Big Data Security on Hadoop Open Source Frame for Healthcare Data Management using One-Time-Pad Encryption Algorithm (Published)

The study elicited knowledge about the factors associated with one-time pad encryption/decryption with big data in healthcare; formulate an assembled algorithms model for one-time pad encryption; design and implement the system and evaluating the system performance with the view implementing big data security on Hadoop open-source framework for healthcare data. Literature was sourced to investigate the factors associated with healthcare security attacks and various consequences of breach of data. An assembled algorithm model was formulated using mathematical theory of one-time pad encryption and a model was designed using Universal Modelling Language (UML) and implemented using python programming language, Distributed File System of Hadoop, Yet Another Resource Negotiator called YARN; encryption time and decryption time was adopted for the performance metrics deployed for the evaluation of the developed system. The result showed that as the size of the files increased, the encryption/decryption time keeps increasing as well. While carryout the algorithm evaluation, two different values (file sizes) were used for testing on the Hadoop framework.Securing the healthcare (Ebola) big-data, it was observed that OTP encryption/decryption performed better compared to AES encryption/decryption in term of computational processing time of the healthcare big-data considered. Considering before/after downloading, it was observed that there was need for authentication for another level of security towards securing healthcare records on HDFS. The study concluded that, big data analytics on Hadoop is ideal for today’s big healthcare data and also that One Time Pad encryption algorithm is sufficient to provide needed big healthcare data security.

Keywords: Algorithm, Big Data, Encryption, Hadoop, data security, data vulnerabilities

Biometric Authentication of Remote Fingerprint Live Scan Using Artificial Neural Network with Back Propagation Algorithm and Possibility for Wider Security Applications (Published)

This study is aim to experiments the development of an automated foolproof university library system that integrates fingerprint technique with fingerprint-based Personally Identified Number (PIN)/password architecture for enhanced registration and login security. The development environment for creating the electronic library application for universities as RESTful Web Service is Jersey Framework. This framework implements JAS-RX 2.0 API, which is a de facto specification for developing a RESTful Web Service-based software system. Other necessary programming technologies employed in the research work are JDK, Apache Tomcat and Eclipse, which were set up prior to setting up the Jersey Framework as the development environment. The study is therefore summarized by generating hash digital values of perfectly matched reference shape signatures formed from the extraction of global minutiae features, comparing and further matching each hash value with its corresponding highly encrypted password equivalence for unique establishment of a person’s identity, minimal mean-square errors and unnecessary ambiguity introduced through false positives, as an extended security enhancement measure in biometric systems. , the study investigates the algorithm for generating templates for matching minutiae [10] together with the algorithm for generating reference axis [11], which infers that for a pair of minutiae (p_n , q₀) to match, there exists a reference point that corresponds between the two fingerprint images. The experimental result shows that the Sample fingerprint images were captured using a biometric scanner, which was integrated with the help of JAVA libraries, and stored in a database as raw image files..

 

Keywords: Algorithm, Artificial Neural Network, Biometric, live scan, rest architecture

Searching Isomorphic Graphs (Published)

To determine that two given undirected graphs are isomorphic, we construct for them auxiliary graphs, using the breadth-first search. This makes capability to position vertices in each digraph with respect to each other. If the given graphs are isomorphic, in each of them we can find such positionally equivalent auxiliary digraphs that have the same mutual positioning of vertices. Obviously, if the given graphs are isomorphic, then such equivalent digraphs exist. Proceeding from the arrangement of vertices in one of the digraphs, we try to determine the corresponding vertices in another digraph. As a result we develop an algorithm for constructing a bijective mapping between vertices of the given graphs if they are isomorphic. The running time of the algorithm equal to O(n⁵), where n is the number of graph vertices.

Keywords: Algorithm, Bijective Mapping, Graph, Graph Isomorphism Problem, Isomorphic Graphs, Isomorphism

A Web-Based Clinical Decision Support System for the Management of Diabetes Neuropathy Using Naïve Bayes Algorithm (Published)

Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences. Due to this shortage of specialists, there is a need for a Clinical Decision Support System that will diagnose and manage diabetes neuropathy. This work therefore aimed at designing a web-based Clinical Decision Support System for the management of early diabetes neuropathy. Four pattern classification algorithms (K-nearest neighbor, Decision Tree, Decision Stump and Rule Induction) were adopted in this work and were evaluated to determine the most suitable algorithm for the clinical decision support system. Datasets were gathered from reliable sources; two teaching hospitals in Nigeria, these were used for the evaluation Benchmarks such as performance, accuracy level, precision, confusion matrices and the models building’s speed were used in comparing the generated models. The study showed that Naïve Bayes outperformed all other classifiers with accuracy being 60.50%. k-nearest neighbor, Decision Tree, Decision Stump and Rule induction perform well with the lowest accuracy for x- cross validation being 36.50%. Decision Tree falls behind in accuracy, while k-nearest neighbour and Decision Stump maintain accuracy at equilibrium 41.00%. Therefore, Naïve Bayes is adopted as optimal algorithm in the domain of this study. The rules generated from the optimal algorithm (Naïve Bayes) forms the back-end engine of the Clinical Decision Support System. The web-based clinical decision support system was then designed The automatic diagnosis of diabetes neuropathy is an important real-world medical problem. Detection of diabetes neuropathy in its early stages is a key for controlling and managing patients early before the disabling effect present. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. For further studies, researchers can improve on the proposed clinical decision support system by employing more than one efficient algorithm to develop a hybrid system.

Keywords: Accuracy, Algorithm, Classification, Diabetes, Neuropathy, precision