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COVID-19 has fostered the growth and transformation of digital media in every aspect, importantly in the area of electronic commerce. During these days, many new practices inhabiting in human behaviour bring new norms for the marketers. Use of online shopping has increased incrementally due to the government advisory, changing consumer buying behaviour as well. An amalgamation of the old and new medium of selling goods and services are now bringing new business challenges post pandemic for the marketer, and customer retention is one of them. Henceforth, there is a dearth of studies addressing the impact of dimension of loyalty (behaviour/ attitude) on customer retention in online shopping platforms post pandemic. This study considers post pandemic as the context of research to determine the impact of behavioural and attitudinal loyalty on customer retention for the online shopping platforms. Further research also aims to determine whether the brand image plays a critical role in mediating the relationship between dimensions (Behavioural/ Attitudinal) of loyalty and customer retention for online shopping platforms or not? In the research, primary data represent the respondent from Delhi NCR. The author applied linear regression to validate the research idea. The findings indicate that attitudinal loyalty significantly enforces customer retention over behavioural loyalty. Also, brand image mediates the impact of attitudinal loyalty on retention.

Voting-based consensus clustering is a subset of consensus techniques that makes clear the cluster label mismatch issue. Finding the best relabeling for a given partition in relation to a reference partition is known as the voting problem. As a weighted bipartite matching problem, it is frequently formulated. We propose a more generic formulation of the voting problem as a regression problem with various and multiple-input variables in this work. We demonstrate how a recently developed cumulative voting system is an exception that corresponds to a linear regression technique. We employ a randomised ensemble creation method in which an excess of clusters are randomly chosen for each ensemble partition. In order to extract the consensus clustering from the combined ensemble representation and to calculate the number of clusters, we use an information-theoretic approach. Together with bipartite matching and cumulative voting, we use it. We provide empirical data demonstrating significant enhancements in clustering stability, estimation of the real number of clusters, and accuracy of clustering based on cumulative voting. The gains are made in comparison to recent consensus algorithms as well as bipartite matching-based consensus algorithms, which struggle with the selected ensemble generation technique.

Through the use of blockchain technology, sensitive information may be securely communicated without the need to replicate it, which can assist in decreasing medical record mistakes and saving time by eliminating the need to duplicate information. Furthermore, the information is timestamped, which further enhances the security of the data even further. The deployment of blockchain technology in a range of healthcare situations may enhance the security and efficiency of payment transactions. In this way, only those who have been allowed access to patient medical information can see or modify such information. It is proposed in this study that blockchain technology be used to provide an accessible data storage and retrieval mechanism for patients and healthcare professionals in a healthcare system that is both safe and efficient. As of 1970, a variety of traditional knowledge-based approaches such as Personal Identification Recognition Number (PIRN), passwords, and other similar methods have been made available; however, many token-based approaches such as drivers’ licenses, passports, credit cards, bank accounts, ID cards, and keys have also been made available; however, they have all failed to establish a secure and reliable transaction channel. Because they are easily misplaced, stolen, or lost, they are usually unable to protect secrecy or authenticate the identity of a legitimate claimant. Aside from that, personally identifiable information such as passwords and PINs is very prone to fraud since they are easily forgotten or guessed by an imposter. Biometric identification and authentication (commonly known as biometrics) are attracting a great deal of attention these days, particularly in the realm of information security systems, due to its inherent potential and advantages over other conventional ways for identifying and authenticating. As a result of the device’s unique biological characteristics, which include features such as fingerprints, facepalms, hand geometry (including the iris), and the device’s iris, it can be used in a variety of contexts, such as consumer banking kiosks, airport security systems, international ports of entry, universities, office buildings, and forensics, to name a few. It is also used in several other contexts, including forensics and law enforcement. Consequently, every layer of the system—sensed data, computation, and processing of data, as well as the storage and administration of data—is susceptible to a broad variety of threats and weaknesses (cloud). There does not seem to be any suitable methods for dealing with the large volumes of data created by the fog computing architecture when normal data storage and security technologies are used. Because of this, the major objective of this research is to design security countermeasures against medical data mining vulnerabilities that originate from the sensing layer and data storage in the Internet of Things’ cloud database, both of which are discussed in more depth further down. A key allows for the creation of a distributed ledger database and provides an immutable security solution, transaction transparency, and the prohibition of tampering with patient information. This mechanism is particularly useful in healthcare settings, where patient information must be kept confidential. When used in a hospital environment, this method is extremely beneficial. As a result of incorporating blockchain technology into the fog paradigm, it is possible to alleviate some of the current concerns associated with latency, centralization, and scalability.