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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.

The paper aims to craft a non-parametric composite value for the board quality of Indian banks where the weights can be assigned endogenously.

The study employed a non-parametric data envelopment analysis (DEA)-based novel extension known as the benefit of doubt approach. To measure the strength of the Indian bank corporate board in terms of board efficiency (BEF), the study used a mixed approach, i.e. first, the study calculates the percentile ranks of the five attributes that the study assumes are the characteristics of the strong board including board size, number of outside directors, frequency of meetings, non-duality leadership and board gender diversity. Thereafter, the study performs the benefit-to-doubt approach to finally measure the efficiency of the board.

The findings of the study establish that the methodological framework present in the study to measure the strength of the board in terms of BEF has been a much superior method over the other weighted and non-weighted linear average methods.

This methodology aids the shareholders, investors and regulatory bodies in rating the Indian banks based on their strength in terms of better monitoring boards and ensuring a smooth agent–owner relationship.

The benefit of doubt approach has been a unique and novel methodology to craft the composite value for any multidimensional phenomenon. One of the major benefits of using this approach is that it assigns the weights endogenously to each dimension and thereafter collectively determines the efficiency of such a phenomenon.

In the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.

This study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.

Findings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.

This study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.

This study aims to explore and comprehend the reasons behind individual investors’ intention towards socially responsible investment (SRI) in the Indian stock market along with examining the validity of the theory of reasoned action (TRA) model to predict such phenomenon in the Indian context.

The TRA has been used as an underlying framework and has been extended by adding four variables, namely, moral norms, environmental concern, financial literacy and financial performance. The study used a self-administered questionnaire and adopted a convenience sampling method for a survey to collect the data from the individual investors from the capital cities of three states of India. Further, the collected data have been analysed using two-step structural equation modelling.

Results of this study indicate a significant impact of attitude, subjective norms, moral norms, financial literacy and financial performance on investors’ intention towards SRI; however, no significant relation was found between environmental concern and investors’ SRI intention. The multiple squared correlation (R²) shows that the final model could explain 71% of the variance in investors’ intention towards SRI, which signifies a successful implementation of TRA model along with new additions to predict investors’ decision-making behaviour for SRI. Moreover, investors are found to be highly concerned primarily about their financial goals and then for their personal obligation towards society as far as SRI is concerned.

This study reports significant and prominent importance of subjective norms in SRI which could be a strategic theme for the government and the policymakers to influence investors through their opinion leaders to promote SRI. The government should also increase its efforts to facilitate financial literacy among citizens.

Using the TRA model and four variables, namely, moral norms, environmental concern, financial literacy and financial performance addition to its original variables, this study extends the understandings of SRI which is perhaps the novelty of this paper because such examination of SRI has not been conducted, especially in the case of developing countries such as India.