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Investigation of efficient multilevel inverter for photovoltaic energy system and electric vehicle applications
Introduction. This research presents a simple single-phase pulse-width modulated 7-level inverter topology for renewable system which allows home-grid applications with electric vehicle charging. Although multilevel inverters have appealing qualities, their vast range of application is limited by the use of more switches in the traditional arrangement. As a result, a novel symmetrical 7-level inverter is proposed, which has the fewest number of unidirectional switches with gate circuits, providing the lowest switching losses, conduction losses, total harmonic distortion and higher efficiency than conventional topology. The novelty of the proposed work consists of a novel modular inverter structure for photovoltaic energy system and electric vehicle applications with fewer numbers of switches and compact in size. Purpose. The proposed system aims to reduce switch count, overall harmonic distortions, and power loss. There are no passive filters required, and the constituted optimizes power quality by producing distortion-free sinusoidal output voltage as the level count increases while reducing power losses. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and various pulse-width modulation methodologies. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage, current, power losses and number of DC sources are investigated with conventional topology. Practical value. The proposed topology has proven to be extremely beneficial for implementing photovoltaic-based stand-alone multilevel inverter and electric vehicle charging applications. References 16, table 1, figures 18. E. Parimalasundar, R. Jayanthi, K. Suresh, R. Sindhuja. -
A study of Artificial Intelligence impacts on Human Resource Digitalization in Industry 4.0
Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops both AI and the Internet of Things (IoT). Precision, Efficiency, and Flexibility are considered the potential benefits of Industry 4.0. The implementation of Industry 4.0 requires a lot of changes, including the Human Resource (HR) function. In Industry 4.0, the HR capability is more critical and gives an upper hand to the organization. The HR capability should be more cautious and adaptable to adjust to the difficulties and requirements. We study the contributions of AI in HR digitalization and practices in Industry 4.0. 271 HR experts working in Information Technology (IT), Manufacturing, and administration are selected to participate in this review focusing on five AI applications in HR capability and three elements of HR readiness. The information collected was examined utilizing the Statistical Package for Social Sciences (SPSS) tool and Analysis of Moment Structures (AMOS). The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development. Adaptability and human asset capability are upheld by each of the five components of AI application areas of HR. Well-being and Safety improvement were viewed as vital components under the AI application in HR. 2023 The Author(s) -
A new shape of the supply chain during the COVID-19 pandemic
Purpose: The COVID-19 pandemic has created a new normal for international business (IB) activities, leaving them pondering their next steps. The decreasing effectiveness of current vaccines to protect individuals against new variants have created uncertainty on how to respond to the new waves of the COVID-19 infection. This study aims to empirically assesses how IBs perceive the unfolding challenges in the supply chain due to the pandemic and the solutions. Design/methodology/approach: The survey data is obtained from 166 logistics professionals in Hong Kong and India. Findings: The results reveal that returns on investment, logistics, delays and imports are the most affected areas. The most often recommended solutions for supply chain management (SCM) include using local manufacturing capabilities, analytics and automation, offering better customer service, providing more effective transportation means, ensuring diligence around optimization and focusing on sustainability. Originality/value: The findings of this study help to improve supply chain operations. This study also provides recommendations for changes to SCM in response to the new normal. 2022, Emerald Publishing Limited. -
Kerala Development and the Attapadi Adivasi
The development experience of the state of Kerala in southwest India is based generally on democratic principles of equality and popular participation. This article focuses on the lives of the Adivasi1 people of Attapadi in the Palakkad district of Kerala. It argues that the state of Kerala largely treats the Adivasis as secondary citizens and ignores their right to be socially and economically empowered. The state of Kerala takes pride in its positive ranking on human development and social progress indexes but has not done enough to stop Adivasi infants from dying of malnutrition, and Adivasis demands for land rights have been disregarded. As a result, they are forced to live obscure lives in poverty and generally unable to influence their sociopolitical sphere. 2023 The Author(s). -
Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels
