https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.247

Problem frames: analyzing and structuring software development problems. By Michael Jackson. Published by Addison‐Wesley, Boston, Massachusetts, U.S.A., ACM Press Series, 2001. ISBN: 0‐201‐59627‐X, 390 pages. Price: U.K. ?32.95, U.S.A. $49.99, soft cover.

Background: Blockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing.
Objective: This review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs.
Methods: Adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form.
Results: A total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage.
Conclusions: This review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space.
Trial Registration:

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Blockchain Personal Health Records: Systematic Review.

Software development is a complex human endeavour with high failure rates. Although a variety of software development methodologies have been proposed to improve the software development process, there is no universal model for all software development organizations. Virtual reality (VR) is an emerging trend especially for the gaming industry, which should prepare itself for VR development. The goal of this study is to explore potential software development activities and determine whether designing a new software development methodology for VR projects is an important topic for software development organizations working on VR software development. For this purpose, a literature survey has been completed and 71 academic studies have been examined in detail. This study shows that no work is being conducted in the field of developing a new methodology for VR projects. However, the study does show that there are similar endeavours in the field of human computer interaction (HCI), such as game development methodology.

A Literature Survey: Is it Necessary to Develop a New Software Development Methodology for Virtual Reality Projects?

Blockchain technology has gained immense momentum in the present era of information and digitalization and is likely to gain extreme popularity among the next generation, with diversified applications that spread far beyond cryptocurrencies and bitcoin. The application of blockchain technology is prominently observed in various spheres of social life, such as government administration, industries, healthcare, finance, and various other domains. In healthcare, the role of blockchain technology can be visualized in data-sharing, allowing users to choose specific data and control data access based on user type, which are extremely important for the maintenance of Electronic Health Records (EHRs). Machine learning and blockchain are two distinct technical fields: machine learning deals with data analysis and prediction, whereas blockchain emphasizes maintaining data security. The amalgamation of these two concepts can achieve prediction results from authentic datasets without compromising integrity. Such predictions have the additional advantage of enhanced trust in comparison to the application of machine learning algorithms alone. In this paper, we focused on data pertinent to diabetic retinopathy disease and its prediction. Diabetic retinopathy is a chronic disease caused by diabetes and leads to complete blindness. The disease requires early diagnosis to reduce the chances of vision loss. The dataset used is a publicly available dataset collected from the IEEE data port. The data were pre-processed using the median filtering technique and lesion segmentation was performed on the image data. These data were further subjected to the Taylor African Vulture Optimization (AVO) algorithm for hyper-parameter tuning, and then the most significant features were fed into the SqueezeNet classifier, which predicted the occurrence of diabetic retinopathy (DR) disease. The final output was saved in the blockchain architecture, which was accessed by the EHR manager, ensuring authorized access to the prediction results and related patient information. The results of the classifier were compared with those of earlier research, which demonstrated that the proposed model is superior to other models when measured by the following metrics: accuracy (94.2%), sensitivity (94.8%), and specificity (93.4%).

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Diabetic Retinopathy Detection: A Blockchain and African Vulture Optimization Algorithm-Based Deep Learning Framework

Background/Objectives: In Software Engineering discipline, requirement phase plays vital role in success of a project. To make the requirement analysis process more effective, developers adopt several methods like goal-oriented, aspect-oriented, pattern-oriented etc. In the current scenario recurring requirements can be addressed effectively with requirement patterns rather applying a common procedure to all projects. Methods/Statistical analysis: While adopting common procedure to all project results in increasing cost of quality or even project failure due to the fact that every project is not having similar characteristics. To perform requirement process effectively, it is necessary to select a suitable requirement pattern for a project. Selecting relevant, applicable and reliable pattern is challengeable and prior knowledge is required. Findings: The proposed framework groups project characteristics and the pattern sets into k-clusters based on the clustering algorithm. The best suitable pattern will be filtered from the available pattern clusters by applying the filtering algorithm. The suggested patterns along with the situational constraints for a given situation will be stored in the data store. The stack holder can add new pattern details or can update the details of existing patterns. Applications/Improvements: Finds the analysis patterns that matches with the given situational characteristics and requirement of a project. Clustering enhanced with fuzzy to bring the improvement.

Requirement Pattern Extraction using Cluster Based Framework - RAPID

Requirements engineering is one of the necessary and difficult tasks during software development process. The steps in requirements engineering depends very much on the analysts experience and educations. Here, the process starts from decomposition of the environment using problem frames (PF) approach into problem domains where we can get requirements and it gives a formal requirement phenomenon of the interconnected domains. In each step, the requirement reusability can be helped towards the problem frame where analysis pattern can help to overcome the reconstruction of requirements and provide guidance to the analysts. In such cases for solving each problem frame, appropriate patterns should be retrieved and it is used for providing a solution space to it. Once the requirements are clear then they can be included for requirement progression to derive its specification towards the machine with the help of analysis pattern. The influence of the requirement progression of the whole problem will be analyzed. Finally, the given framework is implemented with a case study using ATL(Atlass Transformation Language). In this paper, we propose a new methodological framework for guiding requirements domain to development with the help of analysis pattern.

An approach for decomposing requirements into analysis pattern using problem frames (DRAP-PF)

Electronic health information is an efficient technique for providing health care services to society. Patient health information is stored in the cloud, to allow access of eHealth information from anywhere, and at any time, but the technical problems are security, privacy, etc. Sharing the medical data in a trustless environment is overcome by the proposed framework SeFra. The proposed work provides a secure framework to manage the eHealth record by using blockchain (SeFra). For authentication purposes, a temporal shadow is used and the integrity of health records is ensured by blockchain technology.

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Saravanaguru R.A.K.

Papers

An approach for decomposing requirements into analysis pattern using problem frames (DRAP-PF)

SeFra: A Secure Framework to Manage eHealth Records Using Blockchain Technology