SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…Finally, the Inner-IoU optimization loss function is applied to accelerate model convergence and enhance detection accuracy.ResultsTesting results indicate that SGSNet performs exceptionally well across key metrics, achieving 98.83% precision, 99.45% recall, 99.14% F1 score, 99.50% mAP@0.5, and a loss value of 0.3534. …”
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Myopericarditis secondary to silent autoimmune thyroiditis: a case of severe thyrotoxicosis by Pavel Antonio Montes Hernández, Dylani Rosa Ávila Salcedo, Jorge Alejandro Ayala San Pedro

“…Early recognition and targeted management can reverse myocardial injury and optimize patient outcomes.…”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…We articulate the mathematical characteristics of feature selection driven by NAM, delineate the convergence attributes of the loss function, and present measures of explainability through Shapley Additive Explanations (SHAP). …”
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Routing and Scheduling in Time-Sensitive Networking by Evolutionary Algorithms by Zengkai Wang, Weizhi Liao, Xiaoyun Xia, Zijia Wang, Yaolong Duan

“…Specifically, we introduce a flow grouping method that leverages the greatest common divisor to optimize flow aggregation. On this basis, we develop a flow routing strategy that employs a genetic algorithm, where the evaluation function considers not only flow combinability but also path length and network load. …”
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A Data-Driven Approach Based on Deep Neural Network Regression for Predicting the Compressive Strength of Steel Fiber Reinforced Concrete by Nhat-Duc Hoang, Van-Duc Tran

“…Notably, an asymmetric loss function is used along with Nadam to decrease the percentage of overestimated cases from 50.83% to 27.08%. …”
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Oak-YOLO: A high-performance detection model for automated Oak seed defect identification. by Hao Li, Zhuqi Li, Dongkui Chen, Wangyu Wu, Xuanlong He, Hongbo Mu

“…Additionally, the WIoUv3 loss function is introduced to optimize bounding box regression for complex target shapes and overlapping instances.Extensive experiments were conducted on both single-object and multi-object datasets. …”
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Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model by Yiqing Yang, Guoyin Zhang, Yanxia Wu, Zhixiang Zhao, Yan Fu

“…Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system’s response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems.…”
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DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection by Shaofu Lin, Yang Yang, Xiliang Liu, Li Tian

“…Secondly, a hybrid loss function has been developed with weights optimized employing the Bayesian Optimization algorithm to provide a strategic method for parameter tuning in similar research. …”
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Anesthetic management of folders with severe kyphosis in ankylosing spondylitis: a single-center retrospective case series study by Lin Peng, Qiang Li, Lingxi Zheng, Deng Zhao, Qiang Fu, Qiang Fu

“…It also covered strict preoperative anesthetic evaluations, establishing an optimal fluid pathway during surgery, precise anesthetic monitoring and management, and applying postoperative multimodal analgesia and rehabilitation exercises to optimize perioperative anesthetic management.ResultsPreoperative cardiopulmonary function exercises were required to ensure patients could withstand surgery and anesthesia. …”
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ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao, Wenfeng Li

“…Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. …”
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A target detection model HR-YOLO for advanced driver assistance systems in foggy conditions by Yao Zhang, Na Jia

“…Furthermore, The Wise Intersection over Union (WIoU) loss function is introduced to enhance target localization accuracy. …”
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SGRiT: Non-Negative Matrix Factorization via Subspace Graph Regularization and Riemannian-Based Trust Region Algorithm by Mohsen Nokhodchian, Mohammad Hossein Moattar, Mehrdad Jalali

“…Additionally, this paper presents a solution for addressing the Stiefel manifold problem and utilizes a Riemannian-based trust region algorithm to optimize the loss function. The outcome of this optimization process is a new representation of the data in a transformed space, which can subsequently serve as input for the NMF algorithm. …”
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ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact by Xi Liu, Hui Hwang Goh, Haonan Xie, Tingting He, Weng Kean Yew, Dongdong Zhang, Wei Dai, Tonni Agustiono Kurniawan

“…Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. …”
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AGEN: Adaptive Error Control-Driven Cross-View Geo-Localization Under Extreme Weather Conditions by Mengmeng Xu, Hongxiang Lv, Hai Zhu, Enlai Dong, Fei Wu

“…Additionally, to further enhance model robustness, we innovatively introduce an Adaptive Error Control (AEC) module based on fuzzy control to optimize the loss function dynamically. Specifically, by adjusting loss weights adaptively, the AEC module allows the model to better handle complex and challenging scenarios. …”
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Linear adaptive filtering of random sequences on basis of deterministic approach by V. A. Artemiev, A. O. Naumov, L. L. Kokhan

“…In order to obtain a recursive filtering algorithm, it is proposed to extend the structure of the method loss function by  including in loss function an additional term that defines the estimate extrapolation for the next measurement period. …”
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YOLO-SUMAS: Improved Printed Circuit Board Defect Detection and Identification Research Based on YOLOv8 by Ying Tang, Runhao Liu, Sheng Wang

“…Aiming at the demand for defect detection accuracy and efficiency under the trend of high-density and integration in printed circuit board (PCB) manufacturing, this paper proposes an improved YOLOv8n model (YOLO-SUMAS), which enhances detection performance through multi-module collaborative optimization. The model introduces the SCSA attention mechanism, which improves the feature expression capability through spatial and channel synergistic attention; adopts the Unified-IoU loss function, combined with the dynamic bounding box scaling and bi-directional weight allocation strategy, to optimize the accuracy of high-quality target localization; integrates the MobileNetV4 lightweight architecture and its MobileMQA attention module, which reduces the computational complexity and improves the inference speed; and combines ASF-SDI Neck structure with weighted bi-directional feature pyramid and multi-level semantic detail fusion to strengthen small target detection capability. …”
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COVID-19 detection using federated machine learning. by Mustafa Abdul Salam, Sanaa Taha, Mohamed Ramadan

“…During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models' loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.…”
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Expectation-maximization alternating least squares for tensor network logistic regression by Naoya Yamauchi, Hidekata Hontani, Tatsuya Yokota, Tatsuya Yokota

“…Unlike conventional gradient-based methods, which suffer from vanishing gradients and inefficient training, our proposed approach can effectively minimize squared loss and logistic loss. To make ALS applicable to logistic regression, we introduce an auxiliary function derived from Pólya-Gamma augmentation, allowing logistic loss to be minimized as a weighted squared loss. …”
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DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR by Xiaolong Wei, Ling Yin, Liangliang Zhang, Fei Wu

“…To achieve this, we introduce three main enhancements: (1) ResNet18 as the backbone network to improve feature extraction and reduce model complexity; (2) the integration of recalibration attention units and deformable attention mechanisms in the neck network to enhance multi-scale feature fusion and improve localization accuracy; and (3) the use of the Focaler-IoU loss function to better handle the imbalanced distribution of target scales and focus on challenging samples. …”
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Investigations on higher-order spherical harmonic input features for deep learning-based multiple speaker detection and localization by Nils Poschadel, Stephan Preihs, Jürgen Peissig

“…The trained neural networks, optimized with a single loss function for the combined tasks of detection and localization, are then evaluated in detail for overall SDL performance as well as their performance in the sub-tasks of detection and, particularly, localization. …”
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