Project Implementation Decision Using Software Development Life Cycle Models: A Comparative Approach

Selection of a suitable Software Development Life Cycle (SDLC) model for project implementation is somewhat confusing as there are a lot of SDLC models with similar strengths and weaknesses. Also, the solutions proffered among the researchers so far have been the qualitative comparative analysis of SDLC models. Hence, this paper proposes a comparative analysis of SDLC models using quantitative approach in relation to strengths and weaknesses of SDLC models. The study adapted comparative analysis and Software Development Life Cycle (SDLC) models features’ classification using ten characteristics such as project complexity, project size, project duration, project with risk, implementation/initial cost, error discovery, associated cost, risk analysis, maintenance and cost estimation. A quantitative measure that employs online survey using experts in software design and engineering, project management and system analysis was carried out for the evaluation of SDLC models. Purposeful Stratified Random Sampling (SRS) technique was used to gather the data for analysis using XLSTAT after pre-processing, taking into consideration both benefit and cost criteria. The overall performance evaluation showed that Spiral-Model is the best followed by V-Model and lastly Waterfall Model with comparative values of 38.63%, 35.76% and 25.61% respectively. As regards cost estimation, Waterfall Model is the most efficient with value of 41%, then V-Model with 31% and lastly Spiral Model with 28%. V-Model has great error recovery capability with value of 45% which is closely followed by Spiral Model with 37% and lastly Waterfall Model with 18%. The study revealed that, a model with efficient risk assurance does not guarantee efficient cost management. In the future work, more characteristics regarding SDLC models shall be considered.

Examining Information System’s Usage and Performance Indicators Using Technology Utilization, Satisfaction and Performance

While the development of information systems in workplaces with the aim of achieving cost-effectiveness, efficiency and quality of service delivery remain sacrosanct, issues of effective utilization and its resultant implications on organizational performance remain critical from one context to another. Unfortunately, few studies had considered focusing on these causal relationships among information system deployments in the construction industry especially in developing countries like Nigeria. This work modelled the interactions causal relationships associated with task technology fit, system usage and performance variables using the TUSPEM model. Through convenience and stratified sampling techniques, the views of 136 senior staff including top level management staff, sectional heads and other senior staff of a construction firm in Nigeria were sought. Smart PLS structural equation modeling software was used for the analysis of the dataset. The result showed significant relationships between causal variables in the TUSPEM model such as Application utilization to performance (t-value 2.44, P< 0.02), utilization to user satisfaction (t-value 2.87, P< 0.01). TTF to performance (t-value 2.86, P< 0.06), satisfaction (t-value 4.40, P< 0.00), User attitude to utilization (t-value 5.40, P< 0.00). Computer 2self-efficacy to utilization (t-value 4.47, P< 0.00). User satisfaction to performance (t-value 2.47, P< 0.01). Critical appraisal and integration of quality feedback on information system usage and its resultant effects on the numerous information systems being deployed must not be sideline if the sustainability of information system is anything to go by. Other implications are discussed.

Modelling and Implementation of a Device Based Digital Identity Management System

Identity management concerns transcends technical design issues as users disposition, awareness and attitude play a vital role in the effectiveness of such systems. A framework that supported some important digital identity management system security related features was presented in a previous study. In this current study, an attempt to empirically demonstrate the functioning of these features and to access how user’s awareness, disposition and attitude impact on the proper functioning of such systems was undertaken. To achieve these, a system was modeled and implemented using object oriented analysis and design method. The developed system empirically generates forensic evidence and an implicit mutual authentication scheme. The resulting system developed was tested for these features. The results obtained showed that the framework did provide for reliable forensic evidence generation and it also provided for the implementation of an implicit form of mutual authentication scheme. However, the outcome of a brief test conducted to gauge the reliability of this implicit mutual authentication scheme showed that the support provided was not very reliable based on these users’ factors (awareness, attitude and deposition)

