Artificial Neural Network Expert System Research Articles

Prediction of load in a long bone using an artificial neural network prediction algorithm

The hierarchical nature of bone makes it a difficult material to fully comprehend. The equine third metacarpal (MC3) bone experiences nonuniform surface strains, which are a measure of displacement induced by loads. This paper investigates the use of an artificial neural network expert system to quantify MC3 bone loading. Previous studies focused on determining the response of bone using load, bone geometry, mechanical properties, and constraints as input parameters. This is referred to as a forward problem and is generally solved using numerical techniques such as finite element analysis (FEA). Conversely, an inverse problem has to be solved to quantify load from the measurements of strain and displacement. Commercially available FEA packages, without manipulating their underlying algebraic formulae, are incapable of completing a solution to the inverse problem. In this study, an artificial neural network (ANN) was employed to quantify the load required to produce the MC3 displacement and surface strains determined experimentally. Nine hydrated MC3 bones from thoroughbred horses were loaded in compression in an MTS machine. Ex-vivo experiments measured strain readings from one three-gauge rosette and three distinct single-element gauges at different locations on the MC3 midshaft, associated displacement, and load exposure time. Horse age and bone side (left or right limb) were also recorded for each MC3 bone. This information was used to construct input variables for the ANN model. The ability of this expert system to predict the MC3 loading was investigated. The ANN prediction offered excellent reliability for the prediction of load in the MC3 bones investigated, i.e. R2 ≥ 0.98.

Research on architecture of intelligent design platform for artificial neural network expert system

Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

Artificial Neural Network Expert System for Integrated Heart Rate Variability

This paper describes the combination of an expert system for bio-information with smart devices using a wireless sensor network. A wireless bio-sensor module acquires physiological signals, includi...

The EEG assessment of low-grade hepatic encephalopathy: Comparison of an artificial neural network-expert system (ANNES) based evaluation with visual EEG readings and EEG spectral analysis

Objective The EEG provides an objective staging of hepatic encephalopathy (HE), but its interpretation may be biased by inter-observer variability. This study aims at comparing an entirely automatic EEG classification of HE based on an artificial neural network-expert system procedure (ANNES) with visual and spectral analysis based EEG classifications. Methods Two hundred and thirty-eight consecutive cirrhotic patients underwent closed-eye EEG. They were followed up for up to one-year to detect bouts of overt HE and death. The EEG was classified by ANNES, qualitative visual reading, main basic rhythm frequency and spectral analysis. The classifications were assessed on the basis of: (i) match with liver function, (ii) prognostic value and (iii) repeatability. Results All classifications were found to be related to the severity of liver failure, with cognitive findings and a history of previous bouts of HE. All of them had prognostic value on the occurrence of overt HE and on survival. The ANNES based classification was more repeatable than the qualitative visual one, and had the advantage of detecting low power EEG, but its efficiency in analyzing low-grade alterations was questionable. Conclusions An entirely automatic – ANNES based – EEG classification of HE can improve the repeatability of EEG assessment, but further improvement of the device is required to classify mild alterations. Significance The ANNES based EEG grading of HE needs further improvements to be recommended in clinical practice, but it is already sufficient for detecting normal and clearly altered EEG tracings.

Electroencephalographic staging of hepatic encephalopathy by an artificial neural network and an expert system

1 Done under the auspices of the CIRMAN MEC, University of Padova, Italy. Aim of the study. – To provide an objective EEG assessment of hepatic encephalopathy (HE), we set up and tested an entirely automatic procedure based on an artificial neural network-expert system software (ANNESS). Patients and methods. – A training set sample of 50 EEG (group A) and a test sample of 50 EEG (group B) of 100 cirrhotic patients were considered. The EEGs had been visually classified by an expert electroencephalographer, using a modified five-degree Parsons-Simith classification of HE. The efficiency of the ANNESS, trained in group A, was tested in group B. Results. – Both the ANNESS and the visually-based classifications were found to be correlated to liver insufficiency, as assessed by the Child–Pugh score (Spearman's coefficient ρ = 0.485, P < 0.0001; ρ = 0.489, P < 0.0001, respectively) and by the biochemical indexes of hepatic function (bilirubin: ρ = 0.31 vs. 0.27; albumin: ρ = –0.13 vs. –0.18; prothrombin time ρ = –0.35 vs. –0.52). The classifications were found to be correlated to each other ( ρ = 0.84 P < 0.0001, Cohen's kappa = 0.55). However, the ANNESS overestimated grade 2 EEG alterations. Conclusion. – An ANNESS-based classification of EEG in HE provided data comparable with a visually-based classification, except for mild alterations (class 2) that tended to be overestimated. Further optimization of automatic EEG staging of HE is desirable, as well as a prospective clinical evaluation.

Artificial neural network techniques for estimating heavy convective rainfall and recognizing cloud mergers from satellite data

Abstract This research presents an artificial neural network (ANN) technique for heavy convective rainfall estimation and cloud merger recognition from satellite data. An Artificial Neural network expert system for Satellite-derived Estimation of Rainfall (ANSER) has been developed in the NOAA/NESDIS Satellite Applications Laboratory. Using artificial neural network group techniques, the following can be achieved: automatic recognition of cloud mergers, computation of rainfall amounts that will be ten times faster, and average errors of the rainfall estimates for the total precipitation event that will be reduced to less that 10 per cent.

A conceptual ANN-ES scheme for power system security assessment and preventive control

Proposes a coupled artificial neural network-expert system (ANN-ES) scheme for on-line security assessment and preventive control of power systems. A conceptual scheme for this purpose is presented.