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    ESTIMATION OF SOFTWARE COMPLEXITY OF CALCULATION OF AUTOREGRESSION COEFFICIENTS AT DIGITAL SPECTRAL ANALYSIS by Andrey Zuev, Andrey Ivashko, Denis Lunin

    Published 2022-03-01
    “…Tasks to be solved: selection of spectral analysis methods suitable for diagnostics of technological equipment, analysis of methods for calculating autoregression coefficients and derivation of relations for estimating software complexity of algorithms and calculation of numerical estimates of addition and multiplication for some algorithms, adaptation of developed methods and estimates to microcontrollers. spectrum Applied methods: algorithm theory, Fourier transform, natural series, microcontroller programming. …”
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    Variable Selection for Generalized Single-Index Varying-Coefficient Models with Applications to Synergistic G × E Interactions by Shunjie Guan, Xu Liu, Yuehua Cui

    Published 2025-01-01
    “…A three-step variable selection method for single-index varying-coefficients models was proposed in recent research. …”
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    NEW ORGANIZATION PROCESS OF FEATURE SELECTION BY FILTER WITH CORRELATION-BASED FEATURES SELECTION METHOD by Olga Solovei

    Published 2022-09-01
    “…The computation complexity of the proposed approach to feature selection doesn’t depend on dataset’s dimensions which makes it robust to different data varieties; it eliminates the time needed for feature subsets’ search as subsets are selected randomly. …”
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    Long‐Term Maintenance of Complex Chromosomal Inversion Polymorphism in Drosophila mediopunctata by Fabiana Uno, Felipe Bastos Rocha, Louis Bernard Klaczko

    Published 2024-10-01
    “…Our findings reveal that moderate selection coefficients, such as s = 0.0407, are sufficient to maintain the observed LD for the most common haplotypes, albeit leading to an unstable equilibrium. …”
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    Influence of Mismatch of Parameters of Quadrature Channels on the Work of the Adaptive Selection System for Moving Targets by S. I. Ziatdinov

    Published 2020-07-01
    “…Materials and methods. The method of complex variable is used, in which the input and output signals of the adaptive selection system for moving targets are represented as a pair of complex-conjugate components. …”
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    q-Fractional Hesitant Fuzzy Sets and Their Correlation Coefficients: Multi-Criteria Decision Making Technique for Selection of Agricultural Land to Cultivate Apples Crops by Nadeem Ajaib, Khizar Hayat, Dragan Pamucar, Kamran Kausar

    Published 2025-01-01
    “…The q-Fractional Fuzzy Sets (q-FrFSs) offers information in Membership Grade (MG) and Non-membership Grade (NMG) of an object; however, both grades have the hesitancy factor because complex information usually does not give single MG and single NMG. …”
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    THE SELECTION OF THE INITIAL MATERIAL FOR VARIOUS DIRECTIONS OF MAIZE BREEDING ON THE BASIS OF MULTI-CRITERIA ESTIMATION by G. Ya. Krivosheev, A. S. Ignatiev, A. G. Gorbacheva, I. A. Vetoshkina

    Published 2019-07-01
    “…Average variability was identified in grain productivity (V = 14.8–18.2%), drought resistance index (V = 10.5%), harvesting moisture of grain (V = 18.6%). The use of the complex of main economically valuable traits for the integrated assessment (SD), taking into account the specified contributions (Rk) and weight coefficients (Wi), made it possible to rank the initial material according to its value for various areas of selection. …”
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    Crashing Fault Residence Prediction Using a Hybrid Feature Selection Framework from Multi-Source Data by Xiao Liu, Xianmei Fang, Song Sun, Yangchun Gao, Dan Yang, Meng Yan

    Published 2025-02-01
    “…Maximal information coefficient analysis is then applied to rank features within each cluster and select the most relevant ones, forming an optimized feature subset. …”
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    A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

    Published 2024-11-01
    “…Four correlation-based feature selection algorithms, Pearson correlation coefficient (PCC), Spearman correlation coefficient (SCC), mutual information (MI), and minimum redundancy maximum relevance (mRMR), are used for feature selection, and the same number of features are selected for both experiments. …”
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    Channel estimation method based on compressive sensing for FBMC/OQAM system by Weina YUAN, Qiu YAN

    Published 2019-12-01
    “…In mobile-to-mobile sensor networks,the channel estimation for FBMC/OQAM system can be investigated as a compressive sensing problem to raise frequency spectrum efficiency by exploiting the sparse nature of wireless channels.Firstly,a novel orthogonal matching pursuit algorithm with selection weak strategy and regularization based on Tanimoto coefficient (T-SWROMP) was proposed to improve the accuracy of LS channel estimation.Then,T-SWROMP methods with auxiliary pilot and coding were used to estimate channel frequency response for FBMC/OQAM system.The experimental results demonstrate the proposed method has lower complexity than the traditional SWOMP method.In addition,it achieve best performance among the traditional OMP,SWOMP and ROMP methods under dual-selective channels.…”
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    UV–Vis Detection of Thioacetamide: Balancing the Performances of a Mn(III)-Porphyrin, Gold Colloid, and Their Complex for Selecting the Most Sensitive Material by Camelia Epuran, Ion Fratilescu, Ionela Fringu, Anca Lascu, Liliana Halip, Mihaela Gherban, Eugenia Fagadar-Cosma

    Published 2025-05-01
    “…The optical detection of thioacetamide was investigated using a metalated porphyrin, Mn(III)-5,10,15,20-tetrakis-(3,4-dimethoxyphenyl)-21H,23H-porphyrin chloride (Mn-3,4-diMeOPP), a gold colloid solution (AuNPs), and a complex formed between them (Mn-3,4-diMeOPP–AuNPs) in order to select the most sensitive material and to achieve complementarity between methods. …”
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    A data-driven hybrid framework for voltage transformer ratio error prediction: Addressing challenges in complex power systems by Jiuxi Cui, Zhenhua Li, Heping Lu, Feng Zhou, Haoyu Chen, Zhenxing Li

    Published 2025-09-01
    “…Then, the Maximum Information Coefficient (MIC) quantifies the nonlinear relationship between environmental factors and ratio errors, reducing redundant features. …”
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