Predicting web user behavior using learning-based ant colony optimization

TL;DR: A framework for users’ behavioral profiling formation is presented and customer behavioral patterns are used for customer identification in the e-Commerce environment and the results imply that the accuracy of the neural network approach in customer pattern behavior recognition increases when the number of customers grows.

TL;DR: A new concept “interest pheromone” is defined and a simple algorithm based on improved Ant Colony Optimization (ACO) is proposed to mine users’ dynamic interest and Experimental results illustrate that the proposed paradigm can truly capture users' browsing preference effectively.

TL;DR: A novel ant-based parameter estimate algorithm to accurately track multiple cells in a series of low-contrast image sequences is presented, which is more accurate and robust than other popular tracking methods.

TL;DR: The ensemble machine learning methods incorporating random subspace and random forest employing genetic fuzzy rule-based systems as base learning algorithms were developed in Matlab environment and applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions.

TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.

TL;DR: We present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext to produce better search results.

TL;DR: The author examines the importance of (sub)sequence comparison in molecular biology, core string edits, alignments and dynamic programming, and a deeper look at classical methods for exact string matching.

TL;DR: An artificial ant colony capable of solving the travelling salesman problem (TSP) is described, an example of the successful use of a natural metaphor to design an optimization algorithm.