Master-slave group based model for co-ordinator selection, an improvement of bully algorithm

Mishra, Balmukund; Singh, Ninni; Singh, Ravideep

doi:10.1109/pdgc.2014.7030789

Cited by 20 publications

(7 citation statements)

References 7 publications

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“…The CNN model is used to segment the tumor in this strategy, although the results are limited in accuracy and sensitivity because of the limitations of the CNN model [ 18 ]. According to the findings of another study [ 19 , 20 ], brain tumors can be detected using both hand-created and deep learner features.…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Kolla

Mishra

Huq

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

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Section: Related Workmentioning

confidence: 99%

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Kolla

Mishra

Huq

et al. 2022

Computational Intelligence and Neuroscience

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“…When recommendations on indirect trust computations are used, there is a chance of a collusion attack where nodes group and raise trust levels to become a legitimate nodes. [32], [33].…”

Section: A Attack Modelmentioning

confidence: 99%

Security in IoT Mesh Networks Based on Trust Similarity

Athota

Reddy²,

Singh

et al. 2022

IEEE Access

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Internet of Things (IoT) Mesh networks are becoming very popular to enable IoT devices to communicate without relying on dedicated PC services. Internet of Things (IoT) implicitly uses mesh networks. IoT connectivity to cloud and edge computing is in vogue. A Wireless Mesh Network (WMN) is a multi-hop and distributed wireless network with mesh routers and mesh clients. Data originating from mesh clients are forwarded to destinations through mesh routers. In IoT Mesh networks, mesh clients are IoT devices. The crucial security issue with these networks is the lack of a trusted third party for validation. However, trust between nodes is required for the proper functioning of the network. WMNs are particularly vulnerable as they rely upon cooperative forwarding. In this research work, a secure and sustainable novel trust mechanism framework is proposed. This framework identifies the malicious nodes in WMNs and improves the nodes' cooperation. The proposed framework or model differentiates between legitimate and malicious nodes using direct trust and indirect trust. Direct trust is computed based on the packet-forwarding behavior of a node. Mesh routers have multi radios, so the promiscuous mode may not work. A new two-hop mechanism is proposed to observe the neighbors' packet forwarding behavior. Indirect trust is computed by aggregating the recommendations using the weighted D-S theory, where weight is computed using a novel similarity mechanism that correlates the recommendations received from different neighbors. Dynamic weight computation calculates the overall trust by using several interactions. We present the evaluations to show the effectiveness of the proposed approach in the presence of packet drop/modification attacks, bad-mouthing attacks, on-off attacks, and collusion attacks by using the ns-2 simulator.

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“…Fine-tuning [ 31 – 35 ] consists in removing the final set of fully linked layers from a pretrained CNN and replacing them with a new set of fully linked layers. All of the layers below the head are frozen, and their weights cannot be changed because of this.…”

Section: Process Flow and Algorithmmentioning

confidence: 99%

Deep Learning and Transfer Learning for Malaria Detection

Jameela

Athotha

Singh

et al. 2022

Computational Intelligence and Neuroscience

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Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.

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Master-slave group based model for co-ordinator selection, an improvement of bully algorithm

Cited by 20 publications

References 7 publications

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Security in IoT Mesh Networks Based on Trust Similarity

Deep Learning and Transfer Learning for Malaria Detection

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