Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches by Myung-Su Yi, Byung-Keun Lee, Joo-Shin Park

“…The findings underscore the importance of understanding the correlation between vessel parameters and incident outcomes to enhance risk management strategies. …”
Get full text

Machine learning-based seismic response forecasting using feature mapping algorithms and scientometric analysis of nailed vertical excavation in a soil mass by Surya Muthukumar, Dhanya Sathyan, Premjith B, Sanjay Kumar Shukla

“…The influence of the design parameters on the seismic stability of nailed soil excavation is investigated using multivariate regression analysis. …”
Get full text

Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

“…Additionally, scanning electron microscopy (SEM) was employed to examine dominant wear mechanisms under extreme wear conditions, revealing adhesion, abrasion, oxidation, and delamination as primary degradation processes. Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. …”
Get full text

THE EFFECT OF CUTTING KNIFE TYPES AND FORAGE FEEDING SPEEDS ON THE PERFORMANCE OF SOME TECHNICAL INDICATORS FOR THE FORAGE CHOPPING MACHINE MODEL (CH922DH) by Khalid Al aubedy, Saif Rawdhan

“…In this study, experimental work was undertaken to investigate the engineering factors that influence the agricultural residual chopping machine's performance for producing unconventional feed. …”
Get full text

A Data-Driven Strategy for Long-Term Agrarian Sustainability using the Application of Machine Learning Algorithms to Predictive Models for Pest and Disease Management by Almusawi Muntather, Ameer S. Abdul, Lalitha Yaragudipati Sri

“…In order to address these problems, this study presents the Pest and Disease Management Machine Learning Algorithm (PDM MLA), a data driven pest and disease control approach. …”
Get full text

Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance... by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Anchal Sharma, Surita Maini, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid, Rohit Bansal

“…Traditional diagnostic techniques, based on hormone level measurements (TSH, T3, FT4, T4, and FTI), are usually lengthy and laborious. This study uses machine learning (ML) algorithms and feature selection based on GA to improve the accuracy and efficiency of diagnosing thyroid disorders using the UCI thyroid dataset. …”
Get full text

Development of an Analytical Model for Determining the Magnetic Flux of Scattering through the Gears of the Stator of a Synchronous Electric Machine with a Fractional Gear Winding by A. V. Menzhinski, S. V. Panteleev, A. N. Malashin

“…The article presents a two-dimensional finite element model of the magnetic field of a magnetic system of a synchronous electric machine with fractional gear windings. The specific features of the distribution of magnetic fluxes (main effect, edge effect, scattering) in the magnetic system have been revealed and equivalent circuits of the magnetic circuit of the electric machine under study have been constructed at different positions of the stator gear relative to the rotor poles. …”
Get full text

Improving Atmospheric Correction Algorithms for Sea Surface Skin Temperature Retrievals from Moderate-Resolution Imaging Spectroradiometer Using Machine Learning Methods by Bingkun Luo, Peter J. Minnett, Chong Jia

“…The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. …”
Get full text

Treatment Technology for Grout Eruption during Large Diameter Slurry Shield Tunneling Machine Crossing the Water-Rich Sand Layer at the River Bottom by SUN Mingxiang

“…[Result & Conclusion] Through comprehensive treatment methods such as performing water-based operations on the river surface, drilling and grouting to reinforce the soil above, in front of, and on both sides of the shield, adjusting the mud ratio, injecting tail-seal grease, bypassing pipelines, circularly reverse-washing the slurry chamber and air-cushion chamber, and adjusting the tunneling parameters in the reinforcement section, the shield machine is successfully extricated from trouble and smoothly passes through the grout-eruption section. …”
Get full text

Development of a Predictive Model for the Biological Activity of Food and Microbial Metabolites Toward Estrogen Receptor Alpha (ERα) Using Machine Learning by Maksim Kuznetsov, Olga Chernyavskaya, Mikhail Kutuzov, Daria Vilkova, Olga Novichenko, Alla Stolyarova, Dmitry Mashin, Igor Nikitin

“…In this study, we evaluated a suite of 27 machine learning models and, following systematic optimization and rigorous performance comparison, identified linear discriminant analysis (LDA) as the most effective predictive approach. …”
Get full text

Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest by Qiang Wu, Qiang Wu, Fang Zhang, Yuchang Fei, Zhenfen Sima, Shanshan Gong, Qifeng Tong, Qingchuan Jiao, Hao Wu, Jianqiu Gong, Jianqiu Gong

“…After screening predictive variables by LASSO regression, three predictive models selected using the LazyPredict package, namely logistic regression (LR), support vector machine (SVM) and random forest (RF), were established respectively. …”
Get full text

Evaluating Land Subsidence Triggered by the 7.8 M_w Turkey-Syria Earthquake Using an Advanced Machine Learning Model by R. Jena, A. Shanableh, A. Shanableh, R. Al-Ruzouq, R. Al-Ruzouq, M. B. A. Gibril, N. A. Hammouri, A. Elawady, H. Shanableh

“…Sentinel-1 Synthetic Aperture Radar (SAR) data were processed to detect surface deformation near the epicenter and quantify the affected region's vertical displacement. An extreme learning machine model was developed using nine parameters, including slope, curvature, sediment thickness, soil thickness on slopes, peak ground acceleration, hydrologic soil, Vs30, land cover, and landslide probability. …”
Get full text

Design and Implementation of a Low-Pressure Briquetting Machine for the Use of <i>Pinus</i> spp. Wood Residues: An Approach to Appropriate Rural Technology by Mario Morales-Máximo, Víctor Manuel Ruíz-García, José Guadalupe Rutiaga-Quiñones, Luis Bernado López-Sosa

“…The design of the manual briquetting machine was developed through a participatory approach with community users. …”
Get full text

Integrated Water Vapor Estimation During Clear Skies Using a Ground-Based Infrared Radiometer and the Light Gradient Boosting Machine Method by Wenyue Wang, Catalina Medina Porcile, Wenzhi Fan, Klemens Hocke

“…New algorithms of retrieving atmospheric integrated water vapor (IWV) under clear-sky conditions for the infrared radiometer using linear regression, quadratic regression (QR), and light gradient boosting machine (LightGBM) methods are developed in this work. …”
Get full text

Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement by Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wójcikowski, Bogdan Pankiewicz

Get full text

Research on the Mechanism of the Impact of Green View Index of Urban Streets on Thermal Environment: A Machine Learning-Driven Empirical Study in Hangzhou, China by Qiguan Wang, Yanjun Hu, Hai Yan

Get full text

Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…In parallel, the study applied machine learning (ML) models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—to predict the mechanical properties of SMA based on input mix parameters. …”
Get full text

Spatiotemporal analysis of thermal islands in a semi-arid city: A case study of Kermanshah, Iran using machine learning and remote sensing by Peyman Karami, Seyed-Mohsen Mousavi

“…The sample size for assessing the impact of environmental parameters on LST variations was determined using the Cochran formula. …”
Get full text

Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets by Konstantin Sharafutdinov, Sebastian Johannes Fritsch, Mina Iravani, Pejman Farhadi Ghalati, Sina Saffaran, Declan G. Bates, Jonathan G. Hardman, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert

“…<italic>Goal:</italic> Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. …”
Get full text

Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis by Huili Zhao, Shenao Zhang, Lang Chen, Xin Liu, Aihong Cao, Peng Du

Get full text