The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper we introduce the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilisation of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.

No abstract

No abstract

No abstract

CAPTCHA is a security technique to allow a computer application to distinguish between computer and human access. Most of now a day CAPTCHA words have their characters connected together which makes them very difficult to be segmented and recognized. In this paper we present an efficient technique that first benefits from the intersections between characters in a word and then segment it based on the recognition of each segmented character. Edge corners (ECs) are used in both segmentation and recognition phases. A novel fuzzy logic‐based scheme is proposed to match characters using their ECs. Experimental results show the efficiency of the proposed scheme in terms of success rate using a large set of CAPTCHA's words. Copyright © 2015 John Wiley & Sons, Ltd.