The good, the better and the challenging: Insights into predicting high-growth firms using machine learning by Sermet Pekin, Aykut Şengül

“…Our findings indicate that XGBoost achieves the highest performance, followed by Random Forest and Logistic Regression, demonstrating their effectiveness in distinguishing between high-growth and non-high-growth firms. …”
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A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation by Jing Liu, ChongZhao Han, XiangHua Yao, Feng Lian

“…A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. …”
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Machine Learning Methods for Forecasting Intermittent Tin Ore Production by Nabila Dhia Alifa Rahmah, Budhi Handoko, Anindya Apriliyanti Pravitasari

“…This study's findings show that a serial combination of the Random Forest classification method and CatBoost forecasting can produce accurate tin ore production forecasts for the selected CSD (RMSE = 0.271, MAE = 0.179, MAE = 0.730, F1-score = 0,80). …”
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Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. by Lathan Liou, Elizabeth Mostofsky, Laura Lehman, Soziema Salia, Francisco J Barrera, Ying Wei, Amal Cheema, Anuradha Lala, Andrew Beam, Murray A Mittleman

“…In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. …”
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Correspondence: Can Menstrual Cycle Length Predict Cardiovascular Risk in Healthy Indian Females? by Avnesh Kumar Singh, Shikha Singh

“…There are two main varieties: probability sampling and non probability sampling. Convenient non random sampling was incorrectly referred to as simple random sampling in the article. …”
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Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents by Minqing Zhu, Peng Shi, Hongjun Cui, Xinye Li, Xinwei Ma

“…The bounded rationality characteristics of travelers, such as the time perception difference coefficient, psychological threshold effect coefficient, and scale effect, are introduced into the generalized random regret minimization (GRRM) model. An improved generalized random regret minimization (IGRRM) model is constructed based on travelers’ route choice behavior under unexpected accidents. …”
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“Pharmacological” analysis of atrial fibrillation maintenance mechanism: reentry, wavelets, or focal? by Alexander Burashnikov

“…Improvement in AF-org caused by anti-AF agents before AF cardioversion is coherent with simultaneous multiple random reentries and wavelets. However, simultaneous multiple reentries are unlikely to occur regularly (most of the contemporary AF mapping studies report either a single reentry at a time or no reentry at all), and the ability of random wavelets to maintain AF is speculative. …”
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Sufficient statistics for the Pareto distribution parameter by I. S. Pulkin, A. V. Tatarintsev

“…For the product of the sample elements considered as a random variable the distribution function and probability density are found; mathematical expectation, higher moments, and differential entropy are calculated. …”
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Optimal investment game for two regulated players with regime switching by Lin Xu, Linlin Wang, Hao Wang, Liming Zhang

“…We provided a numerical method using a Markov chain approximation scheme and presented several numerical examples to illustrate the impact of regime switching and random time solvency on optimal policies.…”
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The influence of social media applications on learning English as a second language by Fatma Dashti, Hanady M. Abdulsalam

“…Four teachers were asked to provide random grades of their students to check how they are affected. …”
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A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques by Ali Soleymani, Fatemeh Arabgol

“…The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.…”
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The Effect of Resampling Techniques on Model Performance Classification of Maternal Health Risks by Nia Mauliza, Aisha Shakila Iedwan, Yoga Pristyanto, Anggit Dwi Hartanto, Arif Nur Rohman

“…The results showed that the SMOTE-ENN and ADASYN-ENN methods significantly improved the model's performance in predicting maternal disease. Random Forest and Decision Tree algorithms showed the best results in terms of accuracy and consistency. …”
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The Profit Rate-Interest Rate Nexus: Evidence from Machine Learning Algorithms by Mehmet Yeşilyaprak, Ali Polat, Önder Özgür, Süleyman Şahal

“…Second, the study uses random forest, bagging, and boosting algorithms as methodological tools and benefits from the apparent advantages these algorithms have empirically.…”
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Failure Mechanism and Factor of Safety for Spatially Variable Undrained Soil Slope by Liang Li, Xuesong Chu

“…The undrained shear strength of cohesive soil slope is modeled by a one-dimensional random field in the vertical direction. The FS and FM for a specific realization of random field are determined by SRT embedded in FEM- and FDM-based software (e.g., Phase2 6.0 and FLAC) and LEM, respectively. …”
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Mesoscopic Numerical Computation of Compressive Strength and Damage Mechanism of Rubber Concrete by Z. H. Xie, Y. C. Guo, Q. Z. Yuan, P. Y. Huang

“…This paper presents the results of a study, based on the Walraven equation of the production of a mesoscopic random aggregate structure containing various rubber contents and aggregate sizes. …”
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PROBABILITY DISTRIBUTION OF SINGLE-LAYER CYLINDRICAL VESSEL BURST PRESSURE by LIU Cen, YANG Fan, LIU Bing, YUAN XiaoHui, ZHANG HongWei, LIU XiaoNing

“…Research results indicated that:（ 1） Based on the engineering practice of Chinese standard,the application range of the formula can be divided into the 3 groups according to the design pressure which are no exceeding 35 MPa,between35 MPa and 100 MPa,and no more than 100 MPa,respectively;（ 2） According to the relationship between design pressure and burst pressure,the actual burst pressure of the formula application range sample was no more than 105. 5 MPa,between 91. 0 MPa and 329. 6 MPa,and no more than 329. 6 MPa,respectively;（ 3） When the significance was 0. 05,the ratio of the samples measured burst pressure and mid-diameter formula and Tresca formula calculated values is 6 random variables with the normal distribution.（ 4） When the 2-sides confidence was 98%,the distribution parameters value range of 6 random variables were obtained respectively.…”
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Fatigue Strength Analysis of Cracked Wind Turbine Planetary Gear Train Under Variable Load Excitation by Ben Chen, Shijing Wu, Pan Zheng, Jun Wang, Qiang Chen, Sujie Gao, Jianhua Zhou

“…The external excitation caused by random wind load can greatly affect the fatigue life of the wind power transmission system. …”
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A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System by Xiaohu Li, Feng Xu, Jinhua Zhang, Sunan Wang

“…In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. …”
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Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients by Melis Merve Doğançay, özlem Ege Oruç, Melike Şırlancı Tüysüzoğlu, Zeynep Altın

“…The random forest model with 9 variables was the best performing model. …”
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Applied Comparison of Meta-analysis Techniques by Li Wang, Colin Lewis-Beck, Elyse Fritschel, Erdem Baser, Onur Baser

“…Basic meta-analysis models are fixed-effects, random-effects, and meta-regression.…”
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