Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…Nevertheless, the implementation of effective predictive models in clinical practice remains limited, primarily due to the lack of robust and interpretable tools. …”
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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

“…The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. …”
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Enhanced safety and efficiency in traction elevators: a real-time monitoring system with anomaly detection by Safa Ozdemir, Osamah N. Neamah, Raif Bayir

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Machine learning unveils hypoxia-immune gene hub for clinical stratification of thyroid-associated ophthalmopathy by Lu Chen, Sha Wang, Jinwei Wang, Bei Xu, Jia Tan, Xiaobo Xia

“…Hypoxia exposure aggravated the above dysregulation and promoted proliferation and adipogenesis of orbital fibroblasts. A predictive model was developed using four machine learning algorithms and validated for its effectiveness in diagnosing TAO and assessing disease severity. …”
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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

“…ABSTRACT Background This systematic review investigates the use of machine learning (ML) algorithms in predicting survival outcomes for ovarian cancer (OC) patients. …”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

“…For w/h > 1, the sensitivity order of the influencing factors was as follows: width > inclination > height; SVM is the best model for the gently inclined pillar strength prediction (R2=0.921; REVS=0.926; RMAE=1.225; RMSE=2.367), and the model prediction performance is further improved after combining the optimizations of GP and IQPSO algorithms (R2=0.976; REVS=0.977; RMAE=0.465; RMSE=0.862). …”
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Streamlined Model Predictive Control for a Triple-Optimization of Thermal Comfort, Heating Costs, and Photovoltaic Self-Consumption by Vukasin Klepic, Magdalena Wolf, Tobias Proll

“…This paper introduces a straightforward Model Predictive Control (MPC) method for residential buildings equipped with thermally activated building structures (TABS), photovoltaics (PV), and heat pumps (HP). …”
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A Predictive Analysis of the Business Environment of Economies along the Belt and Road Using the Fractional-Order Grey Model by Lingling Pei, Jun Liu

“…This paper determined the optimal order of FGM (1, 1) model through particle swarm optimization algorithm and combined with the World Bank business environment data to predict and analyze the business environment of economies along the Belt and Road. …”
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Squat Motion of a Humanoid Robot Using Three-Particle Model Predictive Control and Whole-Body Control by Hongxiang Chen, Xiuli Zhang, Mingguo Zhao

“…In this paper, we apply three-particle model predictive control (TP-MPC) combined with weight-based whole-body control (WBC) to a humanoid robot. …”
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Nonlinear Model Predictive Control Energy Management Strategy for Hybrid Power Ships Based on Working Condition Identification by Yucheng Yan, Zhichao Chen, Diju Gao

“…Secondly, based on the identification results, a nonlinear model predictive control algorithm is designed to adjust the generator speed and the battery current to achieve energy optimization control under constraints. …”
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Predictive risks assessment of power supply interruption to oil production consumers, taking into account changes in significant factors by V. M. Levin, N. P. Guzhov

“…The functions of forecasting the probabilities of power supply failures of oil production facilities are obtained depending on the integral assessment of their technical condition, which, along with the severity of the consequences of failures, make it possible to predict risks.CONCLUSION. A computational procedure has been developed that includes mathematical models and an algorithm for prioritizing equipment with a strategy "according to technical condition" when putting it into repair based on a forecast of the risks of power supply disruption to consumers. …”
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Machine-learning-based predictive classifier for bone marrow failure syndrome using complete blood count data by Jeongmin Seo, Chansub Lee, Youngil Koh, Choong Hyun Sun, Jong-Mi Lee, Hong Yul An, Myungshin Kim

“…To support primary physicians, we developed a predictive model using basic demographics and CBC data collected retrospectively from two major hospitals in South Korea. …”
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Model Predictive Control of Nonlinear System Based on GA-RBP Neural Network and Improved Gradient Descent Method by Youming Wang, Didi Qing

“…A model predictive control (MPC) method based on recursive backpropagation (RBP) neural network and genetic algorithm (GA) is proposed for a class of nonlinear systems with time delays and uncertainties. …”
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Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework by Kaushik Sathupadi, Sandesh Achar, Shinoy Vengaramkode Bhaskaran, Nuruzzaman Faruqui, M. Abdullah-Al-Wadud, Jia Uddin

“…Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. …”
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Optimizing smart inverter control for improved distribution network hosting capacity: A model predictive control approach by Muhammad Kamran Khan, Kimmo Kauhaniemi, Hussain Sarwar Khan

“…Furthermore, we delve into the implementation of direct power finite control set model predictive control (DP-FCS-MPC) aimed at augmenting hosting capacity. …”
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Establishment of a predictive model for spontaneous preterm birth in primiparas with grade A1 gestational diabetes mellitus by Ting Sun, Yangyang Zhang, Chunzhi Xie, Anyi Teng, Shi Lin, Hui Zhang, Yan Li

“…The factors influencing SPB were explored, and a prediction model based on a random forest algorithm was constructed.ResultsShort CL in the second trimester, a family history of preterm birth, a high pre-pregnancy and prenatal body mass index, the use of assisted reproductive technology, and a high fasting blood glucose level in the first trimester were important risk factors for SPB in primiparas with grade A1 GDM. …”
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Predictive Models of Patient Severity in Intensive Care Units Based on Serum Cytokine Profiles: Advancing Rapid Analysis by Cristiana P. Von Rekowski, Tiago A. H. Fonseca, Rúben Araújo, Ana Martins, Iola Pinto, M. Conceição Oliveira, Gonçalo C. Justino, Luís Bento, Cecília R. C. Calado

“…Naïve Bayes models were developed using the full dataset or with feature subsets selected either through an information gain algorithm or univariate data analysis. Strong predictive models were achieved for IMV (AUC = 0.891) and mortality within homogeneous (AUC = 0.774) or more heterogeneous (AUC = 0.887) populations utilizing two to nine features. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…AI integrated approaches such as stress prediction, irrigation optimization, and image-based phenotyping have enhanced agriculture, while machine learning models like Random Forest and Gradient Boosting improve stress management. …”
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…This study examines building a predictive model using the XGBoost algorithm, an ensemble learning approach known for handling huge datasets and producing robust predictions. …”
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A Computationally Efficient Model Predictive Control Energy Management Strategy for Hybrid Vehicles Considering Driving Style by Yalian Yang, Yuqi Chen, Changdong Liu

“…The driving-style adaptive Pontryagin’s minimum principle for model predictive control (DSA-PMP-MPC) algorithm was designed as a real-time energy management strategy for Hybrid Electric Vehicles (HEVs). …”
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