Business 2.0 : a novel model for delivery of business services

Web 2.0, regardless of the exact definition, has proven to bring about significant changes to the way the Internet was used. Evident by key innovations such as Wikipedia, FaceBook, YouTube, and Blog sites, these community-based Website in which contents are generated and consumed by the same group of users are changing the way businesses operate. Advertisements are no longer dasiaforcedpsila upon the viewers but are instead dasiaintelligentlypsila targeted based on the contents of interest. In this paper, we investigate the concept of Web 2.0 in the context of business entities. We asked if Web 2.0 concepts could potentially lead to a change of paradigm or the way businesses operate today. We conclude with a discussion of a Web 2.0 application we recently developed that we think is an indication that businesses will ultimately be affected by these community-based technologies; thus bringing about Business 2.0 - a paradigm for businesses to cooperate with one another to deliver improved products and services to their own customers.<br /

Prediction of wool knitwear pilling propensity using data mining techniques

This thesis examined the application of data mining techniques to the issue of predicting pilling propensity of wool knitwear. Using real industrial data, a pilling propensity prediction tool with embedded trained support vector machines is developed to provide high accuracy prediction to wool knitwear even before the yarn is spun

ó-SCLOPE : clustering categorical streams using attribute selection

Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose &sigma;-SCLOPE, a novel algorithm based on SCLOPE&rsquo;s intuitive observation about cluster histograms. Unlike SCLOPE however, our algorithm consumes less memory per window and has a better clustering runtime for the same data stream in a given window. This positions &sigma;-SCLOPE as a more attractive option over SCLOPE if a minor lost of clustering accuracy is insignificant in the application.<br /

Chemical reaction monitoring via the light focusing in optofluidic waveguides

This paper studies the light focusing phenomenon in optofluidic waveguides and uses it to monitor chemical reactions. Firstly, the relationship between the light focusing pattern and its contributing factors is investigated experimentally. Next, a characterization experiment is conducted to validate the use of light focusing pattern as an indicator of diffusion properties. The sensitivity and the limit-of-detection (LOD) are measured to be 1.54 μm/(μm2/s) and 3.93 × 10−12 m2/s in the over-mixed region, respectively. Then, the sucrose hydrolysis reaction is monitored using the proposed optofluidic method as a demonstration. The initial hydrolysis rate of this reaction is measured to be 19.62 μM/min, which agrees reasonably well with the reported value. Lastly, this method is extended to determine the diffusion coefficient of binary solutions. The diffusion coefficients of ethylene glycol and glycerol in water are measured to be 5.56 ± 0.12 × 10-10 and 7.01 ± 0.20 × 10-10 m2/s, respectively. This study demonstrates a new method for potential integrated biochemical sensing and paves the way for a broad range of sensing applications in microreactors, chemical synthesis, and quantification of biomolecular interactions.National Research Foundation (NRF)This work is supported by National Research Foundation, Singapore under Competitive Research Program (Program No.: NRF2014NRFCRP001-002)

Prediction of wool knitwear pilling propensity using support vector machines

The propensity of wool knitwear to form entangled fiber balls, known as pills, on the surface is affected by a large number of factors. This study examines, for the first time, the application of the support vector machine (SVM) data mining tool to the pilling propensity prediction of wool knitwear. The results indicate that by using the binary classification method and the radial basis function (RBF) kernel function, the SVM is able to give high pilling propensity prediction accuracy for wool knitwear without data over-fitting. The study also found that the number of records available for each pill rating greatly affects the learning and prediction capability of SVM models.<br /