Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework by Ayele Tesema Chala, Mais Mayassah, Clara Beatrice Vilceanu, Richard Ray

“…Variations in soil properties, subsurface conditions, and seismic inputs introduce complexities that challenge the reliability of predictions. This study introduces a Bayesian generalized linear model (GLM) to probabilistically predict peak ground acceleration (PGA) while accounting for uncertainties associated with geotechnical variability. …”
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Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields by Qassim A. Talib Al-Shujairy, Suhad M. Al-Hedny, Mohammed A. Naser, Sadeq Muneer Shawkat, Ahmed Hatem Ali, Dinesh Panday

“…Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. …”
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Polymer Property Prediction Model for an Industrial Two-Stage Ethylene-Propylene Copolymerization Reactor by Hu Li, He Lei, Hui-Long Wei, Zheng-Hong Luo

“…A comprehensive model for the product property prediction based on coupling polymerization kinetics, product property model, and continuously stirred tank reactor model was established to simulate a two-stage industrial ethylene-propylene copolymerization reactor. …”
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Establishment of a prediction model for long-term infections in patients undergoing peritoneal dialysis by Mengmeng Liu, Qian Lu, Jinzhu Sun, Shengnan Dai, Feng Yan, Chun Yang, Qiuhua Zhang

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A novel prediction model of grounding resistance based on long short-term memory by Xinghai Pu, Jing Zhang, Fei Wang, Shuai Xue

“…This study aims to investigate the use of Long Short-Term Memory (LSTM) models for predicting temporal variations in grounding resistance using time series data. …”
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Comparison of machine learning model performance for predicting the climate variables in Johor Bahru, Malaysia by Farid Zamani Che Rose, Nur Aqilah Khadijah Rosili, Muhammad Fadhil Marsani

“…Results showed that RF outperforms the other ML models in prediction performance by exhibiting the lowest error for both training and testing data. …”
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Enhanced LSTM-based AI model for accurate dissolved oxygen prediction in aquaculture systems by Ala Saleh Alluhaidan, Prabu P, Romana Aziz, Shakila Basheer

“…Experimental results demonstrate that the enhanced LSTM model achieves a prediction accuracy of 92.28 %, outperforming existing models such as IFP (81.97 %), DOE (63.81 %), and DOP (86.79 %). …”
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Density Measurement and Prediction Model of the CO2–Water-Bearing Crude Oil System by Menglan Li, Yingzhu Guan, Weiqi Wang

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Hydraulic permeability prediction from attenuation in an oil well using the squirt flow model by Joseline Mena-Negrete, Raúl Del Valle García, Enrique Coconi-Morales

“…The correlations are the means to establish the predicted hydraulic permeabilities from sonic and ultrasonic data. …”
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Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning by Jiazheng Zhu, Xize Huang, Xiaoyu Liang, Meng Wang, Yu Zhang

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Heat pump drying kinetics modeling and prediction for Lentinus edodes based on orthogonal experimental by Yaxiang Hou, Weidong Wu, Jing Wang, Yingying Yang, Hua Zhang

“…Then, the expression of moisture ratio of L. edodes with respect to drying time, air supply temperature (AST), loading density (LD) and circulating air volume (CAV) was proposed, and interaction effects of the three key parameters on the drying time were studied based on the prediction model. The findings indicated a substantial agreement between the predicted and experimental values. …”
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Predicting the Aquatic Toxicity of Pharmaceutical and Personal Care Products: A Multitasking Modeling Approach by Amit Kumar Halder, Tanushree Pradhan, M. Natália D. S. Cordeiro

“…This study aims to develop and validate robust in silico models for predicting the aquatic toxicity of PPCPs. …”
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RETRACTION: Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern by null Complexity

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Monthly Attenuation Prediction for Asphalt Pavement Performance by Using GM (1, 1) Model by Limin Tang, Duyang Xiao

“…In addition, this uncertainty is not accurately reflected by the pavement performance prediction model. In particular, the gray GM (1, 1) model is very suitable due to it is ability to better predict the existing situation of a domestic asphalt pavement along with the actual performance of a road surface of the “small sample, poor information” gray system. …”
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LSTM model to predict missing data of dissolved oxygen in land-based aquaculture farm by Sang-Yeon Lee, Deuk-Young Jeong, Jinseo Choi, Seng-Kyoun Jo, Dae-Heon Park, Jun-Gyu Kim

“…A long short-term memory (LSTM) model is introduced to predict missing datapoints of dissolved oxygen (DO) in an eel (Anguilla japonica) recirculating aquaculture system. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…Employing machine learning approaches to handle the extensive and disaggregated data, a novel stacked ensemble learning model is proposed. This model integrates a Random Forest (RF), three-layer Deep Neural Networks (DNN), Support Vector Machine Radial (SVM-R), and a Gradient Boosting Model (GBM) meta layer to enhance prediction accuracy. …”
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Artificial Neural Network-based Model for Predicting Cardiologists’ Over-apron Dose in CATHLABs by Reza Fardid, Fatemeh Farah, Hossein Parsaei, Hadi Rezaei, Mohammad Vahid Jorat

“…Therefore, it is essential to develop a model for predicting cardiologists’ radiation exposure. …”
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An ultrasound model for predicting recurrence of papillary thyroid carcinoma after complete endoscopic resection by Bin Lu, Yibo Zhou, Xiaofeng Lu, Wenchao Weng, Shengye Wang, Jianlin Lou

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Developing a model using neural networks to predict wheat production in the Kirkuk governorate by Yousif Hilal, Ahmet ELIÇIN, Arkan Sedeeq, Ahmed Shahin

“…The research shows that the trained models produced a minimum error, indicating that the test model can predict wheat yield production in the Kirkuk governorate.…”
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Improving the prediction of potato yield gaps: Solanum-model parameterization and evaluation in southwestern China by Ying Wang, Muhammad Abdul Rehman Rashid, Shumin Liang, Zulfiqar Ali Sahito, Ju Yang, Zhou Li, Zuo Hu, Zhechao Pan, Qijun Sui

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