Mobile Anomaly Detection Based on Improved Self-Organizing Maps

Anomaly Detection Using Self-Organizing Maps-Based K-Nearest Neighbor Algorithm

Self-organizing maps have been used extensively for condition-based maintenance, where quantization errors of test data referring to the self-organizing maps of healthy training data have been used as features. Researchers have used minimum quantization error as a health indicator, which is sensitive to noise in the training data. Some other researchers have used the average of the quantization...

Improved Self-Organizing Maps and Speech Compression

Recent developments of Self-Organizing Maps or Kohonen networks become more and more interesting in many fields such as: pattern recognition, clustering, speech recognition, data compression, medical diagnosis... Kohonen networks is unsupervised learning models. The results obtained by the Kohonen networks are dependent on their parameters such as the architecture of the Kohonen map, the later ...

Video Navigation Based on Self-Organizing Maps

Content-based video navigation is an efficient method for browsing video information. A common approach is to cluster shots into groups and visualize them afterwards. In this paper, we present a prototype that follows in general this approach. The clustering ignores temporal information and is based on a growing self-organizing map algorithm. They provide some inherent visualization properties ...

Steel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps

Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...

Self-organizing maps based on differential equations