Evaluation of flow resistance is necessary for the computation of conveyance capacity in open channels. The significance of the friction factor in channels with bedload conditions is paramount. The response of flow resistance in gravel-bed channels in bedload transport conditions is distinct from that of a fixed bed. The paper studies the different empirical approaches in the literature to determine the friction factor under bedload transport conditions and proposes an expression by genetic programming for the same. Various hydraulic and geometric parameters affect flow resistance in the bedload transport condition. The present study includes bed slope, relative submergence depth, aspect ratio, Reynolds number, and Froude number as influencing factors for such flow conditions. A wide range of experimental datasets is employed to determine the effect of these influencing parameters and develop a customised single expression for the friction factor. The experimental data set has also been moderated for sidewall corrections. The predictability of the proposed model is compared to various empirical equations from the literature. Unlike the existing models, the proposed model provides a more extensive expression for effectively predicting the friction factor for a wide range of datasets. The conveyance capacity of a river is validated from the estimated value of friction factor, as compared to other standard models. The developed Multi-Gene Genetic Programming (MGGP) model reasonably predicts discharge in the rivers, signifying that the model can competently be applied to field study within the specified range of parameters. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Electrochemical non-enzymatic strategy with green synthesized Fe2O3CuO nanocomposite for detection of amiprofos-methyl herbicide in industrial effluents and soils
Iron oxide-Copper oxide nanoparticles composite (Fe2O3CuO NPs) was synthesized through a green phytosynthetic approach using Ocimum sanctum Linn (commonly known as Tulsi) leaf extract. The evaluation of electrocatalytic properties were evaluated by carrying out electrochemical detection of amiprofos-methyl (APM), an organophosphorus herbicide. It is moderately toxic to mammals and aquatic biodiversity and is considered to be an acetylcholinesterase inhibitor. The presence of specific natural phytochemicals such as eugenol, naringenin, apigenin, quercetin, and high amount of ascorbic acid in the aqueous extract of Ocimum sanctum Linn plant parts, has been widely used for the synthesis of various metallic nanoparticles where these compounds serve as reducing, stabilizing, and capping agents. The synthesized Fe2O3CuO NPs were characterized using scanning electron microscope (SEM), energy dispersive X-ray analysis (EDX), X-ray diffraction analysis (XRD), UVVis spectroscopy, Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). The modified electrode was electrochemically characterized by cyclic voltammetry and differential pulse voltammetry (DPV) techniques for the detection of APM. The electrochemical signals have increased by three folds in the detection of APM with Fe2O3CuO nanocomposite compared to the bare glassy carbon electrode. The electrochemical sensor showed a linear range of 0.05 to 30 g/mL with a limit of detection of 0.0065 g/mL. The developed electrochemical sensor was successfully applied for the detection of APM in different water and soil samples with recoveries ranging from 96.00?99.00%. The electrode showed good stability and reproducibility over a period of 10 days with a 95% of peak current than the former. The newly synthesized nanoparticles, thus, proved to be an interesting material for electrochemical and biological studies. 2023 The Authors -
Effect of hooked end steel fibers on strength and durability properties of ambient cured geopolymer concrete
Growing carbon emissions in the construction industry have warranted the use of alternative materials such as geopolymer concrete. At the same time exposure of concrete material to harsh environmental conditions has compelled to design of durable geopolymer concrete. The use of hooked-end steel fibers in conventional fiber-reinforced concrete has proven to improve its crack resistance, and thus, positively influence the durability properties of concrete structures. Nevertheless, limited studies explore the effect of hooked-end steel fibers on the strength and durability properties of ambient cured geopolymer concrete with a low NaOH content (i.e., 8 M concentration). In this study, ambient cured geopolymer concrete was prepared by fly ash, ground granulated blast furnace slag (GGBS), NaOH, Na2SiO3, manufactured sand, and natural coarse aggregates. Additionally, hooked-end steel fibers with an aspect ratio of 67 were added to the mix by volume fraction in dosages of 0 %, 0.5 %, 1 %, 1.5 %, and 2 %. The experimental results showed that the addition of fibers reduced the workability with a minimum slump of 70 mm and a maximum Vee Bee time of 8 s for mixes with 2 % steel fibers. The addition of fibers improved the compressive strength, split tensile strength, and flexural strength of geopolymer concrete, with a maximum strength of 41.44 MPa, 4.28 MPa, and 5.23 MPa at an optimum fiber dose of 1 %, respectively. Above the optimum dose, the strength of the steel fiber-reinforced geopolymer concrete (SFRGPC) was reduced. The depth of water penetration reduced in SFRGPC when compared to GPC. Moreover, the resistance to chloride ion penetration was not significantly affected by addition of steel fibers till optimum dose of 1 %. The scanning electron microscopic results revealed the positive effect of steel fibers in restricting the progression of cracks. This has resulted in smaller crack width in the SFRGPC when compared to GPC. Overall, steel fibers in optimum dose have improved the performance of geopolymer concrete and this will contribute towards low carbon material. 2023 The Authors -
