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Articles by Shahnorbanun Sahran
Total Records ( 12 ) for Shahnorbanun Sahran
  Moaath Shatnawi , Mohammad Faidzul Nasrudin and Shahnorbanun Sahran
  Polar Particle Swarm Optimizer (Polar PSO) is a modified version of Particle Swarm Optimization (PSO) algorithm that used a mapping function that takes position of particles in polar space and converts them to Cartesian space and vice versa. The conversion is necessary since the particles are initialized and evaluated in Cartesian space while their movements are in polar space. The conversion however distorts the position of particles even though they were initially uniformly distributed. So, this conversion is believed to be the reason behind the Polar PSO performs poorly compared to the original Cartesian PSO, especially in high dimensions. This study proposes an initialization method in polar space for Polar PSO. It uses a distribution function to avoid the points being distributed near the polar origin. This method will reduce the number of conversion and in the same time diverse the position of particles to cover a sufficiently large portion of the search space. The proposed method is tested in Ackley, DeJong, Rastrigin, Rosenbrock, Griewangk, Quartic, Salomon and Dixon benchmark functions. The results show that the polar initialization improves slightly the performance of the Polar PSO. Although, the polar initialization is useful in reducing the distortion during conversion the Polar PSO can be further improved by enhancing its movement in polar space.
  Ali Taei Zadeh , Muriati Mukhtar , Shahnorbanun Sahran and MR Khabbazi
  Service-oriented Architecture (SOA) as an ideal architecture to support today’s business challenges such as agility and flexibility acts like a bridge between IT and business domains. There are intensified SOA applications at large enterprises. However, small and medium enterprises cannot obtain SOA benefits due to the lack of compatible solutions of SOA implementation challenges. Service identification is the first step in service modeling that extracts services as building blocks of SOA. Determining suitable inputs for SME service identification based on their conditions is a key factor for developing related identification method. This paper proposes SME based criteria to evaluate current inputs of service identification. To assess the inputs that are collected from variety of sources a mapping process between enterprise goals and collected inputs is presented.
  Zahra Lotfi , Shahnorbanun Sahran and Muriati Mukhtar
  Product quality is one of the key competitive factors that will enable firms to survive and succeed in the global market place. Product quality is an important dimension of operational performance in supply chain management that has not received sufficient attention from the research community. Previous researches in this field have neglected to consider the relationship between supply chain integration and dimensions of product quality. Hence, the effect of supply chain integration on quality performance has received less attention. Therefore consideration must be given to the development of collaborative activities between manufacturer, supplier and costumer which enables firms to work together and improve product quality. Accordingly, the primary focus of this study is to investigate the relationship between dimensions of product quality and supply chain integration. The dimensions considered for product quality are design quality and conformance quality, whereas the dimensions for supply chain integration are customer integration, supplier integration and internal integration. The relationships between these dimensions are then embodied in a framework which will be validated. This research adopts both a qualitative conceptual approach and a quantitative approach in the development of the framework. The literature is consulted in identifying the dimensions of supply chain integration and product quality. These dimensions are then modeled into a questionnaire survey and are administered to identify manufacturing companies. Validity and reliability of the scales for the construct of interest were assessed through a factor analysis and Cronbach-alpha test. The results provide considerable support for our product quality-supply chain integration framework that can be used in the manufacturing sector.
  Zahra Lotfi , Fariza Hanum MD Nasaruddin , Shahnorbanun Sahran and Muriati Mukhtar
  Nowadays, educational technology is getting more important for the learning environment. Much research has been done to investigate the effectiveness of e-learning and development of collaborative activities. It is thus viable to develop a web based system so that collaborative activities between users can take place. This means that users can collaborate in doing assignments and projects. In this study the web based system and results from its evaluation are described. The requirements for the web based system are gathered via interviews and surveys. This resulted in the identification of tools and features suitable for collaborative learning which forms the attributes of the system. The evaluation of the system is carried out via questionnaires distributed to learners and teachers after they used the system. Statistical analysis carried out on the collected data revealed that the respondents all agreed to the system being suitable for collaborative learning. The proposed model Lotfi VCL (Lotfi Virtual Collaborative Learning) gives an application of the tools and features of collaborative learning software suitable for learners’ and teachers’ collaborative activities. The study concludes by highlighting the research contributions and future enhancements of the system.
