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Articles by Shahrul Azman Noah
Total Records ( 3 ) for Shahrul Azman Noah
  M.M. Abdulrazzaq and Shahrul Azman Noah
  Content-based image retrieval systems offer solution to store and search the ever increasing amount of digital images currently in existence. These systems retrieve and extract the images based on low level features, such as color, texture and shape. However, these visual features did not enable users to request images based on semantic meanings. Semantic retrieval is of highly importance in various domains and in particular the medical domain that contain images from various medical devices such as MRI and X-ray. Image annotation or classification systems can be considered as a solution for the limitations of existing CBIR systems. In this study, it was proposed a new approach for image classification using multi-level features and machine learning techniques, particularly the K-Nearest Neighbor (kNN) classifier. We experimented the proposed approach on 9000 images available from the ImageCLEFmed2005 dataset. Principle Component Analysis (PCA) was performed to reduce the feature vectors. The accuracy results achieved 89.32% and 92.99% for the respective 80 and 90% of training images. The results show improvement as compared to previous studies for the same dataset.
  Juzlinda Ghazali , Shahrul Azman Noah and Lailatulqadri Zakaria
  Annotating images with text is one of the approaches to represent semantic meaning of images. Automatic classification of images into various semantic categories is one of the many steps required to perform the automatic textual annotation as exhibited in many research in this area. However, little or none researchers in this area do provide detail evaluation for selecting the best or suitable technique for performing image classification. The majority of the researchers mainly select any of the available machine learning technique and apply it as part of their proposed approaches and algorithms. In this study, six techniques were reviewed, which are SVM, Multilayer Perceptron, Bagging, DECORATE, C4.5 Decision Tree and Random Forest using 429 Flickr images relating to Malaysian tourism. Image feature extraction using 3D Colour Histogram with 64 and 216 bins were done. The results show that DECORATE has the best accuracy.
  Opim Salim Sitompul and Shahrul Azman Noah
  Applications of artificial intelligence (AI) technology in the form of knowledge-based systems within the context of database design have been extensively researched particularly to provide support within the conceptual design phase. However, a similar approach to the task of data warehouse design has yet to be seriously initiated. In this paper, we proposed a design methodology for conceptual data warehouse design called the transformation-oriented methodology, which transforms an Entity-Relationship (ER) model into a multidimensional model based on a series of transformation and analysis rules. The transformation-oriented methodology translates the ER model into a specification language model and transformed it into an initial problem domain model. A set of synthesis and diagnosis rules will then gradually transform the problem domain model into the multidimensional model. A prototype KB tool called the DWDesigner has been developed to implement the aforementioned methodology. The multidimensional model produces by the DWDesigner as output is presented in a graphical form for better visualization. Testing has been conducted to a number of design problems, such as university, business and hospital domains and consistent results have been achieved.
 
 
 
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