Machine learning and FEM-driven analysis and optimization of deep foundation pits in coastal area: A case study in Fuzhou soft ground by Shuhong Wang, Bowen Han, Jianhui Jiang, Natalia Telyatnikova

“…Stability is analyzed using in-situ monitoring data from the R4 area, and the deformation of the support system is predicted using machine learning. The predicted maximum lateral deformation of the support system may reach the warning value, necessitating corrections to the existing support parameters. …”
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Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing by Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu

“…In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. …”
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Machine learning-based evaluation of performance of silicon nitride waveguide fabrication: Gradient-boosted forests for predicting propagation and bend excess losses by Hinum-Wagner Jakob Wilhelm, Hoermann Samuel Marko, Feigl Gandolf, Schmidt Christoph, Kraft Jochen, Bergmann Alexander

“…The impact of waveguide geometry and layer properties on loss was examined using a full factorial design of experiment. These machine learning models’ predictive accuracy and ability to capture complex relationships between fabrication parameters and different loss mechanisms were assessed. …”
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PECULIAR FEATURES OF MACHINING MARKS FORMATION ON SURFACE ОF TITANIUM SPECIMEN WITH SINGLE ELECTRO CONTACT ACTION OF WIRE ELECTRODE-TOOL by M. G. Kiselev, A. V. Drozdov, S. G. Monich, P. S. Bogdan

“…The paper presents an investigation of shape and geometry parameters of machining marks obtained on the surface of titanium specimen with a single electro contact action of a wire electrode-tool. …”
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Machine learning model based on dynamic contrast-enhanced ultrasound assisting LI-RADS diagnosis of HCC: A multicenter diagnostic study by Meiqin Xiao, Yishu Deng, Wei Zheng, Lishu Huang, Wei Wang, Hao Yang, Danyan Gao, Zhixing Guo, Jianwei Wang, Chaofeng Li, Fang Li, Feng Han

“…Purpose: To investigate the viability of employing machine learning methods based on quantitative parameters of contrast-enhanced ultrasound for distinguishing HCC within LR-M nodules. …”
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Assessing the impact of reverse osmosis plant operations on water quality index improvement through machine learning approaches and health risk assessment by Fariba Abbasi, Azadeh Kazemi, Ahmad Badeenezhad, Mostafa Moazamfard, Raham Armand, Amin Mohammadpour

“…But in the outlet Flow, the parameters exceeding the standard level were TH, SO4, Cl, and TDS. …”
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Prediction of heat transfer in a rotor-stator cavity cooled by multiple jets: Integration of CFD models and machine learning for performance optimization by Abdellatif EL hannaoui, Rachid Boutarfa

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Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study by Nai-Yu Kuo, Hsin-Jung Tsai, Shih-Jen Tsai, Albert C Yang

“…The Questionnaire Model (Model-Questionnaire) was designed to distinguish OSA from primary insomnia using demographic information and Pittsburgh Sleep Quality Index questionnaires, while the Saturation Model (Model-Saturation) categorized OSA severity based on multiple blood oxygen saturation parameters. The performance of the sequential machine learning models in screening and assessing the severity of OSA was evaluated using an independent test set derived from TVGH. …”
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Integrating artificial Intelligence-Based metaheuristic optimization with Machine learning to enhance Nanomaterial-Containing latent heat thermal energy storage systems by Ali Basem, Hanaa Kadhim Abdulaali, As’ad Alizadeh, Pradeep Kumar Singh, Komal Parashar, Ali E. Anqi, Husam Rajab, Pancham Cajla, H. Maleki

“…The proposed strategy combines machine learning algorithms, including multilayer perceptron neural network (MLPNN), generalized additive model (GAM), Gaussian kernel regression (GKR), support vector machine (SVM), and Gaussian process regression (GPR) with artificial intelligence-based metaheuristic optimization algorithms (PSO and GA) to optimize their structural/training parameters. …”
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Modelling and Prediction of Particulate Matter, NOx, and Performance of a Diesel Vehicle Engine under Rare Data Using Relevance Vector Machine by Ka In Wong, Pak Kin Wong, Chun Shun Cheung

