Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks

Tamilvizhi, T.; Surendran, R; Romero, Carlos Andres Tavera; Sendil, M. Sadish

doi:10.32604/iasc.2022.026331

Cited by 26 publications

(17 citation statements)

References 18 publications

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“…Among other things, the AlexNet model is a more complex variant of the LeNet5 [12] model (in that it has a greater number of layers than LeNet5), and a considerable lot of the best models in the PC vision field are extended forms of the AlexNet model, including ZFNet [13], VGGNet [14], GoogleLetNet [15], and RestNet [16] to name a few. Many classic Machine Learning (ML) methods have been outperformed by CNNs in a variety of computer vision applications, including object identification and recognition, among other things [17][18][19][20]. The categorization of brain tumors is a more challenging research problem to answer than the classification of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal¹,

Thanarajan²,

Alotaibi³

et al. 2023

Computer Systems Science and Engineering

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Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a U-Net and a 3D CNN, we present a major yet easy combinative technique that results in improved and more precise estimates. The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on. Using the dataset, two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region. Then, the estimates was made by two separate models that were put together to produce the final prediction. In comparison to current state-of-the-art designs, the precision (percentage) was 98.35, 98.5, and 99.4 on the validation set for tumor core, enhanced tumor, and whole tumor, respectively.

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Section: Related Workmentioning

confidence: 99%

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal¹,

Thanarajan²,

Alotaibi³

et al. 2023

Computer Systems Science and Engineering

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“…β shows the contraction expansion factor and the variable of quantum PSO. For avoiding the premature convergence, Rout et al enhanced the quantum PSO [28], presenting mbest in the model and in the following equation:…”

Section: Image Classification Using Dsae Modelmentioning

confidence: 99%

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Tamilvizhi¹,

Rajendran

Anbazhagan

et al. 2022

Mathematical Problems in Engineering

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Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.

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“…[20], [21]. The presented TAMOMO-SCRP model selects the secured optimum CH in the cluster to attain the secured data broadcast on the network [22], [23]. The aim is for choosing one of the optimum numbers of nodes like CHs.…”

Section: B Design Of Clustering Techniquementioning

confidence: 99%

“…The aforementioned objective is converting the multiobjective function to a single objective. Therefore, the normalized procedure (F(x)) was executed for all the objectives α 1 , α 2 , α 3 , α 4 in (22).…”

Section: B Design Of Clustering Techniquementioning

confidence: 99%

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

2022

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Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.INDEX TERMS Industrial Internet of Things, clustering, bald eagle search algorithm, trust aware protocols, security.

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Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks

Cited by 26 publications

References 18 publications

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

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