Home made cake batik / Aronick Edward Majimbun... [et al.]

For this semester of July 2010 - November 2010, under the Business Plan Project of ENT 300, we altogether 4 person have decided to establish a new business which are known as Home Made CAKE BATIK. This business is about food production. We are planning to start running this business on January 2011. Our business chooses this food industry field because this is something that can be recognizing as an innovation and new product in Malaysia. Cake Batik is typically known all around Malaysia for the taste of “Cocoa” and “Milo” and it’s a traditional delicacy of Malaysia during “Hari Raya” but it doesn’t have any shop outlet and not been recognized by outside our Malaysian country. As an entrepreneur, we can see this is something new, and therefore we want to take the opportunity start from now on. Cake Batik is a cake that being mixed with Marie biscuit and with only two taste which is Milo and Cocoa. That is why we are taking this kind of business opportunity. The histories about this Cake Batik never reveal but for sure it has been discovered in Malaysia. That is why we are taking a step ahead to run this business opportunity to take the advantage to upgrade the making of Cake Batik into more creative and innovative way. So, we take this opportunity to do this food production business with a small starting cost as a start. Our base of operation is in Putatan area where there is a very developing area and very suitable condition for this business to grow. So, our main operation is to produce Cake Batik and distribute it directly to the customer. Last but not least, we believe that our Home Made CAKE BATIK has high potential in commercial business and we are very confident, our objective will be achieved

An Experimental Study of the Application of Gravitational Search Algorithm in Solving Route Optimization Problem for Holes Drilling Process

Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

Classification System for Wood Recognition using K-Nearest Neighbor with Optimized Features from Binary Gravitational Algorithm

Woods species recognition is a texture classification difficulty that has been studied by many researchers years ago. The species of the wood are identified by the proposed classification using the textural type that can be observed on the structural features for example the colour of the woods, weight, texture and other features. Any mistakes on texture recognition will affect the economic benefits of wood production where it is an important basis for an identification of woods. Besides, to guide a person to be skilled in wood recognition, it will take a long time and the result the wood sample can be biased. These kinds of problem can be a motivation to develop a system that can recognize the wood effectively. This project will try to integrate both attempts by proposing a classification system consists of feature extractor, classifier and optimizer. The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM’s feature selection and parameters. For this project, images of wood knot from CAIRO UTM database are used for benchmarking the proposed system performance. The result shows that the proposed approach can perform as good as previous literatures with fewer features used as input for the classifier