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COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The purpose of this paper is to investigate the impact of the US unconventional monetary policy surprises on the management of trilemma in India.

This paper uses the event study approach along with OLS and MANOVA to examine the impact.

The results validate the existence of trilemma in India for the period from October 2008 to December 2017. The results also show that monetary policy independence still exists in India in the wake of greater spillover effects during the Federal Open Market Committee announcement days. The spillover effects on USD-INR exchange rates and capital flows are found to be statistically significant. The MANOVA results show that the trilemma in India is influenced by around 20% by the changes in the US monetary policy.

The above approach of event study combined with MANOVA in this subject area has not been used before to the best of the authors’ knowledge. Further, there are only a few studies that exist on the spillover effects of the US monetary policy actions on the management of trilemma in India.

This paper deals with the estimation of the stress–strength reliability R = P(X < Y), when X and Y follow (1) independent generalized gamma (GG) distributions with only a common shape parameter and (2) independent Weibull random variables with arbitrary scale and shape parameters and generalize the proposal from Kundu and Gupta (2006), Kundu and Raqab (2009) and Ali et al. (2012).

First, a closed form expression for R is derived under the conditions (1) and (2). Next, sufficient conditions are given for the convergence of the infinite series expansions used to calculate the value of R in case (2). The models GG and Weibull are fitted by maximum likelihood using Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton method. Confidence intervals and standard errors are calculated using bootstrap. For illustration purpose, two real data sets are analyzed and the results are compared with the existing recent results available in the literature.

The proposed approaches improve the estimation of the R by not using transformations in the data and flexibilize the modeling with Weibull distributions with arbitrary scale and shape parameters.

The proposals of the paper eliminate the misestimation of R caused by subtracting a constant value from the data (Kundu and Raqab, 2009) and treat the estimation of R in a more adequate way by using the Weibull distributions without restrictions in the parameters. The two cases covered generalize a number of distributions and unify a number of stress–strength probability P(X < Y) results available in the literature.

The aim of this study is to provide a review of the research studies that took place in the previous years regarding Mangiferin and its potential use in the treatment of various disorders such as diabetes, cardiovascular diseases, cancer and human immunodeficiency virus.

Mangiferin can be easily extracted from the plant at a very low cost to treat some common as well as deadly diseases, which will be a miracle in herbal treatment. In support of this, the recent related articles were considered to understand the bioactive compound “Mangiferin” to treat lifestyle diseases. Various review articles and research papers were collected from international and national journals, and internet sources were also being used to compile the current manuscript.

Mangiferin has been shown to have the potential to mitigate many life style diseases such as diabetes, obesity, hypertension, fatty liver, atherosclerosis, dyslipidemia and diabetes-related end-organ complications such as retinopathy, neuropathy and nephropathy.

In this comprehensive review, this study evaluates Mangiferin and its lifestyle disease-modifying properties for nutraceuticals purposes.

Diabetic retinopathy (DR) is a central root of blindness all over the world. Though DR is tough to diagnose in starting stages, and the detection procedure might be time-consuming even for qualified experts. Nowadays, intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases. Therefore, a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.

The proposed DR diagnostic procedure involves four main steps: (1) image pre-processing, (2) blood vessel segmentation, (3) feature extraction, and (4) classification. Initially, the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filter. In the next step, the blood vessel segmentation is carried out using a segmentation process with optimized gray-level thresholding. Once the blood vessels are extracted, feature extraction is done, using Local Binary Pattern (LBP), Texture Energy Measurement (TEM based on Laws of Texture Energy), and two entropy computations – Shanon's entropy, and Kapur's entropy. These collected features are subjected to a classifier called Neural Network (NN) with an optimized training algorithm. Both the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm (MLU-DA), which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the NN. Finally, this classification error can correctly prove the efficiency of the proposed DR detection model.

The overall accuracy of the proposed MLU-DA was 16.6% superior to conventional classifiers, and the precision of the developed MLU-DA was 22% better than LM-NN, 16.6% better than PSO-NN, GWO-NN, and DA-NN. Finally, it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting DR.

