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The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.

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 adoption of electronic health records (EHRs) and digitization of health data over the past decade is ushering in the next generation of digital health tools that leverage artificial intelligence (AI) to improve varied aspects of health system performance. The decade ahead is therefore shaping up to be one in which digital health becomes even more at the forefront of health care delivery – demanding the time, attention, and resources of health care leaders and frontline staff, and becoming inextricably linked with all dimensions of health care delivery. In this chapter, we look back and look ahead. There are substantive lessons learned from the first era of large-scale adoption of enterprise EHRs and ongoing challenges that organizations are wrestling with – particularly related to the tension between standardization and flexibility/customization of EHR systems and the processes they support. Managing this tension during efforts to implement and optimize enterprise systems is perhaps the core challenge of the past decade, and one that has impeded consistent realization of value from initial EHR investments. We describe these challenges, how they manifest, and organizational strategies to address them, with a specific focus on alignment with broader value-based care transformation. We then look ahead to the AI wave – the massive number of applications of AI to health care delivery, the expected benefits, the risks and challenges, and approaches that health systems can consider to realize the benefits while avoiding the risks.

Introduction: Healthcare facilities have witnessed deterioration, limited employee engagement, and communication gaps due to a lack of wireless technology. The Internet makes work and life quicker and more intelligent. The Internet of Things (IoT) is a scheme of interconnection equipped with unique identifiers in recent years. Artificial intelligence (AI) and IoT advancement allow employees to develop competent and predictive services and solutions in human resource (HR) practices. This chapter has been formulated to summarise and classify the existing research and better understand the past, present, and future of employee engagement by improving IoT interrelated devices in the healthcare industry.

Purpose: This study aims to categorise and overcome the challenges involved in HR practices. Effectively embracing IoT application-connected devices in the healthcare industry can enhance human resources management’s (HRM) role and measure performance assessment to improve employee engagement and productivity.

Methodology: In this study, the authors develop propositions dependent on a theory-based review. A systematic analysis was applied to minimise the challenges of HRM. The subject-related articles from different journal sources, like Scopus, Emerald, Web of Science, Springer, etc., were analysed based on engagement criteria. It was graphically recorded in a collective and informative way to emphasise the review outcomes. The study has presented the positive impacts of AI and IoT on engagement in health care.

Summary: This chapter accumulated theory-based knowledge about healthcare employee engagement and how IoT-based technology like AI can optimise employees’ engagement effectively. Further, it draws comparative benefits for a workforce to execute performance advancements and create future progressive aspects for healthcare employees.

Diabetes is a chronic disease and the major types of diabetes are type 1 and type 2. On aging, people with diabetes tend to have long-term problems in hypertension, coronary artery disease, obesity, and nerves. Given the increasing number of complications in recent years, by 2040, 624 million people will have diabetes worldwide and l in 8 adults will have diabetes in the future. Machine learning (ML) is evolving rapidly, many aspects of medical learning use ML. In this study, tension-type headaches (TTH) were associated with diabetes using SPSS, Pearson correlation, and ANOVA tests. Data were collected from Delhi NCR Hospital. It contains 30 diabetic subjects. The purpose of this study was to correlate diabetes analysis from TTH and other diseases using the latest technologies to analyze the Internet of Things and Big Data and Stress Correlation (TTH) on human health. The authors used Pearson correlation to correlate study variables and see if there was any effect between them. There was an important relationship between the percent variable, the total number of individuals, the number of individuals, and the minimum variable. The age (field) of the number of individuals to one of the total number of individuals showed a strong correlation (1.000) with a significant value of p (1.000). Overall, cases of TTH increased with age in men and do not follow the pattern of change in diabetes with age, but in cases of TTH, patterns of headaches such as diabetes increase to age 60 and then tend to decrease.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Industry 4.0 and the new technological transformation process it represents pose major challenges for the world today. Now, a new period has been entered, which will affect every point of daily life, from production to trade, and from health to entertainment. In terms of its scope and complexity, this period is unlike any that humanity has experienced before. The concept of Industry 4.0 is an external reflection of the innovations and applications made and to be made not only in today but also in the production methods of the future. Because this change and development occurs as a result of a certain accumulation and continues to occur continuously, it is evident that strategy is of great importance in today’s constantly changing and developing world. It is of great importance for leaders to develop strategies for evaluating the opportunity areas emerging by anticipating change in the Industry 4.0 process and to carry organizations and society into the future. Strategic leaders can do this effectively. At this point, strategic leadership is an important issue. Based on this idea, in this study, the strategic leadership issue from Industry 4.0 perspective will be examined in a theoretical context.