Basin dependencies of tropical cyclone genesis environment and possible future changes revealed by machine learning methods by QiFeng Qian, YeFeng Chen, XiaoJing Jia, Hao Ma, Wei Dong

“…Ocean basins are categorized into three groups based on PCA, and three MaxEnt machine learning (ML) models are developed to predict TC genesis under future scenarios. …”
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Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning by Hao Xu, Wenchao Wu, Yuntian Chen, Dongxiao Zhang, Fanyang Mo

“…This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. …”
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Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods by Meisheng Xiao, Qianhui Zheng, Paul Popa, Xinlei Mi, Jianhua Hu, Fei Zou, Baiming Zou

“…This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings. …”
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Modeling the Dynamic Variability of Sub‐Relativistic Outer Radiation Belt Electron Fluxes Using Machine Learning by Donglai Ma, Xiangning Chu, Jacob Bortnik, Seth G. Claudepierre, W. Kent Tobiska, Alfredo Cruz, S. Dave Bouwer, Joseph F. Fennell, J. Bernard Blake

Subjects: “…machine learning…”
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RETRACTED: Using machine learning algorithms to solve data classification problems using multi-attribute dataset by Borodulin Aleksey, Gladkov Alexey, Gantimurov Andrei, Kukartsev Vladislav, Evsyukov Dmitriy

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Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model by A. G. Smirnov, M. Berrendorf, Y. Y. Shprits, E. A. Kronberg, H. J. Allison, N. A. Aseev, I. S. Zhelavskaya, S. K. Morley, G. D. Reeves, M. R. Carver, F. Effenberger

Subjects: “…machine learning…”
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Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. by Ziyi Mo, Adam Siepel

“…We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.…”
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Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms by Jinmei Liu, Juan Luo, Xu Chen, Jiyi Xie, Cong Wang, Hanxiang Wang, Qi Yuan, Shijun Li, Yu Zhang, Jianli Hu, Chen Shi

“…We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain. …”
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Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction by Emmanuel de-Graft Johnson Owusu-Ansah, Richard Doamekpor, Richard Kodzo Avuglah, Yaa Kyere Adwubi

Subjects: “…credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms…”
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Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach by Sara Malek, Arshia Eskandari, Mahkame Sharbatdar

“…Computational fluid dynamics (CFD) provides valuable insights but is computationally intensive, prompting exploration of machine learning (ML) models for efficient hemodynamics prediction. …”
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Predictive Analysis of Structural Damage in Submerged Structures: A Case Study Approach Using Machine Learning by Alexandre Brás dos Santos, Hugo Mesquita Vasconcelos, Tiago M. R. M. Domingues, Pedro J. S. C. P. Sousa, Susana Dias, Rogério F. F. Lopes, Marco L. P. Parente, Mário Tomé, Adélio M. S. Cavadas, Pedro M. G. P. Moreira

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Investigating the Efficiency of Machine Learning Methods for Simulating the Effects of the Paper-Pulp Industry on Aqua Ecosystem Pollution by Mahmoud Ahmadi, Mehran Davallo, Mohsen Jahanshahi, Vahid Kiarostami, Majid Peyravi

“…To overcome these difficulties, this paper aims to use machine learning tools for analyzing the pollutant space. …”
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Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model by Godfrey A. Mills, Daniel K. Acquah, Robert A. Sowah

“…In this paper, we present a hybrid intrusion detection system that combines supervised and unsupervised learning models through an ensemble stacking model to increase the detection accuracy rates of attacks in networks while minimising false alarms. Three machine learning algorithms comprising a multilayer perceptron neural network, a modified self-organizing map, and a decision tree were used for the detection framework. …”
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Deciphering Car Crash Dynamics in Greater Melbourne: a Multi-Model Machine Learning and Geospatial Analysis by Christopher JOHNSON, ZHOU Heng, Richard TAY, SUN Qian(Chayn)

Subjects: “…|car crash dynamics|hexagonalization|multi-model machine learning|spatial planning intervention…”
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Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

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Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning by Jian Wang, Chengsong Duan, Qiao Yu, Cheng Yang

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Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning by Qiqiang Liang, Xin Xu, Shuo Ding, Jin Wu, Man Huang

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Spatiotemporal variation and influencing factors of phosphorus in Asia’s longest river based on receptor model and machine learning by Gege Cai, Jiamei Zhang, Wanlu Li, Jiejun Zhang, Yun Liu, Shanshan Xi, Guolian Li, Haibin Li, Xing Chen, Fanhao Song, Fazhi Xie

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Development and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke by Yanan Lin, Yan Li, Yayin Luo, Jie Han

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