This paper adopts the latest optimization algorithm called MLU-DA-Neural Network with optimal gray-level thresholding for detecting diabetic retinopathy disease. This is the first work utilizes MLU-DA-based Neural Network for computer-aided Diabetic Retinopathy diagnosis.

Whilst a modest number of investigations have been undertaken concerning nanofluids (NFs), the exploration of fluid flow under exponentially stretching velocities using NFs remains comparatively uncharted territory. This work presents a distinctive contribution through the comprehensive examination of heat and mass transfer phenomena in the NF ND–Cu/H₂O under the influence of an exponentially stretching velocity. Moreover, the investigation delves into the intriguing interplay of gyrotactic microorganisms and convective boundary conditions within the system.

Similarity transformations have been used on PDEs to convert them into dimensionless ODEs. The solution is derived by using the homotopy analysis method (HAM). The pictorial notations have been prepared for sundry flow parameters. Furthermore, some engineering quantities are calculated in terms of the density of motile microbes, Nusselt and Sherwood numbers and skin friction, which are presented in tabular form.

The mixed convection effect associated with the combined effect of the buoyancy ratio, bioconvection Rayleigh constant and the resistivity due to the magnetization property gives rise to attenuating the velocity distribution significantly in the case of hybrid nanoliquid. The parameters involved in the profile of motile microorganisms attenuate the profile significantly.

The current simulations have uncovered fascinating discoveries about how metallic NFs behave near a stretched surface. These insights give us valuable information about the characteristics of the boundary layer close to the surface under exponential stretching.

The novelty of the current investigation is the analysis of NF ND–Cu/H₂O along with an exponentially stretching velocity in a system with gyrotactic microorganisms. The investigation of fluid flow at an exponentially stretching velocity using NFs is still relatively unexplored.

Purpose: This study links SDG goal 9 of industry, innovation strategies and another infrastructural environment to branding relating to destination and interactive marketing. Digital marketing tools with various applications aim to offer hi-tech services to customers in interactive marketing services, namely multiple goods and services, data and innovative techniques in the tourism and travel sector. Exploring the study would add to the existing literature supporting interactive marketing procedures and destination branding. Branding relating to destinations fosters tourists around the globe facilitating economic growth and development and supporting the local economies.

Need for the Study: It is observed from the literature that very few studies have been identified across the globe from various researchers on the interactive marketing and destination branding that ensures brand loyalty and reassesses the intent of the tourists just before the epidemic pandemic in the form of COVID-19. Artificial Intelligence, as part of information technology, offers various interactive marketing services in the form of different social media marketing strategies, attractive websites for tourists and travel providers and image building on destination branding. This study would help fill the marketing gap, which results in branding relating to destination, brand loyalty and reassessing the intent of various tours and travel plans just before the pandemic.

Methodology: The study focused on the literature, demonstrating the stimulus organism methodology and examining the impact of potential marketing strategy, which is interactive focusing on the destination branding, loyalty relating to the brand and also procedures to revert with the intent that would motivate and facilitate the customer’s confidence showering the loyalty relating to the brand in the travel and tourism sector.

Findings and their Practical Implications: The study revealed that the marketing relating to interactive methodologies in the hi-tech digital approaches ought to be carried out to create opportunities for prospective tourists willing to get information about various tourists destinations with the help of various marketing techniques such as different social media applications, easy access of websites for accessing the tourist destinations and relevant information, accessing images pertinent to the tourism destinations with the ease of chat box and providing suitable audios and video sources to the potential customers.

The purpose of this study is to explore the factors that influence sustainable homestay development and suggest a model for adopting and implementing the homestay concept and its contribution towards sustainable rural development in Uttarakhand, India.

Researchers have collected a total of 360 responses from tourists, homestay owners/villagers and managers associated with homestay businesses in Uttarakhand to examine, assess and analyse the data with the help of different statistical tools such as SPSS and AMOS to validate the concept of homestay and its impact on sustainable rural development.

The analysis uncovered that collaborative consumption, sharing economy and family feeling and community development are positively associated with and, in return, community development affects sustainable development.

This study enables us to explain the “collaborative consumption” in the context of homestays functional in the state of Uttarakhand only.

