Development of Prepaid Electricity Payment System for a University Community Using the LUHN Algorithm

This work presents a University Community based electricity prepaid billing system. Generally in Nigeria, electricity customers face a lot of problems with respect to their electricity bills from the distribution companies. The challenges they face include wrongly calculated bills as a result inaccurate reading of meters, general human errors in bill preparation among others. In some other semi-automated systems in which prepaid meters are used, consumers waste much time in purchasing utility units for electricity. This is the case presently at the university community we are considered in this work. This paper presents the design and implementation of a combination of a web-based and SMS alert prepaid electricity system called for the community. The implementation of the system was done using C# programming language and Microsoft SQL Server as the database platform. The system incorporates the Luhn algorithm for generating pins for use on the simulated prepaid meters. The system is able to run on the university intranet and can also serve as internet based application

Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

A roadmap to integrated digital public health surveillance: The vision and the challenges

The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems have to utilize the potential created by new situationaware realtime signals from social media, mobile/sensor networks and citizens' participatory surveillance systems providing invaluable free realtime event-based signals for epidemic intelligence. However, rather than improving existing isolated systems, an integrated solution bringing together existing epidemic intelligence systems scanning news media (e.g., GPHIN, MedISys) with real-time social media intelligence (e.g., Twitter, participatory systems) is required to substantially improve and automate early warning, outbreak detection and preparedness operations. However, automatic monitoring and novel verification methods for these multichannel event-based real time signals has to be integrated with traditional case-based surveillance systems from microbiological laboratories and clinical reporting. Finally, the system needs effectively support coordination of epidemiological teams, risk communication with citizens and implementation of prevention measures. However, from computational perspective, signal detection, analysis and verification of very high noise realtime big data provide a number of interdisciplinary challenges for computer science. Novel approaches integrating current systems into a digital public health dashboard can enhance signal verification methods and automate the processes assisting public health experts in providing better informed and more timely response. In this paper, we describe the roadmap to such a system, components of an integrated public health surveillance services and computing challenges to be resolved to create an integrated real world solution

Terms extractions: an approach for requirements reuse

This paper presents a solution to a requirements reuse problem that utilises natural language processing and information retrieval technique. We proposed a semi-automated approach to extract the software features from online software review to assist the process to reuse natural language requirements. We have conducted an experiment to compare the manual feature extraction versus the semi-automated feature extraction. We used compilations of software review from the Internet as a source of this extraction process. The extracted software features are compared against the features obtained manually by human and the evaluation results obtained in terms of time, precision, recall, and F-Measure indicate a promising result

Serious games to improve health professional´s skills when caring for cardiac patients

This study focuses on a coronary care unit, aiming to identify ways in which these formal caregivers try to solve their problems and educational needs However, to intervene with non formal education and training actions, using innovative ways of designing, starting and delivering training, in which the participants involved organize themselves and take an active role in terms of the selection of objectives and contents This dynamic includes serious games as an element capable of producing attention, memory, and motivation for participants, providing them with effective and affective experiences A scoping review on the topic highlights the potential of serious games in the development of affection, attention, memory, and motivation to learn, nonetheless other factors should be considered, such as collaborative learning environments

Secure Storage Model for Digital Forensic Readiness

Securing digital evidence is a key factor that contributes to evidence admissibility during digital forensic investigations, particularly in establishing the chain of custody of digital evidence. However, not enough is done to ensure that the environment and access to the evidence are secure. Attackers can go to extreme lengths to cover up their tracks, which is a serious concern to digital forensics – particularly digital forensic readiness. If an attacker gains access to the location where evidence is stored, they could easily alter the evidence (if not remove it altogether). Even though integrity checks can be performed to ensure that the evidence is sound, the collected evidence may contain sensitive information that an attacker can easily use for other forms of attack. To this end, this paper proposes a model for securely storing digital evidence captured pre- and post-incident to achieve reactive forensics. Various components were considered, such as integrity checks, environment sandboxing, strong encryption, two-factor authentication, as well as unique random file naming. A proof-of-concept tool was developed to realize this model and to prove its validity. A series of tests were conducted to check for system security, performance, and requirements validation, Overall, the results obtained showed that, with minimal effort, securing forensic artefacts is a relatively inexpensive and reliable feat. This paper aims to standardize evidence storage, practice high security standards, as well as remove the need to create new systems that achieve the same purpose