AI Innovations: Ensemble Learning and Energy-Efficient Knowledge Injection

Abstract

This research expands on the recent rapid advancements in Artificial Intelligence (AI) by focusing on its practical applications and environmental impact. It introduces a novel ensemble learning system for Network Intrusion Detection that integrates a one-dimensional Convolutional Neural Network (1D-CNN), an FT-Transformer, and XGBoost. It leverages the spatial pattern recognition of the 1D-CNN, the self-attention capabilities of the FT-Transformer, and the efficiency of XGBoost to significantly enhance detection accuracy, precision, recall, and F1-score compared to existing systems. Toward heart disease prediction, this research develops an ensemble model that combines BERT, FT-Transformer, and XGBoost, which excels at extracting meaningful features, capturing temporal patterns, and handling structured data. It improves diagnostic accuracy and has the potential to revolutionize healthcare diagnostics. In addition, this research examines the environmental impact of Large Language Models (LLMs), focusing on their energy consumption during the knowledge-injection process. When using knowledge injection, we compare the energy efficiency of Fine-tuning and Retrieval-Augmented Generation methods. The findings underscore the importance of enhancing AI's practical capabilities while mitigating its carbon footprint.