Guest Editorial

The International Conference on Information and Communications Systems (ICICS2011) is a forum for Industry Professionals and Academics from Companies, Governmental Agencies, and Universities around the world to present their latest research results, ideas, developments and applications in all areas of Computer and Information Sciences. The topics that have been covered in the ICICS2011 include, but are not limited to: Artificial Intelligence, Mobile Computing, Networking, Information Security and Cryptography, Web Content Mining, Bioinformatics and IT Applications, Database Technology, Systems Integration, Information Systems Analysis and Specification, and Telecommunications. We selected 11 high quality papers (out of 54 papers, which were presented at the ICICS2-11) and invited the authors of the selected papers to extend them and submit them for a complete new peer-review for consideration in this Special Issue (SI). The final decision for the inclusion in the SI has been strictly based on the outcome of the review process. The main objective of the SI is to make available the latest results in the field to the research community and report state-of-the-art and in-progress research on all aspects of information and communication systems. The selected papers span a broad range on the information retrieval, E-business and Internet. The contributions of these papers are outlined below. Jackson et. al, have studied the boundaries of natural language processing techniques in extracting Knowledge from emails, where they aimed to determine if natural language processing techniques can be used to fully automate the extraction of knowledge from emails. Based on the system built by the authors and it has been shown that although the f-measure results are world leading, there is still a requirement for user intervention to enable the system to be accurate enough to be of use to an organisation. On the hand, Al-Dwairi and Alsalman fcused on a very major problem in the World Wide Web where they proposed a lightweight system to detect malicious websites online based on URL lexical and host features and call it MALURLs. The system relies on Na ve Bayes classifier as a probabilistic model to detect if the target website is a malicious or benign. It introduces new features and employs self learning using Genetic Algorithm to improve the classification speed and precision. The system achieves an