Spamming has become a time consuming and expensive problem for which several new directions have been investigated lately. This paper presents a new approach for a spam detection filter. The solution developed is an offline application that uses the k-Nearest Neighbor (kNN) algorithm and a pre-classified email data set for the learning process.
The beginning of knowledge is the discovery of something we do not understand. ~ To my family ~ ii ACKNOWLEDGEMENTS I would like to express my gratitude towards my scientific advisor, Prof. dr. eng. Sergiu Nedevschi, for all the guidance and constructive observations he has given me throughout my PhD research period. Also, this thesis would probably not… (More)
This paper focuses on solutions to two NP-Complete problems: k-SAT and the knapsack problem. We propose a new parallel genetic algorithm strategy on the CUDA architecture, and perform experiments to compare it with the sequential versions. We show how these problems can benefit from the GPU solutions, leading to significant improvements in speedup while… (More)
In the field of data mining, one of the main objectives is to achieve the highest possible classification accuracy. This paper presents a classifier fusion system based on the principles of the Dempster-Shafer theory of evidence combination. It allows one to combine evidence from different sources and arrive at a degree of belief (represented by a belief… (More)
Current important challenges in data mining research are triggered by the need to address various particularities of real-world problems, such as imbalanced data and error cost distributions. This paper presents Distributed Evolutionary Cost-Sensitive Balancing, a distributed methodology for dealing with imbalanced data and -- if necessary -- cost… (More)
This paper presents a system for identifying communities in networks built based on opinions and social data. We show how we can build graphs from opinions and social interactions and how we identify the community structure of these graphs. We handle both types of data: one-dimensional and multidimensional. As community detection method, we use the Infomap… (More)