Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces

Garg, Meenakshi; Dhiman, Gaurav

doi:10.33969/jiec.2020.21003

Cited by 51 publications

(12 citation statements)

References 20 publications

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“…The momentous standards of VANET are the dedicated short-range communication (DSRC) protocol, IEEE 802.11 [2], and wireless access in vehicular environment (WAVE) [3,4]. Delays due to traffic, traffic that leads to congestion, consumption of energy, and the emission of pollution are the disputable in traffic management for smart cities [5][6][7][8][9]. The traffic management must effectuate the smart system for parking, an intelligent system for vehicles in routing management, and an intelligent system that predicts the traffic [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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“…In the future, this technique can be extended to support modern networks, which could involve heterogeneous IoT networks [16]. Further use of artificial intelligence and optimization can be found in [36,[40][41][42].…”

Section: Discussionmentioning

confidence: 99%

Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network

Kumar

Malik

Lohani

2021

Wireless Communications and Mobile Computing

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Recently, there have been exploratory growth in the research of wireless sensor network due to wide applications like health monitoring, environment monitoring, and urban traffic management. Sensor network applications have been used in habitat monitoring, border monitoring, health care, and military surveillance. In some applications, the security of these networks is very essential and need robust support. For a network, it is very important that node in the network trust each other and malicious node should be discarded. Cryptography techniques are normally used to secure the networks. Key plays a very important role in network security. Other aspects of security such as integrity, authentication, and confidentiality also depend on keys. In wireless sensor network, it is very difficult to manage the keys as this includes distribution of key, generation of new session key as per requirements, and renewal or revoke the keys in case of attacks. In this paper, we proposed a scalable and storage efficient key management scheme (SSEKMS) for wireless sensor networks that establish the three types of keys for the network: a network key that is shared by all the nodes in the network, a cluster key shared for a cluster, and pairwise key for each pair of nodes. We analysed the resiliency of the scheme (that is the probability of key compromise against the node capture) and compared it with other existing schemes. SSEKMS is a dynamic key management system that also supports the inclusion of the new node and refreshes the keys as per requirements.

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“…Also, all the basic functionalities and modalities of the normal artificial neural networks (ANN) still hold for the CNNs. These networks are specialized for image pattern recognition, and this differentiates them from the ANNs [ 41 ]. The classical ANNs suffer from a drawback of low computational efficiency for image-related data due to the complexity in calculations.…”

Section: Image Classification Using Convolution Neural Networkmentioning

confidence: 99%

An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks

Juneja

Juneja²,

Behl³

et al. 2021

Computational and Mathematical Methods in Medicine

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There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding, k -means clustering, and edge detection. The CNN cannot scan and process the whole image at an instant; it needs to recursively scan small pixel spots until it has scanned the whole image. Spatial transformation layers and VGG19 have been used for the purpose of feature extraction, and ReLU activation function has been employed due to its inherent low complexity and high computation efficiency; finally, stochastic gradient descent has been used as an optimizer. The main advantage of the current method is that it retains the essential features of the image for prediction along with incorporating a considerable dimensional reduction. The model delivered substantial improvement over existing research in terms of precision, f -score, and accuracy of prediction. This model if used precisely can be very effective for healthcare practitioners in determining the thoracic or pneumonic symptoms in the patient at an early stage thus guiding the practitioner to start the treatment immediately leading to fast improvement in the health status of the patient.

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Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces

Cited by 51 publications

References 20 publications

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network

An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks

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