Výsledky vyhledávání - Predictive Learning Models classification

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  1. 1

    Machine learning predictive classification models for the carcinogenic activity of activated metabolites derived from aromatic amines and nitroaromatics Autor Halabi, Andrés, Rincón, Elizabeth, Chamorro, Eduardo

    ISSN: 0887-2333, 1879-3177, 1879-3177
    Vydáno: England Elsevier Ltd 01.06.2022
    Vydáno v Toxicology in vitro (01.06.2022)
    “… (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models…”
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    Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils Autor Sabatino, Manuela, Fabiani, Marco, Božović, Mijat, Garzoli, Stefania, Antonini, Lorenzo, Marcocci, Maria Elena, Palamara, Anna Teresa, De Chiara, Giovanna, Ragno, Rino

    ISSN: 1420-3049, 1420-3049
    Vydáno: Switzerland MDPI AG 25.05.2020
    Vydáno v Molecules (Basel, Switzerland) (25.05.2020)
    “… A subset of essential oils was experimentally tested in an in vitro model of HSV-1 infection and the determined IC50s and CC50s values were used in conjunction with the results…”
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    Primary Hyperhidrosis and Sensitive Skin: Exploring the Link with Predictive Machine Learning-Based Classification Models Autor McCormick, Erika T, Choi, Joung Min, Azim, Sara Abdel, Whiting, Cleo, Pieretti, Lisa, Zhang, Liqing, Friedman, Adam

    ISSN: 1545-9616
    Vydáno: United States 01.10.2024
    Vydáno v Journal of drugs in dermatology (01.10.2024)
    “…)-exempt survey was disseminated by the International Hyperhidrosis Society. A predictive classification model for SS was built using random forest machine learning algorithms…”
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    Machine learning-based classification models for non-covalent Bruton’s tyrosine kinase inhibitors: predictive ability and interpretability Autor Li, Guo, Li, Jiaxuan, Tian, Yujia, Zhao, Yunyang, Pang, Xiaoyang, Yan, Aixia

    ISSN: 1381-1991, 1573-501X, 1573-501X
    Vydáno: Cham Springer International Publishing 01.08.2024
    Vydáno v Molecular diversity (01.08.2024)
    “…In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton’s tyrosine kinase (BTK…”
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    Predictive Maintenance using Machine Learning Based Classification Models Autor Chazhoor, Anisha, Mounika, Y, Vergin Raja Sarobin, M, Sanjana, M V, Yasashvini, R

    ISSN: 1757-8981, 1757-899X
    Vydáno: IOP Publishing 01.10.2020
    “… Several predictive models using machine learning on the Semiconductor Manufacturing process dataset (SECOM…”
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    A Piecewise Linear Regression and Classification Algorithm With Application to Learning and Model Predictive Control of Hybrid Systems Autor Bemporad, Alberto

    ISSN: 0018-9286, 1558-2523
    Vydáno: New York IEEE 01.06.2023
    “…This article proposes an algorithm for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space…”
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    Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors Autor Anand, Vatsala, Khajuria, Ajay, Pachauri, Rupendra Kumar, Gupta, Vinay

    ISSN: 2045-2322, 2045-2322
    Vydáno: London Nature Publishing Group UK 19.08.2025
    Vydáno v Scientific reports (19.08.2025)
    “… In this study, different machine learning models were used to detect and classify kidney tumors…”
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    Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models Autor Wang, Marcus W H, Goodman, Jonathan M, Allen, Timothy E H

    ISSN: 1520-5010, 1520-5010
    Vydáno: United States 15.02.2021
    Vydáno v Chemical research in toxicology (15.02.2021)
    “… The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology…”
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    Photoreceptor layer thinning is an early biomarker for type 2 diabetes: a cohort study in UK Biobank Autor Ma, Yiyuan, Wu, Yue, Hu, Leyi, Zhang, Xinyu, Zheng, Danying, Liu, Zhenzhen, Jin, Guangming

    ISSN: 1476-5454, 1476-5454
    Vydáno: England 01.12.2025
    Vydáno v Eye (London) (01.12.2025)
    “…) images, excluding individuals with neurological or ocular diseases. Cox proportional hazard models were used to analyse the association between baseline POS thickness and incident T2D…”
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    Classification and predictive models using supervised machine learning: A conceptual review Autor Pienaar, M A, Naidoo, K D

    ISSN: 2078-676X, 2078-676X
    Vydáno: South Africa 01.04.2025
    “…Supervised machine learning models (SMLMs) are likely to be a prevalent approach in the literature on medical machine learning…”
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    A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT) Autor Ghasemkhani, Bita, Balbal, Kadriye Filiz, Birant, Derya

    ISSN: 2227-7390, 2227-7390
    Vydáno: Basel MDPI AG 01.09.2024
    Vydáno v Mathematics (Basel) (01.09.2024)
    “…This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT…”
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    Early predictive model for breast cancer classification using blended ensemble learning Autor Mahesh, T. R., Vinoth Kumar, V., Vivek, V., Karthick Raghunath, K. M., Sindhu Madhuri, G.

    ISSN: 0975-6809, 0976-4348
    Vydáno: New Delhi Springer India 01.01.2024
    “… A Blended ensemble learning, which is an innovative approach, has been utilized for the classification of breast cancer and this model performs…”
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    Identifying Suspected Instances of Fraud Utilizing Machine Learning Predictive Classification Models that Minimize Manual Labor to Inspect Anomalies Autor Andrews, Kyle

    ISBN: 9798516068157
    Vydáno: ProQuest Dissertations & Theses 01.01.2021
    “… The bank provided a client list of 139 clients with over 12 million transactions for 2020 to identify suspected instances of bank wire fraud and create a predictive classification machine learning…”
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    Dissertation
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    Experimental application of classification learning to generate simplified model predictive controls for a shared office heating system Autor Bursill, Jayson, O'Brien, William, Beausoleil-Morrison, Ian

    ISSN: 2374-4731, 2374-474X
    Vydáno: Taylor & Francis 28.05.2019
    “…The application of model-based predictive control (MPC) to commercial buildings has been a topic of increasing interest in the literature…”
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    The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions Autor Ermers, Nick J., Hagoort, Karin, Scheepers, Floortje E.

    ISSN: 1664-0640, 1664-0640
    Vydáno: Frontiers Media S.A 25.05.2020
    Vydáno v Frontiers in psychiatry (25.05.2020)
    “… predictions and detect relevant clusters. Due to the multidimensionality at play in the pathogenesis of depression, it is suggested that machine learning could contribute to improving classification…”
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    Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine Autor Liu, Jinlei, Dan, Wenchao, Liu, Xudong, Zhong, Xiaoxue, Chen, Cheng, He, Qingyong, Wang, Jie

    ISSN: 2047-2501, 2047-2501
    Vydáno: Cham Springer International Publishing 06.04.2023
    “…Backgrounds Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke…”
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    Pollution risk assessment by designing predictive binary classification models of substituted benzenes centered on data mining and machine learning techniques Autor N’guessan, Aubin, Dali, Brice, Esmel, Elvice Akori, Moussé, Logbo Mathias, Ziao, Nahossé, N’guessan, Raymond Kré, Megnassan, Eugene

    ISSN: 1614-7499, 0944-1344, 1614-7499
    Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2025
    “… In this study, we evaluated four binary classification machine learning (ML) models (support vector machine, k -nearest neighbor, CART decision tree and random forest…”
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