The land registry system is one of the very important department in any governance system that stores the records of land ownership. There are various issues and loopholes in the existing system that give rise to corruption and disputes. This requires a significant chunk of valuable government resources from judiciary and law enforcement agencies in settling these issues. Blockchain technology has the potential to counter these loopholes and sort out the issues related with land registry system like tempering of records, trading of the same piece of land to more than one buyer. In this paper, a secure and reliable framework for land registry system using Blockchain has been proposed. The proposed framework uses the concept of smart contract at various stages of the land registry and gives an algorithm for pre-agreement. First, we describe the conventional land registry system and reviews the issues in it. Then, we outline the potential benefits of employing Blockchain technology in the land registry system and presented a framework. Finally, a number of case studies are presented.

Blockchain technology is a private, secure, trustworthy, and transparent information exchange performed in a decentralised manner. In this case, the coordination and validation efforts are simplified as the records are designed to update regularly and there is no difference in the two databases. This review focuses on how the blockchain addresses scalability challenges and provides solutions in the healthcare field through the implementation of blockchain technology. Accordingly, 16 solutions fell under two main areas, namely storage optimization and redesign of blockchain. However, limitations persist, including block size, high volume of data, transactions, number of nodes, and protocol challenges. This review consists of six stages, namely identification of research question, procedures of research, screening of relevant articles, keywording based on the abstract, data extraction, and mapping process. Through Atlas.ti software, the selected keywords were used to analyse through the relevant articles. As a result, 48 codes and 403 quotations were compiled. Manual coding was performed to categorise the quotations. The codes were then mapped onto the network as a mapping process. Notably, 16 solutions fell under two main areas, namely storage optimization and redesign of blockchain. Basically, there are 3 solutions compiled for storage optimization and 13 solutions for the redesign of the blockchain, namely blockchain modelling, read mechanism, write mechanism, and bi-directional network.

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a twofold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cuttingedge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images. INDEX TERMS Breast cancer, histopathology medical images, deep learning, transfer learning, data augmentation, pre-processing, classification.

Disruptive technology, blockchain is propelling a technological intervention in healthcare due to its unique features and advantages. The healthcare industry is migrating to Health 4.0. Therefore, peer-to-peer (P2P) transactions in a decentralized and distributed manner make blockchain more lucrative to serve the needs of the healthcare industry of today. The revolutionary system blockchain has been discussed in the field of healthcare over the past five years. Hence, a systematic investigation of the existing body of knowledge concerning blockchain research in the healthcare domain is essential. The motivation of this study is to support further study based on the current research trend analysis through graphical visualization and analysis of the bibliographic material. Therefore, this study maps the expansion of scientific and academic research conducted concerning blockchain relevant to healthcare by utilizing a bibliometric analytic method to understand the state of the art. Bibliometric statistics have been utilized to analyze current scientific articles published in the Scopus database from 2016 to 2019. In addition, an overview of the publication trends over the first three months of 2020 has been undertaken to understand the research trend for the current year so far. The study serves the purpose of mapping research development trends in this area. The outcome discovered some beneficial insights such as the yearly trend of publications, top listed authors, institutes, countries and publishers from around the world. Moreover, this paper assists scholars to develop a theoretical framework to provide a primary source of reference in this field for further study of blockchain technology in the healthcare domain. INDEX TERMS Blockchain, bibliometric analysis, Health 4.0, healthcare, Scopus database.