Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods by Weinan Li, Weinan Li, Weinan Li, Lisen Liu, Jianing Li, Weiguang Yang, Weiguang Yang, Yang Guo, Yang Guo, Longyu Huang, Longyu Huang, Zhaoen Yang, Jun Peng, Jun Peng, Xiuliang Jin, Xiuliang Jin, Yubin Lan, Yubin Lan

“…This study introduces a novel approach combining machine learning with feature selection to identify sensitive spectral features for accurate and efficient detection of cotton Verticillium wilt.MethodsWe conducted comprehensive hyperspectral measurements using handheld devices (350–2500 nm) to analyze cotton leaves in a controlled greenhouse environment and employed Unmanned Aerial Vehicle (UAV) hyperspectral imaging (400–995 nm) to capture canopy-level data in field conditions. …”
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An Application of Machine Learning Methods to Cutting Tool Path Clustering and RUL Estimation in Machining by Fabio C. Zegarra, Juan Vargas-Machuca, Avid Roman-Gonzalez, Alberto M. Coronado

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Health Workers’ Perspectives on Mobile Health Care Learning Stickiness: Mixed Methods Study by Sabila Nurwardani, Putu Wuri Handayani

“…Quantitative data processing was conducted using covariance-based structural equation modeling, whereas qualitative analysis was conducted on the data from 15 respondents using the content analysis method. ResultsOn the basis of the web-based questionnaire (quantitative) results, we found that cognitive (P=.01) and emotional (P=.004) app relationship quality affected health workers’ stickiness in mobile learning. …”
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PV Production Forecast Using Hybrid Models of Time Series with Machine Learning Methods by Thomas Haupt, Oscar Trull, Mathias Moog

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A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

“…This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). …”
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Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis by Hajar Moradmand, Lei Ren

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Evaluation Methods, Indicators, and Outcomes in Learning Health Systems: Protocol for a Jurisdictional Scan by Shelley Vanderhout, Marissa Bird, Antonia Giannarakos, Balpreet Panesar, Carly Whitmore

“…Search results will be screened, extracted, and analyzed to inform a descriptive review pertaining to our main objectives. Evaluation methods and approaches, both within learning cycles and at the system level, as well as frameworks, indicators, and target outcomes, will be identified and summarized descriptively. …”
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Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis by Hamed Hajishah, Danial Kazemi, Ehsan Safaee, Mohammad Javad Amini, Maral Peisepar, Mohammad Mahdi Tanhapour, Arian Tavasol

“…Method The study followed PRISMA 2020 guidelines and was registered in the PROSPERO database (CRD42023481167). …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…The experimental results showed that among the eight traditional machine learning methods, the KNN classifier achieved the highest detection accuracy of 96.97% in the left temporal lobe region. …”
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The Application of Spiritual-Based Adult Education Methods to Increase Students' Independent Learning Motivation by Rizki Ajhari Panjaitan, Eva Imania Eliasa

“…The recommendations produced aim to optimize the use of the spiritual andragogy method in higher education, with a focus on improving student learning outcomes.…”
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Hybrid Time Series Methods and Machine Learning for Seismic Analysis and Volcano Eruption Predict by Fridy Mandita, Ahmad Ashari, Moh. Edi Wibowo, Wiwit Suryanto

“…Notably, the experimental learning rate was set at 0.01. The results demonstrated that the proposed hybrid method outperformed stand-alone time series techniques, achieving an accuracy of 0.93 to 0.95. …”
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Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications by Ying Li, Yanwu Yang, Yuchu Chen, Chenfei Ye, Ting Ma

“…We discuss 3 main categories of SSL methods: contrastive learning, generative learning, and generative-contrastive methods, outlining their basic principles and representative methods. …”
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Application of machine learning methods to restore size-sex composition in catches of snow crab by I. S. Chernienko, A. G. Slizkin, V. V. Kulik

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Modelling the Temperature of a Data Centre Cooling System Using Machine Learning Methods by Adam Kula, Daniel Dąbrowski, Marcin Blachnik, Maciej Sajkowski, Albert Smalcerz, Zygmunt Kamiński

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Analysis of Demographic, Familial, and Social Determinants of Smoking Behavior Using Machine Learning Methods by Joanna Chwał, Małgorzata Kostka, Paweł Stanisław Kostka, Radosław Dzik, Anna Filipowska, Rafał Jan Doniec

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Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods by Aina Zhang, Xiaoming Kong, Bowen Liu, Nan Li, Yude Bu, Zhenping Yi, Meng Liu

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Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

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Retracted: Digital Effectiveness in Video Conference Methods on Internet Learning Environments of Higher Education by Journal of Mathematics

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The Application of the Approach of “Cognitive Structure” in the Teaching Methods of the Holy Quran in Respect to Meaningful Learning by Zahra Safian Jozdani, Reza Saadat Nia

“…It is believed that with relation to its teaching responsibility and upright guidance the holy Quran defines its method. With considering the holy Quran and the text of education with analytic-descriptive method this note is going to extract the methods that the Quran has applied for this aim while explaining the special position of the cognitive structure and its soundness in the process of teaching. …”
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USING MACHINE LEARNING METHODS FOR PREDICTION OF DURABLE ECONOMIC DEVELOPMENT: ROMANIA CASE STUDY by Dumitrel-Victor TIȚA, Daniel NIJLOVEANU, Nicolae BOLD, Doru-Anastasiu POPESCU

“…After using traditional statistic methods for presenting these correlations, we will create trend predictions using Machine Learning (ML) methods using the traditional ML methodology. …”
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