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

Households’ Decision to Participate in Cooperative Organizations: Evidence from Farmers in Akwa Ibom State, Southern Nigeria

Farmer organizations are effective mechanisms for increasing agricultural production, income and reducing poverty. Regrettably, farmers have not taken advantage of the lofty benefits accruable to those who voluntarily join these organizations. The study estimated the factors influencing household’s decision to participate in cooperative organizations and also tested the level of agreement among identified constraints linked with participation. Multistage sampling procedure was employed to select 120 farmers for the study. Primary data were obtained using questionnaires. Data were analyzed using Probit model and Kendall’s coefficient of concordance. Results of analyses revealed that the mean age, years of educational attainment household size and years of farming experience were 32, 15, 5 and 7 respectively. Result of probit analysis further indicate that age of the farmer, farm income, household size, participation in meeting, major decision maker, distance of farm to the nearest road and farmers social status were the most critical factors influencing household’s decision to participate in cooperative organizations. Result of Kendall’s coefficient of concordance revealed that there was 0.42 (moderate agreement) between the ranking of constraints associated with farmers' participation in cooperative organizations. Furthermore, findings showed that the top five factors limiting households’ decision to participate in cooperative organizations were inadequate capital accumulation, high embezzlement of funds, poor leadership, recurring internal crises and lack of initiative. Policies to provide good and accessible roads, increase farmers incomes and encourage youths are rational options that will enhance effective participation in cooperative organizations

Comparative Analysis of Neural Network Models for Petroleum Products Pipeline Monitoring

In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonlinear problems. Monitoring of variations in Petroleum Products Pipeline (PPP) attributes (flow rate, pressure, temperature, viscosity, density, inlet and outlet volume) which changes with time is complex due to existence of non linear interaction amongst the attributes. The existing works on PPP monitoring are limited by lack of capabilities for pattern recognition and learning from previous data. In this paper, NN models with pattern recognition and learning capabilities are compared with a view of selecting the best model for monitoring PPP. Data was collected from Pipelines and Products Marketing Company (PPMC), Port Harcourt, Nigeria. The data was used for NN training, validation and testing with different NN models such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Feed Forward (GFF), Support Vector Machine (SVM), Time Delay Network (TDN) and Recurrent Neural Network (RNN). Neuro Solutions 6.0 was used as the front-end-engine for NN training, validation and testing while My Structured Query Language (MySQL) database served as the back-end-engine. Performance of NN models was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient (r), Akaike Information Criteria (AIC) and Minimum Descriptive Length (MDL). MLP with one hidden layer and three processing elements performed better than other NN models in terms of MSE, MAE, AIC, MDL and r values between the computed and the desired output

A Dataset-Driven Parameter Tuning Approach for Enhanced K-Nearest Neighbour Algorithm Performance

The number of Neighbours (k) and distance measure (DM) are widely modified for improved kNN performance. This work investigates the joint effect of these parameters in conjunction with dataset characteristics (DC) on kNN performance. Euclidean; Chebychev; Manhattan; Minkowski; and Filtered distances, eleven k values, and four DC, were systematically selected for the parameter tuning experiments. Each experiment had 20 iterations, 10-fold cross-validation method and thirty-three randomly selected datasets from the UCI repository. From the results, the average root mean squared error of kNN is significantly affected by the type of task (p9000, as optimal performance pattern for classification tasks. For regression problems, the experimental configuration should be7000≤SS≤9000; 4≤number of attributes ≤6, and DM = 'Filtered'. The type of task performed is the most influential kNN performance determinant, followed by DM. The variation in kNN accuracy resulting from changes in k values only occurs by chance, as it does not depict any consistent pattern, while its joint effect of k value with other parameters yielded a statistically insignificant change in mean accuracy (p>0.5). As further work, the discovered patterns would serve as the standard reference for comparative analytics of kNN performance with other classification and regression algorithms