Data Mining and Fusion Framework for In-Home Monitoring Applications
Abstract
:1. Introduction
1.1. Sensor Data Fusion Architectures
1.2. Data Mining Concepts
2. Related Work
2.1. Object Detection
2.2. Automobile Systems
2.3. Healthcare Applications
2.4. Cluster-Based Analysis
3. Materials and Methods
4. Results
4.1. Conceptual Findings
4.2. Homogeneous Data Analysis
4.3. Heterogeneous Data Analysis
4.4. Proposed Data Fusion Framework
5. Discussion
5.1. The Proposed Framework vs. Others
5.2. Advantages of the Proposed Framework
5.3. Limitation of the Proposed Framework
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classification Techniques | Application of Classification Techniques | Clustering Techniques | Application of Clustering Techniques |
---|---|---|---|
Neural Network | E.g., stock market prediction [13] | Partition-based | E.g., medical datasets analysis [14] |
Decision Tree | E.g., Banking and finance [15] | Model-based | E.g., multivariate Gaussian mixture model [16] |
Support Vector Machine | E.g., big data analysis [17] | Grid-based | E.g., large-scale computation [18] |
Association-based | E.g., high dimensional problems [19] | Density-based | Applications with noise. E.g., DBSCAN [20] |
Bayesian | E.g., retrosynthesis [21] | Hierarchy-based | E.g., Mood and abnormal activity prediction [22,23] |
Parameters | ODMS (%) | RMS (%) | WDMS (%) | Anaconda (%) |
---|---|---|---|---|
Ease of Use Interface | 96.0 | 94.0 | 91.0 | 78.0 |
Functionality and Features Management | 95.0 | 96.0 | 92.0 | 78.0 |
Software Integration | 94.0 | 95.0 | 90.0 | 76.0 |
Performance Index | 95.0 | 95.0 | 91.0 | 77.0 |
Advanced Features Incorporation | 95.0 | 94.0 | 92.0 | 77.0 |
User Rating on Implementation | 90.0 | 67.0 | 73.0 | 77.0 |
Average Rating | 94.2 | 90.2 | 88.2 | 77.2 |
Model | ODMS CA (%) | WDMS CA (%) | RMS CA (%) |
---|---|---|---|
Naive Bayes | 79.9 | 77.0 | 80.8 |
Generalised Linear Model | NA | NA | 82.7 |
Logistic Regression | 94.1 | 74 | 22.9 |
Fast Large Margin | NA | NA | 83.3 |
Deep Learning/Neural Network | 94.2 | NA | 86.1 |
Decision Tree | 62.3 | 77.0 | NA |
Random Forest | 73.9 | 83.0 | 55.1 |
Stochastic Gradient Descent | 94.5 | 71.0 | 87.1 |
Support Vector Machine | 94.0 | 75.0 | 78.3 |
Average based on Available Models | 84.7 | 76.2 | 72.0 |
Model | RMS CA (%) | WDMS CA (%) | ODMS CA (%) |
---|---|---|---|
Naive Bayes | 60.4 | 67.0 | 80.7 |
Generalised Linear Model | 60.7 | NA | NA |
Fast Large Margin | 62.2 | NA | NA |
Deep Learning/Neural Network | 59.2 | NA | 98.9 |
Decision Tree | 54.3 | 64.0 | 99.5 |
Decision table | NA | 69.0 | NA |
Random Forest | 59.2 | 70.0 | 89.9 |
Stochastic Gradient Descent | 60.1 | NA | 99.3 |
Support Vector Machine | 61.3 | 48.0 | 98.4 |
K-Nearest Neighbours | NA | NA | 99.1 |
CN2 Induction | NA | NA | 99.5 |
J48 | NA | 70.0 | NA |
Average | 59.7 | 64.7 | 95.7 |
Model | AUC (%) | CA (%) | F1 (%) | Precision (%) | Recall (%) | Log Loss (%) |
---|---|---|---|---|---|---|
Random Forest | 85.2 | 96.8 | 96.0 | 95.8 | 96.8 | 0.2 |
Neural Network | 95.5 | 98.6 | 98.6 | 98.6 | 98.6 | 0.1 |
K-Nearest Neighbours | 95.5 | 95.5 | 94.6 | 93.7 | 95.5 | 0.1 |
CN2 Induction | 87.8 | 94.6 | 94.6 | 94.6 | 94.6 | 0.1 |
Average | 91.0 | 96.4 | 96.0 | 95.7 | 96.4 | 0.1 |
Model | AUC (%) | CA (%) | F1 (%) | Precision (%) | Recall (%) | Log Loss (%) |
---|---|---|---|---|---|---|
Random Forest | 100.0 | 99.5 | 99.5 | 99.5 | 99.5 | 0.0 |
Neural Network | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 |
K-Near Neighbours | 98.7 | 98.2 | 98.0 | 98.0 | 98.2 | 0.1 |
CN2 Induction | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 0.0 |
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Ekerete, I.; Garcia-Constantino, M.; Nugent, C.; McCullagh, P.; McLaughlin, J. Data Mining and Fusion Framework for In-Home Monitoring Applications. Sensors 2023, 23, 8661. https://doi.org/10.3390/s23218661
Ekerete I, Garcia-Constantino M, Nugent C, McCullagh P, McLaughlin J. Data Mining and Fusion Framework for In-Home Monitoring Applications. Sensors. 2023; 23(21):8661. https://doi.org/10.3390/s23218661
Chicago/Turabian StyleEkerete, Idongesit, Matias Garcia-Constantino, Christopher Nugent, Paul McCullagh, and James McLaughlin. 2023. "Data Mining and Fusion Framework for In-Home Monitoring Applications" Sensors 23, no. 21: 8661. https://doi.org/10.3390/s23218661
APA StyleEkerete, I., Garcia-Constantino, M., Nugent, C., McCullagh, P., & McLaughlin, J. (2023). Data Mining and Fusion Framework for In-Home Monitoring Applications. Sensors, 23(21), 8661. https://doi.org/10.3390/s23218661