Ihsan Mohd Yassin

Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network

Asphyxia occurs in infants with neurological level disturbance, which is found to affect sound of cry produced by infants. The infant cry signals with asphyxia have distinct patterns which can be recognized with pattern classifiers such as Artificial Neural Network (ANN). This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating between healthy and infants with asphyxia from their cries, of ages from zero to seven months old, with an input feature reduction algorithm, Orthogonal Lest Square (OLS) analysis, in contrast to direct selection. The infant cry waveform served as input to Mel Frequency Cepstrum (MFC) analysis for feature extraction. The MLP classifier performance was examined with different combination in number of coefficients, filter bank and hidden nodes. It is found that the OLS algorithm is effective in enhancing the accuracy of MLP classifier while reducing the computation load. Both the average and highest MLP classification accuracies with coefficients being ranked by OLS algorithm have consistently displayed better score than those by direct selection. The highest MLP classification accuracy of 94% is obtained with 40 filter banks, 12 highly ranked MFC coefficients and 15 hidden nodes.

Comparison between Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms for Breast Cancer Diagnosis using MLP

Computerized diagnostic tools have received significant attention over the past few decades, in order to assist medical practitioners in diagnosis of disease based on a variety of test results. It provides a fast and accurate method for diagnosis, particularly in cases where medical practitioners need to deal with difficult diagnosis problems. In this paper, we present an examination of two popular training algorithms (Levenberg-Marquardt and Scaled Conjugate Gradient) for Multilayer Perceptron (MLP) diagnosis of breast cancer tissues. We test the performance of the training algorithms using features extracted from the Wisconsin Breast Cancer Database (WBCD), a benchmark dataset that has been extensively used in literature for breast cancer diagnosis. Based on our results, we conclude that both algorithms were comparable in terms of accuracy and speed. However, the LM algorithm has shown slightly better advantage in terms of accuracy (as evidenced in the average training accuracy and MSE) and speed (as evidenced in the average training iterations) on the best MLP structure (with 10 hidden units).

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.

Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia

The infant cry signals with asphyxia have distinct patterns which can be recognized using pattern classifiers such as Artificial Neural Network (ANN). This study investigates the effect of selecting infant cry features using the Binary Particle Swarm Optimization on the performance of Multilayer Perceptron (MLP) classifier in discriminating between healthy and infants with asphyxia from cry signals. The feature extraction process was performed by MFCC analysis. The MLP classifier performance was examined using various combination of number of coefficients. It was found that the BPSO helps to enhance the classification accuracy of MLP classifier while reducing the computational load. The highest MLP classification accuracy achieved was 95.07%, which was obtained when 26 MFCC filter banks, 14 selected MFC coefficients and 5 hidden nodes were used.

Series-cascaded ring resonators for compact microwave filter with high rejection

Very selective fifth order bandpass ring filter is presented based on two series-connected quarter-wavelength side-coupled ring resonators. As the connection between the two quarter-wavelength lines forms a half-wavelength resonator, an extra pole is produced, leading to a total of five poles since each ring resonator presents two poles. This adds to the compactness of the overall filter and improves the selectivity and rejection. Centered at 2 GHz, the filter layout is simulated and implemented on FR4 substrate using microstrips. Filter simulation and measurement results are presented to validate the idea.

Logical Effort using a Novel Discrete Particle Swarm Optimization algorithm

Designers often need to choose the gate sizes for logic circuit designs to estimate the delay of the circuit. Simulation and timing analysis are poor tools for this task because they cannot be modified for better results. Hence, the method of Logical Effort (LE) provides a simple method to overcome these design problems. We apply a novel Discrete Particle Swarm Optimization algorithm (DPSO) to solve the LE problem for electronic circuits. The method uses the rescaling function commonly used to scale datasets in neural networks to convert continuous-valued PSO values into discrete values. The proposed algorithm was successfully applied on a three-stage NAND gate circuit, and has been shown to work well, with 100% accuracy in all test runs.

Heat exchanger modeling using NARX model with binary PSO-based structure selection method

This paper explores the application of Non-Linear Autoregressive Model with Exogenous Inputs (NARX) system identification of heat exchanger system. Model structure selection was performed using the Binary Particle Swarm Optimization (BPSO) algorithm. The application of BPSO for model structure selection represents each particles position as binary values, which were used to select a set of regressors from the regressor matrix. Parameter estimation was then performed using Householder-based QR factorization method. Tests performed on the heat exchanger system defined the model with a maximum lag of five, while fulfilling all model validation criterions.

Classification of Parkinson's disease based on Multilayer Perceptrons Neural Network

Parkinsons disease (PD) is the second commonest late life neurodegenerative disease after Alzheimers disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD. The dataset information of this project has been taken form the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of 92.95% while SCG obtained 78.21% accuracy.

Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation

This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was created by combining the shapes and size of various abnormalities and pasting it onto normal brain image. Several types of backgrounds were tested — low, medium and high grey levels. The experimental results show good segmentation for medium and low background levels value for both light and dark abnormality levels over different backgrounds.

Classification of infant cries with hypothyroidism using Multilayer Perceptron neural network

Hypothyroidism occurs in infants with insufficient production of hormones by the thyroid gland. The cry signals of babies with hypothyroidism have distinct patterns which can be recognized with pattern classifiers such as Multilayer Perceptron (MLP) artificial neural network. This study investigates the performance of the MLP in discriminating between healthy infants and infants suffering from hypothyroidism based on their cries. The infant cries were first divided into one second segments, and important features were extracted using Mel Frequency Cepstrum Coefficient (MFCC) analysis. Two methods were then used to select which MFCC coefficients to be used as features for the MLP: direct selection or Fishers Ratio analysis (F-ratio analysis). Their performances were compared with experimental results showing that MLP was able to accurately distinguish between the two cases. The classification performance of MLP trained with F-Ratio analysis is found to be better compared to direct selection method.