Mediating Role of Financial Literacy in Relationship between Financial Stress & Financial Resilience among MSME in Bangalore
The outbreak of COVID-19 virus brought down the economies of various countries and the livelihoods of the public. World economy witnessed a lower amount of the economic growth. Indian economy also fared poor during the pandemic. The pandemic generated unemployment, job loss, pay cut, and closure of many business organizations in India. Unemployment and low income caused psychological stress and financial stress among the public and among the businessmen especially micro, and small entrepreneurs (MSES). The Indian economy is bouncing back to normalcy. MSES exhibit stronger resilience during the hard time. The study is interested to measure and analyse the determinants that promote financial resilience among the MSES in India. Based on existing literature, financial literacy is chosen to study the nexus between financial stress and financial resilience of the entrepreneurs. 2023, Indian Institute of Finance. All rights reserved. -
The Belt and Road Initiative: Issues and Future Trends
The Belt and Road Initiative (BRI) is a China-led plan that involves infrastructure and construction projects in more than 140 countries, out of which 65 countries account for 30% of the worlds gross domestic product, 35% of the worlds trade, 39% of the global land, 64% of the worlds population, 54% of the worlds CO2 emissions and 50% of the worlds energy consumption (Du & Zhang, 2018, China Economic Review, 47, 189205). The project announced in 2013 is often considered Chinese Premier Xi Jinpings dream. It quickly grew in sectoral and geographical complexity from the Arctic to deep oceans, to Latin American countries, Africa and even collaborations in maritime and outer space. Nine years into the making, the project suffered disruption in the wake of the COVID-19 pandemic. Travel restrictions and lockdowns led to suspension and slowdown in the project. However, the Chinese leadership continues to remain optimistic regarding the BRI and is opting for digital, health and sustainability models to keep the initiative running. The article analyses the strategic and economic significance of the BRI from its inception to now. It focuses on the impact of the pandemic on the BRI and stakeholders responses to the project, and looks into attempts by China to make it a success in the post-pandemic world. 2023 Indian Council of World Affairs(ICWA). -
Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study
The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four(4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( 9.19kcalmol?1) against the target protein NiV G, when compared to the control drug, Ribavirin ( 6.95kcalmol?1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-PoissonBoltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( 218.664kJmol?1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( 83.812kJmol?1). 2023, King Abdulaziz City for Science and Technology. -
RayleighBard Convection in a Bi-viscous Bingham Fluid with Weak Vertical Harmonic Oscillations: Linear and Non-linear Analyses
Linear and weakly non-linear stability analyses of RayleighBard convection in a bi-viscous Bingham fluid layer are performed in the presence of vertical harmonic vibrations. In the linear analysis, expression is obtained for the correction Rayleigh-number arising due to the vibrations. The non-linear-analysis based on the GinzburgLandau equation is used to compute the Nusselt-number in terms of the correction Rayleigh-number. The mean-Nusselt-number is then obtained as a function of the scaled-Rayleigh-number, the frequency and the amplitude of modulation, the Prandtl number, and the bi-viscous Bingham fluid parameter. The non-autonomous amplitude-equation is numerically solved using the RungeKuttaFehlberg45 method. It is found that the influence of increasing the amplitude of modulation is to result in a delayed-onset situation and thereby to an enhanced-heat-transport situation. For small and moderate frequencies, the influence of increasing the frequency of oscillations is to decrease the critical Rayleigh-number. However, the mean-Nusselt-number decreases with increase in the frequency of oscillations only in the case of small frequencies. An increase in the value of the bi-viscous Bingham fluid parameter leads to advanced-onset and thereby to an enhanced-heat-transport situation. At very large frequencies, the effect of modulation on onset and heat-transport ceases. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Next generation employability andcareer sustainability inthehospitality industry 5.0
