Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

“…For gamma prediction, the ADA-KNN model outperforms other models, with an R² value of 0.9545 on the test set, an MSE of 4.5908E-03, and a MAE of 1.42730E-02. …”
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Learning behavior analysis and personalized recommendation system of online education platform based on machine learning by Feng Ma

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Enhancing drought prediction through machine learning: advanced techniques combining phenotypic and agrometeorological data by Efrem Yohannes Obsie, Yongguo Liu

“…This study proposes a multimodal framework that combines drought-stressed winter wheat images with field-collected agrometeorological data to develop robust machine-learning models. Two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were evaluated as predictive models. …”
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Comprehensive Review of Collaborative Data Caching in Edge Computing by Abdul Tawab Akrami, Girija Attigeri, Manjula C. Belavagi

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Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations by Xiaozhi Lu, Farag M. A. Altalbawy, Tarak Vora, R. Manjunatha, Debasish Shit, Shirin Shomurotova, Akshay Kumar, Atreyi Pramanik, Ajay Sharma, Raed H. C. Alfilh, Samim Sherzod, Mohammad Mahtab Alam

“…This study aims to develop a reliable predictive model for mud loss volume using machine learning techniques to improve drilling efficiency and reduce non-productive time. …”
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ProWaste for proactive urban waste management using IoT and machine learning by Thompson Stephan, S. M. Hari Krishna, Chia-Chen Lin, Ujjwal Sumesh, Saurabh Agarwal, Hyunsung Kim

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Optimization of Serum and Salivary Cortisol Interpolation for Time-Dependent Modeling Frameworks in Healthy Adult Males by Nathaniel T. Berry, Travis Anderson, Christopher K. Rhea, Laurie Wideman

“…Second- and third-degree polynomial regressions were determined to be the optimal models for fitting salivary. TOST tests determined that serum data and estimated 24 h output from these models (with interpolation) provided statistically similar estimates to the observed data (<i>p</i> < 0.05). …”
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Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning by Yuhao Xie, Xiangfu Wang

“…Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. …”
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Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification by Haniyeh Rafiepoor, Mohammad M. Banoei, Alireza Ghorbankhanloo, Ahad Muhammadnejad, Amirhossein Razavirad, Saeed Soleymanjahi, Saeid Amanpour

“…Predictive partition analysis was employed to establish the decision tree model to prioritize markers for clinical use. ML models have also been developed to predict TNM stage and different subtypes of GC. …”
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Replacing Gauges with Algorithms: Predicting Bottomhole Pressure in Hydraulic Fracturing Using Advanced Machine Learning by Samuel Nashed, Rouzbeh Moghanloo

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The Optimization Research Approaches for Renewable Energy Output Forecasting by Jie SHI, Xiaofei LIU

“…The characteristics of wind power output and forecasting model are fully considered to propose piecewise support vector machine (PSVM) and neural network (NN) model; the effort of weather condition on photovoltaic is analyzed to optimize the forecasting model. …”
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Predictive Modeling of Credit Card Rejection Using Machine Learning Algorithms: A Comparative Study by Shengyu Gu

“…The aim of this project is to predict, by the use of machine learning models, whether a certain individual will get their credit card application rejected. …”
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Multiobjective Optimization of Turbine Coolant Collection/Distribution Plenum Based on the Surrogate Model by Junsheng Chai, Zhenyu Wang, Xuanling Zhao, Chunhua Wang

“…Based on these data sampling, least square support vector machine (LS-SVM) was used for the surrogate model, and a kind of chaotic optimization algorithms was used for searching for the Pareto solution set. …”
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Integrating Artificial Intelligence and Computational Algorithms to Optimize the 15-Minute City Model by Marwa Abouhassan, Samah Elkhateeb, Raneem Anwar

“…Furthermore, it elaborates on how the integration of artificial intelligence (AI) and computational tools can be utilized in optimizing the 15-minute city model. We reveal how AI-driven algorithms, machine learning techniques, and advanced data analytics can enhance urban planning, improve accessibility, and foster social integration. …”
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An optimized stacking-based TinyML model for attack detection in IoT networks. by Anshika Sharma, Shalli Rani, Mohammad Shabaz

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Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach by Camilo Chacon Sartori, Christian Blum, Filippo Bistaffa, Guillem Rodriguez Corominas

“…The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. …”
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Prediction of loess collapsibility coefficient using bayesian optimized random forest model by Wan Zhang, Jiangtao Guo, Zhaopeng Li, Ruifang Cheng, Cuiping Ning, Hongfeng Niu, Ze Liu

“…However, the process of hyperparameter optimization in previous studies was not sufficiently comprehensive, leading to suboptimal model performance. …”
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Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load by Andrei-Nicolae Bădăluţă, Sergiu-Valentin Galaţanu, Jaroslav Kováčik, Liviu Marşavina

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Comprehensive MILP Formulation and Solution for Simultaneous Scheduling of Machines and AGVs in a Partitioned Flexible Manufacturing System by Cheng Zhuang, Jingbo Qu, Tianyu Wang, Liyong Lin, Youyi Bi, Mian Li

“…The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while considering various workshop layouts and operational constraints. …”
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