2020
DOI: 10.1109/access.2020.2967629
|View full text |Cite
|
Sign up to set email alerts
|

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

Abstract: By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeli… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
17
0

Year Published

2020
2020
2022
2022

Publication Types

Select...
5
1

Relationship

1
5

Authors

Journals

citations
Cited by 19 publications
(17 citation statements)
references
References 49 publications
0
17
0
Order By: Relevance
“…Both algorithms depend on the exchange of information among members of the population, using deterministic and probabilistic rules to improve research results. However, PSO is computationally more efficient than GA [ 41 ].…”
Section: Resultsmentioning
confidence: 99%
“…Both algorithms depend on the exchange of information among members of the population, using deterministic and probabilistic rules to improve research results. However, PSO is computationally more efficient than GA [ 41 ].…”
Section: Resultsmentioning
confidence: 99%
“…Some of prediagnose on test set are shown in Table VI. According to formula (15) and Table VI, the value of the log loss function of the LightGBM-FA model on the test set is shown in formula (19). The prediagnosis accuracy of LightGBM-FA is 0.67274 and its merror is 0.32726.…”
Section: Learning and Prediagnosis Results Lightgbm-famentioning
confidence: 99%
“…Some of prediagnose on the test set are shown in Table III. Firstly, we use the log loss function to evaluate the effect of the model, as shown in formula (15)…”
Section: Figure 4 Part Of the 72nd Tree Diagram Of Xgboostmentioning
confidence: 99%
See 1 more Smart Citation
“…Such models, however, use a single threshold to determine how sensitive information in specific environments is hidden. [15][16][17][18][19] But it is unreasonable in real applications. A long pattern is more specific than a short pattern.…”
Section: Introductionmentioning
confidence: 99%