Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm by Behrang Beiranvand, Taher Rajaee, Mehdi Komasi

“…Also, RF, MARS, and GMDH models were created based on a proposed scenario to predict settlement using points located in a cluster. …”
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Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels by Hui Jin, Ling Zhang, Yan Sun, Ya Xu, Man Luo

“…Five machine learning models—CoxPHSurvival, FastKernelSurvivalSVM, GradientBoostingSurvival, RandomSurvivalForest, and ExtraSurvivalTrees—were used to predict cardiovascular mortality. …”
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PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE by Delvian Christoper Kho, Hindriyanto Dwi Purnomo, Hendry Hendry

“…Results indicate that XGBoost and XGBoost Random Forest consistently outperform other models in predicting stock price movements. These findings highlight the potential of Gradient Boosting models in providing accurate and reliable predictions, offering valuable insights for investors, financial analysts, and researchers to enhance investment strategies and adapt to market fluctuations effectively.…”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…Compared to a baseline model from the literature, O-LGB achieved significant improvements in predictive performance. …”
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Predicting Annual Tigris River Streamflow at Kut Barrage using SAMS Program by Ghufran R. AL-Youdawi, Laith B. AL-Badranee

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Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics by Nuray Vakitbilir, Amanjyot Singh Sainbhi, Abrar Islam, Alwyn Gomez, Alwyn Gomez, Kevin Yuwa Stein, Logan Froese, Tobias Bergmann, Davis McClarty, Rahul Raj, Frederick Adam Zeiler, Frederick Adam Zeiler, Frederick Adam Zeiler, Frederick Adam Zeiler

“…It emphasizes the importance of linear multivariate statistical models, particularly autoregressive (AR) models and the Kalman filter, in time series modeling and prediction of cerebral processes. …”
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Synthetic graphs for link prediction benchmarking by Alexey Vlaskin, Eduardo G Altmann

Subjects: “…link prediction…”
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Unleashing Predicting a Comparative Study of Machine Learning for Bankruptcy Risk Prediction by Yupeng Li, Ke Sun

“…Predictive analytics are advanced through the utilization of sophisticated machine learning methodologies, specifically the Random Forest classification model (RFC), The Grasshopper Optimizer Algorithm (GOA), and the Artificial Rabbits Optimizer (ARO). …”
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Predicting OSA Using Radiographs of the Airway Anatomy by Du M, Gui Y, Guo Y, Liu J, Deng W, Huang J, Liu T, Zhang X, Pang F

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A hybrid ARIMA-BP approach for superior accuracy in predicting traffic accident losses by Jian Liu, Zhuqing Zhang, Bin Lyu, Rui Feng, Ye He

Subjects: “…Traffic accident prediction…”
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Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, E. D. Emtseva, A. A. Vishnevskiy

“…Aim. To develop predictive models of obstructive coronary artery disease (OPCA) in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) based on the predictive potential of cardiometabolic risk (CMR) factors.Material and methods. …”
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Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome by M. M. Tsivanyuk, B. I. Geltser, K. I. Shakhgeldyan, E. D. Emtseva, G. S. Zavalin, O. I. Shekunova

“…Models based on XGBoost and ANN had less predictive value.…”
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Deep-Learning-Based AI-Model for Predicting Dental Plaque in the Young Permanent Teeth of Children Aged 8–13 Years by Banu Çiçek Tez, Yasin Güzel, Bahar Başak Kızıltan Eliaçık, Zafer Aydın

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Validation of a predictive model for platelet transfusion refractoriness in patients with hematological diseases by HUANG Xiulan, YUE Shuhan, CAI Qun, LU Liqi, HE Mengzhen, LEI Qiao, LIU Caoyi, ZHANG Jingwei

“…[Objective] To validate and optimize the platelet transfusion refractoriness (PTR) prediction model for patients with hematological disorders established by our center. …”
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A prediction model for guiding tumor microwave ablation surgery based on simulation by Lu Qian, Yamin Yang, Pan Chen, Jia Liu, Xiaofei Jin, Zhiyu Qian, Chunxiao Chen

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Boosting Cervical Cancer Prediction Leveraging a Hybrid FT-Transformer Model by Md Emamul Hossen, S. M. Mahim, Shakib Al Hasan, Md Khairul Islam, Md Shohidul Islam, Salahuddin Khan, Mohammad Alibakhshikenari, Peiman Parand, Md Sipon Miah

“…To address this critical clinical need, we propose an innovative Hybrid FT-Transformer model that synergistically integrates a Feature Tokenization (FT) Transformer with Depthwise convolutional neural networks and Long Short-Term Memory (LSTM) networks for precise CC prediction. …”
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Milling-Force Prediction Model for 304 Stainless Steel Considering Tool Wear by Changxu Wang, Yan Li, Feng Gao, Kejun Wu, Kan Yin, Peng He, Yunjiao Xu

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Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…Multiple tests were performed by different machine learning classifiers, mainly in predicting learning style and recommending an assessment methodology. …”
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Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model by De Zhao, Hua Wang, Zhiyuan Liu

“…Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). …”
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Spherical assembly model and prediction of pressure loss of porous media flow in goaf by Fangxing CHEN, Xiaoli HAO, Shiqiang CHEN, Yiqun LI, Shilin LI, Yi LU

“…The model is used to predict the pressure loss, and the simulation results are compared with the predicted values of Ergun equation. …”
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