Modeling of Critical Food Supply Chain Drivers Using DEMATEL Method and Blockchain Technology

The main purpose of this paper is to identify the critical drivers of the food supply chain (FSC) in the Indian context and find cause–effect relationships among the identified drivers using a decision-making trial and evaluation laboratory (DEMATEL)-based method. After a review of the literature and discussion with food chain experts, fourteen drivers have been identified for this study. Critical drivers and their causal relationships are explored through the cause-and-effect diagram. Results of this study show that the drivers namely "Shift towards a sustainable food system in India” (D7), "Social requirements on food security and safety” (D13), and "Growing attention towards food SCM amidst pandemic Covid-19” (D1) are the top three critical and influential drivers. It has been observed that limited research studies are done to identify and analyze the FSC drivers specific in the Indian context. Recent advancements in Blockchain technology have paved the path for improving the performance of the food supply chain with appropriate Blockchain technology intervention. Blockchain technology (BT) can be a new driver in the FSCM. This paper proposes a conceptual framework for the implementation of Blockchain technology in the food supply chain. This paper attempts to draw the attention of policymakers to develop a new sound policy with the help of Blockchain technology to ensure food security. © 2023, The Institution of Engineers (India).

Impact of the UK's COVID-19 lockdown on the residential electricity consumption

Extreme disruptive scenarios such as pandemic lockdown force people to alter regular daily routines, impacting their energy consumption pattern. The implication of such a disruptive scenario for a more extended period on energy consumption is uncertain. This study aimed to investigate the impact of COVID-19 lockdown on residential electricity consumption in 100 houses from the southwestern UK. For the study, we analysed highly granular (1-minutely) electricity consumption data for April-September 2020 compared to the same months in 2019 for the same houses. Our study showed statistically significant differences during the lockdown period (the analysed six months) in energy demand. The minutely average electricity demand was 1.4-10% lower during April-September 2020 than in 2019. Our analysis showed that not all houses had similar type of changes during the lockdown. Some houses demonstrated a 38% increase in electricity demand, whereas some houses showed a 54% reduction during the lockdown period compared to 2019. Some houses showed significantly higher electricity use during the morning and afternoon than in 2019, which might be due to working and schooling from homes during the lockdown. © International Building Performance Simulation Association, 2022

Estimating Medical Insurance Cost using Linear Regression with HyperParameterization, Decision Tree and Random Forest Models

The total health expenditure refers to the total public and private funds spent on health services and amenities, medical and surgical bills and all other healthcare facilities provided. The financing for health is of great significance and plays a crucial role in health systems. To enhance the productivity of human capital, the efficiency and delivery of healthcare services must be uplifted. Reports have shown that from year 2000 to 2018 there had been a gradual escalation of global health expenses and is standing on around 10% of the total GDP of the world. Out of pocket expenses are also high in least developed nations that have lower per capita income. Even though the World Health Organization (WHO) sanctions loans to these countries, these nations are bound to use the money on industrialization only and not their healthcare, education and public welfare sectors. With inflation, the expenses of first-rate healthcare are also rising which makes it fundamental to have health and life insurance plans. Health insurance schemes insured around 514 million people in India in the year 2021, most of which were covered under government schemes only. Since the advent of COVID-19 people have realized the need for having a insurance plan. Most of the companies that are based on the health insurance sector use predictive modelling to improve their services and business process. Machine Learning (ML) algorithms are used to train a model and provide insurance costs estimations. Past data is searched for any pattern or trend in the behaviour history of consumers and then future estimations are evaluated. The proposed project is comprised of different regression models like Linear regression with hyperparameterization , regressors like Decision Tree and Random forest to estimate the approximate insurance expenditure. © 2023 IEEE.

SARS-CoV-2 Mortality Risk Prediction Using Machine Learning Algorithms to Aid Medical Decision-Making

The coronavirus has affected the world in every possible aspect such as loss of economy, infrastructure, and moreover human life. In the era of growing technology, artificial intelligence and machine learning can help find a way in reducing mortality so, we have developed a model which predict the mortality risk in patients infected by COVID-19. We used the dataset of 146 countries which consists of laboratory samples of COVID-19 cases. This study presents a model which will assist hospitals in determining who must be given priority for treatment when the system is overburdened. As a result, the accuracy of the mortality rate prediction demonstrated is 91.26%. We evaluated machine learning algorithms namely decision tree, support vector machine, random forest, logistic regression, and K-nearest neighbor for prediction. In this study, the most relevant features and alarming symptoms were identified. To evaluate the results, different performance measures were used on the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.