Cognitive intelligence in fog computing-inspired veterinary healthcare. (May 2021)

• Record Type: Journal Article
• Authors: Bhatia, Munish; Ahanger, Tariq Ahamed; Tariq, Usman; Ibrahim, Atef
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Cloud computing -- Health Severity Index (HSI) -- Time Sensitivity Parameter (TSP) -- Recurrent Neural Network (RNN); 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.2021.107061
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: A comprehensive smart home-based health monitoring framework for veterinary is proposed in this research. It focuses on the distant monitoring of the health condition of domestic animals within the home environment by utilizing Internet of Medical Things (IoMT) technology. Fog computing is used to formulate a time-based data accumulation and the corresponding Health Severity Index (HSI) is established for determining the animal's health severity. Also, Time Sensitivity Parameter (TSP) is specified for time-sensitive veterinary healthcare, for which Self Organized Mapping (SOM) is incorporated. Furthermore, Recurrent Neural Network (RNN) model is utilized to provide predictive healthcare services in a time-sensitive manner. The system is implemented over several challenging datasets for validation purposes. Based on the results, the presented framework can acquire a high measure of Precision (94.60%), Accuracy (94.71%), Sensitivity (94.55%), and F-measure (93.48%).