  Nahlah Shatnawi , Shahnorbanun Sahran and Mohammad Faidzul
  The Bees Algorithm (BA) is a new population-based optimization algorithm inspired by the foraging nature of bees. In the basic version of the Bees Algorithm, the algorithm performed a combination of neighborhood search and global search. However, the current BA has the disadvantage of not fully imitate all physical and social aspect of bees’ nature. In this study, enhancements to the BA will be introduced as Memory-based Bees Algorithm (MBA) by adding memory (local and global) to two types of bees to make the algorithm more natural. The results of comparing the proposed Local-MBA, global-MBA and MBA (combination of Local-MBA and global-MBA) are tested using several benchmark functions. They had obtained approximately 59.34, 73.02, 74.9 and 75.44% improvement on mean number of evaluations over the basic BA, respectively. Novel fitness values of two engineering design problems are obtained by applying MBA. The proposed algorithms have great potential to be used in many optimization problems.
  Nahlah Shatnawi , Mohammad Faidzul and Shahnorbanun Sahran
  Image thresholding was the process of converting grayscale or even color images into images that had fewer classes of possible pixel values. Thresholding methods could involve finding either a single threshold value (bi-level) or multiple thresholds (multilevel). Bi-level thresholding method was straightforward, but multilevel methods involved exhaustive searching that required large amounts of computation time. One meta-heuristic optimization method to solve the computation time problem was based on bee’s behavior in nature. The recently introduced variant of this method was the Bees Algorithm (BA). BA mimics honey bee foraging activities. It had been proven to be the most powerful fair optimization method for sampling a large solution space because of its fair random sampling. In this study, Otsu’s BA-based method was used to reduce computation time in multilevel image thresholding. Two standard images, Lena and Peppers, were thresholded using the peak signal-to-noise ratio as the image quality index. The effectiveness of the proposed method in terms of its peak signal noise ratio and computation time was measured. The results were then benchmarked against other optimization algorithms, such as Artificial Bee Colony (ABC), Honey Bee Mating Optimization (HBMO), Particle Swarm Optimization (PSO) and excessive search. The experiments showed that the quality of images generated by the BA was the best among all of the methods. The BA also used the shortest computation time to find more than 4 thresholds. This result demonstrates that the BA was an outstanding method for optimizing multilevel image thresholding, especially for large threshold values.
  Waleed Khamees Alomoush , Siti Norul Huda Sheikh Abdullah , Shahnorbanun Sahran and Rizuana Iqbal Hussain
  Fuzzy clustering algorithms suffer from some weakness. The main weakness including the inclination to be trapped in local optima and vulnerable to initialization sensitivity. This study proposed a new approach called (FFCM) to solve Fuzzy C-Means (FCM) initialization problem using firefly algorithm to find optimal initial cluster centers for the FCM, thus improve all applications related fuzzy clustering such as image segmentation. The new approach (FFCM) has been evaluated in MRI Brain segmentation problem using simulated brain dataset of McGill University and MRI real images from IBSR center benchmark datasets. The experiments indicate encouraging results after applying (FFCM) and compared the outcomes with FCM random initialize cluster center.
  Abdullah H. Almasri and Shahnorbanun Sahran
  Assigning threshold value plays an important role in the temporal coding Spiking Neural Network (SNN) as it determines when the neuron should fire, the time window parameter plays a significant role in the SNN performance. This study does two things: First it proposes a mathematical method to find out the threshold boundary in the temporal coding SNN models and second it outlines the input time window boundary which leads to specify the spike time boundary. The latter was used at the former. The threshold boundary method was applied to two learning algorithms i.e., Spiking-Learning Vector Quantization (S_LVQ) and Self-Organizing Weight Adaption for SNN (SOWA_SNN), for both classification and clustering pattern recognition applications, respectively. This method finds the threshold boundary mathematically in both learning models above and observes that the minimum and maximum value of the threshold does not depend on the time input window, time coding or delay parameters in SNN. With regard to the input time window, it finds that specification beyond the parameter boundary affects the computational network cost and performance; also it finds that the delay and the time coding parameters play a significant role in assigning the time window boundary.
