Zakaria Hussain

Intelligent medical disease diagnosis using improved hybrid genetic algorithm--multilayer perceptron network.

An improved genetic algorithm procedure is introduced in this work based on the theory of the most highly fit parents (both male and female) are most likely to produce healthiest offspring. It avoids the destruction of near optimal information and promotes further search around the potential region by encouraging the exchange of highly important information among the fittest solution. A novel crossover technique called Segmented Multi-chromosome Crossover is also introduced. It maintains the information contained in gene segments and allows offspring to inherit information from multiple parent chromosomes. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of multi-layer perceptron network in medical disease diagnosis. Compared to the previous works, the average accuracy of the proposed algorithm is the best among all algorithms for diabetes and heart dataset, and the second best for cancer dataset.

Dynamic modeling and control of wheel-chaired elliptical stepping exercise

This paper presents the model development of Functional Electrical Stimulation (FES)-assisted wheel-chaired elliptical stepping exercise. This exercise specially designed for persons with hemiplegic or spinal cord injury (SCI). The design involves the development of humanoid, wheelchair and elliptical stepping machine models. A feedback control system is used to implement the control and coordinate the exercise movement. Knee extension of the subject is initiate to lead the elliptical stepping movement. The torque production and cadence speed of the exercise is observed at different setting of the control gain. The result obtained shows that the implementation of the exercise required an adequate amount of torque applied to produce the well coordinate knee movement for left and right knee. Integration with muscle model and implementation of more advance control system is required for further improvement to the exercise model.

Gel electrophoresis image segmentation with Otsu method based on Particle Swarm Optimization

Gel electrophoresis (GE) is an important tool in genomic analysis. It is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. Numerous pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Then multilevel thresholding using Otsu method based on Particle Swarm Optimization is applied. The experimental results show that the PSO-Otsu successfully segmented all the bands.

Performance comparison of gradient descent and Genetic Algorithm based Artificial Neural Networks training

One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding, the main objective of this paper is to make another comparison between the variations of gradient descent and Genetic Algorithm (GA) based ANNs training with special emphasize given on the developed algorithm and comparison methodology. Besides, the effect of the crossover operation on GA training is also being investigated. The comparison is done using cancer and diabetes benchmark dataset. The result shows that the overall classification error percentage of the family of GA is slightly better than those of gradient descent on cancer dataset. On the other hand, gradient descent is much better than GA on diabetes.

Intelligent Breast Cancer Diagnosis Using Hybrid GA-ANN

Breast cancer prevails as one of the infamous deathly diseases among women worldwide. Early detection and treatment of breast cancer can increase the survival rate of patients. Presently, the method of diagnosis depends on the human experiences. The method is time-consuming, subjected to human error and cause unnecessary burden to radiologists. This paper introduces an automatic breast cancer diagnosis technique using a genetic algorithm (GA) for simultaneous feature selection and parameter optimization of artificial neural networks (ANN). The performances of the proposed algorithm employing three different variations of the backpropagation technique for the fine tuning of the weight of ANN are compared. The algorithm is called the GAANN_XX where the XX refers to the back-propagation training variation used. The proposed algorithms called GAANN_RP produces the best and average, 99.43% and 98.29% correct classification respectively on the Wiscinson Breast Cancer Dataset.

Identification of Hammerstein-Weiner System for Normal and Shading Operation of Photovoltaic System

This paper present an identification of model system performance for Photovoltaic (PV) System under normal and shading operating condition in UiTM Pulau Pinang, Malaysia of 2.4 kW systems. A system identification approach was implemented by employing a Hammerstein-Weiner (HW) model as model structure. The approach concerned on the estimation of the photovoltaic system basis of observed data. The nonlinearity input and output are taken from irradiance and dc output current data of the real system severally. These data were used in Hammerstein-Weiner model to generate a black-box model structure which provides a flexible parameterization for nonlinear models. The best fit nonlinear model when using data from normal operating condition is when HW model incorporate with piecewise linear as the input channel and wavelet network estimators as output channel. For normal operation of PV system, the percentage of best fit was 96.51% by means of bn = 1, fn = 3, and kn = 2 of the linear model order. While the percentage best fit model generate considering shading effect was 86.32% with bn = 1, fn = 3, and kn = 1 of the linear model order. The modelling is implemented using system identification toolbox of Matlab software package.

Multilevel thresholding of gel electrophoresis images using firefly algorithm

Gel electrophoresis (GE) is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. A few pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Multilevel thresholding using Otsu method based on Firefly Algorithm is developed. The experimental results show that the Otsu-FA produced good separation of DNA bands and its background.

Electrically Stimulated Quadriceps Muscle Model for Wheel-chaired Elliptical Stepping Exercise

This paper presents the development of physiologically based mathematical model of quadriceps muscle and the implementation on Functional Electrical Stimulation (FES) assisted wheel-chaired elliptical stepping exercise. Two muscles that make up quadriceps muscle namely rectus femoris and vasti were developed. The muscle models were integrated with humanoid, elliptical stepping ergometer and wheelchair model to simulate the wheel-chaired elliptical stepping exercise. During contraction, both rectus femoris and vasti muscles produce active and passive joint moments at knee and hip joint and hence drive the movement of the elliptical stepping exercise. At appropriate position and timing, a constant 220 μs of stimulation pulse width and 30 Hz of stimulation frequency is delivered to both left and right quadriceps muscles. The produced active, passive and total joint moments during the stimulation were recorded and analyzed. From the analysis, it was confirmed that the produced joint moments were able to drive the movement of the elliptical stepping exercise. The exercise may be performed smoothly with appropriate pattern of FES delivered to quadriceps muscle through the implementation of more advanced control strategy.

Gel Electrophoresis Image Segmentation with Kapur Method Based on Particle Swarm Optimization

Gel electrophoresis (GE) is an important tool in genomic analysis. It is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. Numerous pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Then multilevel thresholding using Otsu method based on Particle Swarm Optimization is applied. The experimental results show that the PSO-Otsu successfully segmented all the bands.

Dynamic modeling and simulation of free swinging shank for hemiplegics

The lower extremities of a humanoid model having the parameters of stroke patient is designed to promote an understanding of its paralysis behaviour. The assemblies of the model are designed using SolidWork, a three dimensional computer-aided design (CAD) software and converted into SimMechanics models for further simulations. The humanoid-lower extremities model is designed based on anthropometric data of a healthy human and modified accordingly to changes of body composition and weight of stroke patients. Difference of 0.56 kg between paretic and non-paretic leg might gives significant results in control simulations. The developed model can offer prediction and much insight into the nature of movement generation and behaviour of the real stroke system.