Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution by Mengyang Qu, Gyanendra Sharma, Naoki Wada, Hisaki Ikebata, Shigeyuki Matsunami, Kenji Takahashi

“…The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. …”
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Optimization of exhaust catalytic manifold of internal combustion engine by Vladimir K. Nikolaev, Arkady A. Skvortsov, Zamir F. Khudayarov, Roman S. Nikolaev

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Performance of machine learning algorithms to evaluate the physico-mechanical properties of nanoparticle panels by Derrick Mirindi, James Hunter, David Sinkhonde, Tajebe Bezabih, Frederic Mirindi

“…This review analyzes secondary data on nanoparticle integration in board production, aiming to evaluate the relationships among physical (water absorption (WA) and thickness swelling (TS)) and mechanical (modulus of rupture (MOR), modulus of elasticity (MOE); and internal bond (IB) strength) properties and to predict performance using machine learning (ML) algorithms. These algorithms include Pearson correlation, hierarchical clustering, and decision tree (DT) models. …”
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Design of cross-reactive antigens with machine learning and high-throughput experimental evaluation by Chelsy Chesterman, Thomas Desautels, Luz-Jeannette Sierra, Kathryn T. Arrildt, Adam Zemla, Edmond Y. Lau, Shivshankar Sundaram, Jason Laliberte, Lynn Chen, Aaron Ruby, Mark Mednikov, Sylvie Bertholet, Dong Yu, Kate Luisi, Enrico Malito, Corey P. Mallett, Matthew J. Bottomley, Robert A. van den Berg, Daniel Faissol

“…Moreover, limited data on antigen-antibody binding in public databases constrains the training of machine learning models. To address these challenges, we used computational models to predict fHbp properties and machine learning was applied to select both the most promising and informative mutants using a Gaussian process (GP) model. …”
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Methodology for Integrated Design Optimization of Actuation Systems for Exoskeletons by Daniel Greve, Christian Kreischer

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Investigation of Droplet Spreading and Rebound Dynamics on Superhydrophobic Surfaces Using Machine Learning by Samo Jereb, Jure Berce, Robert Lovšin, Matevž Zupančič, Matic Može, Iztok Golobič

“…In this work, we employed a collection of 1498 water–glycerin droplet impact experiments on monolayer-functionalized laser-structured aluminum samples to train, validate and optimize a machine learning regression model. To elucidate the role of each influential parameter, we analyzed the model-predicted individual parameter contributions on key descriptors of the phenomenon, such as contact time, maximum spreading coefficient and rebound efficiency. …”
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Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh. by Arman Hossain Chowdhury, Md Siddikur Rahman

“…Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data.…”
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A machine learning algorithm for personalized healthy and sustainable grocery product recommendations by Laura Z.H. Jansen, Kwabena E. Bennin

“…To assess the impact of integrating healthiness and sustainability information of food choices in predicting an item to buy, we employ three food recommendation models: a Baseline popularity-based model, Restricted Boltzmann Machine (RBM), and Variational Bayesian Context-Aware Representation (VBCAR) based on (1) preferences, (2) preferences and health, (3) preferences and sustainability, and (4) all combined attributes. …”
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An informativeness score for optimal mixed datasets using Gaussian process regression by Cameron J LaMack, Eric M Schearer

“…In this study, we present a method for calculating an optimally informative dataset for numerous subsystem models. …”
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Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on... by Weiye Zhang, Weiye Zhang, Yan Li, Pengwei Shao, Yuxuan Du, Ke Zhao, Jiawen Zhan, Lee A. Tan

“…Subgroup analyses, nonlinear analyses, and ROC analyses further supported these findings. Machine learning feature selection identified 19 variables, with the Random Forest model demonstrating optimal performance.ConclusionBoth WWI and BMI were associated with increased CLBP risk, with WWI potentially serving as a more sensitive predictive indicator. …”
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Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd by Xiao Guo, Hongyu Huang, Haiyan Wang, Chang Cai, Ying Wang, Xiaohua Wu, Jian Wang, Baogen Wang, Biao Zhu, Yun Xiang

“…In this study, we employed NIRS along with machine learning and neural network-based methods to model and predict protein and free amino acids (FAAs) of bottle gourd. …”
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Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning by Mir Amir Mohammad Reshadi, Fereidoun Rezanezhad, Ali Reza Shahvaran, Amirhossein Ghajari, Sarah Kaykhosravi, Stephanie Slowinski, Philippe Van Cappellen

“…Using this dataset, we trained and tested three optimized gradient boosting Machine Learning (ML) models. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

“…SVM closely followed it with a score of 0.895, while the other two models returned worse results. Some results from the application of the optimal model after specific weather events are also presented in this study. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…The core idea of dynamic modeling is that the machine learning model is trained using a sliding time window, so that the relationship between predictors and predictands is optimized for a specific and recent period rather than for the entire time span. …”
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Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo. by Audêncio Victor, Diego Augusto Medeiros Santos, Eduardo Koerich Nery, Danilo Pereira Mori, Pamella Cristina de Carvalho Lucas, Denise Cammarota, Guillermo Leonardo Florez Montero, Fabiano Novaes Barcellos Filho, Ana Lúcia Frugis Yu, Telma Regina Marques Pinto Carvalhanas

“…This study aims to develop machine learning (ML) models to classify the aetiology of bacterial meningitis using data from the Notifiable Diseases Information System (SINAN) in São Paulo State, Brazil.…”
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Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean by Dandan Li, Shaojun Zheng, Chenyu Zheng, Lingling Xie, Li Yan

“…To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. …”
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Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States. by Mukhtar Ijaiya, Erica Troncoso, Marang Mutloatse, Duruanyanwu Ifeanyi, Benjamin Obasa, Franklin Emerenini, Lucien De Voux, Thobeka Mnguni, Shantelle Parrott, Ejike Okwor, Babafemi Dare, Oluwayemisi Ogundare, Emmanuel Atuma, Molly Strachan, Ruby Fayorsey, Kelly Curran

“…Nigeria, with the second-largest HIV epidemic globally, faces challenges in achieving its HIV epidemic control goals by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Based on the optimal model (RF), the importance of the selected seven factors was determined. …”
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Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals by Abdul Azziz Abd Talib, Chun Yee Lim, Chin Kian Liew

“…A-scan signals were analysed to extract key features for machine learning models, with Gradient Boosting Classifier emerging as the optimal model, achieving an accuracy of 97% in defect classification. …”
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Parameters analysis and process optimization of shell precision thermoforming by Cheng Haozhe, Xu Shubo, Sun Kangwei, Wang Shengliang, Pan Yuefei, Ma Hailong, Ren Guocheng, Li Jianing, Zhao Zhongkui

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