Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. …”
Get full text

Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

“…The best performing algorithm was extreme gradient boosting (XGboost) using 9 features, with an AUCPR of 0.9974 (95% CI 0.9961–0.9984), significantly better than the LR model (AUCPR = 0.9791, 95% CI 0.9651–0.9904). Conclusion Supervised machine learning demonstrates efficacy in determining blood gas sample type from ICU patients. …”
Get full text

Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine by FU Qiang, HU Dong, YANG Tongliang, LUO Guoqing, TAN Weimin

“…In response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, based on compressed sensing (CS) and deep multi-kernel extreme learning machine (D-MKELM) theory, a CS-DMKELM intelligent diagnosis model for rolling bearings was proposed. …”
Get full text

Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning by Tong Li, Yunpeng Li, Wenxin Cheng, Jufeng Zheng, Lianqing Li, Kun Cheng

“…This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N₂O emissions from Chinese upland fields. …”
Get full text

Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine by FU Qiang, HU Dong, YANG Tongliang, LUO Guoqing, TAN Weimin

“…ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. …”
Get full text

A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing by Lurui Wang, Mehdi Jadidi, Ali Dolatabadi

“…To bypass the high cost of purely CFD-driven optimization, we construct a two-stage machine learning (ML) framework trained on 48 high-fidelity CFD simulations. …”
Get full text

Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions by Themba Mashiyane, Lagouge Tartibu, Smith Salifu

“…The methodology involves applying FEA simulation (using Abaqus) to model the strain, stress, and buckling behavior of the oil storage tank. …”
Get full text

Prediction of copper contamination in soil across EU using spectroscopy and machine learning: Handling class imbalance problem by Chongchong Qi, Nana Zhou, Tao Hu, Mengting Wu, Qiusong Chen, Han Wang, Kejing Zhang, Zhang Lin

“…This study underscores the utility of the optimized model for managing soil Cu pollution and provides a valuable reference for addressing imbalanced learning challenges in soil pollution research.…”
Get full text

Leveraging Machine Learning in Next-Generation Climate Change Adaptation Efforts by Increasing Renewable Energy Integration and Efficiency by Izabela Rojek, Dariusz Mikołajewski, Marek Andryszczyk, Tomasz Bednarek, Krzysztof Tyburek

“…This article examines the growing role of machine learning (ML) in promoting next-generation climate change adaptation through the improved integration and performance of renewable energy systems. …”
Get full text

Enhancing high pressure pulsation test bench performance: a machine learning approach to failure condition tracking by Aslı Aksoy, Ömer Haki

“…In highly automated machining systems, it is vital to reduce the number of unplanned machine downtimes due to equipment failure, as these can lead to significant losses in resources. …”
Get full text

Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

“…This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. …”
Get full text

Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security by Sonam Bhardwaj, Ayon Tarafdar, Manoj Baghel, Triveni Dutt, Gyanendra Kumar Gaur

Get full text

Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission by Jakub Gęca, Dariusz Czerwiński, Bartosz Drzymała, Krzysztof Kolano

“…An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. …”
Get full text

Temperature Analysis of Secondary Plate of Linear Induction Motor on Maglev Train Under Periodic Running Condition and Its Optimization by Wenxiao Wu, Yunfeng He, Jien Ma, Qinfen Lu, Lin Qiu, Youtong Fang

Get full text

Regression Analysis of Heat Release Rate for Box-Type Power Bank Based on Experimental and Machine Learning Methods by Shihan Luo, Hua Chen, Xiaobing Mao, Wenbing Zhu, Yuanyi Xie, Wenbin Wei, Mengmeng Jiang, Chenyang Zhang, Chaozhe Jiang

“…Based on the experimental data, HRR prediction models were constructed using decision tree regression (DT), K-nearest neighbor regression (KNN), and linear regression (LR). …”
Get full text

Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications by Sodiq Abiodun Kareem, Makinde Akindeji Ibrahim, Justus Uchenna Anaele, Olajesu Favor Olanrewaju, Emmanuel Omosegunfunmi Aikulola, Michael Oluwatosin Bodunrin

“…Recent studies confirm that ML models have been instrumental in improving MXene synthesis processes, enabling higher yields and optimization of properties, better purity, and scalability through real-time process control and reinforcement learning. …”
Get full text

Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. …”
Get full text

Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines by Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen, Li Zhang, Chuyang Wang

“…To overcome the computational complexity, poor real-time performance, and limited generalization of existing methods like GRU-GAN and SOM-LSTM, this study proposes a hybrid framework combining Bayesian multiple imputation with a Support Vector Machine (SVM) for data repair. The framework first employs an adaptive Kalman filter to denoise raw data and remove outliers, followed by Bayesian multiple imputation that constructs posterior distributions using normal linear correlations between historical and operational data, generating optimized imputed values through arithmetic averaging. …”
Get full text

A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…In combination with pose graph optimization, this model enabled precise TBM positioning. …”
Get full text

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio by Houzhong Zhang, Yiyun Qi, Weijian Si, Chengyin Zhang

Get full text