A hybrid deep learning classifier and Optimized Key Windowing approach for drift detection and adaption

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 classifier due to optimal hyper parameter tuning, which is performed by the proposed intelligent preying algorithm. The other advantage of the proposed model lies in adapting the classifier with varying data patterns, which is performed using OKW. The experimentation is done using the benchmark dataset, such as Apache, Hadoop, Linux, Spark, and Cloud monitoring. The experimental analysis demonstrates the proposed model attaining higher sensitivity, accuracy, specificity, precision, and recall for Hadoop data. This shows a higher result than that of the competent techniques.