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A hybrid binary logical regression -gradient decent approach for dynamic cooperative spectrum sensing in CRN

INTERNATIONAL JOURNAL OF ELECTRONICS 2024年

作者： Supraja, C. Thandapani, Kavitha Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Elect & Commun Engn Chennai 600062 India

This paper proposes hybrid binary logical regression with a gradient decent optimisation (GDO) algorithm for spectrum sensing. From multiple secondary users (SU) systems, all signal vectors are collected with cognitive radios (CRs), and the features associated with the signal vector are considered for decision statistics. The decision statistics are modelled with a hybrid binary logical regression with gradient decent (BLR-GD) algorithm, which improves efficiency. The gradient decent (GD) is accomplished with binary logical regression (BLD), in which the regression coefficients are calculated efficiently. For evaluating the performance of the proposed algorithm, the spectrum sensing is evaluated in low signal-to-noise ratio (SNR) and high SNR scenarios. The detection probability, accuracy, F-measure and ROC curve performance of the proposed approach will outperform other existing approaches. The latency value of the proposed approach has reached 0.009 ms, which shows the efficiency of the proposed approach with 5 G communication features.

关键词： 5G technology cognitive radio network spectrum sensing binary logical regression gradient descent algorithm

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Support vector machine classification model for color fastness to ironing of vat dyes

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TEXTILE RESEARCH JOURNAL 2021年第15-16期91卷 1889-1899页

作者： Yu, Xinliang Wang, Hanlu Hunan Inst Engn Coll Mat & Chem Engn Hunan Prov Key Lab Environm Catalysis & Waste Reg Xiangtan 411104 Hunan Peoples R China Guangdong Univ Petrochem Technol Coll Chem Engn Maoming Peoples R China

Investigations of quantitative relationships between structure and color fastness of dyes are crucial in seeking novel dyes. For the first time, this work reported a classification model based on a quantitative structure-property relationship to predict color fastness to ironing of vat dyes. By performing binary classification analysis based on a support vector machine (SVM) and genetic algorithm, 56 vat dyes in the training set together with seven molecular descriptors were used to develop the classification model, which was validated with 59 vat dyes in the test set. The optimal SVM model (C = 208.465 and gamma = 5.9692) possesses overall accuracy of 91.1% for the training set and 83.1% for the test set, which is more accurate than those from the binary logistic regression model (87.5% and 81.4%, respectively). Furthermore, the mechanism of molecular descriptors correlated with color fastness to ironing of vat dyes is discussed.

关键词： binary logical regression color fastness support vector machine vat dyes

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