Application of Conversational Intelligent Reporting System Based on Artificial Intelligence and Large Language Models

This article explores how machine learning techniques can be used to drive digital authentication to prevent fraud in digital payment technologies. First, it introduces the development trend and fraud risk of digital payment technology, and then analyzes the limitations of traditional authentication methods, focusing on the potential of machine learning in digital authentication. It then explores specific application scenarios of machine learning in digital authentication, including data collection and preparation, feature engineering, model selection and training, as well as real-time monitoring and anti-fraud processing. Finally, current challenges and solutions are discussed, as well as the future of machine learning in digital payment technology. Through in-depth analysis of these contents, the article aims to provide readers with valuable insights to help them better use machine learning technology to improve the security and reliability of digital payments and promote the sustainable development of the digital economy.

Machine Learning-Driven Digital Identity Verification for Fraud Prevention in Digital Payment Technologies

Qin,

Zhong,

Wang

et al. 2024

Robot Navigation and Map Construction Based on SLAM Technology

Li,

Fan,

Ding

et al. 2024

SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in the field of robotics, which realizes the autonomous navigation of robots in unknown environments through real-time positioning, mapping and path planning. This paper first introduces the basic principle and workflow of SLAM technology, including sensor data fusion, state estimation and map construction. Then, by comparing and analyzing the map construction methods of traditional raster map and visual SLAM technology, the advantages and disadvantages of different map representations are shown. Finally, combined with the practical application scenario, the wide application of SLAM technology in logistics, intelligent manufacturing and other fields is discussed, and its future development direction is prospected.

Current State of Autonomous Driving Applications Based on Distributed Perception and Decision-Making

Wang,

Lei,

Shui

et al. 2024

This article reviews the key role of distributed cloud architecture in autonomous driving systems and its integration with intelligent computing networks. By spreading computing resources across multiple geographic locations, the distributed cloud enables localized processing and storage of data, reducing latency and improving real-time decision making in autonomous vehicles. The article points out that the combination of distributed cloud technology and intelligent computing network provides a powerful solution to meet the challenges of autonomous driving technology. By dynamically allocating computing resources and deeply integrating cloud, network, and chip technologies, distributed cloud gives autonomous driving systems enhanced data processing capabilities to ensure stable and reliable performance in a variety of driving scenarios. Finally, the paper highlights that the synergy of distributed cloud and intelligent driving technology marks an important milestone for intelligent transportation systems, heralding the accelerated adoption of distributed cloud solutions in the automotive industry, driving the pace of innovation and transformation.