TY - JOUR
T1 - Bio-inspired Search Approach Cross-Domain Location Mapping for Smart Mobile Service System
AU - Agbehadji, Israel Edem
AU - Abayomi, Abdultaofeek
AU - Mutanga, Murimo B.
AU - Awuzie, Bankole O.
AU - Ngowi, Alfred B.
N1 - Publisher Copyright:
© 2022. Israel Edem Agbehadji, Abdultaofeek Abayomi, Murimo B. Mutanga, Bankole O. Awuzie and Alfred B. Ngowi.This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license
PY - 2022
Y1 - 2022
N2 - The health care service sector is very critical in every country. Although governments have made significant efforts to improve health infrastructure and train more qualified health professionals, the majority of them cannot find jobs within public health facilities. Furthermore, patients queue at health care facilities for hours daily for basic health care services while job creation and reducing these long queues remains a challenge in most developing countries. By leveraging the ubiquitousness of mobile technology, patients can be assisted to request health care services, to be delivered at their locations by qualified healthcare professionals. Given the critical nature of health care services, it is imperative to deliver services on-time without delays. In this regard, finding an optimal path between the service originator and the location of health care professionals is important. In this study, location data was used to provide near-optimal location information by mapping location data between patient and health professional domains. Our mapping approach centers on the hunting behavior of the Kestrel bird and through this, an algorithm is proposed for cross-domain location mapping. The mathematical model that is proposed in this study's major contribution as well as the application of the Kestrel-based Search Algorithm (KSA) for location data generation to find the optimal distance from an initial location. The result is promising in terms of the optimal distance between two locations using the haversine and equirectangular approximation formulas. The KSA was juxtaposed with other renowned meta-heuristic algorithms such as the BAT, Wolf Search Algorithm with Minus Previous Step (WSA-MP), and Ant Colony Optimization (ACO).
AB - The health care service sector is very critical in every country. Although governments have made significant efforts to improve health infrastructure and train more qualified health professionals, the majority of them cannot find jobs within public health facilities. Furthermore, patients queue at health care facilities for hours daily for basic health care services while job creation and reducing these long queues remains a challenge in most developing countries. By leveraging the ubiquitousness of mobile technology, patients can be assisted to request health care services, to be delivered at their locations by qualified healthcare professionals. Given the critical nature of health care services, it is imperative to deliver services on-time without delays. In this regard, finding an optimal path between the service originator and the location of health care professionals is important. In this study, location data was used to provide near-optimal location information by mapping location data between patient and health professional domains. Our mapping approach centers on the hunting behavior of the Kestrel bird and through this, an algorithm is proposed for cross-domain location mapping. The mathematical model that is proposed in this study's major contribution as well as the application of the Kestrel-based Search Algorithm (KSA) for location data generation to find the optimal distance from an initial location. The result is promising in terms of the optimal distance between two locations using the haversine and equirectangular approximation formulas. The KSA was juxtaposed with other renowned meta-heuristic algorithms such as the BAT, Wolf Search Algorithm with Minus Previous Step (WSA-MP), and Ant Colony Optimization (ACO).
KW - Bio-inspired algorithm
KW - Cross-domain location mapping
UR - https://www.scopus.com/pages/publications/85129773202
U2 - 10.3844/jcssp.2022.281.296
DO - 10.3844/jcssp.2022.281.296
M3 - Article
AN - SCOPUS:85129773202
SN - 1549-3636
VL - 18
SP - 281
EP - 296
JO - Journal of Computer Science
JF - Journal of Computer Science
IS - 4
ER -