Oluwole Charles Akinyokun

Fuzzy logic-driven expert system for the diagnosis of heart failure disease

The heart is a key organ of the human system which pumps and circulates blood throughout the body. Its failure to perform thesefunctions often leads to total breakdown of the entire body system and in most cases results in death. In several countries of theworld, significant numbers of heart failure patients are being reported dead due to inaccurate and untimely diagnosis. Recently,expert system approach, which is anchored on Information Technology, has emerged as a strong tool for solving this problem.This paper reports on the development of aWeb and Fuzzy Logic-based Expert System for the diagnosis of heart failure disease.The proposed system consists of a Knowledge Base (which is made up of a Database), a Fuzzy Logic component, a FuzzyInference Engine and a Decision Support Engine which comprises of cognitive and emotional filter as well as Tele-medicinefacilities. The system was implemented using Hypertext Preprocessor (PHP), JavaScript and Hypertext Mark-up Language(HTML) with My Structured Query Language (MySQL) as the Database Management System. Performance analysis based ondata on selected heart failure patients and survey of some experts on heart failure disease at the State Specialist Hospital, Akure,Nigeria shows satisfactory performances of the system.

A neuro-fuzzy decision support model for therapy of heart failure

Most medical decision support systems focus on diagnosis with little emphasis on therapy to the effect that though an accurate diagnosis is undertaken patients still have problems of drug misuse as a result of inaccurate therapy. The purpose of this paper is to design an assistive model for therapy of heart failure using artificial neural networks (ANN). Artificial neural networks have been found to be a very veritable tool in learning from existing datasets and based on the results, can perform accurate prediction on the data it has not encountered before through generalisation. It was based on this that 134 datasets on heart failure were collected from three hospitals and trained in a feed forward back propagation learning neural networks. This was further refined through the fuzzy system and some decision support filters. Results obtained from the neuro-fuzzy system indicate that the model has the ability to refine and enhance the physician’s ability to prescribe an appropriate therapy based on the diagnosis. This study is one of the few attempts at utilising soft computing technology in the diagnosis and therapy of cardiovascular diseases. The authors had previously developed neuro-fuzzy models for diagnosis of heart failure.

A hybrid knowledge discovery system for oil spillage risks pattern classification

The complexity and the dynamism of oil spillages make it difficult for planners and responders to produce robust plans towardstheir management. There is need for an understanding of the nature, sources, impact and responses required to prevent or controltheir occurrence. This paper develops an intelligent hybrid system driven by Sugeno-Type Adaptive Neuro Fuzzy InferenceSystem (ANFIS) for the identification, extraction and classification of oil spillage risk patterns. Dataset consisting of 1008records was used for training, validation and testing of the system. Result of sensitivity analysis shows that Cause, Locationand Type of spilled oil have cumulative significance of 85.1%. Optimal weights of Neural Network (NN) were determined viaGenetic Algorithm with hybrid encoding scheme. The Mean Squared Error (MSE) of NN training is 0.2405. NN training,validation and testing results yielded R > 0.839 in all cases indicating a strong linear relationship between each output andtarget data. Rule pruning was performed with support (15%) and confidence (10%) minimum thresholds and antecedent-size of3. The performance of the ANFIS was evaluated with eight different types of membership functions (MFs) and two learningalgorithms. The model with triangular MF gave the best performance among all other given models while hybrid-learningalgorithm performed better than back propagation algorithm. The ANFIS model reported in the paper adopted triangular MFand hybrid learning algorithm for the predication and classification of oil spillage risk patterns. Average training and testingMSE of the model is 0.414315 and 0.221402 respectively. The knowledge mining results show that ANFIS based systemsprovide satisfactory results in the prediction and classification of oil spillage risk patterns.

Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management

This paper reports the findings from the experimental study of an intelligent system driven by Neural Network (NN), Fuzzy Logic (FL) and Genetic Algorithm (GA) for knowledge discovery and oil spillage risk management. Application software was developed in an environment characterized by 11Ants Analytics, Matrix Laboratory (MatLab), Microsoft Excel, SPSS and GraphPadInstat as frontend engines; Microsoft Access Database Management System as backend engine and Microsoft Windows as platform. 11Ants Analytics served as a tool for oil spillage indicators rank analysis and predictive model building. Matlab served as a tool for the extraction of patterns from 11Ants Analytics Model of oil spillage. Microsoft Excel serves as an interface between 11Ants Analytics and Matlab. Microsoft Excel, SPSS and GraphPadInstat serve as tools for the generation of relevant statistics. Indicators of oil spillage risks serve as input to the NN. GA is used to provide optimal set of parameters for NN training while FL used for modelling imprecise knowledge and provision of membership functions for the GA and NN. Data on Oil Spill incidences associated with oil exploration activities in the Niger Delta Region of Nigeria were collected from National Oil Spill Detection and Response Agency (NOSDRA) and used to assess and evaluate the practical function of the intelligent system. Adaptive Neuro-Fuzzy Inference System (ANFIS) driven by Mamdani’s inference mechanism was used to predict and estimate oil spillage risks. The findings from the experimental study are presented.

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.

Development of agent-based system for monitoring software resources in a network environment

Mobile agent is becoming an emerging tool for monitoring and managing computer networks. Its usefulness in this regard emanates from its ability to communicate with other agents and devices, and navigate a computer network to collect data and take actions autonomously. In this research, an investigation of the use of an agent-based system to monitor the software tools on the nodes of a computer network is carried out. The proposed framework adopts a multi-agent system approach combining a static server agent with a mobile monitor agent which move around and extract data from each node via the server agent. The system was tested in a computer network environment which is characterized by a Windows NT. The programming and mobility infrastructure is the C\#, an object-oriented and multifunctional programming scheme. The performance of the proposed agent-based system and Remote MONitoring (RMON) system are simulated and the results obtained show the cost of service, query time and delay overhead is lower in the agent-based system than that of RMON.

Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease

Abstract In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.