Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model by Chuansheng Zhang, Minglai Yang

“…Based on the commonly recommended PSO-ELM model for ET₀ prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. …”
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Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai, Bo Jiang

“…This study provides a reliable and efficient machine learning method for ship resistance prediction.…”
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Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model by Beevi Fathima, N Santhi Dr, N Ramasamy Dr

“…Stroke is a critical condition marked by the death of brain cells due to inadequate blood flow, necessitating improved predictive models for stroke lesions. The accuracy and flexibility required to forecast and classify stroke lesions is lacking in current approaches, which compromise patient outcomes. …”
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Integrating Multi-Layer Perceptron Regression with Innovative Optimization for Accurate Building Cooling Load Prediction by Subhiya Zeynalli, Baharak Eslami

“…This study explores the application of a machine learning model called Multi-Layer Perceptron Regression (MLPR) for building cooling demand prediction. Through a hybridization technique with two cutting-edge optimization algorithms, the Brown Bear Optimization Algorithm (BBOA) and the Non-Monopolize Search Algorithm (NMSA), it sets out to explore its optimization potential. …”
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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…ML models captured complex relationships in data, leading to accurate predictions and early issue detection. This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). …”
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Development and validation of a carotid plaque risk prediction model for coal miners by Yi-Chun Li, Yi-Chun Li, Yi-Chun Li, Tie-Ru Zhang, Tie-Ru Zhang, Tie-Ru Zhang, Fan Zhang, Fan Zhang, Fan Zhang, Chao-Qun Cui, Chao-Qun Cui, Chao-Qun Cui, Yu-Tong Yang, Yu-Tong Yang, Yu-Tong Yang, Jian-Guang Hao, Jian-Ru Wang, Jiao Wu, Hai-Wang Gao, Ying-Bo Liu, Ming-Zhong Luo, Li-Jian Lei, Li-Jian Lei, Li-Jian Lei

“…The area under the curve (AUC), sensitivity, and specificity of the model constructed based on the XGBoost algorithm were 0.846, 0.867, and 0.702, respectively.ConclusionsIt is possible to predict the presence of carotid plaque using machine learning. …”
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Patients with metabolic syndrome and premature atrial contractions: predicting the atrial fbrillation onset by A. I. Olesin, I. V. Konstantinova, V. S. Ivanov

“…Aim. To develop an algorithm for the prediction of the atrial fbrillation onset in patients with metabolic syndrome (MS) and premature atrial contractions (PACs) in a prospective study. …”
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Intelligent prediction for face straightness based on sensor data and human operation information by SUN Yan, FU Xiang, WANG Ranfeng, JIA Yifan, ZHANG Zhixing

“…A machine learning classification algorithm was employed to establish a prediction model for the face straightness of the current mining cycle. …”
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A network security situation prediction method based on hidden Markov model by Xiong ZHAN, Hao GUO, Peng ZHANG, Shu MAO

“…Aiming at the dynamic and real-time characteristics of network security situation prediction,a network security situation prediction model based on hidden Markov model（HMM）was designed.According to the HMM,the situation in t+1 from t time can be calculated.Meanwhile,the maximum entropy algorithm was combinated with HMM to improve the accuracy rate.The analysis and simulation experiments show that the method has the characteristics of autonomous learning and active defense,and it also balance the speed and accuracy.…”
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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings by Chuanxiang Ren, Chunxu Chai, Changchang Yin, Haowei Ji, Xuezhen Cheng, Ge Gao, Heng Zhang

“…Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. …”
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Durability Test and Service Life Prediction Methods for Silicone Structural Glazing Sealant by Bo Yang, Junjin Liu, Jianhui Li, Chao Wang, Zhiyuan Wang

“…A recursive algorithm was developed to predict TBS degradation under actual service conditions based on the degradation model and environmental records, with verification through outdoor aging tests. …”
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Two Machine-learning Hybrid Models for Predicting Type 2 Diabetes Mellitus by Rahman Farnoosh, Karlo Abnoosian, Rasha Abbas Isewid

“…The samples are categorized into three classes: diabetic (Y), nondiabetic (N), and predicted diabetic (P). The dataset contains twelve attributes and includes outlier data. …”
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Study on Predicting Blueberry Hardness from Images for Adjusting Mechanical Gripper Force by Hao Yin, Wenxin Li, Han Wang, Yuhuan Li, Jiang Liu, Baogang Li

“…Firstly, a chimpanzee optimization algorithm (ChOA) was used to optimize a prediction model that established a mapping relationship between fruit diameter, thickness, weight, and fruit hardness. …”
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Towards Predicting Business Activity Classes from European Digital Corporate Reports by Péter Molnár, Alex Suta, Bence Lukács, Árpád Tóth

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Fault prediction of smart meter based on spatio-temporal convolution neural network by Gao Wenjun, Xue Binbin, Pang Zhenjiang

“…Then, combined with CNN, the fault prediction model of smart meter is established, and the model parameters are optimized by adaptive momentum estimation (Adam) algorithm. …”
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PREDICTING AND ANALYZING OF TURKISH SUGAR PRICE WITH ARCH, GARCH, EGARCH AND ARIMA METHODS by Mehmet Arif ŞAHİNLİ

“…Mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute deviation (MAD) were used to determine the fit model for making predicting. In this study, we found the best model as a GARCH (1,1) model.…”
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Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices by Dong-Rui Chen, Chuang Liu, Yi-Cheng Zhang, Zi-Ke Zhang

“…In addition, we propose an extremes indicator through the network, which is constructed from the price time series using a weighted visual graph algorithm. Experimental results on 12 stock indices show that the proposed indicators can predict financial extremes very well.…”
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The Method of Predicting the Ｒise of Temperature by Combining Fuzzy System and Ｒecursive Least Square by WANG Gang, ZHANG Bo, WANG Guan, YE San-pai

“…For the problem that the rise of temperature of fitting is too high to control，a new method of predicting the rise of temperature has been put forward. …”
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Research on wind temperature prediction of tunneling working site based on PSO−SVR by Yanhe LI, Zhijun WAN, Zhenzi YU, Hong GOU, Wanli ZHAO, Jiale ZHOU, Peng SHI, Zheng ZHEN, Yuan ZHANG

“…By comparing with the MLR model estimated by the least square method and the conventional SVR model calibrated by the “trial and error” method, the advantages of the PSO-SVR algorithm are analyzed. The PSO-SVR algorithm model was applied to predict airflow temperature in J-24120 protective airway of Pingmei No.10 Coal Mine. …”
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