Optimal deep learning approaches and healthcare big data analytics for mobile networks toward 5G. (October 2021)

• Record Type: Journal Article
• Authors: Pustokhin, Denis A.; Pustokhina, Irina V.; Rani, Poonam; Kansal, Vineet; Elhoseny, Mohamed; Joshi, Gyanendra Prasad; Shankar, K.
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: 5G networking -- Big data analytics -- Map reduce -- Disease diagnosis -- Feature selection; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107376
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Propose an optimal Deep Learning Approaches and Healthcare Big Data Analytics for Mobile Networks in 5G environment Develop a new big data analytic-based feature selection and DBN based disease diagnosis model. Design a Link-based Quasi Oppositional Binary Particle Swarm Optimization (LQOBPSO) algorithm for feature selection. Abstract: Recent developments in wireless networking, big data technologies like 5G networks, healthcare big data analytics, Internet of Things (IoT) and other advanced technologies in wearables and Artificial Intelligence (AI) have enabled the development of intelligent disease diagnosis models. The current study devises a new big data analytic-based feature selection and Deep Belief Network (DBN)-based disease diagnostic model. To reduce the number of features and curse of dimensionality, a Link-based Quasi Oppositional Binary Particle Swarm Optimization Algorithm is used in feature selection to narrow down an optimal set of features. The application of quasi-oppositional mechanism in BPSO algorithm helps in increasing the convergence rate. Followed by, the DBN model is applied as a classifier to diagnose the existence of disease from feature-reduced data. A series of experiments was conducted to emphasize the performance of the presented model. The obtained experimental outcomes showcased that the presented model yielded better results in several aspects. Graphical abstract: Image, graphical abstract