2020
DOI: 10.3390/s20205811
|View full text |Cite
|
Sign up to set email alerts
|

An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Abstract: In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
12
0
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
6
2
1

Relationship

2
7

Authors

Journals

citations
Cited by 21 publications
(13 citation statements)
references
References 27 publications
(29 reference statements)
0
12
0
1
Order By: Relevance
“…We chose Euclidean distance [22][23][24] as our distance metric. Take one of the small datasets for example, the number .…”
Section: Design Choicementioning
confidence: 99%
“…We chose Euclidean distance [22][23][24] as our distance metric. Take one of the small datasets for example, the number .…”
Section: Design Choicementioning
confidence: 99%
“…The proposed model contains two core contributions: (1) the model was deployed on the cloud server, and (2) its deployment on the edges majorly contributes toward adaptability by continuously updating. The authors used online training (OT) and online classifier updating (OCU), presented in [36], with some internal tweaking parameters to make the approach suitable for the federated machine learning environment. Averaging Mechanism: The global model (server) collects the trained weights from all local models (edges).…”
Section: Methodsmentioning
confidence: 99%
“…The cloud-based adaptive ensemble CNN was inspired by a previously proposed approach [36]. The primary difference is that this study restructured the previously proposed framework into the federated machine learning-based architecture.…”
Section: Federated Machine Learning-based Algorithm For Cloud Servermentioning
confidence: 99%
“…The implementation of ML techniques is vital for dynamic operations of the systems with continuous and automated learning [10]. Other than pattern recognition, ML technologies are adopted for the self-learning of the big-data based systems connected via the internet of things (IoT) integrated with digital technologies [11]. Likewise, for the construction progress detection technologies, the trend of integration with ML techniques for the digitalisation of the monitoring process has also been increased in recent times [12].…”
Section: Introductionmentioning
confidence: 99%