An Artificial Immune System for recommending relevant information through political weblog

Nasir, Ahmad Nadzri Muhammad; Selamat, Ali; Selamat, Hafiz

doi:10.1145/1806338.1806416

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scope of applications of AIS include, but are not restricted to: pattern recognition (Alexandrino et al, 2009), fault and anomaly detection (Kessentini et al, 2010), data analysis (data mining, classification etc.) (Nasir et al, 2009;Kodaz et al, 2009), agentbased systems (Hilaire et al, 2008), scheduling (Yu, 2008), machine learning, autonomous navigation and control (Zhang et al, 2009), search and optimization methods (Rodionov et al, 2011), artificial life, and security of information systems (Yu, 2011).…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Application of an Artificial Immune System to Predict Electrical Energy Fraud and Theft

Astiazara¹,

Barone²

2012

Proceedings of the 14th International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

This paper describes the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. The field of Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, like pattern recognition, fault and anomaly detection, data analysis, agent-based systems and others. Although its potential, AIS still is not applied as much other techniques such as Artificial Neural Nets are. Various works compare AIS with other techniques using toy problems. But how much efficient is AIS when applied to a real world problem? How to model and adapt AIS to a specific domain problem? And how would be its efficiency compared to traditional algorithms? On the other hand, many companies perform activities that can be improved by Computational Intelligence, like predicting fraud. Electrical energy fraud and theft cause large financial loss to energy companies and indirectly to the whole society. This work applies AIS to predict electrical energy fraud and theft, analyzes efficiency and compares against other classifier methods. Data sample used to training and validation was provided by an electrical energy company. The results obtained showed that AIS has the best performance.

show abstract