Enhanced slope stability prediction using ensemble machine learning techniques by Devendra Kumar Yadav, Swarup Chattopadhyay, Debi Prasad Tripathy, Pragyan Mishra, Pritiranjan Singh

“…This study presents a machine learning (ML) model for evaluating slope stability that meets high precision and speed criteria in slope engineering. …”
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RESONANCE BEHAVIOUR ANALYSIS OF CARRYING SYSTEM IN HEAVY MACHINE WITH TRAVELING-COLUMN by Yu. V. Vasilevich, S. S. Dovnar

“…A finite elements method (FEM)-analysis has been carried out with the purpose to study dynamics of carrying system in a machine tool of unique height. This is a one-off machine with high traveling column and a support with horizontal slide can move vertically along the column. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

“…In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil – nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs. …”
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Comparative Study of Friction Models in High-Speed Machining of Titanium Alloys by Fan Yi, Ruoxi Zhong, Wenjie Zhu, Run Zhou, Li Guo, Ying Wang

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Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis by Helong Yu, Kang Yuan, Wenshu Li, Nannan Zhao, Weibin Chen, Changcheng Huang, Huiling Chen, Mingjing Wang

“…The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. …”
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About the trustworthiness of physics-based machine learning – considerations for geomechanical applications by D. Degen, D. Degen, D. Degen, M. Ziegler, M. Ziegler, O. Heidbach, O. Heidbach, A. Henk, K. Reiter, F. Wellmann, F. Wellmann

“…To overcome the challenge of trustworthiness, we propose the usage of a novel hybrid machine learning method, namely the non-intrusive reduced-basis method, as a surrogate model. …”
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AI-powered machine learning models for monitoring and optimization of biodrying process by Abhisit Bhatsada, Panida Payomthip, Tanik Itsarathorn, Ye Nyi Nyi Lwin, Eka Wahyanti, Sirintornthep Towprayoon, Suthum Patumsawad, Chart Chiemchaisri, Komsilp Wangyao

“…This study integrates machine learning and deep learning techniques to develop predictive models to capture these complex relationships and improving biodrying efficiency. …”
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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“… Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. …”
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Design and Performance Testing of Seed Potato Cutting Machine with Posture Adjustment by Yingsi Wu, Xiangming La, Xuan Zhao, Fei Liu, Jianguo Yan

“…To address these issues, a seed potato cutting machine capable of posture adjustment, cutting, and spraying was designed. …”
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Investigation of pneumatic transport system of seeding machine "AGRAER-85OH" by V. E. Saitov, R. G. Gataullin

“…The drive fan is an internal combustion engine «HONDA GX-690" (Japan). Experimental machine made it possible to adjust the air flow by changes in the speed of the crankshaft of the engine and to carry out measurements of the parameters of the air flow. …”
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Machine learning in dentistry and oral surgery: charting the course with bibliometric insights by Shuangwei Liu, Yuquan Hao, Shijie Zhu, Liyao Wan, Zhe Yi, Zhichang Zhang

“…Abstract Background We aimed to comprehensively analyze the application of machine learning (ML) in dentistry and oral surgery using bibliometric methods to identify research trends, hotspots, and future directions. …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…The aim of this study is to explore the application of machine learning in predicting the treatment outcome of ECC. …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Then, autoregressive integrated moving average model (ARIMA), extreme learning machine (ELM), support vector regression (SVR), wavelet neural network (Wavelet), recurrent neural network (RNN) and long short-term memory (LSTM) were used to predict COVID-19 epidemic data in Guangdong Province, China; And the prediction performance of each model was compared through prediction accuracy indicators. …”
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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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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…The study considered data from 245 nematode genera and 44 environmental parameters from 100 stations. Data was analyzed by means of a hybrid machine learning (ML) approach, which combines unsupervised and supervised methods. …”
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Development of technology and manufacturing of an import-substituting complex of machines for cultivating crops by R. S. Rakhimov, I. R. Rakhimov, D. A. Yalaletdinov, E. O. Fetisov, Ya. Yu. Khamitov, R. M. Yumaguzhin, A. R. Rakhimzhanov, S. V. Anokhin

“…For large farms with arable land area of over 10 thousand hectares it is necessary to use combined single-purpose machines. Based on the previously conducted studies, using the obtained mathematical models and agrotechnical requirements for developing new machines, calculations were carried out and the design parameters of machine complex for tractors of 1.4...5 traction class were substantiated. …”
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Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization by Chuanwei Zhang, Dingshuai Liu, Paraskevas Tsangaratos, Ioanna Ilia, Sijin Ma, Wei Chen

“…Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. …”
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Design of a Mechanised Sorghum Sieving Machine Used in Agriculture Production. by Amanya, Davis

“…Observations going to the field and observing the farmer's produce per day, questioners asking questions about income and what can be the affordable price for the machine The objectives involved determining the design specification of the sieving machine, selecting and sizing the sieving machine to meet the design specifications, developing the layout of the machine, evaluate the performance of the machine through simulation. …”
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Using machine learning models to predict post-revascularization thrombosis in PAD by Samir Ghandour, Adriana A. Rodriguez Alvarez, Isabella F. Cieri, Shiv Patel, Mounika Boya, Rahul Chaudhary, Rahul Chaudhary, Rahul Chaudhary, Anna Poucey, Anahita Dua

“…Given the high rates of thrombosis post-revascularization, this study aimed to develop a machine learning model (MLM) incorporating viscoelastic testing and patient-specific variables to predict ATE following LER.MethodsWe prospectively enrolled PAD patients undergoing LER from 2020 to 2024, collecting demographic, clinical, and intervention-related data alongside perioperative thromboelastography with platelet mapping (TEG-PM) values over 12 months post-revascularization. …”
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