Design and implementation of intelligent monitoring system for platform security gate based on wireless communication technology using ML

Li, Chunying; Hong-xia, Niu; Shabaz, Mohammad; Kajal, Kumari

doi:10.1007/s13198-021-01402-6

Cited by 19 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed PLA-Deep CNN mechanism is associated to four current techniques: (i) Dolphin Echolocation-based Sine Cosine Algorithm +fuzzy-based Naive Bayes (Dolphin-SCA+FNB) [ 5 ], (ii) DWT-deep belief network (DBN) (DWT+DBN) [ 22 ], (iii) Bayesian HCS-multi-SVNN [ 23 ], and (iv) Fractional—the comparison study for BraTS 2016, 2017, and 2018 data is shown in Tables 2 – 4 .…”

Section: Resultsmentioning

confidence: 99%

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Kalpana

Bennet

Rahmani³

2022

BioMed Research International

View full text Add to dashboard Cite

Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor’s exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that they are unit-wide used for brain tumor feature extraction. The existing research algorithms for brain tumors have some limitations such as different qualities, low sensitivity, and diagnosing the tumor at its stages. In this particular piece of research, an innovative method of optimization known as the procedure for lightning attachment algorithm (PLA) is used, and for the purpose of classification, a CNN model known as DenseNet-169 is applied. PLA was used in order to optimize the growth, and a network model known as the DenseNet-169 model was utilized in order to extract the various growth-optimization choices. First, the MR images of the brain were preprocessed to remove any outliers. Next, the Dense Net-169 CNN model was used to extract network choices from the MR images. In addition, it is used to execute the function of a classifier in order to identify the growth as either an aberrant growth or a traditional growth. In addition, the publicly benchmarked datasets that are widely utilized have validated the algorithmic rule that was granted. The planned system demonstrates the satisfactory accuracy in getting ready to on the dataset and outperforms many of the notable current techniques.

show abstract

Section: Resultsmentioning

confidence: 99%

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Kalpana

Bennet

Rahmani³

2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Finally, the same filter level feature maps are added to reduce the loss of information in the deconvolution process. skip connections and residual structure [19]. Therefore, the network uses 3 modal images as the three channels of the input image and the multimodal 2D-ResUNet segmentation probability map as input two tracks of the image; thus, using the 2D network fast segmentation results to guide 3D model segmentation.…”

Section: Multimodal 2d Convolution the Multimodal 2d-mentioning

confidence: 99%

Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification

Martin

Sharma

Kaur

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients’ multimodal pictures and labels served as training sets, whereas the remaining 75 patients’ multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used. The three models’ performance is compared, and the findings reveal that the multimodal multidimensional fusion model performs best, while the two-modal multidimensional information fusion model performs second. The single-modal multidimensional information fusion model has the poorest performance. In MR images of nasopharyngeal cancer, a convolutional network can precisely and efficiently segment tumors.

show abstract

“…According to experts, connecting defence components to the Internet via a sophisticated information and entertainment device is a security risk. is design enables hackers to remotely manipulate and take over the operation of a vehicle [5,6]. Automotive cyber security is becoming a must-have element as internet connectivity affects every part of our life.…”

Section: Introduction To Risk Management Modelsmentioning

confidence: 99%

Risk-Based Access Control Mechanism for Internet of Vehicles Using Artificial Intelligence

Priscila

Sharma

Vanithamani³

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Internet of Vehicles (IoV) systems are vulnerable to a wide range of attacks because of the lack of security measures. IoV systems can be infiltrated by malicious and unauthorized nodes, which can cause the authenticity, accessibility, and privacy of shared information resources to be compromised. Indeed, the use of an access control system can help; as a result, it is unable to respond to such attacks on time. This paper introduces an artificial intelligence-enabled access control mechanism (AI-ACM) with vehicle nodes and roadside units (RSUs) to overcome these issues. Here, use vehicle nodes as lightweight nodes, while RSUs act as comprehensive and edge nodes to provide access control service. A generative adversarial network (GAN) is used in place of risk prediction (RP) due to the lack of training sets, resulting in a sequence generation rather than an accurate risk prediction. Afterward, the blockchain-based Internet of Vehicles (BIoV) approach is summarized for the security mechanisms of vehicles that are discussed from the aspects of access control and authentication to sustain the distributed processing architecture and solve security issues. The simulation results show that AI-ACM is more accurate than the previous GANs at predicting the future. In addition, the RP model’s access control accuracy can be improved as a result of this technique.

show abstract

Design and implementation of intelligent monitoring system for platform security gate based on wireless communication technology using ML

Cited by 19 publications

References 17 publications

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation

Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification

Risk-Based Access Control Mechanism for Internet of Vehicles Using Artificial Intelligence

Contact Info

Product

Resources

About