Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

Optimized Design of Multilines Center of Subway AFC System via Distributed File System and Bayesian Network Model

Automatic fare collection system (AFCS) is a modern, automatic, networked toll collection system for rail transit ticket sales, collection, billing, charging, statistics, sorting, and management. To realize the subway transit networking operation, this paper designs the subway AFCS based on a distributed file system (DFS), namely, Gluster File System (GlusterFS). Firstly, the multiline center (MLC) in the subway AFCS is designed to analyze the status and current situation of distributed file processing in subway MLC system; secondly, the relevant technical theories are summarized, the Bayesian Network (BN) theoretical model and DFS are explored, and the principles of four DFS are comparatively analyzed; thirdly, the architecture and cluster mode of GlusterFS is expounded, and then based on GlusterFS, the architecture of subway AFCS is discussed. This paper presents several innovation points: first, the subway AFCS is designed based on GlusterFS by analyzing and aiming at the functional requirements, performance requirements, and safety requirements of the MLC subway system; second, the safety risk analysis (SRA) of AFCS is conducted from six security requirements, and a Web scanning system is designed to ensure the system data security. Finally, the design scheme is used to analyze the subway passenger flow and power consumption. The results demonstrate that the design scheme can competently adapt to three different application scenarios. Through comparison of two deployment modes of the Web scanning system, the data security Web scanning system can ensure the safe operation of the AFCS. Furthermore, the statistical analysis of subway passenger flow and power supply data shows that the proposed scheme can support the smooth operation of the subway system, which has significant practical value.

NFC-Blockchain Based COVID-19 Immunity Certificate: Proposed System and Emerging Issues

Vaccine requirements are becoming more mandatory in several countries as public health experts and governments become more concerned about the COVID-19 pandemic and its variants. In the meantime, as the number of vaccine requirements grows, so does the counterfeiting of vaccination documents. Fake vaccination certificates are steadily growing, being sold online and on the dark web. Due to the nature of the COVID-19 pandemic, there is a need of robust authentication mechanisms that support touch-less technologies like Near Field Communication (NFC). Thus, in this paper, a blockchain-NFC based COVID-19 Digital Immunity Certificate (DIC) system is proposed. The vaccination data are first encrypted by the Advanced Encryption Standard (AES) algorithm on Hadoop Distributed File System (HDFS) and then uploaded to the blockchain. The proposed system is based on the amalgamation of NCF and blockchain technologies which can mitigate the issue of fake vaccination certificates. Furthermore, the emerging issues of employing the proposed system are discussed with future directions.

Using HDFS to Load, Search, and Retrieve Data from Local Data Nodes

By utilizing the Hadoop environment, data may be loaded and searched from local data nodes. Because the dataset's capacity may be vast, loading and finding data using a query is often more difficult. We suggest a method for dealing with data in local nodes that does not overlap with data acquired by script. The query's major purpose is to store information in a distributed environment and look for it quickly. In this section, we define the script to eliminate duplicate data redundancy when searching and loading data in a dynamic manner. In addition, the Hadoop file system is available in a distributed environment. Keywords: HDFS; Hadoop distributed file system; replica; local; distributed; capacity; SQL; redundancy

Evaluation of Various DR Techniques in Massive Patient Datasets using HDFS

The objective of comparing various dimensionality techniques is to reduce feature sets in order to group attributes effectively with less computational processing time and utilization of memory. The various reduction algorithms can decrease the dimensionality of dataset consisting of a huge number of interrelated variables, while retaining the dissimilarity present in the dataset as much as possible. In this paper we use, Standard Deviation, Variance, Principal Component Analysis, Linear Discriminant Analysis, Factor Analysis, Positive Region, Information Entropy and Independent Component Analysis reduction algorithms using Hadoop Distributed File System for massive patient datasets to achieve lossless data reduction and to acquire required knowledge. The experimental results demonstrate that the ICA technique can efficiently operate on massive datasets eliminates irrelevant data without loss of accuracy, reduces storage space for the data and also the computation time compared to other techniques.