Shafii Muhammad Abdulhamid

Shafii Muhammad Abdulhamid, Muhammad Shafie Abd Latiff, Syed Hamid Hussain Madni et al.

The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.

Shafii Muhammad Abdulhamid, Gana Usman

The use of information and communication technology in our day to day activities is now unavoidable. In tourism developments, destination information and management systems are used to guide visitors and provide information to both visitors and management of the tour sites. In this paper, information and navigation system was designed for tourists, taking some Niger state of Nigeria tourism destinations into account. The information management system was designed using Java Applet (NetBeans IDE 6.1), Hypertext MarkUp Language (HTML), Personal Home Page (PHP), Java script and MySQL as the back-end integration database. Two different MySQL servers were used, the MySQL query browser and the WAMP5 server to compare the effectiveness of the system developed.

Ismaila Idris, Shafii Muhammad Abdulhamid

An improved email classification method based on Artificial Immune System is proposed in this paper to develop an immune based system by using the immune learning, immune memory in solving complex problems in spam detection. An optimized technique for e-mail classification is accomplished by distinguishing the characteristics of spam and non-spam that is been acquired from trained data set. These extracted features of spam and non-spam are then combined to make a single detector, therefore reducing the false rate. (Non-spam that were wrongly classified as spam). Effectiveness of our technique in decreasing the false rate shall be demonstrated by the result that will be acquired.