Impact of Using the Internet of Medical Things on e-Healthcare Performance: Blockchain Assist in Improving Smart Contract.

Background: This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance. Methods: A quantitative approach used an online survey from public and private hospital administrative departments in Dubai, United Arab Emirates (UAE). ANOVA, t-test, correlation, and regression analysis were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain). Patients and Methods: A mixed method was used in this research, a quantitative approach for data analysis utilizing online surveys from public and private hospitals' administrative departments in Dubai, UAE. A correlation, regression through ANOVA, and independent two-sample t-test were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain). Results: Blockchain application in smart contracts has proven to be significant in the healthcare sector. Results highlight the importance of integrating smart contracts and blockchain technology in the IoMT infrastructure to improve efficiency, transparency, and security. The study provides empirical evidence to support the implementation of smart contracts in the e-healthcare sector and suggests improved e-healthcare performance through this transition. Conclusion: The emergence of e-healthcare systems with upgraded smart contracts and blockchain technology brings continuous health monitoring, time-effective operations, and cost-effectiveness to the healthcare sector.

AI-Based Prediction of Capital Structure: Performance Comparison of ANN SVM and LR Models.

Capital structure is an integral part of the corporate finance that sources the funds to finance growth and operations. Managers always have to maintain value of the firm to be higher than the cost of capital in order to maximize the shareholders wealth. Empirical studies have used sources of finance like debt and equity as variables of capital structure. A choice between debt and equity finance analyzes the firm's ability to perform under the financially constrained environment to attain the sustainable growth. Therefore, it gives rise to a dire need to estimate the cost of capital precisely. We examined the capital structure of top ten market capitalization of the stock markets included in MSCI Emerging index with the use of artificial neural networks, support vector regression, and linear regression in forecasting methods. The capital structure is measured as the proportion of total debt over total equity (Tang et al., 1991). Other financial ratios such as profitability, liquidity, solvent, and turnover ratios were considered as drivers of the capital structure. Applying logistic and hyperbolic tangent activation functions, it was concluded that ANN has a great potential of replacing other traditional forecasting models with the nonstationary data. This research contributes with a new dimension for estimation through different activation functions. There is a possibility of ANN dominance as compared to the other models applied for predictability in financial markets.