The health system in today’s real world is significant but difficult and overcrowded. These hurdles can be diminished using improved health record management and blockchain technology. These technologies can handle medical data to provide security by monitoring and maintaining patient records. The processing of medical data and patient records is essential to analyze the earlier prescribed medicines and to understand the severity of diseases. Blockchain technology can improve the security, performance, and transparency of sharing the medical records of the current healthcare system. This paper proposed a novel framework for personal health record (PHR) management using IBM cloud data lake and blockchain platform for an effective healthcare management process. The problem in the blockchain-based healthcare management system can be minimized with the utilization of the proposed technique. Significantly, the traditional blockchain system usually decreases the latency. Therefore, the proposed technique focuses on improving latency and throughput. The result of the proposed system is calculated based on various matrices, such as F1 Score, Recall, and Confusion matrices. Therefore, the proposed work scored high accuracy and provided better results than existing techniques.

Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.

A blockchain is a new open-source technology who attract whole the business world and technology experts to research, to explore and to understand the potential of this technology in different areas. Blockchain initially released in 2008 by Satoshi Nakamoto as a fundamental technology for the first-ever global decentralized cryptography-based digital currency known as BITCOIN. A blockchain is a transparent and immutable globally distributed ledger, distributed databases, who have global agreement by all its users. It means that when anything written in distributed ledger is cannot be cheated and modified, it will be trusted if the writer of content is trusted. In this research studies, author analyzes the performance of two widely used blockchain platform one is Ethereum and second is Hyperledger Fabric.

End-stage renal disease (ESRD) due to hypertension is common and displays familial aggregation in African Americans, suggesting genetic risk factors, including adrenergic activity alterations which are noted in both hypertension and ESRD. We analysed 554 hypertensive nephrosclerosis participants (without clinically significant proteinuria) from the longitudinal National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) African American Study of Kidney Disease and Hypertension (AASK) cohort to determine whether decline in glomerular filtration rate (GFR) over approximately 3.8 years was predicted by common genetic variation within the adrenergic beta-1 (ADRB1) receptor at non-synonymous positions Ser49Gly and Arg389Gly. The polymorphism at Ser49Gly (though not Arg389Gly, in only partial linkage disequilibrium at r(2) = 0.18) predicted the chronic rate of GFR decline, with minimal decline in Gly(49)/Gly(49) (minor allele) homozygotes compared to Ser(49) carriers; concordant results were observed for haplotypes and diploid haplotype pairs at the locus. An independent replication study in 1244 subjects from the San Diego Veterans Affairs Hypertension Cohort confirmed that Gly(49)/Gly(49) homozygotes displayed the least rapid decline of eGFR over approximately 3.6 years. We conclude that GFR decline rate in hypertensive renal disease is controlled in part by genetic variation within the adrenergic pathway, particularly at ADRB1. The results suggest novel strategies to approach the role of the adrenergic system in the risk and treatment of progressive renal disease.