A Secure Web-Based Universal Basic Educational Administrative Management System: A National Capacity Building Strategy in Education

Education in Nigeria is an instrument for effecting the development of its citizens in particular, and the nation in general. The Universal Basic Education Commission (UBEC) established by UBE Act, 2004 is introduced in Nigeria to ensure unfettered access to nine years of formal basic education as well as reduce the incidence of drop-out from the formal school system through improved relevance and efficiency. In this paper, we design, implement and analyze a secure web-based universal basic educational administrative management system to deal with the problem of administration overload in managing pupils, students, teachers, personnel and curriculum data in both primary and junior secondary schools in Nigeria. We adopt the following sequence to accomplish our goal, requirement analysis, architectural design, application design and implementation. The study explores object-oriented database, PHP (Hypertext Preprocessor), Apache server pages and MySQL DBMS tools. The System prototype is built on a three-tier client server architecture to provide UBEC, primary and junior secondary institutions with broader information availability, better performance, and eliminate internal security problem, paperwork and manpower. The framework will help to improve education capacity building, reliability, robustness and quality. Keywords: Web-based Database, Formal basic Education, Curriculum Data, Object-Oriented Design, Data-Driven System, Universal Basic Education Commission.

Design Methodology of Fuzzy Expert System for the Diagnosis and Control of Obesity

Both developed and developing nations of the world have overtime experienced enormous increase in food and other consumables production. This has led to a rise in calorie intake by people living in these nations of the world. As calorie intake increases in the human system, lack of early detection or control leads to obesity. The study of obesity is gaining utmost importance because of the major health issues associated with it. If an obese prone patient is detected early enough, then quite a number of diseases can be prevented. The ability of fuzzy logic to reason with uncertain and imprecise data in addressing the specific problem of diagnosis and monitoring of diseases in our society cannot be over emphasized. In this paper we design methodology of fuzzy expert system to diagnose and monitor obesity in persons at early stage. The study will help reduce to a great minimum the fast rise of obesity in our society and the world at large. The proposed study is validated with MatLab, and is used as a tracking system with accuracy and robustness. Keywords: Obesity, Fuzzy Inference System, Body Mass Index, Body fat, Waist circumference

A Fuzzy Intelligent Framework for Healthcare Diagnosis and Monitoring of Pregnancy Risk Factor in Women

The harmful effect of pregnancy risk factors to the body cannot be underestimated. Pregnancy risk factors are all the aspects that endanger the life of the mother and the baby. The infant mortality rates are still high in developing countries despite national and international efforts to redress this problem of pregnancy risk factors. The operations of the prediction of pregnancy risk factors are complex and risky due to fluctuation in the diagnosis of these risk factors. This is due to the vagueness, incompleteness, and uncertainty of the information used. Also, the health population index, which is based primarily on the result of medical research, has a strong impact upon all human activities. Medical experts are considered best fit for interpretation of data and setting the diagnosis, but medical decision making becomes a very hard activity because the human experts, who have to make decision, can hardly process the huge amount of data. This paper presents a fuzzy logic model for the diagnosis and monitoring of pregnancy risk factor for in order to make accurate reasoning with huge amount of uncertain knowledge. The model is developed based on clinical observations, medical diagnosis and the expert’s knowledge. Twenty-five pregnant patients are selected and studied and the observed results computed in the range of predefined limit by the domain experts. The model will provide decision support platform to pregnancy risk factor researchers, physicians and other healthcare practitioners in obstetrical. The study will also guide healthcare practitioners in obstetrical and gynecology clinic regions in educating the women more about the pregnancy risk factors and encouraged them to start antenatal clinic early in pregnancy. Keyword: Fuzzy inference System, Artificial Intelligence, Expert System, Pregnancy risk factors, Infant mortality, Pregnancy outcom

Implementation and Evaluation of A Type-1 Fuzzy Logic Controller for Healthcare Diagnosis and Monitoring

Type-1 fuzzy inference systems have shown potential to improve clinician performance by imitating human thought processes in complex circumstances and accurately executing repetitive tasks to which humans are ill-suited. This paper addresses the implementation of a type-1fuzzy model for pregnancy health risk diagnosis and monitoring to enhance control strategies in the medical discipline of diagnosis and monitoring pregnancy health conditions. Twenty-five pregnant patients are selected and studied and the observed results computed in the range of predefined limit by the domain experts. Both the design model and simulation result are same. The system is developed using NETBEANS IDE, JAVA, MYSQL, etc using Windows Vista as operating system platform. Results indicate that, the study has ascertained the association of the risk factors with pregnancy outcomes. It is observed that, the paper will serve as a tool for medical practitioners in educating the women more about the degree of influence of risk on pregnancy impacted by pregnancy risk factors. Thus encourage them to begin antenatal clinic early in pregnancy. It is believed that our application will reduce doctors’ workload during consultation and help to eradicate major negative pregnancy outcomes; thus promoting positive pregnancy outcomes. Keywords: Type-1 fuzzy inference system, Fuzzy logic decision support, Pregnancy health risk, Infant mortalit

