Artificial intelligence/machine learning and journalology: Challenges and opportunities by Prakesh S. Shah, Ganesh Acharya

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Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides by Sukhvir Kaur Bhangu, Nicholas Welch, Morgan Lewis, Fanyi Li, Brint Gardner, Helmut Thissen, Wioleta Kowalczyk

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Emerging Applications of Machine Learning in 3D Printing by Izabela Rojek, Dariusz Mikołajewski, Marcin Kempiński, Krzysztof Galas, Adrianna Piszcz

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A Survey on Privacy-Preserving Machine Learning Inference by Stanisław Barański

Subjects: “…Privacy-Preserving Machine Learning…”
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Improving COPD Readmission Prediction with Optimized Machine Learning by Rajasekaran S, Shaik Mastan Val

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Artificial Intelligence and Machine Learning in Sensing and Image Processing by Jing Chen, Miaohui Wang, Chih-Hsien Hsia

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Synthesis and characterization of machine learning designed TADF molecules by Weimei Shi, Yan Li, Ziying Zhang, Zheng Tan, Shiqing Yang

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Machine Learning for Blockchain and IoT Systems in Smart City by Cheng-Chi Lee, Dinh-Thuan Do, Agbotiname Lucky Imoize

“…Recent advancements in machine learning algorithms have facilitated the rapid use of blockchain technology and the Internet of Things in the design and development of smart cities [...]…”
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Data Poison Detection Schemes for Distributed Machine Learning by Yijin Chen, Yuming Mao, Haoyang Liang, Shui Yu, Yunkai Wei, Supeng Leng

Subjects: “…Distributed machine learning…”
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Discovering nuclear models from symbolic machine learning by Jose M. Munoz, Silviu M. Udrescu, Ronald F. Garcia Ruiz

“…Here, we explore whether symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. …”
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Application of Machine Learning Models for Baseball Outcome Prediction by Tzu-Chien Lo, Chen-Yin Lee, Chien-Lin Chen, Tsung-Yu Hsieh, Che-Hsiu Chen, Yen-Kuang Lin

“…This study aims to develop and evaluate machine learning models that accurately predict game outcomes in the Chinese Professional Baseball League (CPBL). …”
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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction by Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

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Analyzing the speed of sound in neutron star with machine learning by Sagnik Chatterjee, Harsha Sudhakaran, Ritam Mallick

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Machine learning in additive manufacturing: A comprehensive insight by Md Asif Equbal, Azhar Equbal, Zahid A. Khan, Irfan Anjum Badruddin

Subjects: “…Machine learning…”
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Faithful novel machine learning for predicting quantum properties by Gavin Nop, Micah Mundy, Jonathan D. H. Smith, Durga Paudyal

“…Abstract Machine learning (ML) has accelerated the process of materials classification, particularly with crystal graph neural network (CGNN) architectures. …”
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Role of Machine Learning in Liquid Biopsy of Brain Tumours by Zanib Javed, Saqib Kamran Bakhshi, Saad Akhtar Khan, Muhammad Shahzad Shamim

“…Here, we present a literature review on the role of machine learning in liquid biopsy of brain tumours. …”
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Overview of Machine Learning Applications at the Pierre Auger Observatory by Rodriguez Ezequiel

“…The complex spatio-temporal information from shower footprints, comprised of particle arrival times and traces measured by water-Cherenkov detectors, is challenging to analyse with traditional methods but well-suited for machine learning (ML) based analyses. In this contribution, we provide an overview of the ML applications developed to leverage the high event statistics acquired by the Pierre Auger Observatory. …”
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Double machine learning and design in batch adaptive experiments by Li Harrison H., Owen Art B.

“…By extending the framework of double machine learning, we show this rate suffices for our pooled estimator to attain the targeted precision after each batch, as long as nuisance function estimates converge at rate op(N−1⁄4){o}_{p}\left({N}^{-1/4}). …”
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Uncertainty quantification for misspecified machine learned interatomic potentials by Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov, Thomas D. Swinburne

“…Abstract The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. …”
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MACHINE LEARNING AS A CORPORATION'S TOOL FOR SELECTION OF SUPPLIERS by V. V. Baklushinskii, E. V. Pustynnikova

Subjects: “…machine learning…”
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