改进了使用自动编码器和自适应灰狼优化来识别手语的功能减少框架
Rajeev Goel1, Sandhya Bansal2, Kavita Gupta3
1Government College, Naraingarh, India. rcse123@gmail.com.
Scientific reports
|January 17, 2025
概括
本研究介绍了AEGWO-Net,这是自动手语识别 (ASLR) 的先进系统. 它有效地减少了维度,提高了准确性,以提高通信可访问性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自动手语识别 (ASLR) 系统有助于沟通,但面临着高维度的挑战,导致计算需求.
- 在ASLR中",维度的诅咒"是由于过度的特征造成的,导致长时间的培训和高的计算成本.
研究的目的:
- 提出一个集成的机器学习和群集智能技术,以解决ASLR的维度挑战.
- 开发一个强大的和可通用的ASLR系统,提高准确性和效率.
主要方法:
- 使用面向梯度 (HOG) 历史图的特征提取.
- 通过无监督自动编码器减少尺寸.
- 使用改进的灰狼优化器 (GWO) 算法来改进功能集.
- 使用手工人工神经网络 (AEGWO-Net) 进行分类.
主要成果:
- 与PCA-IGWO和KPCA-IGWO相比,AEGWO-Net实现了卓越的性能,精度提高了6%,F1得分提高了4%.
- 该系统在六个不同的数据集上显示了高精度 (98.40%),F1得分 (96.59%),MCC (97.14%) 和AUC (96.21%).
- 与其他现有的群集情报技术相比,AEGWO-Net显示出优越性.
结论:
- 拟议的AEGWO-Net有效地克服了ASLR的维度诅咒.
- 综合方法为手语识别提供了强大的和可通用的解决方案,提高了通信的可访问性.
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