Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids by Shadfar Davoodi, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Valeriy S. Rukavishnikov

“…Therefore, employing radial basis function neural network (RBFNN) and multilayer extreme learning machine (MELM) algorithms integrated with the growth optimizer (GO), predictive hybrid ML (HML) models are developed to reliably predict the FV using only two easy-to-measure input variables: drilling fluid density (FD) and Marsh funnel viscosity (MFV). …”
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Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model by Dan Li, Jiwei Qu, Delan Zhu, Zheyu Qin

“…This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.…”
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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

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A predictive model to identify optimal candidates for surgery among patients with metastatic colorectal cancer by Xiqiang Zhang, Zhaoyi Jing, Longchao Wu, Ze Tao, Dandan Lu

“…The traditional logistic regression model showed good discriminative ability in both the training (area under the curve [AUC]: 0.727 [0.699-0.756]) and test (AUC: 0.741 [0.706-0.776]) sets.ConclusionWe achieved a predictive model which could identify optimal candidates for PTR among mCRC patients with high accuracy.…”
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Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics by Leonidas Theodorakopoulos, Aristeidis Karras, George A. Krimpas

“…These models enable system optimization, reduce the amount of computational overheads, and boost the overall performance of big data applications. …”
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Comparative Analysis of Automated Machine Learning and Optimized Conventional Machine Learning for Concrete’s Uniaxial Compressive Strength Prediction by Chukwuemeka Daniel

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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. …”
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Cross‐Attractor Transforms: Improving Forecasts by Learning Optimal Maps Between Dynamical Systems and Imperfect Models by N. Agarwal, D. E. Amrhein, I. Grooms

“…While advances have been made to reduce errors associated with model initialization and model forecasts, we lack a general framework for discovering optimal mappings between “true” dynamical systems and model phase spaces. …”
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Optimal Control Problem and Its Solution in Class of Feasible Control Functions by Advanced Model of Control Object by Askhat Diveev, Elena Sofronova

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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…Based on these screened features, three models were built: Dynamic Multi-Swarm Particle Swarm Optimization SVM (DMS-PSO-SVM), Particle Swarm Optimization SVM (PSO-SVM), and a standard SVM. …”
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Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing by Eslam Hussein Sayed, Amerah Alabrah, Kamel Hussein Rahouma, Muhammad Zohaib, Rasha M. Badry

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Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation by Yuxin Chen, Mohammad Hossein Kadkhodaei, Jian Zhou

“…This study aims to develop and evaluate a natural gradient boosting (NGBoost) model optimized with Optuna for estimating ground settlement during tunnel excavation, incorporating Shapley additive explanations (SHAP) to perform interpretability analysis on the model’s estimation results. …”
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Leveraging Machine Learning for Optimized Mechanical Properties and 3D Printing of PLA/cHAP for Bone Implant by Francis T. Omigbodun, Norman Osa-Uwagboe, Amadi Gabriel Udu, Bankole I. Oladapo

Subjects: “…machine learning…”
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Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data by Weihao Yang, Ruofan Zhen, Fanyue Meng, Xiaohang Yang, Miao Lu, Yingqiang Song

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The joint policy of production, maintenance, and product quality in a multi-machine production system by reinforcement learning and agent-based modeling by Mohammad Reza Nazabadi, Seyed Najafi, Ali Mohaghar, Farzad Movahedi Sobhani

“…This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. …”
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Stochastic artificial intelligence models for water resources management: innovative riverflow estimation amidst uncertainty by Mojtaba Poursaeid

“…In this study, Kashkan River located in Loristan Province of Iran was studied using data obtained from the database of Iran Water Resources Company (IWRC). Three distinct machine learning (ML) models – Regression Tree (RT), Random Search Regression Tree (RSRT), and Bayesian Optimization Regression Tree (BORT) – were utilized to enhance water resource management practices. …”
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Synthesis of optimal laws for frequency-adjustable hydraulic drives of manipulation systems of mobile machines by Lagerev A.V., Lagerev I.A.

“…Criteria of quality of regulation are formulated, which make it possible to ensure favorable parameters of movement (absence of dynamic movement instability) taking into account the magnitude of the operating operational loads and structural dimensions of the links. Mathematical model is proposed, allowing to synthesize the laws of frequency adjustment, which are optimal for individual quality criteria – the minimum time to work out the movement (single-criterion optimization). …”
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Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling by Rui Fang, Tuo Zhou, Zhuangzhuang Xu, Xiannan Hu, Man Zhang, Hairui Yang

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Optimized ANN Model for Predicting Buckling Strength of Metallic Aerospace Panels Under Compressive Loading by Shahrukh Khan, Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Gyula Varga

“…The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). …”
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Mathematical Modelling and Optimization Methods in Geomechanically Informed Blast Design: A Systematic Literature Review by Fabian Leon, Luis Rojas, Alvaro Peña, Paola Moraga, Pedro Robles, Blanca Gana, Jose García

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