Purpose: With an industry 5.0 revolution taking place in the hospitality industry, a shift from manual to cognitive labor is anticipated, characterized by greater sustainability, resilience and a human-centric approach. In this regard, hospitality educators' ability and willingness to teach novel topics such as automation at work, upskilling of employees, man-machine interaction and service robots have become more important than ever. This study aims to interpret the perspectives of hospitality educators about bridging the gap in the employability skills of (next-gen) hospitality graduates and the concerns relating to career sustainability in times of transition. Design/methodology/approach: A case study method was used given the novelty of the topic in a developing country like India. A qualitative survey with open-ended questions, is employed to understand the viewpoints of Indian hospitality educators, including those with more than 15years of teaching experience. In-depth interviews were conducted with 23 hospitality educators to reach the theoretical saturation point. MAXQDA software was used to analyze the qualitative data collected in the study. Findings: The findings reveal the challenges and motivations of hospitality educators in adapting to frequently changing business environments. In doing so, it sheds light on the methods employed to create a generation of hospitality graduates aligned with the changing dynamics of the industry. Originality/value: The paper presents the viewpoints of hospitality educators in India in relation to a futuristic approach to next-gen employability and career sustainability. Whilst numerous studies have focused on the role of robots and artificial intelligence in replacing the human component of the service environment, the concept of people working alongside advanced technologies is fairly new and needs to be fully explored. 2023, Emerald Publishing Limited. -
Structural health monitoring using AI and ML based multimodal sensors data
Climatic changes, sudden or gradual, influence the structural health of buildings and bridges due to variations in temperature and humidity. Risk and disaster management plays a vital role in the decision-making process for safeguarding structures. Data analytics from sensors systems in smart structures aid in taking appropriate action in securing buildings during natural calamities. The correlation between climate and structural measuring responses can be further improved using artificial intelligence (AI)- machine learning (ML) algorithms to monitor and predict structural health and take any precautionary steps before the event of a casualty. Linear regression is an efficient tool for analyzing structural health. The proposed work's objective is to monitor and predict the structural health and inform the concerned authorities in the event of a failure in advance, using AI-ML approaches. We have analyzed various sensor data sets to predict the health of a structure based on the crack developed. From the data obtained for experimentation, mean width of the crack is observed as 2.38 cm and mean length of the crack is 63.36 cm. 2023 The Authors -
Visual encoding of nudge influencers and exploring their effect on sustainable consumption among children
With the growing number of nuclear families that have a higher disposable income, and a willingness to spend for disparate reasons possibly on the only child in the family, children are unquestionably emerging as a critical market segment that marketers would do well to target. However, while marketing to children is necessary, given the current focus on sustainability, encouraging responsible consumption seems to be a prerequisite. Making children environmentally literate would thereby, significantly help in the ongoing efforts to save our planet from environmental degradation. Based on this backdrop, this study investigates the significance of encouraging children to consume 'sustainably'. Drawing upon Richard H Thaler and Cass R Sunstein's Nudge theory, along with the United Nation's Sustainable Development Goals (UNSDG -12), we employ a novel methodology to visually encode information gleaned from the extant literature. Specifically, we discuss the significance of developing sustainable habits in children and analyze the 'nudges' that motivate children to adopt sustainable habits. Additionally, we specify different nudge elements derived from the extant literature and plot them in a RADAR chart. We observe that 'simplified process' and 'ease of access' nudging have the greatest effect when delivered in school. This study has academic, managerial, and societal implications. The findings of the study would help managers to focus on the nudges in their campaigns. Research scholars and academicians could understand the significance of using the 'RADAR' chart methodology and can expand their studies in various other domains. The present study also helps to understand the extant literature and plan for future research in the domain of sustainable consumption. The findings of the study would help schools and parents understand the effective nudges that result in creating responsible consumers that would largely benefit society. 2023 The Authors -
The impact of slip mechanisms on the flow of hybrid nanofluid past a wedge subjected to thermal and solutal stratification
