Application of Minimax Optimization Mechanism in Chinese-English Machine Translation Quality Estimation by Xiaomei Zhang

“…Traditional MTQE models often suffer from incoherent optimization goals due to their dual phase architecture, limiting their effectiveness. …”
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Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength by She Wang, Qi Zhang

“…The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced metaheuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). …”
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Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology by Raoof Nopour

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Optimization of robotic spray painting trajectories using machine learning for improved surface quality by Ritesh Bhat, M. Karuppasamy, M. Maragatharajan, Anandakumar Haldorai, E. Nirmala, Nithesh Naik

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Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning by Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton

“…This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity. …”
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Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures by Bader Huwaimel, Jowaher Alanazi, Muteb Alanazi, Tareq Nafea Alharby, Farhan Alshammari

“…Furthermore, the study incorporates the use of Glowworm Swarm Optimization (GSO) for hyper-parameter optimization, thereby further elevating the efficacy of the models. …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…These experiments are time-consuming and resource-intensive, requiring large numbers of animals for in vivo optimization. Advances in machine learning (ML) and computing power, data analysis, and model generation using ML techniques, such as neural networks, enable predictive modeling for GET. …”
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Hydrodynamic Shape Optimization of a Naval Destroyer by Machine Learning Methods by Andrea Serani, Matteo Diez

“…This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. …”
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Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings by David Cabezuelo, Izar Lopez-Ramirez, June Urkizu, Ander Goikoetxea

“…Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. …”
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Interpretable machine learning approach for TBM tunnel crown convergence prediction with Bayesian optimization by Wanrui Hu, Kai Wu, Kai Wu, Heng Liu, Weibang Luo, Xingxing Li, Peng Guan

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LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction by Dana R. Hamad, Tarik A. Rashid

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Optimal control of asynchronous drive of auxiliary machines of electric rolling stock by Yu. M. Kulinich, S. A. Shukharev, V. K. Dukhovnikov, D. A. Starodubtsev

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Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis by Basheer Abdullah Marzoog, Peter Chomakhidze, Daria Gognieva, Nina Vladimirovna Gagarina, Artemiy Silantyev, Alexander Suvorov, Ekaterina Fominykha, Malika Mustafina, Ershova Natalya, Aida Gadzhiakhmedova, Philipp Kopylov

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Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models by Alexander Bott, Moritz Siems, Alexander Puchta, Jurgen Fleischer

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Modelling of Electrical Discharge Processes for Optimization of Corona-Protection System of High Voltage Rotating Machines Insulation by Andreev A.M., Andreev I.A., Belko V.O., Reznik A.S., Smirnov A.N., Stepanov A.A.

“…The obtained data have practical importance for the development of the optimal technology for manufacturing the system of electrical insulation of the stator winding of high-voltage air-cooled electric machines.…”
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Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite by CR Mahesha, R .Suprabha, NPG Bhavani, Prashant Sunagar, Raja Ramesh, P. Balamurugan, Rajasekar Rajendran, Anirudh Bhowmick

“…We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). …”
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Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models by Ahmad Alsaber, Anurag Satpathi, Mariam Alsabah, Parul Setiya

“…This research aims to compare the performance of five machine learning models—Elastic Net (ELNET), Random Forest, Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) to identify the most effective approach for potato yield forecasting. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). …”
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Optimization and Evaluation of Calibration for Low-cost Air Quality Sensors: Supervised and Unsupervised Machine Learning Models by Petar Zhivkov

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Optimized Machine Learning Models for Enhanced Stock Market Predictions: A Case Study on the SSE Index by Vahid Babazadeh, Ahmad Faramarzi, Ali Rahnamaei

“…This study aims to introduce a machine learning-based model for Shanghai Stock Exchange Index (SSE) index prediction. …”
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