基于图形和视觉的人工智能系统在亚热带草原民族旅游中的应用
1Academy of fine arts, Inner Mongolia Minzu University, Tongliao Inner Mongolia, 028000, China.
Heliyon
|June 13, 2024
概括
这项研究通过使用深度学习 (DL) 和物联网 (IoT) 增强了民族旅游资源评估. 一个新的融合算法在识别蒙古图案方面实现了98.8%的准确性,促进了文化旅游和视觉设计应用.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 文化遗产研究 文化遗产研究
背景情况:
- 优化民族旅游资源评估和决策对于保护文化遗产和经济发展至关重要.
- 现有的深度学习 (DL) 算法需要改进,以便在视觉设计和文化应用中准确识别模式.
- 物联网 (IoT) 技术的整合为旅游资源的智能管理提供了潜力.
研究的目的:
- 使用DL和物联网优化民族旅游资源的评估和决策.
- 改善蒙古装饰图案的识别和特征提取,用于文化遗产和视觉设计.
- 开发一个智能决策系统,以促进本土旅游业的可持续发展.
主要方法:
- 改进了DL算法,集成ResNet + Canny + 局部二进制模式 (LBP) 来进行特征提取.
- 卷积神经网络 (CNN) 和物联网技术用于模式识别和视觉设计的应用.
- 利用模糊的决策,在不确定性和智能决策系统下进行准确的评估.
主要成果:
- 拟议的融合算法在模式识别方面实现了98.8%的高精度,超过了现有的模型,如AlexNet (90.8%),VGGNet (94.5%) 和ResNet (96.9%).
- 智能决策系统证明了乘数效应,提高了旅游资源管理的准确性,质量和效率.
- 证明了物联网在优化农村旅游智能开发技术方面的有效性.
结论:
- 该研究通过先进的DL和物联网集成成功优化了民族旅游资源评估和决策.
- 开发的融合算法和智能系统为蒙古文化旅游,视觉设计和模式识别研究提供了重要的实际影响.
- 这些发现为通过智能解决方案和模糊决策系统增强农村和城市旅游战略提供了基础.
更多相关视频
相关概念视频
02:16Light Acquisition
8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
01:21Manipulation and Analysis
23
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
23
01:25Selected Data About Geographic Locations
27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27