In Computers & electrical engineering : an international journal
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.
Koppu Srinivas, Maddikunta Praveen Kumar Reddy, Srivastava Gautam
COVID-19, Data cleaning, Deep learning, Disease prediction, Dragonfly optimization, Feature extraction, Fitness basis, Intelligent robotics