Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach by Mehmet Ali Cengiz, Dilek Sabancı

“…Ensemble learning methods are gathered from samples comprising hundreds or thousands of learners that serve the common purpose of improving the stability and accuracy of machine learning algorithms. …”
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Short-term Load Forecasting Based on CNN-LSTM with Quadratic Decomposition Combined by DENG Bowen, XIAO Shenping, LIAO Shiying

“…Short-term power load has strong randomness and volatility, in order to improve the accuracy of load forecasting, this paper proposes a combined forecasting model based on quadratic decomposition, convolutional neural (CNN) network and long short-term memory (LSTM) neural network. …”
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Automated Local Climate Zone Mapping via Multi-Parameter Synergistic Optimization and High-Resolution GIS-RS Fusion by Wenbo Li, Ximing Liu, Alim Samat, Paolo Gamba

“…Traditional methods relying on manual sampling face limitations in scalability, objectivity, and handling spatial heterogeneity. …”
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New Approach to Clustering Random Attributes by Zenon Gniazdowski

“…The proposed method was tested for several sample datasets. It was found that the proposed method is universal. …”
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Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event by Hongwu GUO, Jianfeng CHE, Yixun YAN, Lijie WANG

“…Photovoltaic power output is influenced by season, weather conditions, and other factors with randomness and uncertainty. It is difficult to predict the power output under bad weather with strong volatility. …”
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First density estimates of the Endangered Claire's mouse lemur Microcebus mamiratra and recommendations for its conservation by Luke D. Martin, Herison Razafimanantsoa, Eva S. Nomenjanahary, Sylviane Volampeno, Alison M. Behie

“…We conducted line transect distance sampling surveys of M. mamiratra across several habitat types in and around Lokobe National Park on the island of Nosy Be in north-western Madagascar. …”
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New Randomized Response Procedures by Z. Z. Hussain

“…The cases of positive binomial and negative binomial sampling are also studied. The proposed techniques are exposed to be better at the job than the accustomed randomized response dealings in binomial sampling. …”
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Application of random forest in big data completion by Zheng WANG, Hua REN, Yanping FANG

“…For existing data mining, it is necessary to carry out the data to meet the quality of the data and to achieve sufficient sampling proportion. Relying on the country's existing log retention system, template library design data integrity, authentication could not meet the quality requirements of the data, using the random forest algorithm, the same data with or related data was found, data was completed and data quality was improved, and the template library was extended by optimization of feedback. …”
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A Random Riemann–Liouville Integral Operator by Jorge Sanchez-Ortiz, Omar U. Lopez-Cresencio, Martin P. Arciga-Alejandre, Francisco J. Ariza-Hernandez

“…To illustrate the behavior of this operator, we present two examples involving different random variables acting on specific functions. The sample trajectories and estimated probability density functions of the resulting random integrals are then explored via Monte Carlo simulation.…”
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Evidence synthesis in neuromyelitis optica spectrum disorders: modeling a clinical trial based on published data by D. G. Tolkacheva, I. V. Fateev, K. V. Sapozhnikov, O. N. Mironenko, A. A. Lazarev, V. D. Batorova, A. A. Porozova, A. V. Zinkina-Orikhan

“…Next, a stepwise effective sample size (ESS) simulation was performed in the divosilimab group with power assessment.   …”
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Correlation defense for quantum randomness by N.S. Perminov, O.I. Bannik, D.U. Tarankova, R.R. Nigmatullin

“…Precise SRA identification of the long samples statistics was carried out. The obtained results extend the traditional entropy methods of the useful randomness analysis and open the way for creation of new strict quality quantum standards and defense for physical random number generators.…”
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Field Induced Memory Effects in Random Nematics by Amid Ranjkesh, Milan Ambrožič, Samo Kralj, T. J. Sluckin

“…Furthermore, crossover regime separating external field and random field dominated regime was estimated. We calculated remanent nematic ordering in samples at B=0 as a function of the previously experienced external field strength B.…”
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Link quality prediction based on random forest by Linlan LIU, Shengrong GAO, Jian SHU