“…In this study, the engine speed, load, and coolant temperature are used as the input parameters, while the brake thermal efficiency, brake-specific fuel consumption, concentrations of nitrogen oxides, and particulate matter are used as the output parameters. …”
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Real-Time Estimation of Near-Surface Air Temperature over Greece Using Machine Learning Methods and LSA SAF Satellite Products by Athanasios Karagiannidis, George Kyros, Konstantinos Lagouvardos, Vassiliki Kotroni

“…The air temperature near the Earth’s surface is one of the most important meteorological and climatological parameters. Yet, accurate and timely readings are not available in significant parts of the world. …”
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Robust prediction of chlorophyll-A from nitrogen and phosphorus content in Philippine and global lakes using fine-tuned, explainable machine learning by Karl Ezra Pilario, Eric Jan Escober, Aurelio de los Reyes V, Maria Pythias Espino

“…This paper presents a methodology using 8 popular machine learning (ML) models for estimating Chl-a concentration from nutrient content in lakes. …”
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Evaluation of Prediction Models for the Capping and Breaking Force of Tablets Using Machine Learning Tools in Wet Granulation Commercial-Scale Pharmaceutical Manufacturing by Sun Ho Kim, Su Hyeon Han, Dong-Wan Seo, Myung Joo Kang

“…We identified eight input variables associated with the process and material parameters that control the tableting outcome using feature importance analysis. …”
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Application of machine learning methods on investigation the effect of interrupted deflector's height and downstream depth changes on energy dissipation at flip bucket spillways by Arash Adib, Danial Ebrahimzadeh, Mahmood S. Bejestan, Morteza Lotfirad, Masoud Oulapour

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Sweet Potato Yield Prediction Using Machine Learning Based on Multispectral Images Acquired from a Small Unmanned Aerial Vehicle by Kriti Singh, Yanbo Huang, Wyatt Young, Lorin Harvey, Mark Hall, Xin Zhang, Edgar Lobaton, Johnie Jenkins, Mark Shankle

“…Classical machine learning (ML) approaches and tree-based algorithms, like XGBoost and Random Forest, are implemented. …”
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Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study by Chu Wang, Chu Wang, JunYe Wen, ZiYi Su, HanXiang Yu

“…The variables screened by univariate, multivariate analysis and lasso regression included type of cholecystitis, number of puncture ports, gallbladder adhesion, conservative antibiotic treatment before surgery, gallbladder thickness (mm). The above five parameters were incorporated into the Ml model. Comprehensive analysis revealed that the Light Gradient Boosting Machine (LightGBM) classification model was the optimal model, with the area under the curve (AUC) of the validation cohort was 0.876, the 95% confidence interval was 0.8139–0.938, the accuracy was 0.843, the sensitivity was 0.805, and the specificity was 0.857, with AUC of validation cohort was 0.876. …”
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Double-diffusive magnetoconvection in a tilted porous parallelogrammic domain with discrete heated-cooled segments: Leveraging machine learning and CFD approach by P. Ravindra, M. Sankar, Suresh Rasappan, Wardah Abdullah Al Majrafi, Pugalarasu Rajan, S. Sanal Kumar

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Combined sequential hypothermic oxygenated and normothermic machine perfusion for liver transplant from an expanded criteria donor: first clinical application in Russia by S. V. Gautier, N. V. Grudinin, A. R. Monakhov, M. A. Boldyrev, V. K. Bogdanov, D. M. Bondarenko, S. I. Zubenko, N. P. Mozheiko, M. G. Minina, O. M. Tsirulnikova

“…Objective: to analyze a clinical case series and evaluate the safety and efficacy of a sequential machine perfusion protocol combining dual hypothermic oxygenated perfusion (D-HOPE) and normothermic machine perfusion (NMP) for conditioning and viability assessment of liver grafts retrieved from expanded criteria donors (ECD) in routine clinical practice.Materials and methods. …”
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Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning by Zhongping Tian, Yinning Guo, Xi Chen, Qifeng Zhou, Yuan Liu, Zhizhu Yi, Li Zhang, Li Zhang

“…Machine learning-derived conversion algorithms significantly improve cross-device comparability, offering a robust standardization framework for multicenter research and longitudinal data integration. …”
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Revealing the underlying mechanism in controlling Young’s modulus of additively manufactured Ti-6Al-4V using fuzzified machine learning by Y.T. Liu, C. Chua, V. Soh, Z. Sun, C.K. Chua, S.L. Sing

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