Revolutionizing agricultural stock volatility forecasting: a comparative study of machine learning and HAR-RV models by Houjian Li, Xinya Huang, Fangyuan Luo, Deheng Zhou, Andi Cao, Lili Guo

“…This research contributes to the volatility forecasting literature by advancing the understanding of how machine learning enhances traditional models’ predictive performance.…”
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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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A machine learning-based clinical predictive tool to identify patients at high risk of medication errors by Ammar Abdo, Lyse Gallay, Thibault Vallecillo, Justine Clarenne, Pauline Quillet, Vincent Vuiblet, Rudy Merieux

Subjects: “…Machine learning…”
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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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Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma by Anahita Fathi Kazerooni, Adam Kraya, Komal S. Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R. Lueder, Saksham Phul, Mateusz Koptyra, Phillip B. Storm, Jeffrey B. Ware, Yuanquan Song, Christos Davatzikos, Jessica B. Foster, Sabine Mueller, Michael J. Fisher, Adam C. Resnick, Ali Nabavizadeh

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Artificial Intelligence-Assisted Machine Learning Methods for Forecasting Green Bond Index: A Comparative Analysis by Ahmed İhsan Şimşek, Yunus Emre Gür, Emre Bulut

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Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China by Xinzhi Wang, Qingxia Lin, Zhiyong Wu, Yuliang Zhang, Changwen Li, Ji Liu, Shinan Zhang, Songyu Li

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Analysis of diagnostic genes and molecular mechanisms of Crohn’s disease and colon cancer based on machine learning algorithms by Jie Xiao, Junyao Liang, Tao Zhou, Man Zhou, Dexu Zhang, Hui Feng, Chusen Tang, Qian Zhou, Weiqing Yang, Xiaoqin Tan, Wanjia Zhang, Yin Xu

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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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