ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION

This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro-fuzzy inference system in the prediction of okra yield using environmental parameters such as minimum temperature, relative humidity, evaporation, sunshine hours, rainfall and maximum temperature as input into the neuro-fuzzy inference system, and yield as output. The agro meteorological data used were obtained from the department of agro meteorological and water management, Federal University of Agriculture, Abeokuta and the yield data were obtained from the Department of Horticulture, Federal University of Agriculture, Abeokuta. MATLAB was used for the analysis of the data. From the results, the maximum predicted yield showed that at minimum temperature of 24.4 oc, relative humidity of 78.3% and evaporation of 5.5mm, the yield predicted is 1.67 tonnes/hectare.

Performance Evaluation of Software using Formal Methods

Formal Methods (FMs) can be used in varied areas of applications and to solve critical and fundamental problems of Performance Evaluation (PE). Modelling and analysis techniques can be used for both system and software performance evaluation. The functional features and performance properties of modern software used for performance evaluation has become so intertwined. Traditional models and methods for performance evaluation has been studied widely which culminated into the modern models and methods for system and software engineering evaluation such as formal methods. Techniques have transcended from functionality to performance modeling and analysis. Formal models help in identifying faulty reasoning far earlier than in traditional design; and formal specification has proved useful even on already existing software and systems. Formal approach eliminates ambiguity. The basic and final goal of the performance evaluation technique is to come to a conclusion, whether the software and system are working in a good condition or satisfactorily

Animal diseases and zoonoses at a municipal slaughterhouse in Southwest Nigeria: Three-year retrospective survey (2014–2016)

Slaughterhouses are certified premises where animals are slaughtered and inspected to ensure meats are wholesome and safe for public consumption. To determine the common zoonoses encountered in a municipal slaughterhouse of Abeokuta, Ogun State, Nigeria, a three-year retrospective study was conducted (2014–2016). During the review of slaughterhouse records, the overall throughput of cattle slaughtered was 146,794 (4077.6 ± 413.6; 95% confidence interval [CI] 3923.3–4206.7). Female animal slaughtering outweighed male ones at 5:1 ratio (p < 0.0001). The highest number of cattle were slaughtered in December 2014, December 2015, and August 2016. Of all the total cattle slaughtered, the overall observed prevalences for bovine tuberculosis (BTB), hydatidosis and fasciolosis were estimated as 9514 (6.5%, 264.3 ± 81.7; 95% CI 236.6–291.9), 1851 (1.3%, 55.8 ± 17.3; 95% CI 49.9–61.6) and 845 (0.6%, median = 19.0; 95% CI 18.7–28.3), respectively. On average the highest number of BTB cases was reported in February-March, it declined slightly in October and increased again in November. Similarly, the highest numbers of hydatidosis and fasciolosis were observed in March and February, respectively. A significant (p = 0.02) mean variation of cases of BTB was found across the period and it was higher (p = 0.03) during the wet/rainy season in 2015. Our results emphasized the need to promote coordinated active surveillance for zoonoses detection and mitigation to ensure food safety at farm and slaughterhouse levels. Adequate record keeping for specific organ/meat/carcass condemnation is crucial at postmortem, as this represents a significant loss of animal proteins and revenues. Such data can be used for informed policy to intensify reduction in economic loss associated with animal diseases

Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling

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

The natural resource curse -and- Delivery of health and education services in Nigeria

Essay 1: What are natural resources? Why do some countries thrive with their available resources, while others suffer from what is referred to as the Natural Resource Curse? How did countries such as Botswana escape the resource curse, and why is Nigeria a prime example of the resource curse. This extended paper aims to address the questions mentioned, while attempting to examine the important factors responsible for the resource curse. Essay 2: Delivery of Education and Healthcare services are considered poor in Nigeria thereby making it a source of concern due to the impact on economic and social development. This extended essay aims to identify various bottlenecks hindering proper service delivery, while also providing recommendations to improve current service delivery mechanisms

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&nbsp; 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