Noor Elaiza Abd Khalid

Adapting MapReduce framework for genetic algorithm with large population

Genetic algorithm (GA) is an algorithm that models inspiration from natural evolution to solve complex problems. GA is renowned for its ability to optimize different types of problem. However, the performance of GA necessitates data and process intensive computing when incorporating large population. This research proposes and evaluates the performance of GA by adapting MapReduce (MR), a parallel processing framework introduced by Google that utilize commodity hardware. The algorithm is executed with population size of up to 10 million. Performance scalability is tested by using 1, 2, 3, and 4 node configurations. The travelling salesman problem (TSP) is chosen as the case study while performance improvement, speedup, and efficiency are employed for performance benchmarking. This research revealed that MR can be naturally adapted for GA. It is also discovered that MR can accommodate GA with large population while providing good performance and scalability.

Accuracy Assessment of Urban Growth Pattern Classification Methods Using Confusion Matrix and ROC Analysis

Urban growth pattern can be categorized as either infill, expansion or outlying. Studies on urban growth classification are focusing on the description of urban growth pattern geometric features using conventional landscape metrics. These metrics are too simple and unable to give detailed information on accuracy of the classification methods. This paper aims to assess the accuracy of classification methods that can determine urban growth patterns correctly for a specific growth area. Accuracy assessments are carried out using three different classification methods – moving window, topological relation border length and landscape expansion index. Based on confusion matrices and receiver operating characteristic (ROC) analysis, results show that landscape expansion index has the best accuracy among all.

Optimizing oil palm fiberboard properties using neural network

The shortage of rubber wood (RW) supply has increased the demand to reduce its composition in the Medium Density Fiberboard (MDF). Oil palm biomass such as empty fruit bunch (EFB) has been proven to be an excellent substitute to RW. An accurate percentage combination of RW and EFB will produce a high quality MDF. An MDF needs to be tested in terms of mechanical and physical properties so that it meets the required standard. These tests are costly for they involve high amount of resources. The aim of this research is to optimize the properties of MDF so that quality-testing procedures can be reduced. A prediction model will be used to make predictions on the MDF properties. A stepwise multiple linear regression selects the predictor variables to be used as inputs to the input nodes. With these variables, the multilayer perceptron neural network with various training criteria will train the data and finally produce the prediction. The results produced have shown that some of the property tests can be omitted.

Enhancing Parallel Self-organizing Map on Heterogeneous System Architecture

Self-organizing Map (SOM) is a very popular algorithm that has been used as clustering algorithm and data exploration. SOM consists of complex calculations where the calculation of complexity depending on the circumstances. Many researchers have managed to improve online SOM processing speed using discrete Graphic Processing Units (GPU). In spite of excellent performance using GPU, there is a situation that causes computer hardware underutilized when executing online SOM variant on GPU architecture. In details, the situation occurs when number of cores is larger than the number of neurons on map. Moreover, the complexities of SOM steps also increase the usage of high memory capacity which leads to high rate memory transfer. Recently, Heterogeneous System Architecture (HSA), that integrated Central Processing Unit (CPU) and GPU together on a single chip are rapidly attractive the design paradigm for recent platform because of their remarkable parallel processing abilities. Therefore, the main goal of this study is to reduce computation time of SOM training through adapting HSA platform and combining two SOM training processes. This study attempts to enhance the processing of SOM algorithm using multiple stimuli approach. The data used in this study are benchmark datasets from UCI Machine Learning repository. As a result, the enhanced parallel SOM algorithm that executed on HSA platform is able to score a promising speed up for different parameter size compared to standard parallel SOM on HSA platform.

Parallel Self-organizing Map Using Shared Virtual Memory Buffers

Parallel implementation of Self-organizing Map (SOM) has been studied since last decade. Graphic Processing Unit (GPU) is one of most promising architecture for executing SOM in parallel. However, there are performances issues are highlighted when imposing larger mapping and dataset size onto parallel SOM that executed on the GPU. Alternatively, heterogeneous systems that soldered GPU together with Central Processing Unit (CPU) are introduced in order to improve communication between CPU and GPU. Shared Virtual Memory (SVM) is one of features in OpenCL 2.0 which allows the host and the device to share a common virtual address range. Thus this research proposes to introduce a parallel SOM architecture that suitable for both GPU and heterogeneous system with the aim to compare the performance in term of computation time. The architecture comprises of three kernels that executed on two different platforms (1) discrete GPU platform and (2) heterogeneous system platform that tested using SVM buffers. The experimental results show the parallel SOM running on heterogeneous platform has significant improvement in computation time.

Automating expert role to determine design concept in Kansei Engineering

Affect has become imperative in product quality. In affective design field, Kansei Engineering (KE) has been recognized as a technology that enables discovery of consumer’s emotion and formulation of guide to design products that win consumers in the competitive market. Albeit powerful technology, there is no rule of thumb in its analysis and interpretation process. KE expertise is required to determine sets of related Kansei and the significant concept of emotion. Many research endeavors become handicapped with the limited number of available and accessible KE experts. This work is performed to simulate the role of experts with the use of Natphoric algorithm thus providing sound solution to the complexity and flexibility in KE. The algorithm is designed to learn the process by implementing training datasets taken from previous KE research works. A framework for automated KE is then designed to realize the development of automated KE system. A comparative analysis is performed to determine feasibility of ...

Selfish Gene Image Segmentation Algorithm

The research proposes a selfish gene image segmentation algorithm as an alternative to Genetic Algorithm. Research in Genetic Algorithms originated from Darwin’s theory faced the problem of finding the optimal solution due to its inherent characteristic of genetic drift and premature convergence. Selfish gene views genes as the basic unit in evolution. Thus the color image segmentation algorithm is designed based on virtual population with collection of genes rather than fixed genes chromosomes. The genes are positioned into predetermined loci forming two chromosomes that make up the virtual population in each generation. The chromosomes are rewarded and penalized according to the chromosomes performance. Evaluation with the ground truth images shows that the selfish gene is able to detect the variation of colors very similar to the way eye detect color.

Natphoric computer-aided Kansei Engineering system

Sophisticated consumers and highly competitive environment has forced designers and manufacturers to realize the importance of consumer emotion in creative product design. Kansei Engineering (KE) is a technology that enables discovery of consumers emotions when interacting with a product and use them to formulate a guide in designing products that can win consumers in a competitive market. However, there are not many Kansei experts in the world today and there is no rule of thumb or specific manual for the process. This research proposes a computer-aided Natphoric Kansei Engineering system that will aid users in the process. The system incorporated step-by-step technique of KE type 1 and automate word classification process that usually requires expertise in KE. Result of the system is compared with the one which is done by an expert. From the result, it is shown that the system is able to provide similar output. The system will benefit the product inventors and designers, especially in Malaysia, where there is in need of a good design method to produce innovative product.