The Cognitive Computing Model Based on Machine Learning Algorithms in Artificial Intelligence Environments

Abstract

This study proposes CognitiveNet, a cognitive computing model designed for processing multi-modal long-sequence data. The framework integrates BiLSTM for sequence context extraction, low-rank self-attention (LRSA) for key segment focus, cognitive memory units (CMU) for cross-segment historical information retrieval, and sparse gating for efficient feature selection. Experimental evaluation on the MIMIC-III dataset for classification and NASA CMAPSS dataset for regression demonstrates that CognitiveNet outperforms baseline models, achieving higher accuracy, lower error rates, and improved inference efficiency while maintaining interpretability. Ablation and disturbance experiments confirm the critical contributions of CMU and sparse gating in capturing long-term dependencies and prioritizing core features. While the study focuses on healthcare and aero-engine prognostics, the proposed model provides a generalizable framework for multi-sensor signal processing, with potential applications in electromagnetic signal monitoring, antenna array diagnostics, and high-frequency system predictive maintenance, where efficient handling of long, complex sequences is crucial. This work highlights the capacity of cognitive computing to support real-time, interpretable, and high-precision analysis of complex temporal signals in industrial and engineering contexts.