Publications

This study developed a web-based text editor to eliminate the incompatibility of computer keyboard with the three major indigenous languages in Nigeria. It also aims to reduce the time taken to produce characters with diacritical marks. The editors produced valid Unicode characters and require pressing less buttons to generating all the symbols of the alphabets for the three major indigenous languages in Nigeria. Client-side technologies were used to develop these applications. Three web pages, designated for Yorùbá, Igbo and Hausa language were generated with HTML. CSS was used to define the look and feel of the HTML elements on each page. Regular Expressions implemented in JavaScript functions were used to convert selected ASCII characters into desired Unicode characters. The editors are available at http://www.gazaliwakil.com.ng. The editors work well on latest version of browsers like (Google Chrome, Mozilla Firefox, Opera, and Internet Explorer). They are very light, consume minimal server resources and can work offline. The system was launched Fifty-one (51) times to extract data comprising the Loading, Scripting, Rendering, Painting, System, and Idle time. The obtained result showed that on the average, it takes about 13.77ms to load the HTML DOM elements, 42.83ms to load the javaScript, 13.10ms and 1.73ms for rendering and painting the page by CSS. Additional time taken are 43.91ms and 3,045.10ms for the system and idle time respectively. A total time of 3,160.43ms (3.16s) is required when any of the editors is launched before the page can accept inputs from the users. It also takes the editors 2.66ms to add diacritical marks on a letter. This would, in effect, not reduce the typing speed of users.

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.

In recent years, Cyber Security threat modeling has been discovered to have the capacity of combatting and mitigating against online threats. In order to minimize the associated risk, these threats need to be modelled with appropriate Intelligent User Interface (IUI) design and consequently the development and evaluation of threat metrics. Artificial Intelligence (AI) has revolutionized every facet of our daily lives and building a responsive Cyber Security Threat Model requires an IUI. The current threat models lack IUI, hence they cannot deliver convenience and efficiency. However, as the User Interface (UI) functionalities and User Experience (UX) continue to increase and deliver more astonishing possibilities, the present threat models lack the predictability capacity thus Machine Learning paradigms must be incorporated. Meanwhile, this deficiency can only be handled through AI-enabled UI that utilizes baseline principles in the design of interfaces for effective Human-Machine Interaction (HMI) with lasting UX. IUI helps developers or designers enhance flexibility, usability, and the relevance of the interaction to improving communication between computer and human. Baseline principles must be applied for developing threat models that will ensure fascinating UI-UX. Application of AI in UI design for Cyber Security Threat Modeling brings about reduction in critical design time and ensures the development of better threat modeling applications and solutions.