Fuzzy Rule-based Framework for Effective Control of Profitability in a Paper Recycling Plant

The rapid and constant growth of urban population has led to a dramatic increase in urban solid waste production, with a crucial socio-economic and environmental impact. As the demand for materials continues to grow and the supply of natural resources continues to dwindle, recycling of materials has become more important in order to ensure sustainability. Recycling is one of the best ways for citizens to make a direct impact on the environment. Recycling reduces greenhouse gas emissions that may lead to global warming. Recycling also conserves the natural resources on Earth like plants, animals, minerals, fresh air and fresh water. Recycling saves space in the landfills for future generations of people. A sustainable future requires a high degree of recycling. Recycling industries face serious economic problems that increase the cost of recycling. This highlights the need of applying fuzzy logic models as one of the best techniques for effective control of profitability in paper recycling production to ensure profit maximization despite varying cost of production upon which ultimately profit, in an industry depend. Fuzzy logic has emerged as a tool to deal with uncertain, imprecise, partial truth or qualitative decision-making problems to achieve robustness, tractability, and low cost. In order to achieve our objective, a study of a knowledge based system for effective control of profitability in paper recycling is carried out. The root sum square of drawing inference is found to be the most suitable technique to infer data from the rules developed. This resulted in the establishment of some degrees of influence on the output. To reinforce the proposed approach, we apply it to a case study performed on Paper recycling industry in Nigeria. A computer simulation using the Matlab/Simulink and its Fuzzy Logic Tool Box is designed to assist the experimental decision for the best control action. The obtained simulation and implementation results are investigated and disc

A GIS Performance Analysis of a 3G wireless Cellular Network

In this paper, GIS performance Analysis of a 3G wireless cellular network is presented. The research is motivated by the need for network operators and mobile users to have on the spot assessment of the network performance. This was achieved by studying the effect of population, road structure and visibility on the Erlang traffic of an existing 3G Network. The result expectedly revealed that densely populated areas are likely to experience high traffic and poor signal reception. Road structure analysis shows poor service quality along major roads due to high traffic within the study area while the visibility analysis revealed that the terrain structure of the study area does not support good visibility. Keywords: GIS, 3G wireless cellular Networks, Erlang Traffic, Base Transceiver station

Diagnosis of Prostate Cancer using Soft Computing Paradigms

The process of diagnosing of prostate cancer using traditional methods is cumbersome because of the similarity of symptoms that are present in other diseases. Soft Computing (SC) paradigms which mimic human imprecise data manipulation and learning capabilities have been reviewed and harnessed for diagnosis and classification of prostate cancer. SC technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS) facilitated symptoms analysis, diagnosis and prostate cancer classification. Age of Patient (AP), Pains in Urination (PU), Frequent Urination (FU), Blood in Semen (BS) and Pains in Pelvic (PP) served as input attributes while Prostate Risk (PR) served as output. Matrix laboratory provided the programming tools for system implementation. The practical function of the system was assessed using prostate cancer data collected from the University of Uyo Teaching Hospital. A 95% harmony observed between the computed and the expected output in the ANFIS model, showed the superiority of the ANFIS model over the fuzzy model. The system is poised to assist medical professionals in the domain of diagnosis and classification of prostate cancer for the promotion of management and treatment decisions

Uncertainty and Congestion Elimination in 4G Network Call Admission Control using Interval Type-2 Intuitionistic Fuzzy Logic

The management and control of the global growth and complex nature of wireless Fourth Generation (4G) Networks elicits the need for Call Admission Control (CAC). However, CAC faces the challenge of network congestion, thereby deteriorating the network Quality of Service (QoS) due to inherent imprecision and uncertainties in the QoS data which leads to difficulties in measuring some objective and constraints of QoS using crisp values. Previous researches have shown the strength of Interval Type-2 Fuzzy Logic System (IT2FLS) in coping adequately with linguistic uncertainties. Intuitionistic fuzzy sets (IFSs) have indicated their ability to further reduce uncertainty by handling conflicting evaluation involving membership (M), nonmembership (NM) and hesitation. This paper applies the Interval Type-2 Intuitionistic Fuzzy Logic System (IT2IFLS) in solving CAC problem in order to achieve a better QoS in 4G Networks