Granular-Rule Extraction to Simplify Data

Mashinchi, M.; Selamat, Ali; Ibrahim, Suhaimi; Krejcar, Ondřej

doi:10.1007/978-3-319-15705-4_41

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…It uses averaging techniques to propose a method in which a previous algorithm for association rules mining specifies the minimum support automatically. It uses fuzzy logic to distribute data in different clusters and then tries to introduce to the user the most appropriate threshold automatically [60]. Suggests a two-stage hybrid model for data classification and rule extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

Domb,

Joshi,

Khn

2024

Artificial Intelligence

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IoT comprises sensors and other small devices interconnected locally and via the Internet. Typical IoT devices collect data from the environment through sensors, analyze it and act back on the physical world through actuators. We can find them integrated into home appliances, Healthcare, Control systems, and wearables. This chapter presents a variety of applications where IoT devices are used for anomaly detection and correction. We review recent advancements in Machine/Deep Learning Models and Techniques for Anomaly Detection in IoT networks. We describe significant in-depth applications in various domains, Anomaly Detection for IoT Time-Series Data, Cybersecurity, Healthcare, Smart city, and more. The number of connected devices is increasing daily; by 2025, there will be approximately 85 billion IoT devices, spreading everywhere in Manufacturing (40%), Medical (30%), Retail, and Security (20%). This significant shift toward the Internet of Things (IoT) has created opportunities for future IoT applications. The chapter examines the security issues of IoT standards, protocols, and practical operations and identifies the hazards associated with the existing IoT model. It analyzes new security protocols and solutions to moderate these challenges. This chapter’s outcome can benefit the research community by encapsulating the Information related to IoT and proposing innovative solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

Domb,

Joshi,

Khn

2024

Artificial Intelligence

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“…Q-values are used to minimize QFAM prototyping. Mashinchi et al [17] proposed a granular-rules extraction method to simplify a data set into a granularrule set with unique granular rules. It performs in two stages to construct and prune the granular rules.…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Lightweight Security Framework Suited for IoT

Domb¹

2018

Internet of Things - Technology, Applications and Standardization

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Standard security systems are widely implemented in the industry. These systems consume considerable computational resources. Devices in the Internet of Things [IoT] are very limited with processing capacity, memory and storage. Therefore, existing security systems are not applicable for IoT. To cope with it, we propose downsizing of existing security processes. In this chapter, we describe three areas, where we reduce the required storage space and processing power. The first is the classification process required for ongoing anomaly detection, whereby values accepted or generated by a sensor are classified as valid or abnormal. We collect historic data and analyze it using machine learning techniques to draw a contour, where all streaming values are expected to fall within the contour space. Hence, the detailed collected data from the sensors are no longer required for real-time anomaly detection. The second area involves the implementation of the Random Forest algorithm to apply distributed and parallel processing for anomaly discovery. The third area is downsizing cryptography calculations, to fit IoT limitations without compromising security. For each area, we present experimental results supporting our approach and implementation.

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“…The concept of iterative data granulation can be combined with Fuzzy Logic information granulation theory, by means of clustering algorithms (Mashinchi et al, 2015). Via such computational frameworks, data is simplified (via grouping/clustering) to make it easier to understand and reduce its complexity.…”

Section: Introductionmentioning

confidence: 99%

An evolving feature weighting framework for radial basis function neural network models

2022

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Via Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural-Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W-GrC), there is no work in the literature to demonstrate effectiveness and impact on Type-2 Fuzzy Logic systems (T2-FLS). The main contribution of this paper is to extend W-GrC, for the first time, to both Type-1 and Type-2 models known as Radial Basis Function Neural Network (RBFNN) and General Type-2 Radial Basis Function Neural Network (GT2-RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained.

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Granular-Rule Extraction to Simplify Data

Cited by 7 publications

References 13 publications

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

An Adaptive Lightweight Security Framework Suited for IoT

An evolving feature weighting framework for radial basis function neural network models

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