This investigation aims to inspect the flow and thermal characteristics of hybrid nanoparticles under the effect of thermophoresis and Brownian motion. The hybrid nanofluid is formed by dispersing the silver nanoparticles into the base fluid composed of tungsten oxide and water. The resulting hybrid nanofluid is assumed to flow over a moving wedge. The wedge is a geometry that can be commonly seen in many manufacturing industries, moulding industries, etc., where friction creates more heat and cooling becomes a necessary process. This study currently focuses on such areas of the industries. In this regard, the flow expressions in the form of Partial Differential Equations (PDEs) are obtained by incorporating the modified Buongiorno's model and using boundary layer approximations. The modified Buongiorno model helps us analyze the impact of volume fraction along with the slip mechanisms. Suitable transformations are used to achieve the nondimensional form of governing equations, and further, it transforms the PDE to Ordinary Differential Equation (ODE). The RKF-45 is used to solve the obtained ODE and the boundary conditions. Furthermore, graphic analysis of the solutions for fluid velocity, energy distributions and dimensionless concentration is provided. It was noted that the behavior of the Nusselt and Sherwood numbers was determined by analyzing numerous parameters. The conclusions show that they decrease with greater values of the stratification factors. Additionally, with higher values of the wedge parameter, the magnitude of the velocity field and the thermal boundary layer diminish. 2023 World Scientific Publishing Company. -
Subspace graph topological space of graphs
A graph topology defined on a graph G is a collection T of subgraphs of G which satisfies the properties such as K0, G ? T and T is closed under arbitrary union and finite intersection. Let (X, T ) be a topological space and Y ? X then, TY = {U ? Y: U ? T } is a topological space called a subspace topology or relative topology defined by T on Y. In this P1 we discusses the subspace or the relative graph topology defined by the graph topology T on a subgraph H of G. We also study the properties of subspace graph topologies, open graphs, d-closed graphs and nbd-closed graphs of subspace graph topologies. 2023, Proyecciones. All Rights Reserved. -
Dynamics of chaotic waterwheel model with the asymmetric flow within the frame of Caputo fractional operator
The chaotic waterwheel model is a mechanical model that exhibits chaos and is also a practical system that justifies the Lorenz system. The chaotic waterwheel model (or Malkus waterwheel model) is modified with the addition of asymmetric water inflow to the system. The hereditary property of the modified chaotic waterwheel model is analyzed to determine the system's stability and identify the parameter that contributes to the stability We also examine the factor that leads to the bifurcation. We determine the well-posed nature of the modified system. The modified chaotic waterwheel model is defined with the Caputo fractional operator. The existence and uniqueness, boundedness, stability, Lyapunov stability, and numerical simulation are studied for the modified fractional waterwheel model. The bifurcation parameter and Lyapunov exponent are examined to study the chaotic nature of the system with respect to the fractional order. The nature of the system is captured with the help of the efficient numerical approach AdamsBashforthMoulton Method. The numerical approach demonstrates that the chaotic nature of the modified chaotic waterwheel is changed into unstable nature, which could further reduce to the stable case with suitable values of the parameter. This analysis is justified with the help of Lyapunov exponent. We consider irrational order (?,e) in the present work to illustrate the reliability of fractional order. 2023 Elsevier Ltd -
Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved. -
Modeling Environmentally Conscious Purchase Behavior: Examining the Role of Ethical Obligation and Green Self-Identity
Due to environmental degradation, using environment-friendly products has become necessary to reduce carbon emissions. However, the consumption of such products is still below expectations because these products are usually costlier than their traditional counterparts. The current study aims to investigate consumer behavior towards environment-friendly products using Ajzens theory of planned behavior as a theoretical model. The study seeks to examine the role of the key determinates of environmentally conscious purchase behavior, such as ethical obligation and green self-identity. A total of 386 responses were collected from consumers living in a few major cities of northern India using purposive sampling. The data were analyzed using structural equation modeling in Amos 22.0. The results demonstrated that attitudes towards environment-friendly products perceived behavioral control and green self-identity as the major determinants of green purchase intentions. In addition, attitude was reported to mediate the effect of ethical obligation on green purchase intentions and green self-identity was found to moderate the effect of attitude on green purchase intentions. Additionally, green self-identity was also reported to moderate the relationship between ethical obligation and attitude. The study adds value to the existing literature by signifying the role of green self-identity and ethical obligation in stimulating consumers green purchase intentions. The findings of the study are also meaningful for marketers and policymakers. 2023 by the authors. -
AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd