Browsing by Author "Shafi, A.S.M."

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    COVID-19 detection from chest CT images using optimized deep features and ensemble classification
    (Academic Press, 2024-02-04) Hossain, M.M.; Walid, M.A.A.; Walid, M.A.A.; Galib, S.M.S.; Azad, M.M.; Rahman, W.; Shafi, A.S.M.; Rahman, M.M.
    Diagnosis of COVID-19 positive patients is the eventual move to impede the expansion of coronavirus. Variations of coronavirus make it tough to recognize COVID-19 positive patients through symptoms. Hence, this research aims at a faster and automatic detection approach of COVID-19 disease from the chest Computed tomography (CT) scan images. For the composition of the system, this approach constructs a feature vector from the CT images through the features fusion of two Convolutional neural network (CNN) models namely VGG-19 and ResNet-50. Before the feature fusion, preprocessing techniques are applied to gain more accurate outcomes. Moreover, pertinent features are identified from the feature vector by using several feature optimization methods namely Recursive feature elimination (RFE), Principal component analysis (PCA), and Linear discriminant analysis (LDA), and among them, we have observed PCA as the best preference. Classification is performed on the optimized feature utilizing the Max voting ensemble classification (MVEC). The fused features of VGG-19 and ResNet-50, processed with PCA and MVEC, provide the best outcomes of accuracy, specificity, sensitivity, and precision at 98.51 %, 97.58 %, 99.49 %, and 97.47 %, respectively, after 5-fold cross-validation for the proposed method.
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    Design and Performance Improvement of Microstrip Patch Antenna Using Graphene Material for Communication Applications
    (2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE, 2021-05-26) Mollah, Mohammad Sarwar Hossain; Faruk, Omar; Hossain, Md. Selim; Islam, Md. Tarequl; Shafi, A.S.M.; Molla, M. M. Imran
    In this paper, we discuss the process to fabricate a microstrip patch antenna by using graphene as a substrate material. Ours is the age of modern technology. Wireless communication is one of them. In wireless communication, graphene is widely used in the twenty-first century. Microstrip patch antenna provides better performance and better anticipation compared to other antennas. Microstrip patch antennas are more preferable compared to others because of their low cost, small size, and high performance. The graphene has a reconfigurable surface conductivity that can be tuned to operate at the desired frequencies. The main concept we focus on here to locate the best feed point of a microstrip patch antenna. This paper represents a graphene-based microstrip patch antenna. It has a 2.45 GHz resonant frequency for wireless communication. Here we use CST STUDIO SUITE 2017 software for designing and simulation our antenna. It shows the voltage standing wave ratio (VSWR), return loss, H-plane radiation pattern, and input impedance. The simulation output result shows a gain 6.801 dB and directivity of 7.302 dBi. It has return loss -23.673dB. Finally, we build up a design process of a graphene-based microstrip patch antenna.