Supply chains are evolving into automated and highly complex networks and are becoming an important source of potential benefits in the modern world. At the same time, consumers are now more interested in food product quality. However, it is challenging to track the provenance of data and maintain its traceability throughout the supply chain network. The traditional supply chains are centralized and they depend on a third party for trading. These centralized systems lack transparency, accountability and auditability. In our proposed solution, we have presented a complete solution for blockchain-based Agriculture and Food (Agri-Food) supply chain. It leverages the key features of blockchain and smart contracts, deployed over ethereum blockchain network. Although blockchain provides immutability of data and records in the network, it still fails to solve some major problems in supply chain management like credibility of the involved entities, accountability of the trading process and traceability of the products. Therefore, there is a need of a reliable system that ensures traceability, trust and delivery mechanism in Agri-Food supply chain. In the proposed system, all transactions are written to blockchain which ultimately uploads the data to Interplanetary File Storage System (IPFS). The storage system returns a hash of the data which is stored on blockchain and ensures efficient, secure and reliable solution. Our system provides smart contracts along with their algorithms to show interaction of entities in the system. Furthermore, simulations and evaluation of smart contracts along with the security and vulnerability analyses are also presented in this work.

In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.

We have investigated graphene-based three various refractive index sensors (split ring resonator (SRR), split ring resonator with thin wire (SRRTW), and thin wire (TW) refractive index sensors) for the encoding and sensing-based applications. The sensors are designed to detect the presence of hemoglobin biomolecules with high sensitivity. The results are analyzed in the form of transmittance, and electric field and detailed sensitivity analysis is also carried out for the proposed graphene-based refractive index sensors for four various concentrations of hemoglobin biomolecules. We have also investigated the sensor’s performance in terms of quality factor, Q, and figure of merit (FOM). The encoding of ‘0’ and ‘1’ is attained by varying the graphene chemical potential fulfilling the one-digit coding. An array of these sensors can then be used for encoding-based applications. The detailed analysis of reported sensors is also carried out by checking the effect of varying physical parameters such as substrate thickness, split ring gap, and thin wire width on tunability. These sensors can be applied in biomedical or encoding-based applications. Experiments are performed using XGBoost regressor to determine, whether simulation time and resources can be reduced by using regression analysis to predict the transmittance values of intermediate frequency or not. Experimental results prove that regression analysis using XGBoost Regressor can reduce the simulation time and resources by at least 70 percent.

A secure, smart and peer-to-peer transactions framework can be developed by Blockchain. Blockchain has enormous potential to turn health care systems as a horizontal technology, which has changed many fields of industry. The aim of this article is to critically review 50 papers published between 2015 and 2020 on blockchain-based health systems. 36 of these were journal papers; 7 were from conferences, 4 were from various symposiums; 3 were from seminars and 1 chapter was written in the book. Three key questions will be answered in this report. Firstly, what are the emerging trends of blockchain application development in healthcare from a technical perspective? Secondly, in what ways will the systematic analysis presented here contribute to a better understanding of the potential for incorporating blockchain-based technologies into the healthcare domain? Third, what are the important challenges in adopting blockchain as a solution in the healthcare domain? The descriptive analysis contains in this article shows the statistical statistics on the strategies of these 50 papers reveal that many of the blockchain systems proposed are using privately held blockchain and Ethereum platforms. We also address possible emerging trends of blockchain application such as blockchain integration with artificial intelligence, cloud based solutions and parallel block chain architecture.

Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early‐stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi‐square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K‐nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best‐fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time.

The development of web applications essentially relies on users’ demands who expect highly efficacious but cost effective software services. Managing time and cost so as to develop web applications that cater to the users’ need is a challenge for developers at present. Moreover, low-cost maintenance can only be achieved by enhancing the durability of the web applications. Identifying characteristics of durability is a complex task because the different experts have different opinions regarding the significance of characteristics that determine durability quotient of the web applications. As established by the best practices undertaken in this context, some experts consider quality to be the most important factor for determining durability. Therefore, the present study enlists multi-criteria decision-based symmetrical technique to address the multi-vector option availability for the apt selection of the characteristics for durability. Furthermore, it has also been identified that a numerical assessment of web applications’ durability can affect the service life and low-cost management in web applications. In this context, to achieve high durability and longevity in web applications, this paper attempts to illustrate and perform a numerical evaluation of durability characteristics. By understanding the various characteristics and their significance towards durability, the paper finds that the hesitant fuzzy-based symmetrical technique of the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an effective methodology for evaluating web applications’ durability. For evaluating the quality of the results and establishing their sensitivity, the authors have assessed the outcomes on six different projects of the University. Additionally, results assessed and discussed in this paper would be a conclusive reference for the developers in their attempts to develop highly durable and manageable web applications.