Development of machine learning-based standalone GUI application for predicting hydraulic conductivity and compaction parameters of lateritic soils by Lateef Bankole Adamolekun, Muyideen Alade Saliu, Abiodun Ismail Lawal, Ismail Adeniyi Okewale

“…Hydraulic conductivity and compaction parameters are the key considerations in selecting lateritic soils for engineering construction. …”
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COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA by Fadilah Amirul Adhel, Sri Astuti Thamrin, Siswanto Siswanto

“…SVM cannot choose appropriate parameters so the use of parameters is not optimal. …”
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Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path by Allan Dos S. Braga, Hugo A. O. Da Cruz, Leslye E. C. Eras, Jasmine P. L. Araujo, Miercio C. A. Neto, Diego K. N. Silva, Gervasio P. S. Cavalcante

“…This work presents and evaluates the use of geometric parameters of the environment in the prediction of the electric field in mixed city-river type environments, employing two techniques of Machine Learning (ML) as Artificial Neural Networks (ANN) and Neuro-Fuzzy System (NFS). …”
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Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning by He Jingru, Qian Xuedan, Huang Hu, Lin Bao, Zhang Jun, Zhang Chunxiao, Chen Yuyan

“…This study aims to enhance the sensory evaluation of skin creams by using machine learning to predict sensory attributes based on instrumental parameters, addressing the limitations of conventional methods. …”
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Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis by Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

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AI4EO hyperview challenge: combination of machine learning methods on hyperspectral images to predict the soil parameters by M. Sanità, E. S. Malinverni, R. Pierdicca, A. Mancini, E. Glowienka, L. Nepi

“…The challenge is set in an agricultural area of Poland and the available data are hyperspectral images (150 contiguous hyperspectral bands) and in situ samples for soil parameter measurements. The aim of this challenge was to advance the state of art of soil parameter analysis by hyperspectral images. …”
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A machine learning approach to predict tight-binding parameters for point defects via the projected density of states by Henry Phillip Fried, Daniel Barragan-Yani, Florian Libisch, Ludger Wirtz

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Investigation and optimisation of the functioning parameters of the milking machine electronic unit, diagnosing the state of the udder quarters of cows for mastitis by P. A. Sаvinyh, A. A. Rylov, V. N. Shulatiev, S. A. Ivanovs

“…The article informs on the search of values of setting the parameters of electronic unit of the milking machine providing the current control of physiological state of udder quarters during milking. …”
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Prediction and optimization of WEDM parameters for machining of NiTi-shape memory alloy using ANFIS-PSO approach by Lokesh Kumar, Ashish Goyal, Vimal Kumar Pathak

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Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil by Tzuri Eilat, Alison McQuillan, Amichai Mitelman

“…Accurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. In this study, a case study was used as a benchmark to investigate the sensitivity of the SS-HSM parameters. …”
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Machine learning aided optoelectric characterization modelling and prediction of the IV parameters of perovskite solar cells with > 90% accuracy by Jeykishan Kumar, Bidisha Nath, Tulika Bhattacharjee, Praveen C. Ramamurthy

“…The findings of this research underscore the potential of supervised machine learning as an innovative technique for approximating IV curve parameters of PSC, utilizing EL spectroscopy. …”
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Optimizing WEDM Parameters Using Swarm Intelligence: A Multi‐Objective Approach to Improve Machinability and Cost‐Efficiency by Abhijit Bhowmik, Raman Kumar, Nikunj Rachchh, T. Ramachandran, A. Karthikeyan, Rahul Singh, Deepak Gupta, Dhirendra Nath Thatoi, Bhavik Jain, A. Johnson Santhosh

“…ABSTRACT This study aims to optimize the Wire‐Cut Electrical Discharge Machining (WEDM) parameters for Inconel 800, a high‐performance superalloy known for its remarkable mechanical properties and resistance to elevated temperatures. …”
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Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep by Oswaldo Margarito Torres-Chable, Cem Tırınk, Rosa Inés Parra-Cortés, Miguel Ángel Gastelum Delgado, Ignacio Vázquez Martínez, Armando Gomez-Vazquez, Aldenamar Cruz-Hernandez, Enrique Camacho-Pérez, Dany Alejandro Dzib-Cauich, Uğur Şen, Hacer Tüfekci, Lütfi Bayyurt, Hilal Tozlu Çelik, Ömer Faruk Yılmaz, Alfonso J. Chay-Canul

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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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A deep operator network for Bayesian parameter identification of self-oscillators by Tobias Sugandi, Bayu Dharmaputra, Nicolas Noiray

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METHOD OF DEGRADATION DATA RELIABILITY EVALUATION BASED ON THE LEAST SQUARE SUPPORT VECTOR MACHINE by FENG Shuai, FENG JinFu, WANG Cong, QI Duo, LI YongLi

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Reliability analysis of landing gear operation based on improved support vector machine-based decomposed-coordinated by ZHANG Lei, CHEN Junyu, FENG Jun, FENG Yunwen, LIU Lei

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Estimation of the Double-Cage Model for Three-Phase Induction Machines Using Decision Tree-Based Algorithms by Eduardo Ferreira Rios Oliveira, Rafael Santos, Marcelo Godoy Simoes, Helmo Morales Paredes

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Predicting characteristics of bursty bulk flows in Earth’s plasma sheet using machine learning techniques by Xuedong Feng, Jian Yang, Jacob Bortnik, Chih-Ping Wang, Jiang Liu, Jiang Liu

“…In this study, we employ the XGBoost machine learning algotithm to predict the variation range of several essential BBF properties, including duration, magnetic field, plasma moments, and specific entropy parameters. …”
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Gear Cutting Machining of Klingelnberg Cyclo-palloid Bevel Gear based on Monolithic Cutter Disc by Wang Zhiyong, Yang Weiyin, Zhang Yu, Zhai Huaming

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