Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network by Xiaobin Hong, Guanqiao Chen, Yuanming Chen, Ruimou Cai

“…The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. …”
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Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy by Katta Lakshmi Narasimhamu, Manikandan Natarajan, Pasupuleti Thejasree, Emad Makki, Jayant Giri, Neeraj Sunheriya, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, T. Sathish

“…The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…Experiments on four real-world datasets (133,225 observations) demonstrate that our CEEMDAN–CNN–Transformer framework outperforms 12 state-of-the-art methods, achieving a 13.3% reduction in root mean square error (RMSE) to 0.117, 12.7% improvement in mean absolute error (MAE) to 0.088, and 13.0% improvement in continuous ranked probability score (CRPS) to 0.060. …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…The performance was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Machine Learning-Enhanced Model-Based Optical Proximity Correction Framework With Convolutional Neural Network-Based Variable Threshold Method Near the Diffraction Limit by Jinhao Zhu, Liwan Yue, Ying Li, Xianhe Liu, Qiang Wu, Qi Wang, Yanli Li

“…In CD simulations for typical patterns, the hybrid model reduces error medians and confines the statistical upper and lower limits of the distribution ranges to ±5 nm. …”
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Generating context-specific sports training plans by combining generative adversarial networks. by Juquan Tan, Jingwen Chen

“…Statistical significance is analyzed using ANOVA testing. …”
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DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

“…This study aims to improve the offline detection of signature forgery, especially in cases of skilled forgeries where traditional methods relying on handcrafted features and statistical models often fail to distinguish between genuine and forged signatures. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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Enhanced Fault Localization in Multi-Terminal HVDC Systems Using Improved Gaussian Process Regression by Abha Pragati, Manohar Mishra, Pritam Bhowmik, Josep M. Guerrero, Debadatta Amaresh Gadanayak

“…Subsequently, twelve statistical features (such as mean, median, and standard deviation of voltage and current signals) along with two additional features based on the coefficient of correlations between voltage and current are extracted. …”
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Machine learning - driven solar forecasting in dust-prone regions for sustainable energy systems by Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

“…The LSTM model consistently outperformed the others, achieving a Mean Absolute Error (MAE) of 0.018034 for a 1-hour horizon when dust features were included. …”
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SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…Experimental results show that the approach outperforms alternatives such as long short-term memory (LSTM), ELM, and beluga whale optimization (BWO) for ELM at different prediction starting points.This method has good accuracy in predicting the mean absolute percentage error (MAPE) and root mean square error (RMSE) of RUL in B05, B06, and B07 data sets and is the least error-prone among all models. …”
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BAYESIAN ESTIMATE OF TELECOMMUNICATION SYSTEMS PREPAREDNESS by V. E. Emelyanov, S. P. Matyuk

“…The proposed Bayesian approach has the following advantages: it is possible to conduct quantitative estimates with lack of sufficient statistics on functional use indicators; it takes into account all destabilizing factors of various nature; the presence of a lower mean square error compared to traditional methods. …”
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An intelligent prediction method for rock core integrity based on deep learning by Zhaoxia Hu, Hua Mei, Lei Yu

“…The experimental results show that the accuracy indexes F1, mAP@0.5 and mAP@0.5:0.95 of IDA-RCF are 93.09%, 94.44% and 84.61%, respectively. The relative error between the prediction results and the manual statistical results of the fissure rate is only 4.38%, and the prediction accuracy for the degree of rock core integrity is 93.8%, indicating that the proposed method in this paper is able to accomplish the intelligent evaluation task of rock core integrity with high precision.…”
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Analysis of the health status of patients with type 1 and type 2 diabetes mellitus living in urban and rural areas of the Saratov region (according to the data of the federal regis... by A. D. Ponomarev, G. Yu. Sazanova, M. A. Kunitsyna, L. M. Terina, A. A. Vojteshak

“…In connection with the territorial features of residence and the availability of medical care to residents of urban and rural areas, studies that include an analysis of the incidence of diabetes mellitus, disability and mortality of the urban and rural population from complications of this disease are one of the important mechanisms for monitoring the health status of the population, which predetermines the improvement and implementation strategies aimed at improving the demographic situation.AIM: To conduct a comparative retrospective analysis of the health indicators of patients with type 1 and type 2 diabetes mellitus living in urban and rural areas of the Saratov region.MATERIALS AND METHODS: Information on life expectancy, morbidity, complications, causes of disability, direct causes of death in patients with type 1 and type 2 diabetes mellitus living in the Saratov region in urban and rural areas was obtained from the Federal Register of Patients with Diabetes; information on the urban and rural population of the Saratov region was obtained from official statistical sources published on the website of the Federal State Statistics Service. …”
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Risk assessment and «Safety culture» lifting devices by Prusov A.Yu.

“…The hazardous situation and the likelihood of operator error in the slinging of cargo. The practical example of reduction of risk of operation of the load gripping device for the purpose of increase of safety at its use is considered. …”
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ANALYSIS OF RON 92 OIL BASED ON MORPHOLOGY AND HISTOGRAM TECHNIQUES by Indah Purnama Sari, Zainal Azis, Ahmad Riady Hasibuan

“…Manual methods often require expensive equipment and trained workers and are prone to human error. Therefore, a more efficient and accurate method is needed. …”
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A Robust Regression-Based Modeling to Predict Antiplasmodial Activity of Thiazolyl–Pyrimidine Hybrid Derivatives against <i>Plasmodium falciparum</i> by Kevin S. Umoette, Charles O. Nnadi, Wilfred O. Obonga

“…The models were evaluated using R², mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), <i>p</i>-values, <i>F</i>-statistic, and variance inflation factor (VIF). …”
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Digital phenotyping of Parkinson’s disease via natural language processing by Simona Aresta, Petronilla Battista, Cinzia Palmirotta, Serena Tagliente, Gianvito Lagravinese, Paola Santacesaria, Allegra Benzini, Davide Mongelli, Brigida Minafra, Christian Lunetta, Adolfo M. García, Christian Salvatore

“…PD-MCI). Key linguistic features included retracing, action verb, utterance error, and verbless-utterance ratios. …”
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State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM by Yu Zhao, Yonghong Xu, Yidi Wei, Liang Tong, Yiyang Li, Minghui Gong, Hongguang Zhang, Baoying Peng, Yinlian Yan

“…Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

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