Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. …”
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Fast optimization of the emission reduction pathways of major air pollutants in China: From the perspective of different decision preferences by Yihao Wang, Xuying Wang, Yixuan Zheng, Zeyuan Liu, Yu Zhang, Jiani Kang, Wenbo Xue, Jing Zhang, Yu Lei

“…Six emission reduction scenarios with varying decision preferences were analyzed. …”
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Peak Arm Current Reduction of Modular Multilevel Converter Based on Particle Swarm Optimizer for Circulating Current Injections by Riyadh Toman Thahab, Tahani Hamodi Al-Mhana

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Research on Vibration Reduction Method of Nonpneumatic Tire Spoke Based on the Mechanical Properties of Domestic cat’s Paw Pads by Haichao Zhou, Huiyun Li, Ye Mei, Guolin Wang, Congzhen Liu, Lingxin Zhang

“…The three parameters, the asymmetric arc, the thickness, and the curvature of spokes, were used as design variables to maximize the vibration reduction. The orthogonal experimental, the Kriging approximate model, and the genetic algorithm were carefully selected for optimal solutions. …”
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Security situational awareness of power information networks based on machine learning algorithms by Chao Wang, Jia-han Dong, Guang-xin Guo, Tian-yu Ren, Xiao-hu Wang, Ming-yu Pan

“…To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. …”
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Medical decision support systems for diagnosing diseases based on ensemble learning algorithms by Luma Jarallah

“…This paper proposes a stacked learning model derived from multiple ensembles learning algorithms, including Random Forest, Catboost and XGBoost. …”
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Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting by Aslı BOR, Merve OKAN

“…Our findings indicate that the estimated capability of the Bayesian Regularization algorithm were close to with Levenberg-Marquardt algorithm for training and testing, respectively. …”
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Multi-domain rule-based phenotyping algorithms enable improved GWAS signal by Abigail Newbury, Ahmed Elhussein, Gamze Gürsoy

“…Here, we assess the impact of various rule-based phenotyping algorithms on GWAS outcomes, examining factors such as power, heritability, replicability, functional annotations, and polygenic risk score prediction accuracy across seven diseases in the UK Biobank. …”
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Exploring the limits of scan time reduction for ferumoxytol-enhanced whole-heart angiography in congenital heart disease patients by Ludovica Romanin, Milan Prsa, Christopher W. Roy, Xavier Sieber, Jérôme Yerly, Bastien Milani, Tobias Rutz, Salim Si-mohamed, Estelle Tenisch, Davide Piccini, Matthias Stuber

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Evaluation of the Performance of Unsupervised Learning Algorithms for Intrusion Detection in Unbalanced Data Environments by Gutierrez-Portela Fernando, Almenares Mendoza Florina, Calderon-Benavides Liliana

“…Results showed that K-means++ achieved 95% purity with 95% and 99% prediction accuracies for normal and abnormal data, respectively, while I-forest delivered similar results and excelled in computational efficiency, consuming only 10% of CPU resources compared to 16% for other algorithms. …”
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A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks by Vanja Smailovic, Vedran Podobnik, Ignac Lovrek

“…However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. …”
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Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis by Mario Muñoz, Adrián Rubio, Guillermo Cosarinsky, Jorge F. Cruza, Jorge Camacho

“…Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second with accuracy levels of 89% for consolidation, 92% for B-lines, 66% for A-lines, and 92% for detecting normal lungs compared with an expert opinion.…”
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Multi-objective-based economic dispatch and loss reduction in the presence of electric vehicles considering different optimization techniques by David Hmingthanmawia, Subhasish Deb, Subir Datta, Ksh. Robert Singh, Umit Cali, Umit Cali, Taha Selim Ustun

“…The multi-objective grasshopper optimization algorithm and the ant-lion optimization are compared to observe the minimum cost and total loss of the system. …”
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Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning by Sira Astour, Ahmad Hasan

“…Extensive experiments on a large, balanced dataset containing 491,530 URLs, equally distributed between benign and malicious, showed that ensemble learning models significantly outperform other algorithms. The Bagging classifier, which uses decision trees as the base classifier, achieved an accuracy of 99.01%, a training time of 23.84 seconds, and a prediction time of 0.86 seconds. …”
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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…This confirms that employing Genetic Algorithms (GA) to automate the training and optimization of the model is effective and enhances the neural network's capacity to predict ETo.…”
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Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications by Mande Smita Samrat, M Srinivasulu, Anand Sruthi, K Anuradha, Tiwari Mohit, U Esakkiammal

“…Furthermore, moving past premature convergence provides more robust algorithms that can discover global optima. Moreover, the theoretical aspects of SI algorithms are still in their infancy and propose novel methods to improve predictability and reliability. …”
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Machine learning algorithms to detect patient–ventilator asynchrony: a feasibility study by Guillermo Gutierrez, Kendrew Wong, Arun Jose, Jeffrey Williams

“…The accuracy of these algorithms was evaluated based on their ability to correctly identify epochs, and their clinical reliability was assessed by comparing their predictions to those of clinicians with different levels of experience in asynchrony classification. …”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Methodology The FLA algorithm uses a Support Vector Machine (SVM) classifier to predict fault locations based on key variables like voltage, current, frequency, line impedance, and meteorological conditions. …”
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Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? by P. Sun, P. B. Holden, H. J. B. Birks, H. J. B. Birks

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An Integrated Algorithm with Feature Selection, Data Augmentation, and XGBoost for Ovarian Cancer by Jingxun Cai, Zne-Jung Lee, Zhihxian Lin, Chih-Hung Hsu, Yun Lin

“…First, we can simplify the original genetic dataset through feature selection methods, removing irrelevant variables and noise, thereby improving the model’s predictive accuracy. Following dimensionality reduction, AC-GAN enriches the data, producing more realistic genetic samples to enhance the model’s generalization capacity. …”
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