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