Data mining (DM) is the process of extracting knowledge from data. Knowledge from customer behaviour segmentation is useful for companies in setting the target market and developing a marketing strategy. Recency Frequency Monetary (RFM) model is the most behaviour segmentation used. Many customer-segmentation studies in various application areas use the RFM model that collaborates with DM. With many methods in DM, the selection of appropriate methods can reveal useful hidden patterns in customer segments. This paper aims to analyse DM methods that collaborate with the RFM model and synthesize them to propose a customer segmentation framework. This study uses a comprehensive literature review published in 2015-2020. The most widely used methods are clustering and visualization from seven DM methods analysed. Due to the increased visualization function and the need for customers’ geo-demographic data to be considered in the analysis, this study presents a new framework for using DM methods with the RFM based segmentation in the Geographic Information Systems (GIS) environment. This framework helps analysts utilize DM methods to uncover and understand customer characteristics, so companies can set the target market and develop a marketing strategy to increase their competitive advantage.

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The importance of flood damage assessment has been highlighted by the government as well as by many researchers. Nevertheless, the effort in performing the damage studies is less to be found due to the lack of awareness and some other limitations related to the data and its methodologies. The flood damage data in fact is part of an essential ingredient in developing the flood mitigation policy as well as in evaluating the effectiveness of the current flood reduction measures. However, unlike other kinds flood risk quantification study, damage assessment is the one that less concerned by the researchers. This paper has mainly provides a brief introduction towards the flood damage assessment and certain essential element need to be taken into consideration have been highlighted. An analysis of previous flood damage assessment studies and discussion towards some critical issues are presented in this paper other than proposing granular fuzzy system for enhancement in flood assessment for quality risk analysis.

Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist's knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales.

Otsu method is a global thresholding method that uses between class variance as a discriminant criterion in order to maximize the separation between background and foreground of an image. However, there are problems of biasness in Otsu method. These problems are caused by the differences in class variances. The threshold value obtained by Otsu method will be bias towards the larger class variance component. Hence, in this paper, a new variant of Otsu method by using normalization techniques and their ensembles is proposed. By using normalization techniques, grey level values will be transformed into a smaller range in feature space and this will affect Otsu method as this method depends on grey level values. The domination of certain grey level values also will be eliminated. Rank filtering 1566 Fauziah Kasmin et al. has been applied to eliminate noises and ensemble approaches of normalization techniques are utilized to increase the performance of the proposed method. Ensemble approaches namely Maximum Variance, Majority Voting, Product Rule, Addition Rule and Average Rule have been applied on the binary images obtained. From the experiment on 20 retinal images randomly selected in 50 runs from DRIVE and STARE databases, Maximum Variance shows the most significant result that is 95.39% accuracy. While from the experiment on 15 document images randomly selected in 50 runs from DIBCO2009 and DIBCO2011 databases, Average Rule shows the most significant result that is 97.17% accuracy. This indicate the use of ensembles of normalization techniques can give promising result to improve Otsu method.