An embedded deep-clustering-based load profiling framework

Eskandarnia, Elham; Al-Ammal, Hesham; Ksantini, Riadh

doi:10.1016/j.scs.2021.103618

Cited by 35 publications

(7 citation statements)

References 21 publications

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“…These studies revealed a significant improvement in feature-based clustering over clustering of raw hourly data alone. Another approach is to apply dimensionality reduction algorithms, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and unified manifold approximation and projection (UMAP), directly to high-dimensional data to reduce dimensionality, then perform the clustering, e.g., [4,35,36]. This paper employs the latter approach, but is different from other works.…”

Section: Related Workmentioning

confidence: 99%

Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

Junqi

Niu

et al. 2023

Energy

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Section: Related Workmentioning

confidence: 99%

Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

Junqi

Niu

et al. 2023

Energy

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“…The github code created by [34] was used for implementing the One-Class-Neural Network (OC-NN) classifier. The github code created by [35] and modified by [36] was also used for implementing the DynAE deep clustering model.…”

Section: Experimental Protocolmentioning

confidence: 99%

A Novel Intrusion Detection System for Internet of Healthcare Things Based on Deep Subclasses Dispersion Information

Fouda

Ksantini

Elmedany

2023

IEEE Internet Things J.

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Despite the significant benefits that the Internet of Healthcare Things (IoHT) offered to the medical sector, there are concerns and risks regarding these systems which can delay their wide deployment as they handle sensitive, and often life-critical medical information. In addition to the IoHT concerns and risks, there are security constraints which include hardware, software, and network constraints that pose a security challenge to these systems. Therefore, security measures need to be deployed that can overcome the concerns, mitigate the risks, and meet the constraints of the IoHT. For these reasons, a subclasses intrusion detection system for the IoHT is proposed in this research work based on a novel variation of the standard One-class Support Vector Machine (OSVM), namely Deep Subclass Dispersion Oneclass Support Vector Machine (Deep SDOSVM), which considers subclasses in the target class, i.e. normal class, in order to minimize the data dispersion within and between subclasses, thereby improving the discriminative power and classification performance of the intrusion detection system. A deep clustering model is used for subclasses generation in the proposed Deep SDOSVM approach, namely the Dynamic Autoencoder Model (DynAE), to overcome the drawbacks of the classical clustering algorithms, and further enhance the classification performance of the intrusion detection system. The proposed deep clustering subclasses intrusion detection system was evaluated on the realworld TON IoT dataset, and compared to other state-of-theart one-class classifiers. Experimentation results showed that the proposed approach outperformed the other relevant one-class classifiers for network intrusion detection.

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“…Traditional clustering analysis is evaluated by manual comparison and thus is not authentic. 50 Therefore, a novel, an authentic, meaningful, and useful evaluation method for cloud clusters is introduced in this article.…”

Section: Phase V: Evaluation Of K3cm Clustersmentioning

confidence: 99%

Cloud migration framework clustering method for social decision support in modernizing the legacy system

Aslam,

Rahim,

Watada

et al. 2023

Trans Emerging Tel Tech

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Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

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An embedded deep-clustering-based load profiling framework

Cited by 35 publications

References 21 publications

Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach

A Novel Intrusion Detection System for Internet of Healthcare Things Based on Deep Subclasses Dispersion Information

Cloud migration framework clustering method for social decision support in modernizing the legacy system

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