Machine learning-based urban noise appropriateness evaluation method and driving factor analysis. by Jinlin Teng, Cheng Zhang, Huimin Gong, Chunqing Liu

“…The findings are as follows: 1.Machine learning algorithms can be effectively used for urban noise evaluation. …”
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Password strength verification based on machine learning algorithms and LSTM recurrent neural networks by V. V. Belikov, I. A. Prokuronov

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A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

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Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil by Lei Yan, Yuting Zhu, Haiyan Wang

“…Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…Our objective was to evaluate the sensitivity, specificity, and accuracy of a machine learning (ML) algorithm using publicly available data to predict medical device recalls by the FDA. …”
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Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study by Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi

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Develop Approach to Predicate Software Reliability Growth Model Parameters Based on Machine learning by anfal A. Fadhil, Asmaa’ H. AL_Bayati, Ibrahim Ahmed Saleh

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Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data by Dilan C. Hangawatta, Ameen Gargoom, Abbas Z. Kouzani

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Performance Evaluation of Machine Learning Models for Predicting Energy Consumption and Occupant Dissatisfaction in Buildings by Haidar Hosamo, Silvia Mazzetto

Subjects: “…machine learning models…”
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Machine learning suggests climate and seasonal definitions should change under global warming by Milton Speer, Lance Leslie

“…The greater frequency and variability of floods, heatwaves, and droughts challenge traditional definitions of climate periods as 30-year means. Machine learning (ML) studies, focusing on southern Australia, identified the dominant attributes of these precipitation and temperature events. …”
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Well-Production Forecasting Using Machine Learning with Feature Selection and Automatic Hyperparameter Optimization by Ruibin Zhu, Ning Li, Yongqiang Duan, Gaofeng Li, Guohua Liu, Fengjiao Qu, Changjun Long, Xin Wang, Qinzhuo Liao, Gensheng Li

Subjects: “…machine learning…”
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Machine learning enabled measurements of astrophysical (p,n) reactions with the SECAR recoil separator by P. Tsintari, N. Dimitrakopoulos, R. Garg, K. Hermansen, C. Marshall, F. Montes, G. Perdikakis, H. Schatz, K. Setoodehnia, H. Arora, G. P. A. Berg, R. Bhandari, J. C. Blackmon, C. R. Brune, K. A. Chipps, M. Couder, C. Deibel, A. Hood, M. Horana Gamage, R. Jain, C. Maher, S. Miskovich, J. Pereira, T. Ruland, M. S. Smith, M. Smith, I. Sultana, C. Tinson, A. Tsantiri, A. Villari, L. Wagner, R. G. T. Zegers

“…We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. …”
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BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020. Machine learning (ML) helps identify cancer early, reducing deaths. …”
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LINGUISTIC ANALYSIS FOR THE BELARUSIAN CORPUS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES by Yu. S. Hetsevich, I. V. Reentovich

“…The natural language processing of Belarusian corpus with the help of developed algorithm using machine learning was carried out. The precision of developed models of machine learning has been 80–90 %. …”
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Machine learning-driven identification of critical gene programs and key transcription factors in migraine by Lei Zhang, Yujie Li, Yunhao Xu, Wei Wang, Guangyu Guo

“…This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis. …”
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Review on the Application of Remote Sensing Data and Machine Learning to the Estimation of Anthropogenic Heat Emissions by Lingyun Feng, Danyang Ma, Min Xie, Mengzhu Xi

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PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection by Mehdi Khalil, Chi Zhang, Zhiwei Ye, Peng Zhang

“…The rapid spread of fake news on social media poses a significant threat to modern societies. Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. …”
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Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages by Sijin Lee, Hyun Ji Park, Jumi Hwang, Sung Woo Lee, Kap Su Han, Won Young Kim, Jinwoo Jeong, Hyunggoo Kang, Armi Kim, Chulung Lee, Su Jin Kim

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Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms by Sahar K. Hussin, Salah M. Abdelmageid, Adel Alkhalil, Yasser M. Omar, Mahmoud I. Marie, Rabie A. Ramadan

“…Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. …”
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Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper. by Faye Orcales, Lucy Moctezuma Tan, Meris Johnson-Hagler, John Matthew Suntay, Jameel Ali, Kristiene Recto, Phelan Glenn, Pleuni Pennings

“…It has been suggested that genomic sequencing and machine learning (ML) could make resistance testing more accurate and cost-effective. …”
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