Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning by Federico Rossi, Giancarlo Storti Gajani, Samuele Grillo, Giambattista Gruosso

“…A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. …”
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Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks by Tu Viet Nguyen, Nhan Duc Ho, Hieu Thien Hoang, Cuong Danh Do, Kok-Seng Wong

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Be aware of overfitting by hyperparameter optimization! by Igor V. Tetko, Ruud van Deursen, Guillaume Godin

“…Abstract Hyperparameter optimization is very frequently employed in machine learning. …”
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Prediction model of axillary lymph node status using an automated breast volume ultrasound radiomics nomogram in early breast cancer with negative axillary ultrasound by Qianqing Ma, Qianqing Ma, Junli Wang, Zhengzheng Tu, Jingwen She, Jianhui Zhu, Feng Jiang, Chaoxue Zhang

“…Select the best classifier from 3 machine learning techniques to build Model 1and radiomics-score (RS). …”
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Wind Turbine Blade Fault Detection Method Based on TROA-SVM by Zhuo Lei, Haijun Lin, Xudong Tang, Yong Xiong, He Wen

“…This method integrates the Tyrannosaurus Optimization Algorithm (TROA) with a support vector machine (SVM), aiming to enhance the accuracy and reliability of fault detection. …”
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Designing and planning a bioethanol supply chain network under uncertainty using a data-driven robust optimization model under disjunctive uncertainty sets by Farzaneh MansooriMooseloo, Maghsoud Amiri, Mohammad Taghi Taghavi Fard, Mostafa Hajiaghaei-Keshteli

“…Therefore, the aim of this study is to design and optimize the biomass-to-bioethanol supply chain network using data-driven robust optimization methods and disjunctive uncertainty sets.Methodology: The methodology of this study is a multi-methodology approach based on mathematical modeling and machine learning algorithms. …”
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting by Iman Ahmadianfar, Aitazaz Ahsan Farooque, Mumtaz Ali, Mehdi Jamei, Mozhdeh Jamei, Zaher Mundher Yaseen

“…Hence, a novel hybrid model is provided, incorporating singular value decomposition (SVD) in conjunction with kernel-based ridge regression (SKRidge), multivariate variational mode decomposition (MVMD), and the light gradient boosting machine (LGBM) as a feature selection method, along with the Runge–Kutta optimization (RUN) algorithm for parameter optimization. …”
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Distributed MIMO Networks With Rotary ULAs for Indoor Scenarios Under Rician Fading by Eduardo N. Tominaga, Onel L. A. Lopez, Tommy Svensson, Richard D. Souza, Hirley Alves

“…Considering a spatially correlated Rician fading model, the optimal angular position of the RULAs is jointly computed by the central processing unit using particle swarm optimization as a function of the location of the active devices. …”
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Navigating industry 4.0 and 5.0: the role of hybrid modelling in (bio)chemical engineering’s digital transition by Carina L. Gargalo, Alina A. Malanca, Adem R. N. Aouichaoui, Jakob K. Huusom, Krist V. Gernaey

“…This work investigates the potential of hybrid modelling in the digitalization of the chemical and biochemical industries. …”
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Classification and Recognition Method for Bearing Fault based on IFOA-SVM by Wei Zhang, Zhihua Ma

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Performance prediction of sintered NdFeB magnet using multi-head attention regression models by Qichao Liang, Qiang Ma, Hao Wu, Rongshun Lai, Yangyang Zhang, Ping Liu, Tao Qi

“…Traditional machine learning models based on mathematical and statistical principles are effective for structured data and offer high interpretability. …”
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Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models by Hongxin Wang, Ruiying Jin, Peng Xu, Jiefan Gu

“…BIM-based forward design is now widely used, providing a data foundation for combining HVAC system design with machine learning. This paper proposes an unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results. …”
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Multistage Training of Fuzzy Cognitive Maps to Predict Preeclampsia and Fetal Growth Restriction by William Hoyos, Rodrigo Garcia, Jose Aguilar

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Real-time and resource-efficient banana bunch detection and localization with YOLO-BRFB on edge devices by Shuo Wang, Shuo Wang, Shuo Wang, Lijiao Wei, Danran Zhang, Danran Zhang, Ling Chen, Ling Chen, Weihua Huang, Dongjie Du, Kangmin Lin, Zhenhui Zheng, Zhenhui Zheng, Zhenhui Zheng, Jieli Duan

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A Systematic Review on Intelligent Prediction of Inorganic Building Materials Performance by Mengru Li, Zhenya Zhang, Xianyi Zeng, Zhenglei He

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Formal Verification- and AI/ML-Assisted Radio Resource Allocation for Open RAN Compliant 5G/6G Networks by Tariq Mumtaz, Shahabuddin Muhammad, Faouzi Bouali

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Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis by Jia-Yi Zhang, Ding Li, Guo-Jie Hu

“…Abstract Objective To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance. …”
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A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques by Mamunur Rashid, Tarek Elfouly, Nan Chen

“…The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. In the literature, extensive studies have been conducted on model-based and probabilistic EV charging demand forecasting schemes. …”
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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

“…The data-driven models executed were multilayer perceptron, support vector regressor, random forest, categorical boosting, and light gradient boosting machine. …”
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Development and validation of a spontaneous preterm birth risk prediction algorithm based on maternal bioinformatics: A single-center retrospective study by Yu Chen, Xinyan Shi, Zhiyi Wang, Lin Zhang

“…The performance of five machine learning models was compared using metrics such as the AUC, accuracy, sensitivity, specificity, and precision to identify the optimal predictive model. …”
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