A FrontPage Online News Analysis: An Opinion Mining Approach

The advent of the internet with its attendant democratization of data and deluge of information had given rise to the avalanche of news media agencies. These agencies publish news articles with varying emotional reports especially stories conveying bad sentiments to the public. As major news agencies operate micro-blogging websites and establish their presence on social media channels, the distribution of bad news increases. It has been shown that constant exposure to bad news presented in a body of texts, graphics, and videos/audios contribute to increase in high blood pressure, anxiety attacks, bowel disorders, stroke and/or heart failure. In this work, we presented a sentiment analysis framework to extract news articles from FrontPage of online newspapers and generate contextual wordlists to support positive news broadcasting. Using a set of 12 Nigerian online news channels, we employed a hybrid method of dictionary and corpus-based lexicon approaches to achieve the wordlist derivation. The result advocates for an alternative way of reporting negative news to reduce the adverse impact it has on the masses.

QLREDActive Queue Management Algorithm

The Random Early Detection (RED) algorithm has not been successful in keeping the average queue size low. In this paper, we an improved RED-based algorithm called QLRED which divides the dropping probability function of the RED algorithm into two equal segments. The first segment utilises a quadratic packet dropping function while the second segment deploys a linear packet dropping function respectively so as to distinguish between light and high traffic loads. The ns-3 simulation performance evaluations clearly showed that QLRED algorithm effectively controls the average queue size under various network conditions resulting in a low delay. Replacing/upgrading the RED algorithm in Internet routers requires minimal effort since only the packet dropping probability profile needs to be adjusted.

Application of Convolutional Neural Network (CNN) for the Prediction of Spam Mail

Spam mail has indeed become a global dilemma due to its coevolutionary nature. It has resulted in the loss of organizational resources, possibly financial cost incurred as well as time spent in addressing spam related issues. This has pushed organizations and researchers to the pinnacle of research with the aim of identifying needed solutions. This research paper explores the rich capabilities of Convolutional Neural Network (CNN) for predicting spam mail taking cognizant natural language capabilities. Spam mail prediction was simulated using a simulator built utilizing python programming language to capture the fundamentals of CNN. The CNN training was actualized using 10 epochs. The 1st epoch offers a training time of 4mins, 39s with a loss of 1.7578, accuracy of 0.3508, value loss of 1.2130 and value accuracy 0f 0.5719 while the 10th epoch presents a training time of 4mins, 6s with a loss of 0.5896, accuracy of 0.7936, value loss of 0.8941 and value accuracy of 0.6986.

Assessing the Effects of Coronavirus Outbreaks on the Demand for Electronic Health In Nigeria

Electronic health (e-Health) and Mobile health (m-Health) is perceived as opportunity for patients to access their health care providers in the developing countries during coronavirus pandemic as it has been found to contribute tremendously to health care provision in the developed world even before the pandemic. This study attempts to assess how residents of developing countries annexe e-health and m-health during coronavirus outbreak. More specifically, the study analyses the demand for and adoption of electronic health in the face of coronavirus pandemic in Nigeria (a developing country) using Borgu local government, Niger state as case study. It was found that during the outbreak, residents of the local government did not significantly adopt electronic health during the pandemic majorly due to access to community health worker and cost of adopting electronic health facilities. It was recommended that government and relevant health care agencies that deal policy formulation take necessary measure to encourage wider acceptance of electronic health in Nigeria.

