Cognitive intelligence in fog computing-inspired veterinary healthcare

Munish Bhatia; Tariq Ahamed Ahanger; Usman Tariq; Atef Ibrahim

doi:10.1016/j.compeleceng.2021.107061

Cognitive intelligence in fog computing-inspired veterinary healthcare

Lovely Professional University

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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%).

Original language	English
Article number	107061
Journal	Computers and Electrical Engineering
Volume	91
DOIs	https://doi.org/10.1016/j.compeleceng.2021.107061
State	Published - May 2021

Keywords

Cloud computing
Health Severity Index (HSI)
Recurrent Neural Network (RNN)
Time Sensitivity Parameter (TSP)

Access to Document

10.1016/j.compeleceng.2021.107061

Cite this

@article{5098335214ea43fdb4e926cd31cc6ed0,

title = "Cognitive intelligence in fog computing-inspired veterinary healthcare",

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\%).",

keywords = "Cloud computing, Health Severity Index (HSI), Recurrent Neural Network (RNN), Time Sensitivity Parameter (TSP)",

author = "Munish Bhatia and Ahanger, \{Tariq Ahamed\} and Usman Tariq and Atef Ibrahim",

note = "Publisher Copyright: {\textcopyright} 2021",

year = "2021",

month = may,

doi = "10.1016/j.compeleceng.2021.107061",

language = "English",

volume = "91",

journal = "Computers and Electrical Engineering",

issn = "0045-7906",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Cognitive intelligence in fog computing-inspired veterinary healthcare

AU - Bhatia, Munish

AU - Ahanger, Tariq Ahamed

AU - Tariq, Usman

AU - Ibrahim, Atef

PY - 2021/5

Y1 - 2021/5

N2 - 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%).

AB - 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%).

KW - Cloud computing

KW - Health Severity Index (HSI)

KW - Recurrent Neural Network (RNN)

KW - Time Sensitivity Parameter (TSP)

UR - https://www.scopus.com/pages/publications/85103101476

U2 - 10.1016/j.compeleceng.2021.107061

DO - 10.1016/j.compeleceng.2021.107061

M3 - Article

AN - SCOPUS:85103101476

SN - 0045-7906

VL - 91

JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

M1 - 107061

ER -

Cognitive intelligence in fog computing-inspired veterinary healthcare

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this