A novel hybrid extreme learning machine-based diagnosis model for sensor node faults in aquaculture by Bing Shi, Zelin Gao, Tianheng Pu, Jianming Jiang, Yueping Sun

“…The dataset of water parameters was obtained based on constructing a monitoring system for intensive aquaculture. …”
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SIMULATING MACHINING OF CONTINUOUSLY VARIABLE TRANSMISSION PULLEYS IN COMPLEX BLADE TOOL TRAJECTORY IN ANSYS SOFTWARE PACKAGE by A.A. Generalova, A.A. Nikulin, D.S. Bychkov

Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning by Carlo Ferro, Matteo Cafaro, Paolo Maggiore

“…Following trajectory optimization, the derived data are used to train an ML model that predicts setup parameters, significantly reducing computational costs and time. …”
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Environmental assessment of soluble solids contents and pH of orange using hyperspectral method and machine learning by Mansour Rasekh, Sina Ardabili, Amir Mosavi

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Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data by Arkadiusz Syta, Arkadiusz Podkowiński, Tomasz Chorągiewicz, Robert Karpiński, Jakub Gęca, Dominika Wróbel-Dudzińska, Katarzyna E Jonak, Dariusz Głuchowski, Marcin Maciejewski, Robert Rejdak, Kamil Jonak

“…We collected a dataset comprising 144 corneal scans from adults aged 18–35, including an equal proportion of keratoconus and normal cases. Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. …”
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Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT by He Sun, Shyam Dwaraknath, Handong Ling, Kristin A. Persson, Sophia E. Hayes

“…We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. …”
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Machine Learning-Driven Structural Optimization of a Bistable RF MEMS Switch for Enhanced RF Performance by J. Joslin Percy, S. Kanthamani, S. Mohamed Mansoor Roomi

“…Activation functions were employed within the ML model to improve the accuracy of predicting optimal design parameters by capturing complex nonlinear relationships. …”
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Accurate prediction of salinity in Chott Djerid shallow aquifers, southern Tunisia: Machine learning model development by Zohra Kraiem, Kamel Zouari, Najiba Chkir

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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

“…With the development of new methods such as Machine Learning (ML) algorithms in recent decades, a resolution to these challenges has been identified. …”
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The results theoretical study of the system of vibration protection of the operator of the road sweeping machines on the basis of MTZ-80 by I. A. Teterina

“…The article presents the results of theoretical research aimed at determining the best parameters for the viscosity and stiffness of the cab suspension and seat. …”
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Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights by Selim Demirci, Durmuş Özkan Şahin, Sercan Demirci, Armağan Gümüş, Mehmet Masum Tünçay

“…In this study, a comprehensive machine learning (ML) model was developed to predict and optimize boride coating thickness on steel surfaces based on boriding parameters such as temperature, time, boriding media, method, and alloy composition. …”
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Machine Learning for Predicting Required Cross-Sectional Dimensions of Circular Concrete-Filled Steel Tubular Columns by Anton Chepurnenko, Samir Al-Zgul, Vasilina Tyurina

“…This paper is devoted to the development of machine learning models for predicting the geometric parameters of a circular cross-section for concrete-filled steel tubular (CFST) columns under the combined action of bending moments and compressive axial forces. …”
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Predicting filtration coefficient and formation damage coefficient for particle flow in porous media using machine learning by Xuejia Du, George K. Wong

“…These parameters are typically determined through coreflood tests. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. …”
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Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing by Andrew DuPlissis, Abhishri Medewar, Evan Hegarty, Adam Laing, Amber Shen, Sebastian Gomez, Sudip Mondal, Adela Ben-Yakar

“…To address this challenge, we developed a machine-learning (ML)-based image analysis platform using a 2.5D U-Net architecture (vivoBodySeg) that accurately segments C. elegans in images obtained from vivoChip devices, achieving a Dice score of 97.80%. vivoBodySeg processes 36 GB data per device, phenotyping multiple body parameters within 35 min on a desktop PC. …”
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The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning by Beatrice Borsari, Mor Frank, Eve S. Wattenberg, Ke Xu, Susanna X. Liu, Xuezhu Yu, Mark Gerstein

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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…Abstract In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). …”
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Design and Investigation of Permanent Magnet Traction Machine With Non-Uniform Air Gap for High-Speed Trains by Weiye Li, Gangyan Li, Yinglu Luo, Yu Wang, Jun Peng, Jun Xu, Ao Hu, Mingjie He

“…Firstly, the basic topology of the proposed non-uniform air-gap rotor is studied and the main design parameters of the traction machine used for high-speed rail train with speed over than 400km/h are analyzed. …”
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ASPECTS ABOUT TECHNICAL EXPERTISE OF THE WHELL PROTECTOR DEVICE AND PICK-UP BUNKER OF THE COAL EXTRACTION MACHINE by Alin STĂNCIOIU, Marius Liviu CÎRŢÎNĂ, Constanța RĂDULESCU

“…This paper presents the technical state of the wheel protector and the pick-up bunker of the machine who was removed from the coal extraction machine after the technical expertise. …”
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TriPlaNet: Enhancing machine-paraphrasing plagiarism detection through triplet network and contrastive learning by Deyu Meng, Ziheng Wang, Tshewang Phuntsho, Tad Gonsalves

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