The study results in evidence of crucial implications for policymakers. Policymakers should focus on opportunities in tourism and its integration with economic, environmental and social goals. Homestays will be new avenues for economic and sustainable development.

Homestay offers reasonable and cheap lodging for tourists within the existing ancient homes, typically restored for the guests to form a comfortable stay. Homestay is adopted to facilitate community-based tourism in the state. It also helps in developing a source of livelihood for the community. It is helpful for individuals’ economic, social and aesthetic desires to be consummated by maintaining cultural integrity, ecological processes, biodiversity and natural support systems through homestay, as social entrepreneurship. Homestay has been envisaged as a driver to realise the sustainable development goals by steering the pathways to a property future for all involved within the elected hill states.

This study validates a new homestay model that will be useful for developing community and achieving sustainable development.

This paper explores the survival strategies and coping mechanisms of ethnic minority entrepreneurs operating in the hospitality sector in northern Finland during the ongoing COVID-19 pandemic.

The paper utilizes qualitative research approach based on six exploratory case studies. The in-depth interviews in two phases were conducted with owners/mangers of ethnic minority entrepreneurial restaurants.

The findings reveal that all studied case firms undertook quick adjustments in response to the COVID-19 pandemic. For example, staff working hours were reduced through mutual consultation, and readjustments to the supply chain were made so that critical ingredients remain available despite the forced lockdown and supply chain bottlenecks. However, the readjustment of the supply chain was not visible in all case firms. Some of the owner-managers who were interviewed decided to keep doing business with the ethnic minority suppliers, despite some transportation problems due to lockdowns, especially in the early phases of COVID-19. Findings also suggest that the support grants announced by the state appeared not to be particularly useful for these restaurants due to restrictive eligibility criteria that many microbusinesses potentially fail to meet. Finally, the sample microbusinesses (restaurants) entrepreneurs recognize the importance of home delivery for their business survival, although they were critical of online food delivery service providers (apps) due to their high charges. Some of the case restaurants gave customers incentives for directly ordering from them, as an alternative strategy.

This paper is one of the first studies to specifically highlight the peculiarities of ethnic minority entrepreneurship and survival dynamics in northern Finland, where running the restaurant operations, including ensuring the supply chain management, is more complex than in the case of ethnic minority restaurants in more well-connected European countries and cities with an established history of immigrant businesses. This study is also novel in terms of specifying the strategies adopted by ethnic minority businesses in adjusting to the disruptions caused by the COVID-19 pandemic and surviving through it. At the same time, it has shown limitations and some problems associated with accessing state support announced for the hospitality industry in response to COVID-19. Finally, it offers a new angle by explicitly highlighting the power dynamics between restaurants and food ordering platforms (apps) and the potential alternatives in this specific context.

Purpose: Entrepreneurship is one of the best ways towards self-sufficiency and poverty alleviation for women in a country without guaranteed employment. The research contributes and provides reasons for supporting women’s entrepreneurship, as although it is well-documented that women’s entrepreneurship increases economic growth, other approaches need attention, especially those related to sustainability.

Need for the study: Women have superior emotional intelligence, resilience, and capacity to maintain a healthy work–life balance; women make better business and entrepreneurial leaders. Many businesses see encouraging women to join the workforce as a key to achieving sustainability goals. This study will provide valuable insights into the role of female entrepreneurs in driving sustainable business practices in the USA and India – rural India and urban India.

Methodology: The chapter adopts a descriptive research design methodology in conducting a thorough literature review on the evolution of women entrepreneurship in India. The case-based approach has been adopted to depict women entrepreneurs and their growth stories. The analysis is based on qualitative analysis of secondary data and primary data. This research proposes exciting revelations regarding the trend of women entrepreneurship in India and, from a global perspective, challenges women entrepreneurs face in organised and unorganised sectors in India and policies in different countries that promote women entrepreneurship towards sustainability.

Findings: Women business owners in India help the economy thrive, reduce poverty, and have a greater chance of shaping India’s destiny. This chapter looks at how entrepreneurial actions affect cultural morals and values. Women’s business growth rates vary from country to country, and this variation is influenced by the fact that countries can be broadly categorised as either developed or developing.