Automation of quantum dot measurement analysis via explainable machine learning by Daniel Schug, Tyler J Kovach, M A Wolfe, Jared Benson, Sanghyeok Park, J P Dodson, J Corrigan, M A Eriksson, Justyna P Zwolak

Subjects: “…explainable machine learning…”
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Civil structural health monitoring and machine learning: a comprehensive review by Asraar Anjum, Meftah Hrairi, Abdul Aabid, Norfazrina Yatim, Maisarah Ali

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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types. …”
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RETRACTED: User identification and authentication in browser environments via machine learning by Uymin Anton

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Machine learning-assisted development of gas separation membranes: A review by An Li, Jianchun Chu, Shaoxuan Huang, Yongqi Liu, Maogang He, Xiangyang Liu

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Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing by Mohammad Alsaffar, Abdullah Alshammari, Gharbi Alshammari, Saud Aljaloud, Tariq S. Almurayziq, Fadam Muteb Abdoon, Solomon Abebaw

“…In light of these findings, a hybrid diagnostic tool has been developed that combines several computational intelligence (machine learning) techniques capable of analyzing clinical histories and images of electrocardiogram signals and indicating whether or not the patient has ischemic heart disease with up to 97.01% accuracy. …”
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Multiple testing for signal-agnostic searches for new physics with machine learning by Gaia Grosso, Marco Letizia

“…Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. …”
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Multiple Machine Learning Algorithms-based NBA Team Playoffs Prediction by Yeung Manho

“…With the rapid development of data analytics in sports, it is vital to use machine learning methods to make decisions and predictions. …”
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Research on network traffic classification based on machine learning and deep learning by Yue GU, Dan LI, Kaihui GAO

“…With the continuous development of Internet technology and the continuous expansion of network scale, there are many different types of applications , and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security, accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly, the problem definition and performance metrics of network traffic classification were given.Then, the traffic classification methods based on machine learning and deep learning were introduced respectively, the advantages and disadvantages of these methods were analyzed, and the existing problems were expounded.Next, the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment: dataset, zero-day application identification and the cost of online deployment, and further discusses the challenges faced by the current network traffic classification researches.Finally, the next research direction of network traffic classification was prospected.…”
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Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity by Hongguo Zhao, Peng Liu, Fei Chen, Mengjuan Wang, Jiaxi Liu, Xiling Fu, Hang Yu, Manman Nai, Lei Li, Xinbin Li

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Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology by Ameera S. Jaradat, Mohammad Wedyan, Saja Alomari, Malek Mahmoud Barhoush

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Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches by Md. Atiqur Rahman, Israt Jahan, Maheen Islam, Taskeed Jabid, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Md. Hasanul Ferdaus, Md Mostofa Kamal Rasel, Mahmuda Rawnak Jahan, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Md. Mafiul Hasan Matin, M. Ameer Ali

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Using machine learning to predict outcomes following transcarotid artery revascularization by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al-Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche-Nagle, Mohammed Al-Omran

Subjects: “…Machine learning…”
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Sex identification in rainbow trout using genomic information and machine learning by Andrei A. Kudinov, Antti Kause

“…In our study, we demonstrated the use of the Extreme Gradient Boosting approach from the supervised machine learning gradient boost framework to predict sex from unimputed genomic data, when the suitability of the markers was unknown a priori. …”
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Interpretable machine learning for predicting sepsis risk in emergency triage patients by Zheng Liu, Wenqi Shu, Teng Li, Xuan Zhang, Wei Chong

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Machine learning empowered coherent Raman imaging and analysis for biomedical applications by Yihui Zhou, Xiaobin Tang, Delong Zhang, Hyeon Jeong Lee

“…Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. …”
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Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning by El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan, Said Agoujil

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Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis by Huiyi Zuo, Baoyu Huang, Jian He, Liying Fang, Minli Huang

“… BackgroundIn recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

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Expression‐based machine learning models for predicting plant tissue identity by Sourabh Palande, Jeremy Arsenault, Patricia Basurto‐Lozada, Andrew Bleich, Brianna N. I. Brown, Sophia F. Buysse, Noelle A. Connors, Sikta Das Adhikari, Kara C. Dobson, Francisco Xavier Guerra‐Castillo, Maria F. Guerrero‐Carrillo, Sophia Harlow, Héctor Herrera‐Orozco, Asia T. Hightower, Paulo Izquierdo, MacKenzie Jacobs, Nicholas A. Johnson, Wendy Leuenberger, Alessandro Lopez‐Hernandez, Alicia Luckie‐Duque, Camila Martínez‐Avila, Eddy J. Mendoza‐Galindo, David Cruz Plancarte, Jenny M. Schuster, Harry Shomer, Sidney C. Sitar, Anne K. Steensma, Joanne Elise Thomson, Damián Villaseñor‐Amador, Robin Waterman, Brandon M. Webster, Madison Whyte, Sofía Zorilla‐Azcué, Beronda L. Montgomery, Aman Y. Husbands, Arjun Krishnan, Sarah Percival, Elizabeth Munch, Robert VanBuren, Daniel H. Chitwood, Alejandra Rougon‐Cardoso

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