Abd Rasid Mamat

Silhouette index for determining optimal k-means clustering on images in different color models

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

A Decision Tree Based Recommender System for Backpackers Accommodations

Backpackers often travel for a longer period of time, have their own budgets and requirements on accommodations. The existing systems do not offer personalized recommendation criteria and some proposed inefficient recommender system (RS) for users. Moreover, other than information searching from websites and bloggers, only limited systems were specifically designed for backpackers’ accommodations recommender system. An observation and online survey was conducted to get the information from backpackers regarding their preferences while looking for the accommodations. Fifty (50) respondents were involved in the survey and the data have been analyzed and were classified to build a decision tree. The decision tree model then implemented in the Backpackers’ accommodations Recommender System (BRS). BRS offers a convenient way and solution for backpackers by including decision tree technique in the system to suggest best accommodations suit to backpacker’s preferences.

Noise removal using statistical operators for efficient leaf identification

Plant identification based on leaf shape is becoming a popular trend, since each leaf carries substantial information that can be used to identify plant. This is difficult because the features of a leaf shape can be influenced by other leaves that have similar features but different categories. This paper presents the most popular statistical operators: mean filtering technique (MFT), median filtering technique (MDFT), Wiener filtering technique (WFT), rank order filtering technique (ROFT) and adaptive two-pass rank order filtering technique (ATRFT) for enhancing preprocessing stage. The performance of these techniques was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). Ten features were extracted from the pre-processed leaf images and identification performance was also evaluated using precision and recall. It is found that WFT is the best filtering technique and gives the best identification accuracy of 95.1%.