Research

To assuage the uncertainties of health mishaps, Digital Twin can render insights and predictive analysis of the situation by perceiving the physical environment. For any digital twin collecting the data from physical space is the most important task. But, there is a lack of conceptual data model for digital twin in healthcare sector. For this reason, we proposed a data model and based on this proposed a blockchain integrated healthcare digital twin technology. Moreover, patients will have external data, collecting this type of data is very cumbersome. As, digital twin deals with all the aspects of data pertinent to a particular object for this reason external data can’t be ignored. Also the system will have different types of users having numerous degrees of access control which need to be handled profoundly. The scope of this research are: how the perceived data can be represented structurally for which a concrete mathematical data model is presented as well as the blockchain integrated healthcare digital twin system.

The PoW (Proof of Work) consensus protocol is computationally very costly. So the main purpose is to alleviate the computation cost by reducing some computation in mining. With this in mind, a consensus protocol based Bitcoin cryptocurrency system has been proposed. It is not a fully developed consensus protocol so the implementation is not possible. With the available concept of mining pool, miner will not get the full transactions. By hashing arbitrary numbers, miners have to guess what the true transfer values of the transactions and will check if the sender and receiver are authentic. As going forward, it is found that a myriad things need to be checked like crash fault tolerant, Byzantine fault tolerant, state of the chain throughout the network, and many more. In future, it cannot be developed any further as this system has some flaws which will debase the integrity of the blockchain.

Recommendation system is a structure to help user with different suggestions of things. Nowadays, recommendation systems are impacting people in ways than before which was imperceptible. Moreover, with the advent of different database schema, more sophisticated and diverse data can be easily accumulated respect to an individual user. Whereas, most of the conventional recommendation systems are based on an incentive of recommending items from the same cluster. Despite of its usefulness, currently users are not confined to only one transmission channel, which is decreasing the impact of these recommendation systems. With this problem in mind, in this article, we have implemented a recommendation system incorporating Analytical Hierarchical Process (AHP), Fuzzy Analytical Hierarchical Process (Fuzzy-AHP), and Vikor, which can recommend specific type of an item to specific user by assessing the data. Additionally, We can also devise which online platform will be the most impactful to target this user with the product recommendation. The data set has been acquired after conducting a survey.

Data compression is a technique of representing data in a compact form. It is a process that mitigates the data size, redundancy, and truncate some extra information. A diminutive of an actual data is always propitious as it takes less cost in every way like space, time, computation, bandwidth, and many more. Recent researches lack in providing correct use of compression in different situation. For say, before encrypting a Cryptographic Hash value, Run-length may work better than a Dictionary algorithm to minimize the size and length of the data. With this in mind this article explores two of the lossless compression technique: Run-length and Dictionary compression algorithm. We have implemented both of the code and for comparison the values have been collected for specific reasoning. Lastly, the favorable states for which these two compression algorithms can be used is stated according to the acquired result.