EML-PSP: A novel ensemble machine learning-based physical security paradigm using cross-domain ultra-fused feature extraction with hybrid data augmentation scheme

Abstract

Seismic signals classification has many real-time applications related to monitoring and collecting information for investigations, public safety, and prevention of security breaches. We cross-amalgamated the seismic signals with acoustic data augmentation/feature extraction techniques, keeping the beneficial effects of each domain. In this context, we have identified the human walk from that of an animal by manipulating the seismic response. This work presents a robust automated system for surveillance against noisy environments for the classification of seismic events, which is trained to exploit the collected geo signals, namely the physical security dataset (PSD). In this context, an ensemble machine learning-based integrated physical security paradigm (EML-PSP) framework is proposed for automatically classifying humans and animals on seismic signals through a cross-domain ultra-fused feature extraction (UFFE) module using numerous speech-related feature extraction approaches. For the model to learn considerably, we have introduced a hybrid augmentation module (HAM) to synthesize realistic seismic signals based on multiple acoustic augmentation schemes. The ensemble features with enhanced discrimination power have been used to train ensemble algorithms like light-gradient boosted machine (LGBM), random forest-, and adaptive boosting-models. The exhaustive comparison of the proposed solution has been carried out with other state-of-the-art methods. On exploiting the UFFE-based features, the performance of the LGBM ensemble outnumbered other classifiers with an F1-Score of 0.9961 ± 0.0031. The Matthews correlation coefficient and accuracy were 0.9841 ± 0.0127 and 99.4111 ± 0.0047 percent, respectively. The geo sensor’s PSD results illustrated that the EML-PSP framework has adequate physical security and surveillance prospects.