在使用微系统的商业通信系统中实施可解释的人工智能
Hariprasath Manoharan1, Teekaraman Yuvaraja2, Ramya Kuppusamy3
1Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India.
Science progress
|August 3, 2023
概括
这项研究将可解释的人工智能 (XAI) 与微型电机系统 (MEMS) 集成,以增强银行和金融部门的应用,将数据安全和系统透明度提高96%.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 工程 工程师 工程师 工程师
背景情况:
- 人工智能 (AI) 为银行和金融部门 (BFS) 提供了显著的优势.
- 目前的人工智能系统需要可解释性 (XAI),以获得信任和透明度.
- 现有的传感器模型有局限性,特别是它们的大物理尺寸.
研究的目的:
- 在BFS应用中解决可解释AI的需求.
- 为了克服传统传感器的尺寸限制.
- 加强BFS的数据安全和系统透明度.
主要方法:
- 解释性人工智能 (XAI) 与微型电机系统 (MEMS) 的整合.
- 开发一种新的应用程序,结合实时实验的新数学设计.
- 实施定罪管理,以加强数据安全.
- 在五个场景中测试综合模型,使用多个参数安排和可解释性.
主要成果:
- 结合的XAI和MEMS方法显著减少了数据损坏.
- 与现有模型相比,系统透明度平均提高了96%.
- 在提高BFS应用程序中的数据安全性和可解释性方面已证明有效.
结论:
- XAI和MEMS的集成为BFS挑战提供了一个强大的解决方案.
- 这种方法在系统透明度和数据安全方面提供了实质性的改进.
- 开发的模型为金融领域的AI提供了一个强大而可解释的框架.
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