Abstract Deep learning applications with robotics contribute to massive challenges that are not addressed in machine learning. The present world is currently suffering from the COVID-19 pandemic, and millions of lives are getting affected every day with extremely high death counts. Early detection of the disease would provide an opportunity for proactive treatment to save lives, which is the primary research objective of this study. The proposed prediction model caters to this objective following a stepwise approach through cleaning, feature extraction, and classification. The cleaning process constitutes the cleaning of missing values ,which is proceeded by outlier detection using the interpolation of splines and entropy-correlation. The cleaned data is then subjected to a feature extraction process using Principle Component Analysis. A Fitness Oriented Dragon Fly algorithm is introduced to select optimal features, and the resultant feature vector is fed into the Deep Belief Network. The overall accuracy of the proposed scheme experimentally evaluated with the traditional state of the art models. The results highlighted the superiority of the proposed model wherein it was observed to be 6.96% better than Firefly, 6.7% better than Particle Swarm Optimization, 6.96% better than Gray Wolf Optimization ad 7.22% better than Dragonfly Algorithm.

中文翻译：

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用于智能机器人的深度学习疾病预测模型

摘要 机器人技术的深度学习应用带来了机器学习中没有解决的巨大挑战。当今世界目前正遭受 COVID-19 大流行的影响，每天都有数百万人的生命受到影响，死亡人数极高。该疾病的早期检测将为主动治疗以挽救生命提供机会，这是本研究的主要研究目标。所提出的预测模型通过清理、特征提取和分类的逐步方法来满足这一目标。清理过程构成了缺失值的清理，它是通过使用样条插值和熵相关的异常值检测来进行的。然后使用主成分分析对清洗后的数据进行特征提取过程。引入了面向健身的蜻蜓算法来选择最佳特征，并将得到的特征向量输入深度信念网络。所提出的方案的整体准确性用传统的最先进模型进行了实验评估。结果突出了所提出模型的优越性，其中观察到它比 Firefly 好 6.96%，比粒子群优化好 6.7%，比灰狼优化好 6.96%，比蜻蜓算法好 7.22%。