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.

The multidimensional purposes of grid-tied hybrid renewable system such as tracking of maximum power, increasing the power conversion efficiency, reducing the harmonic distortions in the injected current and control over power injected into the grid are presented in this paper by developing a laboratory-scale setup. To ensure continuous current operation at the shoot through mode of grid connected inverter, a switched Z-source converter is utilized at the PV side. The PWM rectifier connected with the wind turbine transforms AC power into dc. Individual power converters with conventional PI controllers have been dedicated for each power source, and control strategy uses only one reference voltage so as to increase the maximum power tracking speed from both PV and wind sources. The battery energy management is performed by artificial neural network (ANN) to enhance the stable power flow and increase the lifespan of the storage system. Finally, the voltage at the point of common coupling is fed to ANN-based space vector-modulated three-phase inverter and the converted AC power is injected to the grid. The overall system performance is measured by estimating the quality of injected power. A stable operation of the proposed microgrid system is verified by varying input and load at the grid. A continuous-time simulation model is realized in MATLAB and is validated using experimental prototype. This benchmark system provides various research scopes for the future smart grids.

Water is an essential resource for agriculture and the majority of land is irrigated through borewells or wells. The power requirement for an irrigation pump motor is fed by the on-grid power supply but the availability of electricity in rural areas is still questionable. With rising concerns about global warming and the rise in carbon footprints, it is necessary to choose clean and green energy, thereby attaining self-sustainable life. India receives yearly a mean solar irradiation of 6.5 kWh/m²day. Hence, a solar photovoltaic–water-pumping system (SPV–WPS) is a suitable alternative to grid energy; thereby, the farmers would generate electricity through the solar photovoltaic system and become self-sufficient in their energy needs. In this paper, two different agricultural fields in Tamil Nadu, India that deploy flood irrigation and drip irrigation are taken as a case study. The paper discusses the concerns on the use of grid power and their carbon footprint, design and simulation of 4- and 5.5-kW SPV–WPSs using PVsyst 7.1.1, and the advantages of using SPV–WPSs and life-cycle cost analysis on different use cases. The Government of India has introduced a special scheme to promote the installation of SPV–WPSs by offering attractive incentives through PM-Kisan Urja Suraksha evam Utthaan Mahabhiyan (KUSUM) yojana. The results of the case study show that with the use of SPV–WPSs, either with or without subsidy, the farmer could gain a minimum of 250% on the investment with a project lifetime of 25 years.