Machine learning predictions on the output parameters of common rail direct injection engines fueled with ternary blend by Subramanian Karthikeyan, Paramasivam Sathiyagnanam Amudhavalli, Dillikannan Damodharan

“…This study aims to employ a machine learning algorithm (MLA) to predict Common Rail Direct Injection (CRDI) engine emissions and performance using alternative feedstock. …”
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Coordinative Two-Stage Superheated Steam Temperature Linkage Control Based on Multivariable Generalized Predictive Control Algorithm by Sheng TONG, Le LIU, Pengfei WANG

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Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation by Paul Meredith, Christina Saville, Chiara Dall’Ora, Tom Weeks, Sue Wierzbicki, Peter Griffiths

“…ObjectiveThe objective of this study is to explore whether an algorithm could estimate ward workload using existing routinely recorded data. …”
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Predictive Control for an Ankle Rehabilitation Robot Using Differential Evolution Optimization Algorithm-Based Fuzzy NARX Model by Shenglong Xie, Haiming Zhong, Yuntang Li, Su'an Xu, Wenyuan Liu, Shiyuan Bian, Shiwu Zhang

“…In this paper, based on differential evolution (DE) optimization algorithm and fuzzy nonlinear auto regressive with exogenous inputs (NARX) model, an iterative learning model predictive controller is constructed to achieve accurate and robust trajectory tracking control of an ankle rehabilitation robot driven by pneumatic muscle. …”
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FOX-TSA hybrid algorithm: Advancing for superior predictive accuracy in tourism-driven multi-layer perceptron models by Sirwan A. Aula, Tarik A. Rashid

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Enhanced Robust Control of Induction Motor Using Combined Optimal Model Predictive Control With Super-Twisting Algorithm by REZGUI, S.-E., NEMOUCHI, B.

“…This paper presents a novel strategy for induction motor control that combines Optimal Model Predictive Control (OMPC) with the Super-Twisting Algorithm (STA) to enhance the performance of field-oriented control (IFOC) strategy under disturbances and uncertainties. …”
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Energy Scheduling of Hydrogen Hybrid UAV Based on Model Predictive Control and Deep Deterministic Policy Gradient Algorithm by Haitao Li, Chenyu Wang, Shufu Yuan, Hui Zhu, Bo Li, Yuexin Liu, Li Sun

“…To overcome these limitations, this paper proposes a novel energy scheduling framework that integrates the Model Predictive Control (MPC) with a Deep Reinforcement Learning algorithm, specifically the Deep Deterministic Policy Gradient (DDPG). …”
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Clean-Room Air Conditioning Control Based on Improved Min-Max Robust Model Predictive Control Algorithm by Qiang Luo, Ting Wang

“…The Min-Max robust model predictive control algorithm was 0.73 at the lowest and 4.3 at the highest. …”
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Comparative Study of Sphere Decoding Algorithm and FCS-MPC for PMSMs in Aircraft Application by Joseph O. Akinwumi, Yuan Gao, Xin Yuan, Sergio Vazquez, Harold S. Ruiz

“…In this study, we propose a long prediction horizon finite control set model predictive control (FCS-MPC) framework for PMSMs. …”
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EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE by Baljinder Kaur, Manik Rakhra, Nonita Sharma, Monika Mangla

Subjects: “…predictive modeling…”
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Electricity usage prediction using developed human evolutionary optimization algorithm and Xception neural network by Dongxian Yu, Di Wu, Chongyang Liao, Zaihui Cao, Somayeh Pouramini

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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

“…The results indicated that models employing concentration addition (CA), independent action (IA), and molecular descriptors (MD) as variables demonstrated superior predictive abilities. The consensus model combining SVM and RF algorithms (labeled as CM0) demonstrated the highest level of accuracy in fitting the data, with a coefficient of determination of 0.980. …”
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Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey by Manhui Zhang, Xian Xia, Qiqi Wang, Yue Pan, Guanyi Zhang, Zhigang Wang

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Research on the prediction algorithm of tread wear for locomotive wheels based on GA-ridge regression analysis by FANG Xin, LIU Tong, CHENG Yaping, SUN Yuduo, WANG Feier

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Advancing sustainable renewable energy: XGBoost algorithm for the prediction of water yield in hemispherical solar stills by Salwa Ahmad Sarow, Hasan Abbas Flayyih, Maryam Bazerkan, Luttfi A. Al-Haddad, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili

“…An economic analysis revealed a significant reduction in water treatment costs with the optimized system. …”
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Review of Icing Prediction Model and Algorithm for Overhead Transmission Lines Considering Time Cumulative Effects by Chuanqi WANG, Liwen WU, Zhibin DENG, Weifeng DENG, Bin YANG

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Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms by Binglin Song, Ping Liu, Kangrui Fu, Chun Liu

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Predictive Modeling of Yoga's Impact on Venous Clinical Severity Scoring Using Gaussian Process Classification and Advanced Optimization Algorithms by Yazdan Ashgevari, Faranak Kazemi

“…The study employs the Adaptive Opposition Slime Mould Algorithm (AOSM) and Mountain Gazelle Optimizer (MGO) to enhance the predictive capabilities of a Gaussian Process Classification (GPC) model. …”
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Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine learning algorithms (MLAs) in patients with type 2 DM (T2DM).Methods The electronic health records of 12,190 T2DM patients with 3-year follow-ups were extracted, and the dataset was divided into a training and testing dataset in a 4:1 ratio. …”
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Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes by Richard Lupat, Rashindrie Perera, Sherene Loi, Jason Li

“…Here, we propose a novel deep-learning-based algorithm, Moanna, that is trained to integrate multi-omics data for predicting breast cancer subtypes. …”
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