Latest Breaking News On - Drift detection - Page 1 : comparemela.com

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models are developed to detect the concept drift in machine learning techniques. Yet, these models are inadequate for real-time applications due to time and memory constraints. Hence, this research devises dynamic streaming data analytics depending on the optimized hybrid deep learning classifier and Optimized Key Windowing (OKW) approach to effectively handle the time and memory constraints. An optimized hybrid deep learning classifier is the base classifier model developed by integrating deep Long short-term memory (LSTM) and deep Recurrent Neural Networks (RNN) to detect the concept drifts in streaming data. The model's main advantage lies in enhanced accuracy in drift detection by a hybrid clas

Recurrent neural networksOptimized key windowingCloud monitoringData streamDeep learningDrift detectionEtaheuristic algorithmPattern detection