A Novel Pseudo-Siamese Fusion Network for Enhancing Semantic Segmentation of Building Areas in Synthetic Aperture Radar Images by Mengguang Liao, Longcheng Huang, Shaoning Li

“…Finally, the model was optimized using the combined CE-Dice loss function to achieve superior performance. …”
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FM‐YOLOv8:Lightweight gesture recognition algorithm by Fanghai Li, Xitai Na, Jinshuo Shi, Qingbin Sun

“…Recombination convolution can reduce the computation and make up for the loss of precision to some extent. Finally, the MDPIoU loss function is used to optimize target location and prediction, to improve the accuracy of the model. …”
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On shrinkage covariance estimators: how inefficient is 1/N strategy of covariance estimation for portfolio selection in foreign exchange market? by Muhammad Husnain, Shamrez Ali, Qaiser Munir, Ammar Jreisat

“…This study challenges theoretically rigorous shrinkage covariance estimators using multiple evaluation metrics: systematic loss function, risk profile of minimum variance portfolios, Herfindahl index, financial efficiency, and concentration level. …”
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Lightweight blasthole image detection and positioning method by Shan PAN, Ting YU, Zhongwen YUE, Zijian TIAN, Qingyu JIN

“…By incorporating an atrous spatial pyramid pooling module, the receptive field is expanded to capture fine-grained differences between blastholes and shadows formed by rock occlusion in complex surrounding rock backgrounds. Finally, the loss function is optimized to improve the accuracy of blasthole bounding box regression. …”
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Path Planning Method of Mobile Robot Using Improved Deep Reinforcement Learning by Wei Wang, Zhenkui Wu, Huafu Luo, Bin Zhang

“…Finally, the reward function combined with the artificial potential field method is designed to optimize the state-action space. …”
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Improved YOLOv8 Object Detection Method for Drone Aerial Images by Zhong Shuai, Wang Liping

“…Finally, the Inner-IoU is introduced into the original CIoU loss function and optimized into the Inner-CIoU loss function, which enhances the assessment of prediction bounding boxes and improves the model's localization precision. …”
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YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection by Xiang Gan, Shukun Cao, Jin Wang, Yu Wang, Xu Hou

“…At the same time, the Wise-IoU loss function was combined to optimize the training process, which improved the convergence speed and regression accuracy of the loss function. …”
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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…Additionally, an auxiliary bounding box mechanism is incorporated into the CIoU loss function, improving both convergence speed and localization precision. …”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…Finally, the adoption of an adaptively optimized multitask loss function obviates the necessity for manual allocation of weights. …”
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Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue by LIU Yuan, ZHAO Jing, JIANG Guoping, XU Fengyu, LU Ningyun

“…Secondly, to improve the robustness of the model, spatial attention module was designed to enhance the learning of important features. Finally, balance L1 loss was used to optimize the loss function of the baseline algorithm and enhance the stability of the model during the process of detection. …”
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Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm by Lijian Zhang, Guangfu Liu

“…Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.…”
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A growth posture identification method of immature peaches in natural environments by Jidong Lv, Liangliang Niu, Ying Han, Xiaoqin Sun, Lingyun Wang, Hailong Rong, Ling Zou

“…Meanwhile, the original upsampling module was replaced with the CARAFE module to enhance the recognition capability of global features, which considered the impact of immature peaches on the nearby color background. The CIOU loss function was ultimately substituted with the SIOU loss function to further optimize the boundary frame loss and target detection accuracy. …”
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A Strawberry Ripeness Detection Method Based on Improved YOLOv8 by Yawei Yue, Shengbo Xu, Huanhuan Wu

“…Finally, the Wise-IoU (Wise Intersection over Union) loss function optimized the IoU (Intersection over Union) through intelligent weighting and adaptive tuning, enhancing the bounding box accuracy. …”
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On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning by Nansha Gao, Mou Wang, Xiao Liang, Guang Pan

“…Further, deep neural networks are employed to predict the average value of the sound absorption coefficient curve. The overall loss function is derived by combining the mean square error between the expected average sound absorption coefficient and its predicted value and the network-optimized loss function to ensure that the 20 sensitive parameters that meet the acoustic performance can be predicted. …”
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DWS-YOLO: A Lightweight Detector for Blood Cell Detection by Yihai Mao, Hongyi Zhang, Wanqing Wu, Xingen Gao, Zhibin Lin, Juqiang Lin

“…Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. …”
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Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework by Wenxia Yuan, Lingfang Lan, Jiayi Xu, Tingting Sun, Xinghua Wang, Qiaomei Wang, Jingnan Hu, Baijuan Wang

“…Additionally, Shape Weights and Scale Adjustment Factors are introduced to optimize the loss function. The experimental results show that compared with the original YOLOv10, the model generated by the improved algorithm improves the accuracy by 5.88 percentage points, the recall rate by 6.67 percentage points, the balance score by 6.27 percentage points, the mAP value by 4.26 percentage points, the bounding box loss by 18.75%, the classification loss by 27.27%, and the feature point loss by 8%. …”
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Welding Image Data Augmentation Method Based on LRGAN Model by Ying Wang, Zhe Dai, Qiang Zhang, Zihao Han

“…By incorporating the least squares loss function, the gradients of the model parameters were constrained within a reasonable range, which not only accelerated the convergence process but also effectively limited drastic changes in model parameters, alleviating the vanishing gradient problem. …”
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Lightweight Explicit 3D Human Digitization via Normal Integration by Jiaxuan Liu, Jingyi Wu, Ruiyang Jing, Han Yu, Jing Liu, Liang Song

“…Additionally, we introduce an innovative loss function tailored to the geometric properties of normal maps. …”
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Object detection with afordable robustness for UAV aerial imagery: model and providing method by Viacheslav Moskalenko, Artem Korobov, Yuriy Moskalenko

“…The detector training method was developed for the first time, combined the RetinaNet loss function with the gate unit loss function and applied meta-learning to the results of adaptation to various types of synthetic disturbances. …”
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A Machine Learning Model Relating Xrain and Rain Gauge by Miao Zhang, Christopher Gomez, Balazs Bradak, Hotta Norifumi, Shinohara Yoshinori

“…The model takes a logistic activation function, and the loss function is optimized using the Mean Squared Errors and the Mean Absolute Error. …”
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