Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset by Omid Kohandel Gargari, Mobina Fathi, Shahryar Rajai Firouzabadi, Ida Mohammadi, Mohammad Hossein Mahmoudi, Mehran Sarmadi, Arman Shafiee

“…After careful selection of variables related to asthma, various ML algorithms including Support Vector Machine (SVM), Random Forest (RF), AdaBoost (ADA), XGBoost (XGB), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Multi-Layer Perceptron (MLP) were evaluated. …”
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Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage by Xingcheng Wang, Ji Zhang, Xingshuai Ma, Huajie Luo, Laijun Liu, Hui Liu, Jun Chen

“…Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. …”
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Changes in oat grain yield and quality with increased adaptability of cultivars by P. N. Nikolaev, O. A. Yusova, I. V. Safonova, N. I. Aniskov

“…The studies were carried out from 2011 to 2019 in the southern forest steppe of Western Siberia. The content of protein, crude fat and starch in grain, and its hull content were measured according to B. …”
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Effects of <i>Serendipita indica</i> on the Morphological and Physiological Characteristics of <i>Agrostis stolonifera</i> L. Under Drought Stress by Chuhong Lin, Wenxu Hu, Xin Qin, Yongjun Fei, Die Hu

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Synergy of Environmental Education Programs at The Bodogol Nature Conservation Education Center, Complementing The Independent Curriculum at Junior High Schools by Hapriza Aprilia, Rinekso Soekmadi, Hadi Susilo Arifin

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A Tissue Culture Technique for Rapid Clonal Propagation, Microrhizome Induction, and RAPD Analysis of <i>Kaempferia grandifolia</i> Saensouk & Jenjittikul—A Rare Plant Species in T... by Sukanya Nonthalee, Surapon Saensouk, Suthira Maneechai, Phetlasy Souladeth, Piyaporn Saensouk

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RNA-Seq Assembly and Differential Transcriptome Analysis of Korean Medicinal Herb var. by Neha Samir Roy, Jung-A Kim, Ah-Young Choi, Yong-Wook Ban, Nam-Il Park, Kyong-Cheul Park, Hee-sun Yang, Ik-Young Choi, Soonok Kim

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Machine Learning Models for Spring Discharge Forecasting by Francesco Granata, Michele Saroli, Giovanni de Marinis, Rudy Gargano

“…Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was selected as a case study. …”
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The formula for Peace as a result of a future without war in Ukraine by M. Pyvovar

“…Bilateral negotiations commenced in the early days of the invasion: the first meeting occurred at the Belarusian border on 28 February 2022, followed by discussions on humanitarian corridors on 3 March in the Bialowieza Forest, and another on 7 March, which yielded no significant political outcomes. …”
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Heavy Metal Spatial Variation Mechanism and Ecological Health Risk Assessment in Volcanic Island Soils: A Case Study of Weizhou Island, China by Ran Bi, Wei Fu, Xuanni Fu

“…Land use types influence heavy metal content, with higher concentrations in abandoned land and lower concentrations in forest land with dense vegetation and organic matter. …”
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Machine Learning Algorithm for Estimating Surface PM2.5 in Thailand by Pawan Gupta, Shanshan Zhan, Vikalp Mishra, Aekkapol Aekakkararungroj, Amanda Markert, Sarawut Paibong, Farrukh Chishtie

“…The integrated data then used to train and validate a supervised MLA’ random forest’ to estimate hourly and daily PM2.5 concentrations. …”
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Functional connectivity-based compensation in the brains of non-demented older adults and the influence of lifestyle: A longitudinal 7-year study by Pascal Frédéric Deschwanden, Isabel Hotz, Susan Mérillat, Lutz Jäncke

“…Network-based statistics and latent growth modeling were employed to examine changes in structural and functional connectivity, as well as potential functional compensation for declines in processing speed and memory. Random forest and linear regression were used to predict the amplitude of compensation based on demographic, biological, and lifestyle factors. …”
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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability by Sajid Ullah, Xiuchen Qiao, Aqil Tariq

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Efficient surface crack segmentation for industrial and civil applications based on an enhanced YOLOv8 model by Zeinab F. Elsharkawy, H. Kasban, Mohammed Y. Abbass

“…To evaluate the proposed approach and test its generalization ability, nine public datasets comprising images of civil and industrial structures were collected, including CracK500, Crack3238, Crack Forest Dataset, Deepcrack, Rissbilder, Volker, Sylvie, Magnetic Tile, and Pipeline Gamma Radiography Images. …”
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Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization by Xingzhou Liu, Zhi Tian, Chang Chen

“…In addition, five commonly used methods, namely, ΔlogR method, random forest, support vector machine, K-nearest neighbors, and multiple linear regression, were used to predict the TOC content to confirm that the XGBoost model has higher prediction accuracy and better robustness. …”
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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Ethnic group and language biases were weighted using a fair pre-processing algorithm.Results Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). …”
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Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning by Mithila Akter Mim, M. R. Khatun, Muhammad Minoar Hossain, Wahidur Rahman, Arslan Munir

“…These include logistic regression (LRC), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), and bagging classification models. …”
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Novel transfer learning approach for hand drawn mathematical geometric shapes classification by Aneeza Alam, Ali Raza, Nisrean Thalji, Laith Abualigah, Helena Garay, Josep Alemany Iturriaga, Imran Ashraf

“…We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. …”
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Human-Gorilla and Gorilla-Human: Dynamics of Human-animal boundaries and interethnic relationships in the central African rainforest by Takanori Oishi

“…This paper (1) describes the perceptions of the western lowland gorilla (Gorilla gorilla gorilla) by forest dwellers of southeastern Cameroon and (2) investigates the sociocultural dimension of human–gorilla relationships focusing on folk theories of human–animal hybrids in which the gorilla is deeply embedded, enabling us to deal with the symbolic and social aspects of hunter-gatherer–farmer relations. …”
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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

“…Among the ML approaches, MLP model achieved the best performance with a low mean squared error (MSE) of 0.0032 on the test set and 0.0021 on the training set and mean relative error (MRE) of 15.26% on the test set. Also, Random Forest model yielded a higher test MSE of 0.0045 and MRE of 17.83%. …”
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