A Joint Multiple Hypothesis Tracking and Particle Filter Approach for Aerial Data Fusion

d'Apolito, Francesco; Eliasch, Christian; Sulzbachner, Christoph; Mecklenbräuker, Christoph F.

doi:10.23919/fusion49751.2022.9841308

Cited by 1 publication

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to convert the raw data from the bottom layer into meaningful events, the knowledge reasoning module must first apply the rules to the data. [43], [44] Whenever an event is sent from the knowledge reasoning module, it is uniformly processed by the event description module into a simple form. In order to get the lower-level hardware devices to carry out the demand from the top layers, the event decomposition module is used to translate those requirements into commands the system can accept [45].…”

Section: Figure 3: Algorithms For ML In the Iotmentioning

confidence: 99%

“…The proportion of accurately obtained positive observations to all predicted positive observations is known as precision Equations ( 44), ( 45), ( 46) and ( 47). Precision = TP / TP + FP (44) The proportion of correctly detected positive observations to the total number of observations is known as sensitivity/recall. Recall = TP / TP + FN S(45)…”

Section: B Recognition Of Activity In Healthy Older People Using a Ba...mentioning

confidence: 99%

See 1 more Smart Citation

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Saranya

Fatima

2022

IEEE Access

View full text Add to dashboard Cite

The Internet of Things (IoT) in the healthcare market is propelled forward by the implementation of digital systems for monitoring and analysing health problems. IoT and smart devices can contribute to a highly smart environment. Smart medical devices interconnected with smartphone apps can collect medical and other required health data. "Data Fusion (DF)" refers to integrating data and knowledge from multiple sources. However, these techniques are also applied to other domains, including text processing. Using data from multiple distributed sources, the objective of DF in multisensory contexts is to reduce the chance of detection errors and increase their reliability. The objective is to increase scalability, performance efficiency, and identification. A medical device's ability to scale up or down demonstrates its capacity to respond to environmental factors. A more scalable system performs as expected, with no interruptions, and makes the best available use of the resource management it has. To ensure that these tracking devices all work the same way, it is essential to form a specialised group to develop uniformity in areas such as communication channels, aggregation of data, and smart interfaces. The main contribution of this research is pre-processing, DF using the Improved Context-aware Data Fusion (ICDF) algorithm, feature extraction via Improved Principal Component Analysis (IPCA), feature selection through the Enhanced Recursive Feature Elimination (ERFE) algorithm, and a classifier using an ensemble-based Machine Learning (ML) model. The Improved Dynamic Bayesian Network (IDBN) is a good trade-off for tractability, becoming a tool for ICDF operations. The simulation results show that the proposed ICDF model achieves higher performance in terms of 97% accuracy, 96% precision, 97% recall, and 97% F1 score in the healthcare system.

show abstract

Section: Figure 3: Algorithms For ML In the Iotmentioning

confidence: 99%

Section: B Recognition Of Activity In Healthy Older People Using a Ba...mentioning

confidence: 99%

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Saranya

Fatima

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

A Joint Multiple Hypothesis Tracking and Particle Filter Approach for Aerial Data Fusion

Cited by 1 publication

References 15 publications

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

IoT-Based Patient Health Data Using Improved Context-Aware Data Fusion and Enhanced Recursive Feature Elimination Model

Contact Info

Product

Resources

About