A customized ensemble machine learning approach: predicting students’ exam performance by Rasel Ahmed, Nafiz Fahad, Md Saef Ullah Miah, Kah Ong Michael Goh, Mufti Mahmud, M. Mostafizur Rahman

“…The model’s hyperparameters were optimized via GridSearchCV with 10-fold cross-validation, ensuring robustness. …”
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Optimasi Algoritma Support Vector Machine Berbasis Kernel Radial Basis Function (RBF) Menggunakan Metode Particle Swarm Optimization Untuk Analisis Sentimen by Cucun Very Angkoso, Khozainul Asror, Ari Kusumaningsih, Andi Kurniawan Nugroho

“…Penelitian ini bertujuan mengevaluasi efektivitas algoritma Particle Swarm Optimization (PSO) dalam meningkatkan akurasi analisis sentimen pada algoritma Support Vector Machine (SVM) dengan kernel Radial Basis Function (RBF). …”
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Integrating IT and OT for Cybersecurity: A Stochastic Optimization Approach via Attack Graphs by Gonzalo Martinez Medina, Krystel K. Castillo-Villar, Tanveer Hossain Bhuiyan

“…This paper proposes an attack graph-based optimization model to enable cybersecure digital manufacturing. …”
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Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study by Shahryar K. Ahmad, Sujay V. Kumar, Clara Draper, Rolf H. Reichle

“…Abstract Differentiable geoscientific modeling has shown promise for leveraging machine learning (ML) to unify physically based and data‐based modeling. …”
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Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures by Mohammed Alqarni, Ali Alqarni

“…Abstract Analysis of small-molecule drug solubility in binary solvents at different temperatures was carried out via several machine learning models and integration of models to optimize. …”
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Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor by Chen Feng, Chaoshun Li, Li Chang, Zijun Mai, Chunwang Wu

“…This paper proposes an intelligent nonlinear model predictive control (NMPC) strategy, in which hydraulic-mechanical and electrical subsystems are combined in a synchronous control framework. …”
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A Comprehensive Benchmark Dataset for Sheet Metal Forming: Advancing Machine Learning and Surrogate Modelling in Pro-cess Simulations by Heinzelmann Pascal, Baum Sebastian, Riedmüller Kim Rouven, Liewald Mathias, Weyrich Michael

“…Here, surrogate models derived from simulation data by using machine learning provide a promising solution. …”
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Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning by Suranjana V. Mayani, Hessan Mohammad, Soumya V. Menon, Rishabh Thakur, Abdulqader Faris Abdulqader, S. Supriya, Prabhat Kumar Sahu, Kamal Kant Joshi

“…Hyper-parameter optimization was conducted using Successive Halving, a method aimed at efficiently allocating computational resources to optimize model performance. …”
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Application of Machine Learning Technologies for Managing Multifactor Threats in an Integrated Model of Cognitive Security Center at Defense Industry Enterprise by Pavel Panilov, Tatiana Tsibizova

“…The presented innovative model of the cognitive security center, based on machine learning technologies, represents a significant advancement in effectively managing multifactor threats in defense-industrial complex enterprises. …”
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Cardiovascular Disease Detection through Innovative Imbalanced Learning and AUC Optimization by Karthikeyan Palanisamy, Krishnaveni Krishnasamy, Praba Venkadasamy

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Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications by Md. Ashraful Haque, Md. Kawsar Ahmed, Geamel Alyami, Kamal Hossain Nahin, Md Sharif Ahammed, Akil Ahmad Taki, Narinderjit Singh Sawaran Singh, Md Afzalur Rahman, Liton Chandra Paul, Hussein Shaman

“…These results suggest that our optimized prediction model and high-performance MIMO antenna design are well-suited for advancing next-generation THz.…”
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Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization by Long Tsang, Mahdi Akbari, Pouyan Fakharian

“…Therefore, the stacked ensemble-learning model with Bayesian optimization (BO-stacking) is introduced to make predictions of soil liquefaction more accurate. …”
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Comparison and general law research of multiple machine-learning models for proton exchange membrane electrolytic cell parameters prediction by Yukun Wang, Hai-Wen Li, Wenhan An, Yudong Mao, Kaimin Yang, Jiying Liu

“…Abstract This paper presents a simulation-based framework for predicting the performance of proton exchange membrane electrolytic cells (PEMEC). Machine learning techniques are employed to conduct predictive modeling and comparative analysis, with the aim of identifying the optimal machine learning model for evaluating PEMEC parameters. …”
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Advancing lakes algal chlorophyll estimation in the contiguous USA: A comparative study of machine learning models and satellite data by Md Mamun, Xiao Yang

“…This study harnesses the synergy of satellite remote sensing and machine learning (ML) to enhance CHL-a quantification from space. …”
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Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms by Yuyang Chen, Jiansheng Zhong, Haixiang Li, Kunzhe Lin, Liangfeng Wei, Shousen Wang

“…After cross-validation and parameter optimization, the random forest model demonstrated the highest performance, with an accuracy (ACC) of 0.882 and an AUC of 0.96. …”
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Determination and Verification of the Johnson–Cook Constitutive Model Parameters in the Precision Machining of Ti6Al4V Alloy by Piotr Löschner, Munish Kumar Gupta, Piotr Niesłony, Mehmet Erdi Korkmaz, Muhammad Jamil

“…Numerical simulations of the cutting process play a key role in manufacturing and cost optimization. Inherent in finite element analysis (FEA) simulations is the correct description of material behavior during machining. …”
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Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree‐Based Machine Learning Model by Md. Siddikur Rahman, Miftahuzzannat Amrin, Md. Abu Bokkor Shiddik

“…The optimal tree‐based ML model with strong interpretability was created by comparing various ML models using the hyperparameter optimization technique. …”
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Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application by Anupam Yadav, B. Jayaprakash, Laith Hussein Jasim, Mayank Kundlas, Maan Younis Anad, Ankur Srivastava, M. Janaki Ramudu, B. Bharathi, Prabhat Kumar Sahu

“…Abstract A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. …”
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Introducing Spatial Heterogeneity via Regionalization Methods in Machine Learning Models for Geographical Prediction: A Spatially Conscious Paradigm by Lukas Boegl, Ourania Kounadi

“…These findings suggest that RegRF can enhance machine learning models by accounting for spatial phenomena, with potential for further optimization. …”
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Machine Learning Models for Predicting Seismic Response of a Novel Two-Stage Friction Pendulum Isolated Bridge Structure by Hanzlah Akhlaq, Tianbo Peng, Kawsu Jitteh, Muhammad Salman Khan

“…Utilizing this extensive dataset, seven ML models were trained and evaluated using statistical key performance indicators (KPIs). …”
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