Telemedicine can be used by medical practitioners to connect with their patients during the recent Coronavirus outbreak, whilst attempting to reduce COVID-19 transmission among patients and clinicians. Amidst the pandemic, Telemedicine has the potential to help by permitting patients to receive supportive care without having to physically visit a hospital by using a conversational artificial intelligence-based application for their treatment. Thus, telehealth will rapidly and radically transform in-person care to remote consultation of patients. Because of this, it developed a Multilingual Conversational Bot based on Natural Language Processing (NLP) to provide free primary healthcare education, information, advice to chronic patients. The study introduces a novel computer application acting as a personal virtual doctor that has been opportunely designed and extensively trained to interact with patients like human beings. This application is based upon a serverless architecture and it aggregates the services of a doctor by providing preventive measures, home remedies, interactive counseling sessions, healthcare tips, and symptoms covering the most prevalent diseases in rural India. The paper proposes a conversational bot "Aapka Chikitsak" on Google Cloud Platform (GCP) for delivering telehealth in India to increase the patient's access to healthcare knowledge and leve rage the potentials of artificial intelligence to bridge the gap of demand and supply of human healthcare providers. This conversational application has resulted in reducing the barriers for access to healthcare facilities and procures intelligent consultations remotely to allow timely care and quality treatment, thereby effectively assisting the society.

Microservices architectural style is gaining popularity in industry and is being widely adopted by large corporations like Amazon, Netflix, Spotify, eBay, and many more. Several other organizations are also preferring to migrate their existing enterprise scale applications to microservices architecture. Researchers have proposed various approaches for microservices decomposition to be used in migrating or rebuilding a monolithic application to microservices. Applying any available approach to an existing monolithic application is not a straightforward decision; thus, there is a need for guidelines that assist in the migration process. There are various challenges in a migration process because different migration approaches use different sets of input data to identify microservices. Since the available migration techniques are not structured, logically, selection of an appropriate migration strategy is a difficult decision for any system architect. So, it is a recurrent open research question – which migration technique should be adopted to get microservices for a legacy monolithic application? This paper addresses this research challenge by examining existing approaches for microservices migration and groups them based on software development life cycle (SDLC) artifacts. Our research also proposes a microservices prescriptive model (MPM) from the existing prominent microservice migration techniques. This model provides recommendation (1) for refactoring an existing legacy system to microservices, and (2) for new microservices development projects. Our study also helps in gaining more insight about greenfield and brownfield development approaches in microservices applications. Moreover, researchers and practitioners of the field can benefit from this model to further validate their migration approaches based on the available system artifacts.

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