Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images by Thanh Vân Phan, Lama Seoud, Hadi Chakor, Farida Cheriet

“…A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features’ relevance. …”
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Machine Learning-Based Anomaly Prediction for Proactive Monitoring in Data Centers: A Case Study on INFN-CNAF by Andrea Asperti, Gabriele Raciti, Elisabetta Ronchieri, Daniele Cesini

“…We evaluate several methods, including Long Short-Term Memory, Random Forest, and various neural networks, assessing their Accuracy and sensitivity in distinguishing normal from anomalous behaviors. …”
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Credit risk prediction with corruption perception index: machine learning approaches by Cuong Nguyen Thanh, Tam Phan Huy, Tuyet Pham Hong, An Bui Nguyen Quoc

“…Analyzing data from 70 banks over a decade, it employs Decision Tree, Random Forest, Gradient Boosted Trees, and XGBoost models, evaluated using R², RMSE, and MAE. …”
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Deciphering the Immune Subtypes and Signature Genes: A Novel Approach Towards Diagnosing and Prognosticating Severe Asthma Through Interpretable Machine Learning by Yue Hu, Yating Lin, Bo Peng, Chunyan Xiang, Wei Tang

“…We employ single-sample gene set enrichment analysis (ssGSEA) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms to identify differentially expressed immune cells and utilize machine learning techniques, including Extreme Gradient Boosting (XGBoost) and random forest, to predict severe asthma outcomes and identify key genes associated with immune cells. …”
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Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization by Mohammed Alqarni, Ali Alqarni

“…Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance. …”
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POI Data Fusion Method Based on Multi-Feature Matching and Optimization by Yue Wang, Cailin Li, Hongjun Zhang, Baoyun Guo, Xianlong Wei, Zhao Hai

“…Secondly, the random forest algorithm is utilized to dynamically determine the information weights of each attribute and calculate the comprehensive similarity. …”
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Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study by Zainab Abdulelah Al Sudani, Golam Saleh Ahmed Salem

“…In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. …”
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Machines’ Intelligent Fault Diagnosis Based on Hierarchical Refined Composite Generalized Multiscale Fluctuation Dispersion Entropy by Biwen Chen, Changsheng Chen, Zhenlai Ma, Guoping Li, Yi Zhang, Baoyue Li

“…Subsequently, a Random Forest (RF) classifier is employed for fault identification. …”
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Low-Cost Solution for Assessment of Urban Flash Flood Impacts Using Sentinel-2 Satellite Images and Fuzzy Analytic Hierarchy Process: A Case Study of Ras Ghareb City, Egypt by Mohammed Sadek, Xuxiang Li

“…Natural hazards are indeed counted as the most critical challenges facing our world, represented in floods, earthquakes, volcanoes, hurricanes, and forest fires. Among these natural hazards, the flash flood is regarded the most frequent. …”
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Analysis of Determinants of Economic Efficiency in Honey Production in Horo Guduru Zone, Ethiopia: Stochastic Dual Cost Frontier Model Approach by Tolesa Tesema, Megersa Adugna

“…The study suggests policies to address economic inefficiencies by increasing the number of hives, extending the best performers’ experience by increasing the frequency of extension contacts on honey production, facilitating and expanding credit service in the study area, making bee forage access simple, and increasing forest coverage on the land area in line with the current policy of Ethiopia. …”
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Creation and interpretation of machine learning models for aqueous solubility prediction by Minyi Su, Enric Herrero

“…Results: Among the different ML methods, random forest (RF) models obtain the best performance in the different test sets. …”
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Similarity Index among Acacia mangium, Acacia auriculiformis, and its Hybrid Based on the Anatomical Properties of Root, Stem, and Leaf by Sri Sunarti, Visda Fitriana, Suharyanto Suharyanto

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Computational linguistics and natural language processing techniques for semantic field extraction in Arabic online news by Maulana Ihsan Ahmad, Moh. Kanif Anwari

“…The research evaluated five classification models: Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest, and Gradient Boosting. Among these, SVM achieves the highest overall accuracy of 90%. …”
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Aquatic insects (Ephemeroptera, Plecoptera, Trichoptera and Diptera: Tipuloidea) from the upper Neretva in Bosnia-Herzegovina by Wolfram Graf, Ernst Bauernfeind, Marija Ivković, Levente-Péter Kolcsár

“…The extremely high diversity, as well as the enormous abundance of aquatic insects, underline the importance of the upper Neretva as an unimpacted riverine system embedded in dense natural and near-natural forest. …”
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Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis by Onur Baser, Gabriela Samayoa, Nehir Yapar, Erdem Baser

“…In addition to the baseline model, a gradient-boosted classification tree, naïve Bayes, and random forest model were created and compared using receiver operator characteristics, area under the curve, and accuracy. …”
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A Chromosome-level genome assembly of the alpine medicinal plant Bergenia purpurascens (Saxifragaceae) by Xue-Die Liu, Alex D. Twyford, Peter M. Hollingsworth, Ya-Huang Luo, Sheng-Ping Ming, Bo-Yun Yang, Lian-Ming Gao, Shao-Lin Tan

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Identification of Working Trucks and Critical Path Nodes for Construction Waste Transportation Based on Electric Waybills: A Case Study of Shenzhen, China by Jun Bi, Qiuyue Sai, Fujun Wang, Yakun Chen

“…The results show that the XGBoost model can improve the accuracy of the generation of waybill to 90.5% compared with the decision tree model, random forest, and GBDT. Moreover, the density clustering model can discover the hot nodes of construction waste transportation. …”
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Utilizing Machine Learning-based Classification Models for Tracking Air Pollution Sources: A Case Study in Korea by Yelim Choi, Bogyeong Kang, Daekeun Kim

“…Using 972 datasets consisting of five emission sources and 27 air pollutants, different classification models were implemented and subsequently compared: Random Forest (RF), Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (K-NN). …”
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Eucalyptus and Water Use in South Africa by Janine M. Albaugh, Peter J. Dye, John S. King

“…Regardless, the demand for wood products and water continues to rise, providing a challenge to increase the productivity of forest plantations within water constraints. This is of particular relevance for water-limited countries such as South Africa which relies on exotic plantations to meet its timber needs. …”
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Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis by Sara Mumtaz, Shabbab Algamdi, Haifa F. Alhasson, Dina Abdulaziz Alhammadi, Ahmad Jalal, Hui Liu

“…A Gaussian distribution-based classifier is subsequently utilized, achieving an accuracy of 92%, while a Random forest classifier applied to the Grapevine Leave Dataset resulted in an accuracy of 84.63%. …”
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