Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Alsarhan, Ayoub; Kilani, Yousef; Al‐Dubai, Ahmed; Zomaya, Albert Y.; Hussain, Amir

doi:10.1109/tvt.2019.2956228

Cited by 38 publications

(20 citation statements)

References 54 publications

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“…In the future, a re-assembly step will be carried out: in this case, if at least one quadrant is not homogeneous, the entire SEM image will be classified as not homogeneous and then automatically discarded, through use of fuzzy learning approaches (e.g. [46]). In addition, the proposed unsupervised AE based methodology can form in principle the basis to a generative model (e.g.…”

Section: Discussionmentioning

confidence: 99%

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

Ieracitano

Paviglianiti

Campolo

et al. 2021

IEEE/CAA J. Autom. Sinica

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The manufacturing of nanomaterials by an electrospinning process requires accurate and meticulous inspection of Scanning Electron Microscope (SEM) images of the electrospun nanofiber, to ensure no structural defects are produced. The possible presence of anomalies is known to make the nanofibrous material useless in the practical application of any nanotechnology. Hence, automatic monitoring and quality control of nanomaterials has become an important challenge in the context of Industry 4.0. In this paper, we propose a novel automatic classification system for homogenous (anomaly-free) and nonhomogenous (with defects) nanofibers avoiding the processing of the redundant full SEM image. Specifically, the image to be analyzed is partitioned into sub-images (nanopatches) that are then used as input to a hybrid unsupervised and supervised machine learning system. An Autoencoder (AE) is first trained with unsupervised learning to generate a code representing the input image with a number of relevant features. Next, a Multilayer Perceptron (MLP), trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber (NH-NF) and homogenous nanofiber (H-NF) materials. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and recent state-of-the-art techniques, reporting accuracy rates up to 92.5%. In addition, our proposed approach achieves significant model complexity reduction with respect to other deep learning strategies such as Convolutional Neural Networks (CNN). The promising performance achieved in this benchmark study will stimulate the application of our proposed framework in a range of challenging industrial manufacturing tasks.

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Section: Discussionmentioning

confidence: 99%

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

Ieracitano

Paviglianiti

Campolo

et al. 2021

IEEE/CAA J. Autom. Sinica

Self Cite

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“…This variant is used to see the results of variance in data distribution between clusters. The greater the value of V b , the better the cluster [21] [22]. The equation for calculating V b is shown in (4).…”

Section: Discussionmentioning

confidence: 99%

Combined Text Mining: Fuzzy Clustering for Opinion Mining on the Traditional Culture Arts Work

Sonalitha¹,

Zubair²,

Molyo³

et al. 2020

IJACSA

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The Indonesian government is currently intensifying work programs in the field of traditional arts and culture. In order to realize the promotion of the country's culture, the government has enacted a law on cultural promotion. One indicator of the achievement of the promotion of culture, among others, with the collection of data on traditional culture, the data mapping and data inventory can be processed into information and knowledge. In this research, indicators of performance indicators were compiled from connoisseurs of traditional works of art using data in the city of Malang, East Java, Indonesia. The results of the audience's opinion on cultural offerings can be used as a benchmark for the success of the promotion of traditional culture. When the culture is explored and tried to be displayed again, it is important to know the audience's satisfaction and understanding of the display that has just been witnessed. The results of the description of respondents in the form of opinions on the artwork will be collected as data processed using Text Mining with the Clustering of Fuzzy C-Means method to determine the audience's opinion about Feeling , which is related to feelings when viewing the beauty of the artwork, Value is related to the assessment to an art work that can be in the form of art weights contained in the work of art, and Emphasizing , which is related to empathy or respect for the art world, including professions related to the world such as dancers, musicians and others. The results achieved from this study show that has good performance on the proposed method. This can be known from the results of data testing using cluster variance V = 0.00000217901. Based on these values it can be concluded that the value of all cluster variants is good.

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“…For benefit-type attributes, the normalized value of the network attribute G j in network x i can be obtained as the ratio of the difference between a i,j and a min j to that between the maximum and minimum values a values in the network [22]. That is, r i,j = aij −amin j max i aij −amin j , where a min j denotes the minimum value of a that ensures normal communication while max i a ij denotes the maximum a value in the network.…”

Section: Qic Schemementioning

confidence: 99%

“…Subsequently, the dispersion maximization method is used to obtain the weight of each attribute given by w ={w 1 , w 2 , w 3 , w 4 } T , where 4 j=1 w j = 1. Let D ij denotes the distance between node x i and the other nodes with regard to attribute G j [22]. Therefore,…”

Section: Qic Schemementioning

confidence: 99%

QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks

2021

IEEE Access

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In the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high-and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these networks are prone to issues such as access congestion, low QoS, and frequent congestion in one network while other network resources remain idle. Therefore, 5G networks fail to meet the QoS requirements and also prevent effective resource utilization. The deployment of multi-connectivity technologies can facilitate the optimization of the multinetwork convergence-system architecture. However, such technologies are faced with several challenges. Existing literature mainly focuses on the development of a multi-connectivity flowcontrol scheme to determine the secondary nodes (SNs) to which the master node (MN) should distribute data. This paper presents a three-step, QoS-forecasting, intelligent flow-control scheme, wherein the user equipment (UE) determines the data-flow direction based on the network characteristics and load handled by each node. Subsequently, the MN determines the initial user priority based on load balancing, user characteristics, and fairness. Finally, the MN allocates data to each SN in accordance with the QoS and average transmission capability of UE. The simulation results reveal that the proposed algorithm improves the system throughput significantly compared to the single connectivity and traditional fixed-data-split methods. Furthermore, the proposed method facilitates transmission-delay reduction, radio-link failureprobability control, and improved system robustness.

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Novel Fuzzy and Game Theory Based Clustering and Decision Making for VANETs

Cited by 38 publications

References 54 publications

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

Combined Text Mining: Fuzzy Clustering for Opinion Mining on the Traditional Culture Arts Work

QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks

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