Ong Pauline

Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal generalization performance. In this paper, in order to improve the predictive capability of WNNs, the types of activation functions used in the hidden layer of the WNN were varied. The modified WNNs were then applied in approximating a benchmark piecewise function. Subsequently, performance comparisons with other developed methods in studying the same benchmark function were made. An assessment analysis showed that this proposed approach outperformed the rest. The efficiency of the modified WNNs was explored through a real-world application problem-specifically, the prediction of time-series pollution data at Texas of United States. The comparative experimental results showed that integrating different wavelet families into the hidden layer of WNNs leads to superior performance.

Reliable epileptic seizure detection using an improved wavelet neural network.

BACKGROUND Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. AIMS This study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN) to identify the occurrence of seizures. METHOD Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. RESULTS The study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis. CONCLUSION The obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.

Wavelet neural networks applied to pulping of oil palm fronds

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.

On the Use of Wavelet Neural Networks in the Task of Epileptic Seizure Detection from Electroencephalography Signals

Abstract This paper investigates the feasibility and effectiveness of wavelet neural networks (WNNs) in the task of epileptic seizure detection. The electroencephalography (EEG) signals were first pre-processed using discrete wavelet transforms (DWTs). This was followed by the feature selection stage, where two sets of four representative summary statistics were computed. The features obtained were fed into the input layer of WNNs. Three different activation functions were used in the hidden nodes of WNNs – Gaussian, Mexican Hat, and Morlet wavelets. A 10-fold cross validation was performed and the performance assessment revealed that the proposed classifiers achieved high overall classification accuracy, which showed the prominence of WNNs in this binary classification task. The best combination to be used was the WNNs that employed Morlet wavelet as the activation function, with Daubechies wavelet of order 4 in the feature extraction stage. The cross comparison done showed that the classification accuracy achieved by WNNs was comparable to those of other artificial intelligence-based classifiers. It was also demonstrated that a classifier would perform better if input features with higher dissimilarity index were used.

Improved wavelet neural network for early diagnosis of cancer patients using microarray gene expression data

In clinical practice, diagnostic dilemmas are frequently encountered in discriminating the heterogeneous cancers into distinct types. This paper reports an improved machine learning approach based on the wavelet neural network (WNN), which associates a feature selection method, namely, the conditional T-test. It is used in the development of cancer classification by using benchmark microarray data. The experimental results showed that the proposed classifiers achieved a superior accuracy, which ranges from 92% to 100%. Performance comparisons are also made with other classifiers which show that this proposed approach outperforms most of them.

An effective fuzzy C-means algorithm based on symmetry similarity approach

We propose a novel similarity metric based on the concept of symmetry.The similarity measure is integrated in the conventional fuzzy C-means algorithm.The method shows superior partitioning results in simulations.Qualitative and quantitative analysis verify the effectiveness of the algorithm. Fuzzy C-means (FCM) partitions the observations partially into several clusters based on the principles of fuzzy theory. However, minimization on the Euclidean distance in FCM tends to detect hyper-spherical shaped clusters, which is unfeasible for the real world problems. In this paper, an effective FCM algorithm that adopts the symmetry similarity measure is proposed in order to search for the appropriate clusters, regardless of the geometric structures and overlapping characteristic. Experimental results on several artificial and real life datasets with different nature and the performance assessment with other existing clustering algorithms demonstrate its superiority.

Iron Oxide Rust as Raw Material for the Production of Red Pigment in Paint Industry

In this study, the potential of rust as a pigment in paint technology via sintering process was investigated. Iron (III) nitrate was the raw material used to make rust or iron oxide. The characteristics of iron oxide were analyzed. Moreover, iron oxide was mixed with other chemical components to make paint. The properties of paint in both liquid state and solid state were determined by portable field viscometer, pH indicator, glossmeter, pencil hardness test, and tape adhesive test. The optimum ratio of paint components for this study where used iron oxide as pigment. The other properties of pigments and paints will be conducted in the analysis study.

Application of Modified Flower Pollination Algorithm on Mechanical Engineering Design Problem

The aim of the optimization is to obtain the best solution among other solutions in order to achieve the objective of the problem without evaluation on all possible solutions. In this study, an improved flower pollination algorithm, namely, the Modified Flower Pollination Algorithms (MFPA) is developed. Comprising of the elements of chaos theory, frog leaping local search and adaptive inertia weight, the performance of MFPA is evaluated in optimizing five benchmark mechanical engineering design problems - tubular column design, speed reducer, gear train, tension/compression spring design and pressure vessel. The obtained results are listed and compared with the results of the other state-of-art algorithms. Assessment shows that the MFPA gives promising result in finding the optimal design for all considered mechanical engineering problems.

Preparation of Palm Oil Fuel Ash Composite as Green Pigment

In this work, the potential beneficial uses of palm oil fly ash (POFA) as a green pigment in paint technology via sintering process was studied. The POFA composites were sintered in the furnace at temperature 750°C. The obtained green pigment from POFA composites through the processes of mixing, reductive heating, ball milling and sieving was subsequently characterized by X-Ray diffraction technique.

Design optimization of structural engineering problems using adaptive cuckoo search algorithm

This paper proposes an adaptive cuckoo search algorithm (ACSA) for optimization of structural engineering problems. ACSA - an improved cuckoo search algorithm, utilizes an adaptive step size selection strategy its diversification process. This approach improves the convergence characteristic while preserves the balance between intensification and diversification performances in the CSA simultaneously. The effectiveness of the ACSA in solving structural optimization problems is demonstrated in three structural engineering problems. Performance assessment shows that the ACSA outperforms the standard CSA and other methods available in the literature in most of the case studies.