Faradila Naim

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Prediction on Behaviour of Blood Velocity and Mitral Leaflet Displacement in the Different Shapes of Heart Valve during Cardiac Cycle

The number of cases for heart diseases is increasing every day, even though medication technologies are always improving and moving forward. In this paper, the objectives of the study are to investigate the effect of blood flow velocity and leaflet displacement using different shapes of simplified two dimensional heart valve leaflets in the diastole condition. Four different shapes of heart valve were created and the simulation was performed by using Fluid Structure Interaction (FSI). From the results obtained, the triangle shaped leaflet showed it had the highest blood velocity changes and leaflets displacement changes in a one second period when compared to the other three shapes. The outcome simulation result shows that a large vortex formed behind the leaflet and leaflet deformed when the blood flow into left ventricle is agreed with the results in literature. In conclusion, four different shapes of two dimensional model of mitral valve has been developed and investigated for applying the most suitable shape in future artificial valve design.

Speech processing for makhraj recognition

In our daily day, improvement of makhraj for Arabic alphabets is a topic that very useful in many applications and environments. The existing system cannot recognize the appropriate pronunciation of each alphabet with the existence of noise. As an example “ha”, with the disturbance from the noise, the system may recognize wrong alphabet like “kho”. This paper focus on noise removal in makhraj recognition using Least Mean Square (LMS) Algorithm based on Adaptive Filter to search for the optimal solution to adaptive filter, including system identification and noise cancellation. There are 30 Arabic alphabets from until. However, this project will only use 7 alphabets as samples that are from until. The speech processing will be used to obtain same waveform output from two different situations. The filtered data will be processed to match the standard pronunciations and it will be integrated with filter design process in MATLAB. As a result, the waveform of noise cancellation using LMS algorithm is quite similar with the waveform of reference signal. As a conclusion, it is proved that noise cancellation method remove noise from unknown system.

Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders

The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms.

Unclean hand detection machine using vision sensor

This paper will discuss about the unclean hand detection using vision sensor for an automated hand wash screening system. Currently the hand wash screening audit is done manually by an expert to monitor the hand under ultraviolet light once its been washed. Hence, there is a need for more human experts to conduct the screening manually. This project is proposed to automate the hand wash screening audit by using a vision system. The vision system is designed to increase accuracy to detect the unclean area of washed hands. This system will not only detect the unclean area, but will also calculate the percentage of the unclean area which will be used as further analysis of the efficiency of the system. However, we need to build the hand wash prototype using ultraviolet light and a camera that is connected to the computer to process and display the results of hand wash screening.

Feature extraction of pus cells detection and counting in sputum slide images

This paper discusses on a feature extraction of pus cell in sputum slide image. This invention is developed to analyze and count the content of cells specifically pus cell within a biological sample, and more particularly sputum sample which is useful for sputum quality grading. This pus cell detection is addressed by mean intensity and area for single and overlapping pus cells. It is found that mean intensity and area for single pus cell are ranges from 130 - 163 and 35 - 81 respectively. Whereas, with considering other elements exist in sputum image such as epithelial cells and artifacts, the mean intensity for overlapping pus cells are ranges from 130 - 45 with area of 65 - 300. This system reliability is above 80% from the validation results.

Speech processing for makhraj recognition: The design of adaptive filter for noise canceller

In our daily life, improvement of makhraj for Arabic alphabets is a topic that very useful in many applications and environments. The existing systems cannot recognize the appropriate pronunciation of each alphabet with the existence of noise. As an example of “ha”, the system may recognize wrong alphabet like “kho”. This paper focuses on noise removal in makhraj recognition using Normalized Least Mean Square (NLMS) Algorithm based on Adaptive Filter to search for the optimal solution. There are 30 Arabic alphabets from **** until ****. However, this project will only use 7 alphabets as samples, they are **** to ****. The speech processing is used to obtain same waveform output from two different situations. The filtered data is processed to match the standard pronunciations and it is integrated with filter design process in MATLAB. From the result, the waveform of noise cancellation using NLMS algorithm is quite similar with the waveform of reference signal. It is proved that noise cancellation method remove noise from unknown system.

An Improved VEPSO Algorithm for Multi-objective Optimisation Problems

Multi-objective optimisation problem is the problem which contains more than one objective that needs to be solved simultaneously. The vector evaluated particle swarm optimisation algorithm is widely used for such purpose, where this algorithm optimised one objective using one swarm of particles by the guidance from the best solution found by another swarm. However, this best solution is only updated when a solution is better with respect to the optimised objective and results in poor performance. Therefore, the vector evaluated particle swarm optimisation algorithm is improved by incorporating the non-dominated solutions for guiding the particle movement during optimisation. The performance of the improved algorithm is analysed with several performance measures and simulated on various test functions. The results suggest that the improved algorithm outperformed the performance of the original algorithm.

The detection and summation of squamous epithelial cells for sputum quality testing

Sputum with good quality is important to detect diseases. The quality of sputum is determined using Bartletts Criteria by considering the score of squamous epithelial cells (SEC), pus cell (neutrophils) and macroscopy. For squamous epithelial cells, the score is 0 if SEC is less than 10. Whereas if SEC is within 10 to 25, the score is -1 and the score is -2 if the number of SEC is greater than 25. Currently, the detection of SEC in sputum is manually done by technologists. However, the problem with manual detection is time consuming. So, an automated vision system using image processing technique to detect the sputum quality is desirable. Image processing such as image segmentation is used to detect and count the number of SEC, and then the score is determined. Lastly, the percentage of error for this project is calculated.

Automated detection and counting of pus cells on sputum images

This paper discusses on a feature extraction of pus cell in sputum slide image. This invention is developed to analyze and count the content of cells specifically pus cell within a biological sample, and more particularly sputum sample which is useful for sputum quality grading. This pus cell detection is addressed by mean intensity and area for single and overlapping pus cells. It is found that mean intensity and area for single pus cell are ranges from 130-163 and 35-81 respectively. Whereas, with considering other elements exist in sputum image such as epithelial cells and artifacts, the mean intensity for overlapping pus cells are ranges from 130-45 with area of 65-300. This system has the sensitivity of 92.11%, with specificity of 81.82%, accuracy of 87.32% and precision of 85.37.