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Data Mining and Fusion Framework for In-Home Monitoring Applications

Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel...

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Autores principales: Ekerete, Idongesit, Garcia-Constantino, Matias, Nugent, Christopher, McCullagh, Paul, McLaughlin, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650580/
https://www.ncbi.nlm.nih.gov/pubmed/37960361
http://dx.doi.org/10.3390/s23218661
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author Ekerete, Idongesit
Garcia-Constantino, Matias
Nugent, Christopher
McCullagh, Paul
McLaughlin, James
author_facet Ekerete, Idongesit
Garcia-Constantino, Matias
Nugent, Christopher
McCullagh, Paul
McLaughlin, James
author_sort Ekerete, Idongesit
collection PubMed
description Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction.
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spelling pubmed-106505802023-10-24 Data Mining and Fusion Framework for In-Home Monitoring Applications Ekerete, Idongesit Garcia-Constantino, Matias Nugent, Christopher McCullagh, Paul McLaughlin, James Sensors (Basel) Article Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction. MDPI 2023-10-24 /pmc/articles/PMC10650580/ /pubmed/37960361 http://dx.doi.org/10.3390/s23218661 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ekerete, Idongesit
Garcia-Constantino, Matias
Nugent, Christopher
McCullagh, Paul
McLaughlin, James
Data Mining and Fusion Framework for In-Home Monitoring Applications
title Data Mining and Fusion Framework for In-Home Monitoring Applications
title_full Data Mining and Fusion Framework for In-Home Monitoring Applications
title_fullStr Data Mining and Fusion Framework for In-Home Monitoring Applications
title_full_unstemmed Data Mining and Fusion Framework for In-Home Monitoring Applications
title_short Data Mining and Fusion Framework for In-Home Monitoring Applications
title_sort data mining and fusion framework for in-home monitoring applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650580/
https://www.ncbi.nlm.nih.gov/pubmed/37960361
http://dx.doi.org/10.3390/s23218661
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