BMHMCRAS: Design of a Bio-inspired Model for High-Efficiency Multipurpose Data Compression & Representation via Adaptive Signal Analysis
Keywords:
Feature, Extraction, Compression, Selection, Fusion, Bioinspired, MGA, PSOAbstract
Signal processing applications rely on pre-processing, context-based data selection, representation of data into features & their selection of input datasets. These operations enhance the effectiveness of classification, prediction, and other data-dependent applications. Researchers have proposed a variety of machine learning models to perform this task, each with its own nuances, advantages, limitations, and future research directions. However, these models are either application-specific or employ a black-box approach, limiting their scalability and context-specific performance across a variety of applications. This text proposes a novel bio-inspired model that combines high-efficiency multipurpose data compression and representation through adaptive signal analysis in order to circumvent this limitation. The proposed model combines two distinct bio-inspired methods for the parametric tuning of compression and feature extraction models, respectively. Initially, a Particle Swarm Optimizer (PSO) Model is used to determine the optimal compression model parameters. This compression can be lossy or lossless, and it can be altered according to the needs of the deployed applications. This text makes use of an ensemble compression layer that combines Huffman, Run Length Encoding (RLE), Wavelet, ZLib, BZ2, and LZMA, which aided in high-density compression and dataset aggregation. These compressed and aggregated signals are subjected to a feature extraction process that is controlled by a Multiple objective Genetic Algorithm (MGA) Model, which aids in the selection of window sizes, stride sizes, padding sizes, etc. of the feature extraction model. For large-scale feature extraction, this model employs a combination of statistical, convolutional, Fourier, and Cosine transformation techniques. Multiple objective GA models also perform the selection of these features. The fused MGA PSO Model was applied to a wide range of applications, such as electrocardiogram (ECG) classification, plant disease detection, stock market prediction, and credit card fraud detection using various test signals. It was observed that the proposed model could improve classification accuracy by 3.5 percent, classification precision by 4.1 percent, and classification recall by 3.8 percent, while it could also improve prediction accuracy by 5.4 percent, precision by 3.9 percent, and recall by 4.5 percent when averaged across different test sequences. Due to the use of compression layer, the model was also able to reduce the processing delay by 8.5% compared to other contemporary methods.
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