Comprehensive datasets for RNA design, machine learning, and beyond by Jan Badura, Agnieszka Rybarczyk, Tomasz Zok

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Machine Learning Approach to Shield Optimization at Muon Collider by Luca Castelli

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Machine Learning in Hospitality: Interpretable Forecasting of Booking Cancellations by Ismael Gomez-Talal, Mana Azizsoltani, Pilar Talon-Ballestero, Ashok Singh

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Application of machine learning and genomics for orphan crop improvement by Tessa R. MacNish, Monica F. Danilevicz, Philipp E. Bayer, Mitchell S. Bestry, David Edwards

“…Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.…”
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Quantum Machine Learning: Recent Advances, Challenges, and Perspectives by Pradeep Lamichhane, Danda B. Rawat

Subjects: “…Quantum machine learning…”
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Machine Learning-Based Predictive Maintenance for Photovoltaic Systems by Ali Al-Humairi, Enmar Khalis, Zuhair A. Al-Hemyari, Peter Jung

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Design and analysis of quantum machine learning: a survey by Linshu Chen, Tao Li, Yuxiang Chen, Xiaoyan Chen, Marcin Wozniak, Neal Xiong, Wei Liang

Subjects: “…Machine learning…”
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Robust machine-learned algorithms for efficient grid operation by Nicolas Christianson, Christopher Yeh, Tongxin Li, Mehdi Hosseini, Mahdi Torabi Rad, Azarang Golmohammadi, Adam Wierman

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Applications of Machine Learning Technology in Agricultural Data Mining by Petru Alexandru Vlaicu, Basarab Matei

“…The global agricultural sector is undergoing a revolutionary transformation with the growing integration of machine learning (ML) technologies into traditional farming and agronomic practices [...]…”
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CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES by Erol Özçekiç, Ümit Yılmaz

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Robust and Scalable Quantum Repeaters Using Machine Learning by Diego Fuentealba, Jackson Dahn, James Steck, Elizabeth Behrman

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Machine learning approaches for improvement of patient safety in surgery by Philip F. Stahel, Kathryn Holland, Roy Nanz

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An interpretable integrated machine learning framework for genomic selection by Jinbu Wang, Jia Zhang, Wenjie Hao, Wencheng Zong, Mang Liang, Fuping Zhao, Longchao Zhang, Lixian Wang, Huijiang Gao, Ligang Wang

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Machine Learning Helps in Prediction of Tobacco Smoking in Adolescents by Hamidreza Roohafza, Elahe Mousavi, Razieh Omidi, Masoumeh Sadeghi, Mohammadreza Sehhati, Ahmad Vaez

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Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

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Utilizing Statistical Tests for Comparing Machine Learning Algorithms by Hozan Khalid Hamarashid

Subjects: “…machine learning, machine learning assessment, statistical tests, machine learning algorithm, machine learning comparison.…”
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Towards Explainable Machine Learning for Prediction of Disease Progression by Stijn Berendse, Johannes Krabbe, Jonas Klaus, Faizan Ahmed

“…This research focuses on addressing the challenges surrounding interpretability of machine learning techniques in the field of prediction of disease progression. …”
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Machine Learning-Based Weld Classification for Quality Monitoring by Rojan Ghimire, Rajiv Selvam

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Using machine learning algorithms to predict colorectal cancer by Xingjian Xiao, Bo Hong, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Shiyou Liu, Xianglong Xu

“…The objective of this study was to use machine learning and 0.2 million consultation data to predict colorectal cancer and identify important predictors. …”
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Machine learning algorithm for intelligent detection of WebShell by Hua DAI, Jing LI, Xin-dai LU, Xin SUN

“…WebShell is a common tool for network intrusions,which has the characteristics of great harm and good concealment.The current detection method is relatively simple,and easy to be bypassed,so it is difficult to deal with complex and flexible WebShell.To solve these problems,a supervised machine learning algorithm was put forward to detect WebShell intelligently.By learning the features of existing WebShell and non-existing WebShell pages,the algorithm can make prediction of the unknown pages,and the flexibility and adaptability were both very good.Compared with the traditional WebShell detection methods,the experiment proves that the algorithm has higher detection efficiency and accuracy,and at the same time there is a certain probability to detect new types of WebShell.…”
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