A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

Talib, Mohammed Saad; Hassan, Aslinda; Alamery, Thamer; Abas, Zuraida Abal; Mohammed, Ali A.; Ibrahim, Ali Jalil; Abdullah, Nihad Ibrahim

doi:10.1109/access.2020.3020510

Cited by 22 publications

(11 citation statements)

References 46 publications

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“…There are two ways to find the final solution in the solution set: taking the cluster center as the solution and the cluster result as the solution. In order to improve the accuracy of the analysis, subgroups were used as a solution in this sentence [ 16 , 17 ]. Therefore, the average vectors of each group are calculated for each fish, and the position of the fish can be used to represent one pixel in the image.…”

Section: Methodsmentioning

confidence: 99%

High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network

Chen

Zhang

2022

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In order to solve the problem of low efficiency and accuracy of injury image recognition for sports athletes in high-intensity injury treatment, this paper proposes an injury recognition mode based on the deep neural network. In this paper, the image of sports injury is converted to gray level, and the contour of the injury part in the image is extracted according to the combination of adaptive thresholding and mathematical morphology. In this model, the seed points are selected, the active contour is used to approximate the initial contour, and the curve fitting method is used to fit the obtained discrete points to obtain the final damaged contour. The digital matrix is constructed by using the extracted number of pixels at the damaged position and relevant information. The images are arranged into feature vectors with a length of 64 according to the mode of column concatenation. The overall mean vector of the image is calculated. The calculation results, training samples, and image samples to be recognized are substituted into the Euclidean distance to obtain the preliminary recognition results of the damaged position of the image of sports injury. Then, the image segmentation is realized by clustering. The clustering segmentation results are used to color describe the pixel categories of the original image, calculate the relative damage proportion area in the sports injury image, and identify the damage parts of the high-intensity sports injury image. The experimental results show that the recognition rate of the neural network is 80%-100%, and the recognition time of this method is 0-0.6/s. The above method can improve the accuracy of the recognition of the damaged part of the sports injury image and shorten the recognition time and has certain feasibility in determining the sports injury part.

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Section: Methodsmentioning

confidence: 99%

High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network

Chen

Zhang

2022

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“…This technique is used to test the cluster stability by observing the number of vehicles in each cluster at different successive times. All the generated clusters in the proposed approach called CEC-GP [35] are tested at their starting time to indicate the initial number of vehicles. Also, these clusters are tested at different successive times to observe the leaving or the joining vehicles.…”

Section: Resultsmentioning

confidence: 99%

A Novel Stability Evaluation Technique for Clustering Algorithms in Vehicular Ad Hoc Networks

Talib

Hassan

Abas

et al. 2021

Preprint

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The development of the technology and connected devices such as internet of things (IoT), internet of vehicles (IoV), and 5G motivate the researchers to give more attention in the field. Clustering is a key factor in vehicular ad-hoc network (VANET) where a number of vehicles join to form a group based on common characteristics. Vehicles are distinguished by their high mobility in ad hoc vehicle networks. Changes frequently occur in the topology of VANET, causing continuous failures in network communication. In such a dynamic environment, the creation and maintenance of a stable cluster are significant challenges. The evaluation of stability in VANET clustering is an important part to evaluate the clustering approaches. In this paper, a mathematical technique (based on the birth-death process) is created to evaluate the clusters stability based on the number of leaving and joining vehicles to each cluster after its creation. The stability of the created clusters is tested by checking the number of vehicles in each cluster at different successive times. These tests indicate the joining and leaving vehicles to each cluster and their effects on the cluster stability. When the results of the technique show that the standard deviation is small for each cluster, it can be concluded that the proposed clustering algorithm is able to achieve stability in cluster maintaining phase.

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“…To ensure the VANET model has a practical nature, we adopt a realistic mobility model that captures the vehicles' mobility dynamics [33]. The mobility model is based on the acceleration generated from the driving behaviour model.…”

Section: ) Mobility Modelmentioning

confidence: 99%

Reliability-Aware Multi-Objective Optimization-Based Routing Protocol for VANETs Using Enhanced Gaussian Mutation Harmony Searching

et al. 2022

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Vehicular Ad Hoc Networks (VANETs) is an emerging technology that can be applied in intelligent transportation systems. Routing protocols are essential for obtaining reliable VANET networks. This article proposes a novel framework, designated as Reliability Aware Multi-Objective Optimization Based VANETs Routing (RAMO). The framework includes three levels: the first is the simulation of the VANET system; the second is the routing criteria, based on reliability and geometrics; the third level is the routing algorithm. The actual network is the next stage. Furthermore, the framework includes an optimization block that controls the parameters of each of the reliability, geometrical and routing blocks. The optimization is presented under the multi-objective perspective and based on the development of a novel variant of multiobjective harmony searching. This has been designated as Enhanced Gaussian Mutation Harmony Searching (EGMHS), which includes Gaussian mutation, objective decomposition and a harmony memory extraction algorithm. The evaluation was performed based on two levels. The first was the EGMHS evaluation using nine benchmarking mathematical functions, while the second was the RAMO evaluation based on the network simulator. The metrics obtained, including set coverage, delta metric, hyper-volume, packet delivery ratio (PDR) and end-to-end (E2E) delay, demonstrate the superiority over the baseline approaches of both EGMHS and RAMO with EGMHS.

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A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

Cited by 22 publications

References 46 publications

High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network

High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network

A Novel Stability Evaluation Technique for Clustering Algorithms in Vehicular Ad Hoc Networks

Reliability-Aware Multi-Objective Optimization-Based Routing Protocol for VANETs Using Enhanced Gaussian Mutation Harmony Searching

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