基于区间值的双极模糊软聚合运营商的AI驱动技术在电信部门的意义和分类
Jabbar Ahmmad1, Meraj Ali Khan2, Ibrahim Aldayel2
1Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan.
Scientific reports
|April 24, 2025
概括
人工智能 (AI) 在电信方面提供了好处,但也带来了风险. 间隔值双极模糊软集 (IVBFSS) 提供了一个框架来管理AI的双重积极和负面影响.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 电信工程 电信工程 电信工程
背景情况:
- 人工智能 (AI) 显著提高了电信网络效率,流量管理和客户体验.
- 然而,人工智能在电信领域的实施带来了挑战,包括高基础设施成本,数据安全问题,用户隐私问题和潜在的系统故障.
- 现有的决策框架往往缺乏灵活性,无法充分解决人工智能部署固有的双极性 (积极和消极方面).
研究的目的:
- 引入一种新的数学框架,即区间值双极模糊软集 (IVBFSS),能够处理人工智能在电信中的双正负属性.
- 开发IVBFSS数字的基本运算规律和聚合运算符 (算术和几何平均值).
- 通过算法和对电信人工智能技术的分类来证明开发的IVBFSS概念的实际适用性.
主要方法:
- 开发区间值双极模糊软集 (IVBFSS) 数的基本运行规律.
- 引入为IVBFSS量身定制的算术和几何平均积累运算符.
- 创建一个算法来应用IVBFSS来分类人工智能驱动的技术在电信和比较分析提出的方法.
主要成果:
- 该研究成功地开发和定义了IVBFSS的操作规则和聚合运算符.
- 使用IVBFSS框架制定了一个算法来对电信中的AI技术进行分类.
- 对比分析证实了拟议的基于IVBFSS的方法在管理AI复杂性的优势和优势.
结论:
- 间隔值双极模糊软集 (IVBFSS) 提供了一个强大的数学工具,用于解决与电信中人工智能相关的多方面的挑战.
- 开发的聚合运算符和算法为评估和管理人工智能技术提供了一种实用方法,考虑它们的好处和缺点.
- 这项研究通过提供更全面,更平衡的分析视角来增强人工智能在电信领域实施的决策过程.
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