Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…For performance verification, four alternative predictive models were established, including LDA–ANN, support vector machines (SVM), Particle Swarm Optimization (PSO), and a GA-tuned BA–ANN. …”
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A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana. by Gifty Duah, Eric Nyarko, Anani Lotsi

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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

“…This study integrated field data, multispectral imagery, meteorological data, and hydrological data to invert the water quality conditions of aquatic environments. A machine learning model (CNN-RF) was developed to estimate three water quality parameters (TP, DO, COD), and its performance was comprehensively evaluated using four error indices (R², MAE, MSE, MAPE). …”
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Analyzing Momentum Shifts in Tennis: A Machine-Learning Approach to Predicting Match Outcomes by Yuean Xia, Changfeng Li, Tanran Zhang

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Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach by David Carrascal, Paula Bartolomé, Elisa Rojas, Diego Lopez-Pajares, Nicolas Manso, Javier Diaz-Fuentes

“…To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. …”
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Designing for efficiency: an exploration of optimized joint configurations in tyne cultivator design by Elavarasan S, Manimaran G

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Power management in isolated microgrids using machine learning-based robust model predictive control by Chou-Yi Hsu, Amit Ved, Hannah Jessie Rani R, Zayd Ajsan Balsem, Nora Rashid Najem, Abhayveer Singh, P․Sasi Kiran, Ankita Aggarwal, Satish Kumar Samal, Alireza Kamranfar

“…In the proposed fuzzy system, settings are adjusted using a restricted Boltzmann machine (RBM) and contrastive divergence (CD) algorithm, and are then used for MPC optimization. …”
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Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery by Linming Huang, Fen Zhao, Guozheng Hu, Hasbagan Ganjurjav, Rihan Wu, Qingzhu Gao

“…These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). …”
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Development of Anthro-Fitness Model for Evaluating Firefighter Recruits’ Performance Readiness Using Machine Learning by Mohamed Borhanudin Mohd Yusof, Musa Rabiu Muazu, Nazarudin Mohamad Nizam, Abdul Majeed Anwar P. P., Raj Naresh Bhaskar, Razmaan Mohd Azraai Mohd

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Investigation on Thermal Conductivity of Soil Under Freeze–Thaw Action Based on Machine Learning Models by Yuwei Chen, Yadi Min, Haiqiang Jiang, Jing Luo, Mengxin Liu, Enliang Wang, Xingchao Liu, Ke Shi, Xiaoqi Li

“…Based on the variation in the thermal conductivity and the influencing factors of silty clay obtained by the freeze–thaw cycle test, this paper adopted four machine learning models optimized by particle swarm optimization (PSO), including the artificial neural network model (ANN), random forest model (RF), support vector machine model (SVM), and extreme gradient boosting model (XGBoost) to predict the thermal conductivity of the soil. …”
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USING REINFORCEMENT LEARNING ALGORITHMS FOR UAV FLIGHT OPTIMIZATION by O. Dutsiak, V. Yuzevych

“…In this work, the main attention was focused on the study of a sub-method of machine learning, which is called learning with emphasis, with a view to optimizing the flight of a UAV by optimizing the process of constructing its path. …”
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Balancing Complexity and Performance of Machine Learning Models for Avian Pests Sound Detection in Agricultural Environments by Micheline Kazeneza, Anna Sergeevna Bosman, Destiny Kwabla Amenyedzi, Damien Hanyurwimfura, Emmanuel Ndashimye, Anthony Vodacek

“…This research contributes 1) an on-farm audio dataset, 2) comprehensive cross-model evaluation metrics, and 3) deployment optimization strategies for acoustic pest detection systems in resource-constrained agricultural environments.…”
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Numerical model of surface morphology and solid-liquid contact angle in wire electrical discharge machining by Zhi Chen, Yifei Zhou, Zefeng Yang, Zhizhong Zhang, Jian Li, Guojun Zhang, Fenglin Han

“…Combining the established numerical model and multi-objective optimization algorithm, the contact angle of the surface machined by WEDM is greater than 125°, and the stability and controllability of hydrophobic surface preparation are greatly improved. …”
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Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit by Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing ji, Yongsheng Zhang, Shubin Guo

“…Abstract Background Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU). …”
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A Novel Forecasting System with Data Preprocessing and Machine Learning for Containerized Freight Market by Yonghui Duan, Xiaotong Zhang, Xiang Wang, Yingying Fan, Kaige Liu

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Porosity prediction of tight reservoir rock using well logging data and machine learning by Yawen He, Hongjun Zhang, Zhiyu Wu, Hongbo Zhang, Xin Zhang, Xiaojing Zhuo, Xiaoli Song, Sha Dai, Wei Dang

“…These models are further optimized with the particle swarm optimization (PSO) algorithm to enhance their predictive accuracy. …”
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Predicting equilibrium scour depth around non-circular bridge piers with shallow foundations using hybrid explainable machine learning methods by Nasrin Eini, Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Essam Heggy, Marek Kirs

“…Furthermore, both BOA–ANN and STO–ANN outperformed empirical equations and other machine learning techniques. An explicit equation was also derived from the BOA–ANN model. …”
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METHOD OF DEGRADATION DATA RELIABILITY EVALUATION BASED ON THE LEAST SQUARE SUPPORT VECTOR MACHINE by FENG Shuai, FENG JinFu, WANG Cong, QI Duo, LI YongLi

“…The performance degradation model was built in the method which was based on the least square support machine. …”
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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection by Muhammad Altaf, Muhammad Yasir, Naqqash Dilshad, Wooseong Kim

“…Therefore, this study develops a novel framework for accurate fire detection, especially in challenging environments, focusing on two distinct phases: preprocessing and model initializing. In the preprocessing phase, super-resolution is applied to input data using LapSRN to effectively enhance the data quality, aiming to achieve optimal performance. …”
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