Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

11-05-2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing | Issue 5/2019

Neural Computing and Applications 5/2019

EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm

Journal:
Neural Computing and Applications > Issue 5/2019
Authors:
Mohammed K. Hassan, Ali I. El Desouky, Mahmoud M. Badawy, Amany M. Sarhan, Mohamed Elhoseny, M. Gunasekaran

Abstract

In the current decade, ambient assisted living is attracting widespread interest due to the rapidly aging global population. The cloud-based Internet of things (IoT) healthcare systems are facing many barriers to handle the big healthcare data that IoT generates. Edge of things computing is one of the promising solutions. Accordingly, this paper proposes a hybrid ambient assisted living framework with naïve Bayes–firefly algorithm (HAAL-NBFA) for monitoring elderly patients suffering from chronic diseases. This architecture exploits the current advances in the IoT by using ambient and biomedical sensors to collect the data of the elderly patient and then fuse it into context states to predict the health status of the patient in real time using context-awareness techniques. The proposed HAAL-NBFA framework proposes a five-phase classification technique to handle big imbalanced datasets resulting from long-term monitoring of elderly patients. In this paper, the firefly algorithm (FA) has been used to optimize naïve Bayes classifier (NB) which selects the minimum features that give the highest accuracy. The proposed NB-FA acts as a safe-fail module that decides when to stop the system and when to permit its continuation in case of sensor’s failure. The experimental results proved that the proposed HAAL-NBFA had achieved high accuracy and sensitivity in predicting the health status of patients suffering from blood pressure (BP) disorders. Furthermore, the importance of NB-FA in accelerating classifications and maintaining the continuity of HAAL-NBFA’s operation has been proved by experimental results.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 5/2019

Neural Computing and Applications 5/2019 Go to the issue

S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

A new and efficient firefly algorithm for numerical optimization problems

S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

A novel method for solving the fully neutrosophic linear programming problems

S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

Abnormal event detection with semi-supervised sparse topic model

S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

Classifying streaming of Twitter data based on sentiment analysis using hybridization

Premium Partner

    Image Credits