AL-Rafidain Journal of Computer Sciences and Mathematics

Abstract

Most recent studies have focused on using modern intelligent techniques especially those developed In-Door surveillance systems. Such techniques have been built depending on modern Artificial intelligence-based modules. Those modules act like a human brain, they learn and recognize what they learned. The importance of developing such systems came after the requests of customers and establishments to defend their properties and avoid Intruders' damages. This would be provided by an intelligent module that ensure the correct alarm for correct non-secured state, Thus, an Indoor surveillance module depending on Multi-Connect Architecture Associative Memory (MMCA) has been proposed. This proposed system can be trained for more than to shoot. Thus the module can recognize more than one true state that might be secured or non-secured states in real-time. The current study found an accepted accuracy level between (62.778.8%) at first training cycle with two images. While the final result were between (97-100%) at the fifth training cycle with (10) images. It considered a high performance and very excellent results.