COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.
Cognitive Radio Network utilizes the spectrum resources intellectually. Spectrum sensing is the fundamental component of cognitive radio network. However, spectrum sensing is prone to many security attacks caused by malicious users. These attackers try to modify the sensed outcome to degrade the performance of the network. In our proposed model, inorder to identify and resist such malicious activities, Blockchain based technology is used in the fusion center for taking global decisions. The methodology of the proposed model consists of energy detection based Spectrum sensing and SHA-3 employed blockchain based malicious user detection. This detection process includes two phases : Block updation phase and iron out phase. The simulation results of the proposed method show 59% more detection probability at -18dB SNR compared to other conventional methods like equal gain combining (EGC) and Fault-tolerant cooperative spectrum sensing (FTCSS) and 35% more tracking probability when the number of malicious users is decreased. Thus the security of cognitive radio networks can be greatly improved using Blockchain technology.
For the operation of 2.45GHz ISM band, a 2x2 Multiple Input Multiple Output (MIMO) antenna system is designed and fabricated. Complementary Split Ring Resonator (CSRR) is used in the MIMO patch and loaded on its ground plane to miniaturize the single antenna element. The single patch antenna element of 14x18 mm2 is fixed in a board of the Designed MIMO antennae system measuring 100x50x0.8 mm3. The antenna is tested by measuring radiation pattern, gain, VSWR, mutual coupling and return loss. The results of the Designed antenna systems are in good agreement with the simulations. In comparison to a conventional microstrip antenna, the Designed antenna achieves a 75% reduction in the resonant frequency.
Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of 'n' tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled rst based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.
Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.
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