Forecasting Multi-Level Deep Learning Autoencoder Architecture (MDLAA) for Parametric Prediction based on Convolutional Neural Networks

This study presents a data-driven framework for anomaly detection, which is a significant process in modern computing, as the detection of an abnormal signal can prevent a high-risk decision. The proposed Multi-Level Deep Learning Autoencoder Architecture (MDLAA) is used to encode high dimensional input data using CNNs for anomaly detection in High Dimensional Input Datasets (HDDs). MDLAA is based on unsupervised learning, which has a strong theoretical foundation and is widely used for the detection of anomalies in HDDs, but a few limitations significantly reduce its performance. The proposed MDLAA combines multilevel convolutional layers and data preprocessing. The performance of the proposed model was evaluated on a benchmark dataset. Using feature engineering, the proposed algorithm assists in the detection of anomalies that are present in data structures, especially when compared to the ResNet101 feature extractor. The results show that given adequate data, the proposed technique outperformed other previously implemented deep learning approaches and classification models, showing an overall improvement of 2.3% in terms of MSE, F1-score, precision, and accuracy.