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Wireless Sensor Technologies (WST) is entering a new phase. Recent advances offer vast opportunities for research and development. This is the consequence of the decreasing costs of ownership, the engineering of increasingly smaller sensing devices and the achievements in radio frequency technology and digital circuits. The aim of this research was to combine the Wireless Sensor Network with ZigBee...
Understanding the information and clusters hidden inside multidimensional data can be challenging and complicated. Dimension reduction is usually considered as the first step for data analysis and interpretation. The focus of this paper is on the improvement of data clustering performance of Self Organising Maps (SOM) by embedding Auto-Associative Neural Networks (AANN). SOM is known as a computational...
Various face detection techniques has been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge...
The unsupervised learning of Self Organizing Map (SOM) is an effective computational tool in data mining exploration processes. It provides topology preserved data mapping from high-dimensional input space into low-dimensional representation such as two-dimensional map. The visualization and classification of clustered data even with good topological preservation between input and output spaces however...
The Self Organizing Maps (SOM) can be considered as an excellent computational tool and has been applied in numerous application areas. The SOM can be effectively used to visualize and explore the properties of multidimensional data. In this paper, the structure of traditional SOM map has been extended to a three-dimensional Self Organizing Maps (3D-SOM) maps. The purpose of this work was to study...
To interpret the information hidden in multidimensional data can be considered as challenging and complicated task. Usually, dimension reduction or data compression is considered as the first step to data analysis and exploration of multidimensional data. Here, the focus is given to study Auto-Associative Neural Networks (AANNs) technique for data compression and visualization. AANNs have the ability...
In recent years, students' performance in image processing involving the use of mathematics is deteriorated. Their inability to realize the importance of mathematics and its applications in technical subjects are some of the reasons for their poor performance. The conventional teaching approach usually separates the teaching of mathematics from technical subject. This approach does not promote students'...
The security currently become a very important issue in public or private institutions in which various security systems have been proposed and developed for some crucial processes such as person identifications, verification or recognition especially for building access control, suspect identifications by the police, driver licenses and many others. Face recognitions have been an active area of research...
Various face detection techniques has been proposed over the past decade. Generally, a large number of features are required to be selected for training purposes of face detection system. Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. This creates unnecessary computation and usage of large memory space. In this paper we propose to enlarge...
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