Enhancing user experience in VR environments through AI-driven adaptive UI design
This paper presents a new approach to improving user experience in virtual reality (VR)
environments using AI-driven user interface (UI) design. The proposed system uses …
environments using AI-driven user interface (UI) design. The proposed system uses …
LLM-Cloud Complete: Leveraging cloud computing for efficient large language model-based code completion
This paper introduces LLM-CloudComplete, a novel cloud-based system for efficient and
scalable code completion leveraging large language models (LLMs). We address the …
scalable code completion leveraging large language models (LLMs). We address the …
Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning
This study presents a new method for optimising high-risk trading (HFT) strategies using
deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural …
deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural …
AI-driven solar energy generation and smart grid integration: A holistic approach to enhancing renewable energy efficiency
This paper comprehensively analyzes AI-driven solar energy generation and smart grid
integration, focusing on enhancing renewable energy efficiency. The study examines …
integration, focusing on enhancing renewable energy efficiency. The study examines …
Artificial intelligence-based inventory management for retail supply chain optimization: a case study of customer retention and revenue growth
This study explores the evolution of AI-driven product management in the retail industry,
focusing on product quality, customer retention, and revenue growth. From the extensive …
focusing on product quality, customer retention, and revenue growth. From the extensive …
Leveraging Large Language Models for Context-Aware Product Discovery in E-commerce Search Systems
This study presents a new way to improve product discovery in e-commerce research using
large-scale language models (LLMs) for content-aware instruction. We propose a new …
large-scale language models (LLMs) for content-aware instruction. We propose a new …
Optimizing Intelligent Edge Computing Resource Scheduling Based on Federated Learning
This study proposes a novel federated learning framework for optimizing intelligent edge
computing resource scheduling. The framework addresses the challenges of device …
computing resource scheduling. The framework addresses the challenges of device …
Enhancing personalized search with ai: a hybrid approach integrating deep learning and cloud computing
This paper presents a novel hybrid approach for enhancing personalized search by
integrating deep learning techniques with cloud computing infrastructure. The proposed …
integrating deep learning techniques with cloud computing infrastructure. The proposed …
[PDF][PDF] Personalized recommendation systems powered by large language models: Integrating semantic understanding and user preferences
This study proposes a novel personalized recommendation system leveraging Large
Language Models (LLMs) to integrate semantic understanding with user preference s[1] …
Language Models (LLMs) to integrate semantic understanding with user preference s[1] …