Performance of a kinetic model for predicting internal sulphate attack in Portland cement mortars by S.O. Ekolu, J. Dateling, A. Naghizadeh

“…The present study evaluated an existing kinetic model originally proposed for predicting delayed ettringite formation (DEF) in cementitious systems for potential dual employment to predict internal sulphate attack (ISA). …”
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A machine learning model for predicting severity-adjusted in-hospital mortality in pneumonia patients by Jong-Ho Park, Jihye Lim

“…Conclusion These results suggest that further research is needed on models that adjust for the severity of comorbidities for each diagnosis to more accurately predict health outcomes.…”
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Data-driven deep learning model for predicting ambient temperature: environment and solar energy by Reza Hassanian, Nashmin Yeganeh, Ásdís Helgadóttir, Runar Unnthorsson, Morris Riedel

“…The superiority of the model lies in its accurate predictions using limited data and reasonable computational resources. …”
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Construction of geriatric hypoalbuminemia predicting model for hypoalbuminemia patients with and without pneumonia and explainability analysis by Ziqi Liu, Ziqi Liu, Qi Kang, Zhilong Mi, Zhilong Mi, Yuan Yuan, Tiantian Sang, Binghui Guo, Binghui Guo, Binghui Guo, Zhiming Zheng, Zhiming Zheng, Zhiming Zheng, Ziqiao Yin, Ziqiao Yin, Ziqiao Yin, Ziqiao Yin, Wei Tian

“…Through the sensitivity analysis of model, we analysed the important of four examines in patients with and without pneumonia.ResultsThe predicted accuracy of our gradual fusion model was 0.954, which improve the prediction accuracy by nearly 17.6% compared with the classical machine learning method. …”
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Explainable Climate-Based Time Series Modeling for Predicting Chemical Compositions in Tobacco Leaves by Hong He, Yunwei Zhang, Bin Li, Chengjin Tao

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Ultrasound feature-based nomogram model for predicting extrathyroidal extension in papillary thyroid carcinoma by Dong Guo, Chen Chen, Yin Zheng, Yue Shan, Shifei Huang, Tianhan Zhou, Yefei Yao, Zhengxian Zhang, Lu Wang, Dong Xu

“…To develop and validate a nomogram model based on ultrasound features to predict ETE of papillary thyroid carcinoma for preoperative assessment. …”
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Complexity Model for Predicting Oil Displacement by Imbibition after Fracturing in Tight-Oil Reservoirs by Aijun Chen, Yiqing Zhou, Rulin Song, Yangrong Song, Hanlie Cheng, David Cadasse

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Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers by Hengjun Wan, Huaju Tian, Cheng Wu, Yue Zhao, Daiying Zhang, Yujie Zheng, Yuan Li, Xiaoxia Duan

“…Herein, we constructed a multidimensional postoperative delirium risk‐prediction model incorporating multiple demographic parameters and blood biomarkers to enhance prediction accuracy. …”
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A blood test-based machine learning model for predicting lung cancer risk by Lihi Schwartz, Naor Matania, Matanel Levi, Teddy Lazebnik, Teddy Lazebnik, Shiri Kushnir, Noga Yosef, Assaf Hoogi, Dekel Shlomi, Dekel Shlomi

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Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients by Huasheng Lv, Xuehua Bi, Shuai Shang, Meng Wei, Xianhui Zhou, Kai Wang, Baopeng Tang, Yanmei Lu

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Predicting Mortality in Elderly Patients with Hypertensive Crises: Development and Validation of a Risk Model by Erkan Boga

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Predicting the Effectiveness of Resilient Safety in the Building Construction Sector of Rwanda Using the ANN Model by Esperance Umuhoza, Sung-Hoon An

“…Consecutively, an ANN model that could predict the effectiveness of resilient safety was developed. …”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…The purpose of the current study was to develop and validate a clinical risk model to predict malignancy in patients with thyroid nodules.   …”
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The VMD-Informer-BiLSTM-EAA Hybrid Model for Predicting Zenith Tropospheric Delay by Zhengdao Yuan, Xu Lin, Yashi Xu, Ruiting Dai, Cong Yang, Lunwei Zhao, Yakun Han

“…Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and GNSS meteorology. …”
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A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…The comparison of the three models shows that the XGBoost model performs better at predicting auroral oval locations and dealing with noise than the RF and KNN ones. …”
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WoFSCast: A Machine Learning Model for Predicting Thunderstorms at Watch‐to‐Warning Scales by Montgomery L. Flora, Corey Potvin

“…Abstract Developing AI models that match or exceed the forecast skill of numerical weather prediction (NWP) systems but run much more quickly is a burgeoning area of research. …”
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A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incidents by Aziida Nanyonga, Hassan Wasswa, Keith Joiner, Ugur Turhan, Graham Wild

“…To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. …”
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A dual branch model for predicting microseismic magnitude time series named DTFNet by Hao Luo, Zhongyi Liu, Yishan Pan, Liang Wang, Chao Kong, Huan Zhang

“…Due to the complexity and nonlinearity of microseismic data, conventional time series prediction models struggle to forecast them accurately. …”
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Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation by Mi‐Na Kim, Yong Seok Lee, Youngmin Park, Ayoung Jung, Hanjee So, Joonwoong Park, Jin‐Joo Park, Dong‐Joo Choi, So‐Ree Kim, Seong‐Mi Park

“…To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real‐world data. …”
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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…We have shown for the first time in the literature that basic laboratory and semen analysis findings can be used to select patients who will benefit from varicocelectomy with the use of five ML methods. ML models could be identified as a new prediction tool for selecting the patients who will benefit from varicocelectomy. …”
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