A Proposed Framework for Pandemic Control Using Artificial Intelligence: A Case Study of COVID-19 Global Pandemic

A pandemic is a disease outbreak that occurs on a massive scale and rapidly spread across countries or continents. It affects people's daily life and halts businesses, jobs, the economy, social life, religious activities, and many more. In recent times, the world is faced with a unique pandemic that spread across all the seven continents known as Corona Virus (COVID-19). The World Health Organization (WHO) declared the outbreak a Public Health Emergency and therefore, classified it as a pandemic on 30 January 2020. Since then, researchers across multiple domains are putting every effort to come up with solutions to the virus. Most of the researchers have used both traditional and modern techniques to try and challenge the pandemic. Artificial intelligence is a modern technique that allows the management of pandemic and disease control to be easy, accurate, and efficient. Hence, a lot of techniques are proposed using Artificial Intelligence as a tool. In this study, we investigated over 150 research articles from highly reputable corpus of literature in an effort to propose a pandemic control framework that leverages Artificial intelligence and machine learning to improve the efficiency and accuracy of pandemic control and containment. However, the study further investigates the existing techniques for pandemic control, various applications of Artificial Intelligence in Healthcare, as well as proposes some pandemic control techniques using Artificial Intelligence as a platform. The result of this study indicates that Artificial Intelligence is capable of providing efficient mechanisms for pandemic control, prediction, detection, and containment.

Learner’s Generated Digital Media Usage and Effects among Nursing Students in Nigeria

Introduction: The use of learner’s generated digital media (LGDM) in education has accelerated several learning processes that were hitherto very complex and difficult to teach and learn in the past. They come in various multimedia formats to help ease learning and teaching experiences via telecommunication technologies and the Internet. Problem: Learning using LGDM in nursing science requires professional skills, motivations and intentions of users who generate multimedia and deploy them for learning. Understanding LGDM usage in learning and its significant effects may improve academic performance of the learner, motivation and adoption for nursing student. Objective: The aim of this study was to investigate the effects of LGDM on nursing learner’s skills (improved Academic Performances), motivation and intention to use LGDM in their learning programme by formulating hypotheses based on technology acceptance models. Methods: We carried out a survey research methodology of LGDM usage among nursing student of University of Benin Teaching Hospital (UBTH) and National Open University of Nigeria (Benin and Asaba study centre). Nursing student (N=500) of year 3 student was the focus group. Random sampling was used to select the participant (students) and a structured questionnaire was administered to them to provide responses to the closed-ended questions contained therein. Examining the role of Technological Acceptance Model (TAM) and Motivation model in learning established the theoretical framework presented in this research to determine the relationship of LGDM usage to learner intention and academic performance. Structured Equation Modelling (SEM) was used to analyse the theoretical framework path diagram. Results: Among the commonly used LGDM by the nursing students were podcast (video, audio) and e-books alongside with printable learning materials (PDF) for their learning experiences. Although most of those LGDM resources are readily available and free to access, most of the LGDM format require enormous bandwidth to download them hence a burden for learner. The analyses of the path diagram using SEM revealed significant relationships of core framework parameters (Effort Efficiency, Social Influence, Performance expectance) on Behaviour intention, motivation and improved Academic Performances of motivated nursing student who use multimedia for their learning. Conclusions: Nursing students are determined to use LGDM to enhance their learning and improve their academic performance. The findings in this work provide both contextual and practical blueprints for qualitative and collaborative learning and it will benefit education authorities and institutions that offer specialised nursing training and programmes to students for effective and qualitative health care delivery

YorAA: An Authorship Attribution of Yorùbá Texts

The process of establishing the most likely author of a collection of texts or documents whose authorship must be verified is known as authorship attribution. Several studies have been reported in the literature on the task, but rarely any reported work on Yorùbá language texts. In this paper, the development of an automatic Yorùbá written texts authorship attribution system (YorAA) is reported. The literary works of six Yorùbá authors were considered. Stylometry features were extracted from the texts using the BoW approach and lexical/syntactic word frequencies approach. The Support Vector Machine, Multilayer Perceptron and Random Forest algorithms were used for the classification analysis. The experimental results showed that the developed YorAA system achieved accuracy, recall, precision and F1 measures values of 95%, 83%, 84% and 84% respectively on the average, for all the six authors. The results demonstrate that with a database of written texts in Yorùbá language, that is enough to extract relevant stylometry ´ features of the author and appropriate methods and tools applied to such features; the authorship of the texts can be identified or verified.