  Iman Seyedi , Aref Maleki-Daronkolaei , Muriati Mukhtar and Shahnorbanun Sahran
  The aim of this study is to evaluate effects of targeted subsidies on vulnerable food industries in Mazandaran province of Iran in relation to variables including the cost price, competitive advantage, productivity and quality. One of the requirements for entering to the global market and competition in free market is removing governmental subsidies or replace them with targeted subsidies in different parts of industry. As for implementing targeted subsidies plan from early 2010, the effects of determination necessity to perform this plan were felt over industry portion. The considered statistical society consists of all food industry executives impressionable in Mazandaran that their number is about 60 people. According to the Morgan’s table, the statistical sample for this study is determined 45 people. In this study, it was used from survey method and 5-point Likert scale questionnaires with the purpose of measured 4 foregoing variables in the form of 20 questions. The questionnaire stability was calculated by using Cronbach’s alpha by means of SPSS software equal to 0.784 and in assessing the research questions that were used for single-sample t-test hypotheses. The findings have shown that targeted subsidies have been meaningfully effective on decreasing the cost price, creating competitive advantage, quality improvement and enhancing productivity.
  Zahra Lotfi , Muriati Mukhtar and Shahnorbanun Sahran
  The automotive industry is one of the most important industrial sectors in the world. Therefore, consideration must be given to the development of collaborative activities between the automotive industry and supply chain partners to survive and succeed in recent world market. Supply chain integration can collaborate between a manufacturer and its supplier and costumer which enables firms to work together and improve product quality which is an important key competitive capability. This is why, the relationship between supply chain integration and product quality in automotive industry should receive sufficient attention from the research community. Hence, the purpose of the study is to develop and validate the supply chain integration and product quality instrument in the automotive industry. The research methodology for this study was devised based on the literature in general and survey instrument in the automotive industry in particular. The instrument were examined by using a survey conducted in Malaysian Automotive and Supplier Industry for empirical analysis. The study identified indicators of each dimension of supply chain integration; particularly customer integration supplier integration and internal integration and each dimension of product quality in supply chain; specifically design quality and conformance quality and validated a supply chain integration and product quality survey instrument. This questionnaire instrument can be used effectively in any manufacturing firm.
  Awang Hendrianto Pratomo , Anton Satria Prabuwono , Mohd. Shanudin Zakaria , Khairuddin Omar , Md. Jan Nordin , Shahnorbanun Sahran , Siti Norul Huda Sheikh Abdullah and Anton Heryanto
  Problem statement: Robot soccer is an attractive domain for researchers and students working in the field of autonomous robots. However developing (coding, testing and debugging) robots for such domain is a rather complex task. Approach: This study concentrated on developing position and obstacle avoidance algorithm in robot soccer. This part is responsible for realizing soccer skills such as movement, shoot and goal keeping. The formulation of position and obstacle avoidance was based on mathematical approach. This formula is to make sure that the movement of the robot is valid. Velocity of the robot was calculated to set the speed of the robot. The positioning theory including the coordination of the robot (x,y) was used to find the obstacle and avoid it. Results: Some simulations and testing had been carried out to evaluate the usefulness of the proposed algorithms. The functions for shooting, movement and obstacle avoidance had been successfully implemented. Conclusion: The results showed its possibility could be used as strategy algorithms in real robot soccer competition.
  Baher H. Nayef , Siti Norul Huda Sheikh Abdullah , Rizuana Iqbal Hussain , Shahnorbanun Sahran and Abdullah H. Almasri
  Medical image processing and classification are important in medicine. Many Magnetic Resonance Images (MRI) are taken for an individual. To reduce the radiologist workload and to enable more efficiency in brain tumor detection and classification. Many Computer Aided Diagnose (CAD) systems have been developed using different segmentation methods and classification algorithms. This study synthesizes and discusses some studies and their results. A Learning Vector Quantization (LVQ) classifier is used to classify MRI images into normal and abnormal. An initial experiment consisting of normal and abnormal MRI Brain Tumor dataset from UKM Medical Center, to observe various versions of LVQ classifiers performance is conducted.From the extensive and informative studies and numerical experiments, it is expected to obtain better brain tumor classification in the future using Multi pass LVQ classifier which obtained the least standard deviation value (0.4) and the mean accuracy rate is equal to 91%.
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