“…Link quality prediction is vital to the upper layer protocol design of wireless sensor networks.Selecting high quality links with the help of link quality prediction mechanisms can improve data transmission reliability and network communication efficiency.The Gaussian mixture model algorithm based on unsupervised clustering was employed to divide the link quality level.Zero-phase component analysis (ZCA) whitening was applied to remove the correlation between samples.The mean and variance of signal to noise ratio,link quality indicator,and received signal strength indicator were taken as the estimation parameters of link quality,and a link quality estimation model was constructed by using a random forest classification algorithm.The random forest regression algorithm was used to build a link quality prediction model,which predicted the link quality level at the next moment.In different scenarios,comparing with exponentially weighted moving average,triangle metric,support vector regression and linear regression prediction models,the proposed prediction model has higher prediction accuracy.…”
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The Role of Random Allocation in Randomized Controlled Trials: Distinguishing Selection Bias from Baseline Imbalance by Allyn Fives, Daniel W. Russell, Noreen Kearns, Rena Lyons, Patricia Eaton, John Canavan, Carmel Devaney, Aoife O'Brien

“… Background: This paper addresses one threat to the internal validity of a randomized controlled trial (RCT), selection bias. Many authors argue that random allocation is used to ensure baseline equality between study conditions in a given study and that statistically significant differences at pretest mean that randomisation has failed.  …”
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Random Volumetric MRI Trajectories via Genetic Algorithms by Andrew Thomas Curtis, Christopher Kumar Anand

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An investigation into multi-objective decision-making in fresh cold chain supply chain networks within a dual distribution framework by Junyan Sun, Xing Li, Zefei Chen

“…In terms of algorithm design, a hybrid algorithm (LHS-SA-NSGA-II) is proposed, integrating Latin Hypercube Sampling (LHS), Simulated Annealing (SA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). …”
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Bosonic Randomized Benchmarking with Passive Transformations by Mirko Arienzo, Dmitry Grinko, Martin Kliesch, Markus Heinrich

“…We also analyze the sampling complexity of passive bosonic RB by deriving analytical expressions for the variance. …”
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Computational Power of Random Quantum Circuits in Arbitrary Geometries by M. DeCross, R. Haghshenas, M. Liu, E. Rinaldi, J. Gray, Y. Alexeev, C. H. Baldwin, J. P. Bartolotta, M. Bohn, E. Chertkov, J. Cline, J. Colina, D. DelVento, J. M. Dreiling, C. Foltz, J. P. Gaebler, T. M. Gatterman, C. N. Gilbreth, J. Giles, D. Gresh, A. Hall, A. Hankin, A. Hansen, N. Hewitt, I. Hoffman, C. Holliman, R. B. Hutson, T. Jacobs, J. Johansen, P. J. Lee, E. Lehman, D. Lucchetti, D. Lykov, I. S. Madjarov, B. Mathewson, K. Mayer, M. Mills, P. Niroula, J. M. Pino, C. Roman, M. Schecter, P. E. Siegfried, B. G. Tiemann, C. Volin, J. Walker, R. Shaydulin, M. Pistoia, S. A. Moses, D. Hayes, B. Neyenhuis, R. P. Stutz, M. Foss-Feig

“…Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. …”
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Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk by Annisa Damayanti, Anna Baita

“…The approach using the random undersampling technique managed to increase accuracy by 18% from the accuracy before using the random undersampling technique. …”
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Area-Time-Efficient Secure Comb Scalar Multiplication Architecture Based on Recoding by Zhantao Zhang, Weijiang Wang, Jingqi Zhang, Xiang He, Mingzhi Ma, Shiwei Ren, Hua Dang

“…The recoding-k algorithm and randomization-Z algorithm are used to improve security, which can resist sample power analysis (SPA) and differential power analysis (DPA). …”
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