A Data-Driven Intelligent Methodology for Developing Explainable Diagnostic Model for Febrile Diseases by Constance Amannah, Kingsley Friday Attai, Faith-Michael Uzoka

“…A dataset of 3914 patient records from secondary and tertiary healthcare facilities was used to train and validate predictive models, employing Random Forest, Extreme Gradient Boost, and Multi-Layer Perceptron with optimized hyperparameters. …”
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A New Online Monitoring Method for MOA Based on A-VMD and A-SVD by Ying RUAN, Xingwen YE, Mingfeng DENG, Xing WANG, Linyu YANG, Qin SHU

“…Firstly, by sequentially changing the secondary penalty factor and the decomposition layer, and with the energy and loss indicators to measure the effect of VMD decomposition, the optimal parameters of decomposition layers and secondary penalty factor are searched out. …”
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Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining by Suhua Zhou, Wenjie Han, Minghua Huang, Zhiwen Xu, Jinfeng Li, Jiuchang Zhang

“…The ILB model’s performance is assessed using accuracy, area under the ROC curve (AUC), generalization ability, and computation time and compared to four common batch learning models: Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Ten classification algorithms were employed: logistic regression (LR); naive bayes (NaiveBayes); support vector machine (SVM); k nearest neighbors (KNN); random forest (RF); extra trees (ExtraTrees); eXtreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM); gradient boosting (GradientBoosting); and multi-Layer perceptron (MLP). …”
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A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA) by Charmayne Mary Lee Hughes, Yan Zhang, Ali Pourhossein, Terezia Jurasova

“…Model development and internal validation were conducted using data from wave 8 of the English Longitudinal Study of Ageing (ELSA), with external validation using wave 6 data to assess model generalizability.MethodsNine classification algorithms, including Logistic Regression, Random Forest, K-nearest Neighbor, Gradient Boosting, AdaBoost, XGBoost, LightGBM, CatBoost, and Multi-Layer Perceptron, were evaluated and their performance compared.ResultsCatBoost demonstrated the best overall performance in binary classification, achieving high recall (0.951), balanced accuracy (0.928), and the lowest Brier score (0.049) on the internal validation set, and maintaining strong performance externally with a recall of 0.950, balanced accuracy of 0.913, and F1-score of 0.951. …”
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Predictive modeling of mechanical properties in cold recycled asphalt mixtures enhanced with industrial byproducts by Mohsen Amouzadeh Omrani, Rezvan Babagoli, Mehraveh Hasirchian

“…Emulsified cold recycled mixtures are primarily utilized as base layers within a pavement systems. Emulsion-recycled asphalt layers often encounter challenges such as raveling, stripping, weak initial strength, and extended curing periods. …”
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An Adaptive Prediction Framework of Ship Fuel Consumption for Dynamic Maritime Energy Management by Ya Gao, Yanghui Tan, Dingyu Jiang, Peisheng Sang, Yunzhou Zhang, Jie Zhang

“…The effectiveness of the proposed approach was validated using a real-world dataset from an LPG carrier, where it was benchmarked against conventional machine learning models, including Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). …”
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Nanoporous Double-Gyroid Structure from ABC Triblock Terpolymer Thick Films by Karim Aissou, Maximilien Coronas, Daniel Hermida-Merino, Eduardo Solano, Didier Cot, Stéphanie Roualdes, Denis Bouyer, Damien Quemener

“…This mean value was revealed to be nearly equal to that of asymmetric PS-b-P2VP-b-PEO membranes manufactured by NIPS, which have a substructure with an implicit irregular and random distribution of the internal pore structure and a skin layer with P2VP/PEO nanopores arranged into a hexagonal array.…”
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Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning. by Wen Jiang, Wei Liu, Xiaotong Xin, Wei Zhang, Junhui Chen, Jieyu Liu, Yanqi Ma, Cheng Chen, Xiaomei Pan

“…The structure of this model not only focuses on the deep and shallow features of the spectrum, but also the information between different channels, and the self-attention mechanism further extracts the features and outputs the predicted values of Tilianin concentration through the fully connected layer. In this paper, five sets of comparison models are set up, including two machine learning models (Random Forest, K-Nearest Neighbors, Artificial Neural Network) and two deep learning models (Convolutional Neural Network and Variational Autoencoder), and the results show that the model in this paper fits the best, obtaining an R2 of 0.9144, as well as a small error.…”
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An Evaluation of the Causes and Effects of Soil Erosion in Kabale District. A case Study of Ikumba Sub-County. by Immaculate, Kebirungi

