Advanced Large Language Model Integration and Optimization in E-Commerce, Healthcare, and Cybersecurity Applications

Abstract

The rapid evolution of large language models (LLMs) has transformed multiple domains, including e-commerce, healthcare, cybersecurity, and real-time data analytics. These models, characterized by their extensive parameterization and multi-modal capabilities, enable unprecedented natural language understanding, content generation, and predictive intelligence. This research investigates the integration, optimization, and application of LLMs across diverse sectors, emphasizing practical deployment, algorithmic enhancements, and real-world performance outcomes. Methodologically, the study synthesizes approaches in model fine-tuning, hybrid deep learning architectures, and semi-supervised learning, alongside strategies for latency reduction and inference accuracy improvements. Findings highlight that LLMs, when augmented with domain-specific optimization techniques, significantly enhance product recommendation mechanisms, predictive pricing models, medical image reconstruction, and intrusion detection systems. Moreover, the integration of knowledge-enhanced pre-training, context-guided modules, and user privacy-preserving techniques demonstrates both technical feasibility and ethical compliance. The discussion delves into theoretical implications for multi-modal learning, generative adversarial networks, and parameter-efficient finetuning, while acknowledging the constraints of data sparsity, model interpretability, and computational resources. Concluding remarks underscore the transformative potential of LLMs, advocating for continued research in resource-efficient architectures, trustworthy alignment, and cross-domain adaptability.