CIDNet: A Maritime Ship Detection Model Based on ISAR Remote Sensing by Fei Liu, Boyang Liu, Hang Zhou, Song Han, Kunlin Zou, Wenjie Lv, Chang Liu

“…The model is based on the Boundary Box Efficient Transformer (BETR) architecture, which combines super-resolution preprocessing, a deep feature extraction network, a feature fusion technique, and a coordinate maintenance mechanism to improve the detection accuracy and real-time performance of ship targets in complex settings. …”
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Credit Default Risk Measurement and Statistical Analysis Based on Improved GRU Model by Zhifei Yi

“…In terms of the total time required for feature extraction, model training, and testing, the new model only takes 59 ms, which is 57 ms faster than the conventional Logistic algorithm, demonstrating the potential application of the new model in real‐time risk monitoring. …”
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Developing a suicide risk prediction model for hospitalized adolescents with depression in China by Juan Zhao, Juan Zhao, Ying Li, Yangjie Chen, Ahmad Naqib Shuid

“…Feature selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) regression, and key predictors were incorporated into a multivariate logistic regression model. …”
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Insights into the terminal pleistocene climate of Australia from high resolution climate modelling by Andrew L. Lowry, Hamish A. McGowan

“…The simulations were performed using the Weather Research and Forecasting (WRF) model downscaled from the Community Earth System Model (CESM) and compared to pre-industrial (1850 CE) simulations. …”
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Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection by Hyung Rae Lee, Wounsuk Rhee, Sam Yeol Chang, Bong-Soon Chang, Hyoungmin Kim

“…Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. …”
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Three-dimensional CFD analysis of PEMFC with different membrane thicknesses by Mahmut Kaplan

“…Membrane is one of the most important components of a PEMFC and its physical and geometrical features significantly affect PEMFC efficiency. In this paper, a three-dimensional, single-phase computational model has been improved to scrutinize membrane thickness effect on the PEM fuel cell performance using the ANSYS PEM Fuel Cell Module. …”
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Leveraging Radiomics and Genetic Algorithms to Improve Lung Infection Diagnosis in X-Ray Images Using Machine Learning by A. Beena Godbin, S. Graceline Jasmine

“…To optimize the performance of machine learning models, the paper incorporates genetic algorithms for hyperparameter optimization. …”
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Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

“…The primary outcome of PPD was measured with the Edinburgh Postnatal Depression Scale at 6 weeks postpartum. Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…This paper proposes a lightweight framework algorithm based on a contextual semantic fusion model, which is built upon the YOLOv7 model. This framework model consists of three parts: a backbone network, a multi-scale feature representation network, and a detection head. …”
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Stacked hybrid model for load forecasting: integrating transformers, ANN, and fuzzy logic by Elakkiya E, Antony Raj S, Arunkumar Balakrishnan, Bhavyasri Sanisetty, Revanth Balaji Bandaru

“…Conventional techniques, including regression models and ARIMA, frequently perform less well because they are unable to capture the complex multivariate relationships and temporal dependencies present in energy data. …”
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Modelling Associative-Semantic Content as Regards the Logic-of-Semantics Dimension of the Adolescents’ Speech by Наталя Токарева

“…In view of the above, an empirical research of the distinctive features of expanding semantic content of the adolescents' speech consciousness as regards the dimension of the logic-of-semantics organization of the mental and speech activity was performed using the diagnostic potential of a free associative experiment. …”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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Spatio-temporal Matrix Factorization Based Air Quality Inference by Keyong HU, Xiaolan GUO, Guoxiao LIU, Xin YANG, Xupeng WANG

“…In this model, spatial and temporal feature matrices and the air quality matrix are constructed and collaboratively factorized into spatial and temporal feature representations. …”
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YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images by Song Ling, Xianggong Hong, Yongchao Liu

“…The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Based on this reconstruction, the P2 detection layer is established, which optimizes network structure, enhances multi-scale feature fusion performance, and expands the detection network’s capacity for multi-scale complicated targets. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…The model comprehensively matched the multiple requirements of information weighting, temporal dependency modeling, and model optimization in concrete dam deformation prediction, forming a synergistic enhancement effect among methods.The attention mechanism operates on both feature and temporal dimensions to comprehensively enhance the model's focus on critical information. …”
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Detection of Surface Defects of Barrel Media Based on PaE-VGG Model by Hongli Peng, Long Cheng, Jianyan Tian

“…To address the issues of insufficient defect samples and low detection accuracy of barrel media, we propose a detection of the surface defects of barrel media based on a PaE-VGG model. The proposed PaE-VGG model is based on a modification of a state-of-the-art VGG convolutional neural network, incorporating position-aware circular convolution for facilitating location-sensitive global feature extraction. …”
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Load Model Construction and Parameter Identification Method of Inverter-Interfaced Distributed Generator by Jie LI, Jie WANG, Wenteng LIANG, Lushan SHI, Bolun WANG, Yufeng XIONG, Liangju ZHANG, Xia ZHOU

“…Firstly, based on the analysis of the characteristics and common features of IIDG, a comprehensive load model containing IIDG was constructed; According to the unified model, analytical calculations were performed to derive the state differential equation of the IIDG system and the output equation of the system model. …”
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Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach by Chandra Wadde, Gayatri Routhu, Mark Clemente-Arenas, Surya Prakash Gummadi, Rupesh Kumar

“…The CNN architecture consists of 10 layers, including multiple convolutional, pooling, and fully connected layers, designed to effectively extract features and perform classification. The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. …”
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The role of Tei index as an early predictor for evaluating experimental uremic cardiomyopathy model by Shi Cheng, Wei Lu, Jiarui Xu, Yong Yu, Yi Fang, Xiao Tan, Ruizhen Chen, Bo Shen, Xiaoqiang Ding, Yeqing Xie

“…Methods Comprehensive echocardiographic assessment, measurement of blood pressure and serum creatinine, histological analysis, and quantitative polymerase chain reaction (qPCR) were performed to reveal the relationship between Tei index and construction of UCM model. …”
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Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease by Yifan Zhang, Yifan Zhang, Yuexin Yan, Xiangxu Kong, Haijun Zhang, Shengyuan Su

“…Regression analysis and OPLS-DA identified metabolic biomarkers, while pathway analysis examined their relationship to clinical features. An XGBoost-based early prediction model was developed to assess the distinction between PD, prodromal PD, and HC. …”
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