量子遗传算法用于构建语义文本相似性估计框架,用于窃检测应用程序
Saad M Darwish1, Ibrahim Abdullah Mhaimeed2, Adel A Elzoghabi1
1Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, P.O. Box 832, Alexandria 21526, Egypt.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一个新的框架,将语义提取和量子遗传算法 (QGA) 结合起来,用于先进的想法窃检测. 新方法显著提高了检测准确度和效率,与现有的抄袭检测系统相比.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 传统的抄袭检测与转述的内容和结构变化作斗争.
- 现有的机器学习方法往往不足以检测想法抄袭.
- 需要增强的系统来识别复杂的形式的学术不诚实.
研究的目的:
- 用语义提取和量子遗传算法 (QGA) 开发一个统一的框架来检测想法窃.
- 提高抄袭检测系统的准确性并减少其计算时间.
- 解决当前方法在处理语义相似性和重新表述内容方面的局限性.
主要方法:
- 通过WordNet集成语义提取来捕获文档想法.
- 适应量子遗传算法 (QGA) 来识别传达主要思想的连贯句子.
- 使用QGA中的量子位表示来增强基因多样性和并行处理.
主要成果:
- 与现有方法相比,拟议的模型在窃检测方面取得了显著的改进.
- 在真实阳性率 (TPR) 中大约增加了20%,在阳性预测值 (PPV) 中增加了15%,在F-Score中比基因算法 (GA) 和等级 GA (HGA) 提高了10%.
- 较大的数据集的准确率增加了10-15%,表明可扩展性.
结论:
- 集成的语义提取和QGA框架有效地检测到想法窃.
- 该方法在精度和计算效率方面提供了卓越的性能,用于窃的检测.
- 这种方法为识别复杂形式的学术不端行为提供了强有力的解决方案.
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