative predictive model for estimating the loss of thickness that will occur in buried hot-dip galvanized steel as a function of time. …”
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An industrial carbon block instance segmentation algorithm based on improved YOLOv8 by Runjie Shi, Zhengbao Li, Zewei Wu, Wenxin Zhang, Yihang Xu, Gan Luo, Pingchuan Ma, Zheng Zhang

“…YOLOv8-HDSA introduces Focaler-IoU as a loss function to dynamically adjust sample weights to optimize regression performance. …”
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Lightweight insulator target detection algorithm based on improved YOLOX by Bing Zeng, Wei Hua, Dezhi Li, Zhihao Zhou, Hao Wan, Yunmin Xie, Tangbing Li, Yucong Chen, Jianglei Li, Shenli Wang, Shixun Fu, Zihan Jin, Wenhua Zhang

“…Additionally, Coordinate Attention (CA) is integrated into the detection head to improve the precision of small target detection. Finally, the σWIoU loss function replaces the traditional IoU to accelerate convergence and optimize overall performance. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. …”
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Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s by Xiuhong Wang, Qingjie Wang, Yichen Qiao, Xinyue Zhang, Caiyun Lu, Chao Wang

“…Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. …”
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