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This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.

The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.

The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.

This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

In December 2015, 196 countries will meet in Paris to reach a new global climate change agreement. This case looks at the climate negotiation process from the eyes of India's environment minister Mr Prakash Javadekar. In India's context, the energy sector has a big role to play in emission reduction as it is the largest emitter of the GHGs. When compared to US and China, India's per-capita emissions are miniscule but they are expected to rise substantially as the GoI is investing heavily in the infrastructure sector which has a substantial carbon footprint. The case discusses the mandatory emission cuts that India will announce considering the fulfillment of sustainable development goals. The case also points out, the government's promise of providing 24*7 electricity by 2019 which it feels can be an impediment in setting an aggressive emission cut target. The case questions if changes in the portfolio mix can be a part of the solution.

The purpose of this paper is to present the Andaman and Nicobar Islands (ANI) as a case to delineate contextual and often paradoxical challenges in implementing centrally sponsored schemes in a small island economy. Government reports cite ANI's record on health outcomes to be impressive when comparing it with other Union Territories (UTs). However, using examples of two public health and nutrition schemes, the paper argues that unless ANI's development indicators are compared over few years, public policy may fail to identity its development challenges and continue its rhetoric while in reality, health outcomes may be regressing.

The paper used case study research design involving mixed method approach. Quantitative data were secondary in nature, collected from local government departments. Two of the three districts of ANI formed the sample of the study. Main tools used for qualitative analysis were observation and informal interviews with government functionaries and other stakeholders.

The findings point to the paradox of superior public spending and infrastructure in ANI in midst of deteriorating public health and child nutrition outcomes. The paper concluded that any policy making on island's trajectory of development is not only incomplete, but also faulty unless its special context is considered as a starting point.

Ethnographic studies on the health-seeking behaviour of ANI communities can reveal trends in access to health services and its reasons, but that was beyond the scope of the paper.

The paper is arguably the first to identify development paradoxes in implementation of government health and nutrition programmes in ANI. The paper poses further questions on unpacking the paradox of development in the islands. It urges policy makers and administrators to reconsider the efficacy of centrally sponsored schemes that are implemented without being contextualised for a small island economy like ANI.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

The study attempts to investigate the role of social media in spreading awareness regarding ayurvedic immunity boosters (AIB) and changes in diet. Further, the study examines the factors affecting the willingness to pay for ayurvedic immunity boosters (WPIB) during the pandemic and new normal situation with the moderating effect of the “fear of COVID-19 infection.”

The data were collected from millennials in two phases, i.e. the first phase (1 July–August 2021) with 300 respondents and a second phase with (June–August 2022) 257 respondents. An online questionnaire was shared with millennials using the snowball sampling technique. Descriptive statistics with SPSS and SmartPLS 4.0 software were applied to analyze the data.

The results found a variation in AIB content sharing on social media during 2021 and 2022. Results found that respondents reported significant changes in their lifestyle and diet, like consuming honey, khada, tulsi tea, etc. In 2021, health consciousness and trust significantly affected WPIB, whereas in 2022, only health consciousness was substantially affected. Fear of COVID-19 infection moderates the relationship between health consciousness, perceived fear and willingness to pay for ayurvedic products, whereas the effect on consumer preference and trust remains insignificant.

Results could help ayurvedic product manufacturing companies understand the consumers' mindset and the factors that stimulate consumers to buy these immunity boosters. Ayurvedic advertisers should design unambiguous messages that focus on health consciousness and have trustable components to encourage consumers to adopt a healthy lifestyle.

This is one of its kinds of studies that presents the contrasts of how the COVID-19 crisis has significantly changed individuals' dietary intake and affected lifestyle patterns.

The purpose of this paper is to develop an improvised sustainable health-care model by integrating best practices, innovations and new dimensions to the present public health-care system – National Rural Health Mission (NRHM) – for improving the health status of the bottom of pyramid (BoP) in India.

The contribution of NRHM in ensuring the availability of health-care services and improving health indicators has been assessed. Some unique proven models of excellent health-care services and innovations have also been considered in designing an improvised health-care model. The empirical context takes the use of case study research methodology. The data have been extracted from various relevant papers, reports and websites.

Despite substantial augmentation in health infrastructure and human resources, increased local engagement and technology integration, the progress in health indicators during the NRHM has not been fairly better than that before. The present paper provides an improvised model that integrates all the potential stakeholders such as Government, Private health-care services providers, pharmaceutical and insurance companies and BoP community itself to ensuring 5As rather than 4As (Prahalad, 2004) in rural health care.

This study has relied mainly upon the secondary sources of data and some published case studies. The model is a hypothetical framework designed exclusively for rural setups of India.

The study shows the ways and invites all the stakeholders to come forward and build hybrid partnerships not only to develop society but also to develop sustainable BoP markets and earn profits.

The paper brings forth the aspects of achievements and limitations of NRHM in improving BoP health status, and it develops an improvised model to achieve the BoP-health objectives.

This case study would enable students to understand the concept, process and advantages of mergers and acquisitions as a growth strategy with respect to 1mg. Also, the students would be able to use the threats, opportunities, weaknesses and strengths matrix to map 1mg’s strengths, weaknesses, opportunities and threats.

This case study analyses the transformation journey of 1mg to Tata 1mg, one of the most trusted internet pharmacies in India. This case describes a small start-up that was launched in 2013 and had made many acquisitions since then. This case revolves around Tata Digital’s purchase of 1mg. The case starts out by explaining 1mg’s financial situation and why the company was acquired. This case study focuses on how the integration helped Tata Digital and 1mg realize their respective missions. Furthermore, the case study illustrates the benefits and difficulties of this integration.

This case study is basically aimed at postgraduate management students; it can be used in strategic management and health-care courses. Students can understand the concept of diversification and acquisition with the help of this case study. Students can also gain an insight into the organic and inorganic diversification as a growth strategy.

Teaching notes are available for educators only.

CSS 11: Strategy.

To evolve sustainable practices for human resource planning (HRP) for hospitals being set up in Tier 2/3 cities in India. The purpose of this study is to determine the strategic challenges are faced by hospitals in Tier 2/3 cities. To align HRP/workforce planning to departmental strategic/business planning of the hospital in the defined context. To address the dilemmas of the prospective health-care workforce in Tier 2/3 cities.

The integrated methodology adopted uses the grounded approach for building theory from practice. Due to the lack of research in the identified context, the study relies on a review of literature related to health-care practices, HRP and general management inputs to evolve the exact interview questionnaire. The subsequent empirical analysis is based on interviews of niche human resource experts in private hospitals.

The traditional and successful model of private hospitals in the metro cities cannot be transplanted to the Tier 2/3 cities. The thumb rule on a ratio between the doctors, paramedic staff, nurses and other support staff requires to be modified as necessary. Uncertainty in the new context requires the outlook to be basic, uncomplicated, flexible and agile. Attracting and recruitment strategies require adopting an innovative and multipronged approach. Sourcing high-end specialists need not follow the retainership model of employment.

The need to penetrate health-care services to the Tier 2/3 cities within India cannot be disputed. With the increase in disposable income and standard of living in these cities, access to quality and affordable health care is also imperative. The study is a pioneering effort to suggest a practicable and uncomplicated model for hospital setup in the identified context, with a focus on HRP.

This paper offers novel perspectives to the HRP of health-care personnel for a hospital setup in Tier 2/3 cities in India. For the success of private hospitals and their financial viability, this planning is of utmost importance. There is an attempt to fill the knowledge gap in the context which has been rarely explored or ventured. In addition, the policy initiatives suggested are expected to encourage the growth of private hospitals within the Tier 2/3 cities in India.