“…Soil erosion refers to the removal of top layer of soil and subsequently transported and deposited in other low laying area. …”
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Mechanism of Delaying Cartilage Degeneration in Knee Osteoarthritis with Rongjin Niantong Recipe Based on lncRNA NEAT1 and Nrf2/ARE Pathway by FU Changlong, XIE Xinyu, QIU Zhiwei, HUANG Yanfeng, JIN Linglu, LI Xihai

“…The surface layer was destroyed and the transitional layer and the radiation layer became disordered and partial calcified. …”
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Effect of High‐Power LED Barriers on Shear Bond Strength and Curing Time in Orthodontic Brackets With Single‐Component Adhesive by Yasaman Bozorgnia, Mahla Tak, Maryam Jamali

“…Material and Methods Fifty human premolars, extracted for clinical purposes, were randomly allocated into five groups. Three of these groups were cured using a high‐power LED unit with a cellophane layer, applying curing times of 3, 6, and 9 s. …”
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A novel approach for downscaling land surface temperature from 30 m to 10 m using land features multi-interaction by Alfred Homère Ngandam Mfondoum, Sofia Hakdaoui, Ali Mihi, Ibrahima Diba, Mesmin Tchindjang, Luc Beni Moutila, Frederic Chamberlain Lounang Tchatchouang

“…The Radiative Transfer Equation first helped to create an LST15 m layer over Landsat-OLI/TIRS. Next, a bilinear assessment of LST is conducted over elevation and hillshade, so to adjust shadow/brightness. …”
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Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning by Zhongping Tian, Yinning Guo, Xi Chen, Qifeng Zhou, Yuan Liu, Zhizhu Yi, Li Zhang, Li Zhang

“…PurposeTo conduct a systematic comparative analysis of macular retinal thickness, retinal nerve fiber layer (RNFL) thickness, and ganglion cell-inner plexiform layer (GCIPL) thickness measurements between spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) in healthy individuals, while establishing standardized cross-platform conversion algorithms through machine learning methodologies.MethodsIn this cross-sectional investigation, 48 healthy adults (96 eyes) underwent macular retinal thickness assessment (ETDRS grid sectors), RNFL analysis (quadrant sectors), and GCIPL evaluation (six-sector annular divisions) using both SD-OCT (Cirrus HD-OCT 5000) and SS-OCT (Triton DRI-OCT). …”
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Self SOC Estimation for Second-Life Lithium-Ion Batteries by Joelton Deonei Gotz, Emilson Ribeiro Viana, Jose Rodolfo Galvao, Fernanda Cristina Correa, Milton Borsato, Alceu Andre Badin

“…The results showed an RSME below 100 mAh for the first layer and below 1% for the second layer of the system. …”
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Using Plant DNA Barcodes and Functional Traits to Assess Community Assembly of Quercus Forests at Different Scales in the Semiarid Loess Plateau of China by YongFu Chai, TingTing Tian, Luyao Wang, Junxin Wei, Yao Xu, Peiliang Liu, Chengcheng Xiang, Ming Yue

“…Five plots (2500 m2) were constructed within Quercus forests to analyze the functional and phylogenetic structures at three spatial scales (100, 400, 2500 m2) and two vertical structural layers (tree colonization and shrub layer). In contrast to the phylogenetic convergence observed at the genus level, using plant DNA barcodes, we found that the entire forest communities and the tree layer exhibited phylogenetic randomness across all three spatial scales; even the shrub layer showed phylogenetic overdispersion with increasing scale. …”
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Determination of future gully erosion risk and its spatially quantitative interpretability of driving factors in regional scale using machine learning algorithms by Xin Liu, Dichen Wang, Mingming Guo, Xingyi Zhang, Zhuoxin Chen, Zhaokai Wan, Jielin Liu

“…The GERM was realized by four machine learning algorithms including Random Forest (RF), XGBoost, K-Nearest Neighbor (KNN), and Multi-layer perceptron of artificial neural networks (ANN-MLP). …”
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Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…Both deep learning (TabNet, Multi-Layer Perceptron) and machine learning (Random Forest, XGboost) models were employed in constructing model. …”
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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…We evaluate multiple linear regression (MLR), random forest (RF), and multi-layer perceptron neural network (MLPNN) models for their ability to predict the SSRDC values using the selected features. …”
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Research on cross-provincial power trading strategy considering the medium and long-term trading plan by Sizhe Yan, Weiqing Wang, Xiaozhu Li, Hang He, Xin Zhao

“…The algorithm integrates a Circle chaotic map, Sobol sequence, random walk strategy, and filtering technology to enhance optimization capabilities and manage complex constraints. …”
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