Department of Computer Science, School of Engineering, The University of Manchester, Greater Manchester M139PL, UK.
College of Mechanical and Electronic Engineering, Tarim University, Tarim 843300, China.
Comput Intell Neurosci. 2022 May 9;2022:6448302. doi: 10.1155/2022/6448302. eCollection 2022.
This study explores the memory characteristics of elderly individuals to design effective smart devices based on smart memory storage solutions under deep learning to improve the learning efficiency of elderly individuals. The different memory formation stages in the existing human brain are analysed. A smart memory storage solution based on memory-enhanced embedded learning is constructed based on meta-learning under deep learning, which reduces the cost of learning new tasks to the greatest extent. Finally, the performance of the proposed solution is verified using different datasets. The results reveal that the solution based on deep learning has obvious effects on different datasets, with an average accuracy rate of 99.7%. By synthesizing a large number of target sample features, this solution can lower the learning difficulty and improve the learning effect. The proposed elderly oriented smart device effectively reduces the shortcomings in the current market and lowers the learning difficulty, which provides an important reference for further enriching devices in the ageing market.
本研究旨在探索老年人的记忆特点，设计基于深度学习的智能记忆存储解决方案的有效智能设备，以提高老年人的学习效率。分析了现有大脑中不同的记忆形成阶段。基于元学习在深度学习下构建了基于记忆增强嵌入式学习的智能存储解决方案，最大限度地降低了学习新任务的成本。最后，使用不同的数据集验证了所提出解决方案的性能。结果表明，基于深度学习的解决方案对不同的数据集具有明显的效果，平均准确率为 99.7%。通过综合大量目标样本特征，该解决方案可以降低学习难度，提高学习效果。所提出的面向老年人的智能设备有效弥补了当前市场的不足，降低了学习难度，为进一步丰富老龄化市场的设备提供了重要参考。
