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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10650580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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