Prediction of Coalbed Methane Production Using a Modified Machine Learning Methodology by Hongyang Zhang, Kewen Li, Shuaihang Shi, Jifu He

“…The model is trained using the mean absolute error (<i>MAE</i>) loss function, optimized using the Adam optimizer, and finally evaluated using metrics such as <i>MAE</i>, root mean square error (<i>RMSE</i>), and R squared (<i>R</i>²) scores. …”
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DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

“…Fourth, to improve multi-scale adaptability, we replace the standard loss function with Alpha-IoU, enhancing model robustness. …”
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Rice disease detection method based on multi-scale dynamic feature fusion by Qian Fan, Runhao Chen, Bin Li

“…The bounding box regression loss function, inner-WIoU, utilizes auxiliary bounding boxes and scale factors. …”
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Discontinuation of biologic therapy in severe asthma: Evidence and strategies for safe withdrawal: A scoping review by Johanna Ramirez-Villamizar, MD, Ciro D. Ibarra-Enríquez, MD, Juan Sebastián Galindo-Sánchez, MD, Carlos Serrano-Reyes, MD, Liliana Fernández-Trujillo, MD

“…Optimal candidates include those with sustained clinical control, stable lung function, suppressed inflammatory biomarkers, and no need for oral corticosteroids. …”
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Coordinated Reentry Guidance with A* and Deep Reinforcement Learning for Hypersonic Morphing Vehicles Under Multiple No-Fly Zones by Cunyu Bao, Xingchen Li, Weile Xu, Guojian Tang, Wen Yao

“…The A* algorithm generates heuristic trajectories circumventing no-fly zones, reducing the evaluation function by 6.2% compared to greedy methods, while DDPG optimizes sweep angles to minimize velocity loss and terminal errors (0.09 km position, 0.01 m/s velocity). …”
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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…The model introduces the SSPDConv convolution module in the backbone of YOLOv8 to enhance the global information perception of the model under complex backgrounds; a new ESPPFCSPC module is proposed to replace the original spatial pyramid pool SPPF module, which optimizes the multi-scale feature expression; and the MPDIoU loss function is introduced to optimize the problem that the original CIoU is insensitive to the change of target size, and the positioning ability of small targets is improved. …”
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Small object detection algorithm based on improved YOLOv10 for traffic sign by Yukang Zou, Scarlett Liu

“…Additionally, we integrate an attention-guided bidirectional feature pyramid network (EMA-BiFPN) to enhance feature fusion, further improving the detection accuracy for small objects. The MPDIoU loss function is employed during bounding box regression to optimize precision and recall for irregularly shaped targets. …”
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Creation of Single-Bone Forearm in Case of Diffuse Chronic Osteomyelitis of Diaphysis of Radius in an Adolescent Treated with Two Staged Procedure: A Case Report by Animesh Kumar Singh, Sushant Srivastava, Amresh Kumar, Avanish Kumar, Madhusudan Kumar, Abhishek Arunav

“…In cases of extensive bone loss or complex infections, a multidisciplinary team, including infectious disease specialists and plastic surgeons, may be necessary to optimize patient outcomes. …”
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Technical Code Analysis of Geomagnetic Flaw Detection of Suppression Rigging Defect Signal Based on Convolutional Neural Network by Gang Zhao, Changyu Han, Zhongxiang Yu, Zhipan Li, Guoao Yu, Hongmei Zhang, Dadong Zhao, Zhengyi Jiang

“…The single-stage object detection algorithm YOLOv5 (You Only Look Once) based on convolutional neural network model calculation is used, the scale detection layer and positioning loss function of the YOLOv5 algorithm are improved and optimized, and the improved YOLOv5 algorithm is used for experiments. …”
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Improved UAV Target Detection Model for RT-DETR by Yong He, Yufan Pang, Guolin Ou, Renfeng Xiao, Yifan Tang

“…Furthermore, the Focaler-MPDIoU loss function has been developed to address the challenge of suboptimal localization accuracy for hard-to-detect targets and diminutive targets. …”
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Study on PV Defect Detection Based on CWE YOLOv8 by Xiaojuan Zhang, Ruixu Yao, Bo Jing, Xiaoxuan Jiao, Mengdi Ren

“…Additionally, to combat sample imbalance and improve the classification of challenging defects, a dynamic weight-adjusted Focal Loss function is employed. Experimental results show that the proposed CWE-YOLOv8 model achieves an mAP of 87.9%, a 5.1% improvement over the YOLOv8n baseline. …”
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An Improved DeepSORT-Based Model for Multi-Target Tracking of Underwater Fish by Shengnan Liu, Jiapeng Zhang, Haojun Zheng, Cheng Qian, Shijing Liu

“…This study proposes an underwater fish object tracking method based on the improved DeepSORT algorithm, utilizing ResNet as the backbone network, embedding Deformable Convolutional Networks v2 to enhance adaptive receptive field capabilities, introducing Triplet Loss function to improve discrimination ability among similar fish, and integrating Convolutional Block Attention Module to enhance key feature learning. …”
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Rock fracture type recognition based on deep feature learning of microseismic signals by LI Dianze, XU Huajie, ZHANG Bo

“…The extracted deep feature vectors were subsequently fed into an optimized LightGBM classifier (HBL-LightGBM), which was modified with a Hinge Loss function to improve classification performance. …”
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CT-free kidney single-photon emission computed tomography for glomerular filtration rate by Kyounghyoun Kwon, Dongkyu Oh, Ji Hye Kim, Jihyung Yoo, Won Woo Lee

“…The model employed a residual U-Net with edge attention and was optimized using windowing-maximum normalization and a generalized Dice similarity loss function. …”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…Furthermore, we propose an optimized bounding box regression loss function, the Minimum Point Distance Intersection over the Union (MPDIoU), which establishes multi-dimensional geometric metrics to accurately characterize discrepancies in overlap area, center distance, and scale variation between predicted and ground truth boxes. …”
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A study on the detection of conductor quantity in cable cores based on YOLO-cable by Xiaoguang Xu, Jiale Ding, Qi’an Ding, Qikai Wang, Yi Xun

“…Specifically, the Focal loss function is introduced, the C2F structure in the backbone is optimized, the Focal NeXt module is added, and a multi-scale feature (MSF) module is incorporated in the Neck section. …”
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Facial Feature Recognition with Multi-task Learning and Attention-based Enhancements by M. Rohani, H. Farsi, S. Mohamadzadeh

“…First, we introduce an age-specific loss function that minimizes the impact of errors in less critical cases while focusing the learning process on accurate age estimation within sensitive age ranges. …”
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Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n by Lili Song, Haixin Deng, Jianfeng Han, Xiongwei Gao

“…The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. …”
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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Moreover, the EMA-GIoU loss function is proposed to mitigate class imbalance and enhance robustness, particularly in scenarios characterized by skewed class distributions. …”
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YOLOv8-BaitScan: A Lightweight and Robust Framework for Accurate Bait Detection and Counting in Aquaculture by Jian Li, Zehao Zhang, Yanan Wei, Tan Wang

“…The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. …”
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