Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Handling Incomplete Data Classification Using Imputed Feature Selected Bagging (IFBAG) Method(Ios Press, 2021-07-09) Khan, Ahmad Jaffar; Raza, Basit; Shahid, Ahmad Raza; Kumar, Yogan Jaya; Faheem, Muhammad; Alquhayz, HaniAlmost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.Article Citation - WoS: 33Citation - Scopus: 40A Multi-Channel Distributed Routing Scheme for Smart Grid Real-Time Critical Event Monitoring Applications in the Perspective of Industry 4.0(Inderscience Publishers, 2019) Faheem, Muhammed Yasir; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, M. A.; Güngör, Vehbi ÇağrıRecently, the 4th industrial revolution known as Industry 4.0 has paved way for a systematical deployment of the modernised power grid to fulfil the continuously growing energy demand of the 21st century. This paper proposes a novel channel-aware distributed routing protocol named CARP for CRSNs-based SG applications. In CARP, the proposed cooperative channel assignment mechanism significantly improves the detection reliability and mitigates the noise and congested spectrum bands resulting in reliable and high capacity links for CRSNs-based SG applications. Moreover, to support higher capacity data requirements and to maximise the spectrum utilisation, the proposed multi-hop routing mechanism selects a secondary user relay node rich in spectrum information with longer ideal probability at low interference in the network. The extensive simulation results conducted through EstiNet9.0 reveal that the proposed scheme achieves its defined goals compared to existing routing schemes designed for CRSNs-based applications. © 2020 Elsevier B.V., All rights reserved.