Data Mining and Fusion Framework for In-Home Monitoring Applications Article Swipe
Idongesit Ekerete
,
Matías García-Constantino
,
Paul McCullagh
,
Chris Nugent
,
James McLaughlin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202309.1930.v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202309.1930.v1
Sensor fusion algorithms and models have been widely used in recent times. Although research evidence has informed the use of sensor fusion models in diverse applications, there is room for improvement, especially in home-based health monitoring applications which require less supervision and technical knowledge of users. The present work 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. 574 privacy-friendly (binary) images and 1,722 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, K-Nearest Neighbours 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% Precision and 96.4% for Recall.
Related Topics
Concepts
Computer science
Data mining
Sensor fusion
Support vector machine
Machine learning
Software
Random forest
Artificial intelligence
Homogeneous
Naive Bayes classifier
Artificial neural network
Fusion
Stochastic gradient descent
Precision and recall
Philosophy
Thermodynamics
Programming language
Physics
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202309.1930.v1
- https://www.preprints.org/manuscript/202309.1930/v1/download
- OA Status
- green
- Cited By
- 1
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387184833
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387184833Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202309.1930.v1Digital Object Identifier
- Title
-
Data Mining and Fusion Framework for In-Home Monitoring ApplicationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-28Full publication date if available
- Authors
-
Idongesit Ekerete, Matías García-Constantino, Paul McCullagh, Chris Nugent, James McLaughlinList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202309.1930.v1Publisher landing page
- PDF URL
-
https://www.preprints.org/manuscript/202309.1930/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.preprints.org/manuscript/202309.1930/v1/downloadDirect OA link when available
- Concepts
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Computer science, Data mining, Sensor fusion, Support vector machine, Machine learning, Software, Random forest, Artificial intelligence, Homogeneous, Naive Bayes classifier, Artificial neural network, Fusion, Stochastic gradient descent, Precision and recall, Philosophy, Thermodynamics, Programming language, Physics, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2023-09-28 |
| publication_year | 2023 |
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