Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning by Ning Liu, Yu Shen, Shuangcheng Zhang, Xuejian Zhu

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Comparative analysis of the Russian regions using machine learning methods for a set of indicators of electronic services by L. A. Borisova

Subjects: “…machine learning methods…”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

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Machine learning models for predicting the bearing capacity of shallow foundations: A Comparative study and sensitivity analysis by Hamid Mohammadnezhad, Seyedmohammad Eslami

Subjects: “…machine learning…”
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Development of a respiratory virus risk model with environmental data based on interpretable machine learning methods by Shuting Shi, Haowen Lin, Leiming Jiang, Zhiqi Zeng, ChuiXu Lin, Pei Li, Yinghua Li, Zifeng Yang

“…This study aimed to develop a nationwide respiratory virus infection risk prediction model through machine learning approach. We utilized the CRFC algorithm, a random forest-based method for multi-label classification, to predict the presence of various respiratory viruses. …”
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A Real-Time Intelligent System Based on Machine-Learning Methods for Improving Communication in Sign Language by Victor Leiva, Muhammad Zia Ur Rahman, Muhammad Azeem Akbar, Cecilia Castro, Mauricio Huerta, Muhammad Tanveer Riaz

“…Machine learning classifiers, including decision trees, k-nearest neighbors, and support vector machines, achieved accuracy levels of 96%, 96.5%, and 97%, respectively. …”
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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. …”
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Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability by Ahmet Akusta

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Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis by Gaddam Advitha, Allada Nagasai Varaprasad, Koti Vennela Khushi, Pullabhotla Vijay, Sukanta Nayak

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Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

Subjects: “…Machine learning…”
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Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model by Mohammed Majeed Hameed, Mustafa Abbas Abed, Nadhir Al-Ansari, Mohamed Khalid Alomar

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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. …”
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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods by Pragya Kashyap, Kalbhavi Vadhi Raj, Jyoti Sharma, Naveen Dutt, Pankaj Yadav

“…To address this, we developed a machine learning-based approach utilizing resected lung-tissue microbiome of AC and SCC patients for subtype classification. …”
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Comparison of Machine Learning Algorithms for the Prediction of Mechanical Stress in Three-Phase Power Transformer Winding Conductors by Fausto Valencia, Hugo Arcos, Franklin Quilumba

“…To simplify the design, only one hyperparameter was varied on each machine learning technique. The random forests technique had the most accurate results. …”
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Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios by Abbas Yeganeh-Bakhtiary, Hossein EyvazOghli, Naser Shabakhty, Bahareh Kamranzad, Soroush Abolfathi

“…In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. …”
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Using Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed by Eliza Becker, Siavash Khaksar, Harry Booker, Kylie Hill, Yifei Ren, Tele Tan, Carol Watson, Ethan Wordsworth, Meg Harrold

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An Efficient Malware Detection Approach Based on Machine Learning Feature Influence Techniques for Resource-Constrained Devices by Subir Panja, Subhash Mondal, Amitava Nag, Jyoti Prakash Singh, Manob Jyoti Saikia, Anup Kumar Barman

“…This study develops fourteen machine learning models using a five-fold cross-validation technique on the dataset it obtained for research. …”
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Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning by Xiyu Cao, Jianli Ma, Xiaoyi He, Yufei Liu, Yang Yang, Yaqi Wang, Chuantao Zhang

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Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees by Philip H. Williams, Rod Eyles, Georg Weiller

“…Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. …”
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