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

Cognitive Factors in Students' Academic Performance Evaluation using Artificial Neural Networks

Performance evaluation based on some cognitive factors especially Students’ Intelligent Quotient rating (IQR), Confidence Level (CoL) and Time Management ability gives an equal platform for better evaluation of students’ performance using Artificial Neural Network. Artificial Neural Networks (ANN) models, which has the advantage of being trained, offers a more robust methodology and tool for predicting, forecasting and modeling phenomena to ascertain conformance to desired standards as well as assist in decision making. This work employs Machine Learning and cognitive science which uses Artificial Neural networks (ANNs) to evaluated students’ academic performance in the Department of Computer Science, Akwa Ibom State University. It presents a survey of the design, building and functionalities of Artificial Neural Network for the evaluation of students’ academic performance using cognitive factors that could affect student’s performances. Keywords: Cognitive, Intelligent Quotient Rating, Machine Learning, Artificial Neural Network.

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

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