An Effective Framework for Enhancing Performance of Internet of Things using Ant Colony Meta-Heuristic and Machine Learning Algorithms
2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022
; : 2498-2502, 2022.
Article
in English
| Scopus | ID: covidwho-1992632
ABSTRACT
A short time ago Internet of Things (IoTs) is being applied in many fields like healthcare systems, disease forecasting, etc. Even though the IoTs has enormous promise in a variety of applications, there are several areas where it may be improved. In the present work, we have concentrated on improvement of the performance of IoT by adding two technologies such as machine learning algorithms (Naïve Bayes (NB), Random Forest (RF)) and Ant Colony Meta-Heuristic (ACMH) algorithm to select best features from data. The efficient proposed framework applied on the data of SARS-Co V2 for disease prediction to minimize the time consumption and improve the accuracy of forecasting COVID disease. Thus, the lifetime network of IoT will lead to an increase. The performance of proposed work evaluated using reliable metrics such as precision, accuracy, running time, balance accuracy, recall, and F-Measure. We conclude from the results of evaluating, that ML algorithms in IoT achieved best performance than without using ACMH algorithm;RF with ACMH in IoT framework achieved best performance that NB with ACMH algorithm. But NB is best from RF in running time with and without ACMH algorithm. © 2022 IEEE.
Ant Colony FS; COVID; IoT; NB; RF; Ant colony optimization; Decision trees; Forecasting; Heuristic algorithms; Learning algorithms; Machine learning; SARS; Ant colonies; Machine learning algorithms; Meta-heuristics algorithms; Metaheuristic; Naive bayes; Performance; Random forests; Internet of things
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022
Year:
2022
Document Type:
Article
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