Determining Strategies on Playing Badminton using the Knuth-Morris-Pratt Algorithm

Mastery techniques in badminton game are a main ability that must be possessed by players. One part of these techniques is the strategy in proper shuttlecock placement, so that the opposing player is difficult to restore it. Therefore, this study aims to build a computational model and its implementation that are able to provide predictions/recommendations for trainers and players on determining strategies of shuttlecock’s placements and strokes. The proposed model takes into account historical game patterns that have been done by world class athletes. Then, string matching using the Knuth-Morris-Pratt algorithm and a clustering method are utilized to provide solutions to be some strategies on shooting the shuttlecock. The model is then implemented in the R programming language. Several experiments, involving 20 series of world matches collected as historical data, have been conducted to validate the system. From the results obtained, it can be concluded that the system can be used as an alternative tool for players and coaches to determine the strategy in the placement and strokes of shuttlecock on badminton game

A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before