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- 저자 : Muhammad Shahzaib (검색결과 3 건)
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COVID-19 Diagnosis from CXR images through pre-trained Deep Visual Embeddings
Khalid, Shahzaib,Syed, Muhammad Shehram Shah,Saba, Erum,Pirzada, Nasrullah International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.5
COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.
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( Izzah Shahid ),( Muhammad Rizwan ),( Deeba Noreen Baig ),( Rahman Shahzaib Saleem ),( Kauser A. Malik ),( Samina Mehnaz ) 한국미생물 · 생명공학회 2017 Journal of microbiology and biotechnology Vol.27 No.3
Fluorescent pseudomonads have been isolated from halophytes, mesophytes, and xerophytes of Pakistan. Among these, eight isolates, GS-1, GS-3, GS-4, GS-6, GS-7, FS-2 (cactus), ARS-38 (cotton), and RP-4 (para grass), showed antifungal activity and were selected for detailed study. Based on biochemical tests and 16S rRNA gene sequences, these were identified as strains of P. chlororaphis subsp. chlororaphis and aurantiaca. Secondary metabolites of these strains were analyzed by LC-MS. Phenazine-1-carboxylic acid (PCA), 2-hydroxy-phenazine, Cyclic Lipopeptide (white line-inducing principle (WLIP)), and lahorenoic acid A were detected in variable amounts in these strains. P. aurantiaca PB-St2 was used as a reference as it is known for the production of these compounds. The phzO and PCA genes were amplified to assure that production of these compounds is not an artifact. Indole acetic acid production was confirmed and quantified by HPLC. HCN and siderophore production by all strains was observed by plate assays. These strains did not solubilize phosphate, but five strains were positive for zinc solubilization. Wheat seedlings were inoculated with these strains to observe their effect on plant growth. P. aurantiaca strains PB-St2 and GS-6 and P. chlororaphis RP- 4 significantly increased both root and shoot dry weights, as compared with uninoculated plants. However, P. aurantiaca strains FS-2 and ARS-38 significantly increased root and shoot dry weights, respectively. All strains except PB-St2 and ARS-38 significantly increased the root length. This is the first report of the isolation of P. aurantiaca from cotton and cactus, P. chlororaphis from para grass, WLIP and lahorenoic acid A production by P. chlororaphis, and zinc solubilization by P. chlororaphis and P. aurantiaca.
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Development of FPGA-based system for control of an Unmanned Ground Vehicle with 5-DOF Robotic Arm
Abdullah Afaq,Mohammad Ahmed,Ahmed Kamal,Umar Masood,Muhammad Shahzaib,Nasir Rashid,Mohsin Tiwana,Javaid Iqbal,Asadullah Awan 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
This paper discusses the development of a customizable FPGA based system for implementing control algorithms on an Unmanned Ground Vehicle (UGV) and its 5 Degree of Freedom (DOF) manipulator. The compact RIO-9012 is used as a controller which is a reconfigurable embedded control and acquisition system using LabVIEW as the programming platform. The developed system enables the control of UGV and its manipulator using a remote joystick controller via Wi-Fi communication. Apart from Joystick, the system can also be controlled optionally using a keyboard. Accuracy of Joystick control has been enhanced by using point to point mapping technique. A user friendly GUI has been developed to view live video feedback obtained from the onboard cameras to control the UGV accordingly. Different features of UGV like path tracker (tracks its path on Google Maps), variable speed modes, battery indicator, camera switch and selector etc. are also managed in the GUI. The system has been developed so that, in future, it can easily be extended to a fully autonomous system.
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