Privacy-Preserving Authentication and Key-Management Protocol for Health Information Systems

Soni, Mukesh; Singh, Dileep Kumar

doi:10.1201/9781003048848-3

Cited by 50 publications

(9 citation statements)

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“…It has been clearly noticed from Figure 1 that sensing device, group heads (CH) and another intelligent mechanics transmitted information in the direction of sink node or information server. WSNs security plays an important role to deliver secure transmission from sensor nodes to sink node (Reddy and Gayathri, 2020; Soni and Singh, 2021; Nair et al , 2020; Soni et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Kaur

Rani

Dahiya³

2021

WJE

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Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

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Section: Introductionmentioning

confidence: 99%

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Kaur

Rani

Dahiya³

2021

WJE

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“…Just 25 chest X-ray images in each class are based on this model – regular and COVID-19. Moreover, researchers are trying to further apply this proposed method on real-life AI-based approaches; these approaches are based on optimization, Metaheuristi, ML, DL (Dhiman and Kumar, 2017; Dhiman and Kumar, 2018; Dhiman and Kumar, 2019; Dhiman and Kaur, 2019; Kaur et al , 2020) and proposing security protocols for established secure communication among the servers and clients applications (Dehghani et al , 2019; Dehghani et al , 2020a; Dehghani et al , 2020b; Dehghani et al , 2020c; Dhiman, 2019; Dhiman et al , 2021; Dhiman et al , 2020b; Limbasiya et al , 2018; Dhiman et al , 2020a; Soni and Kumar, 2020; Soni and Singh, 2021; Soni et al , 2020a; Soni and Jain, 2018; Soni et al , 2020c; Soni et al , 2021).…”

Section: Background Studymentioning

confidence: 99%

“…and proposing security protocols for established secure communication among the servers and clients applications (Dehghani et al, 2019; Dehghani et al, 2020a; Dehghani et al,Dehghani et al, 2020c;Dhiman, 2019;Dhiman et al, 2021;Dhiman et al, 2020b;Limbasiya et al, 2018;Soni and Kumar, 2020;Soni and Singh, 2021;Soni et al, 2020c;.…”

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confidence: 99%

Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

Kumar¹,

Bajpai

Gupta

et al. 2021

WJE

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Purpose The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images. Design/methodology/approach We used machine learning techniques with convolutional neural network. Findings Detecting COVID-19 symptoms from patient CT scan images. Originality/value This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.

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“…Also, they discussed the impact of COVID-19 on humans (Liu et al, 2018). Moreover researchers are proposing various security protocols (Dhiman et al, 2020a;Pandey et al, 2020;Soni and Kumar, 2020;Soni and Singh, 2021;Nair et al, 2020;Soni et al, 2020a;Patel et al, 2020;Soni et al, 2020b;Chaudhary et al, 2021) for providing secure commendation during the data transmission among servers and clients for generating data sets, that data sets can be used in various machine learning approaches for different methodologies. Bai et al (2020) proposed his study Liu et al (2020) that viral pneumonia and COVID-19 have distinct feature sets, and the primary features are distribution, ground-glass opacity and vascular thickening.…”

Section: Literature Surveymentioning

confidence: 99%

COVID-19 prediction through X-ray images using various layers in convolutional neural network

Mishra

Tiwari

Bajpai

et al. 2021

WJE

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Purpose The purpose of this paper is to focus on the prediction of Coronavirus 2019 (COVID-19) using X-ray image. Design/methodology/approach This study proposed convolutional neural network (CNN) approach to predict COVID-19. Findings Prediction of COVID-19 using CNN. Originality/value The work has implemented multiple CNN models to classify chest X-ray of affected patients by using their chest scans. According to three models, the ResNet-50 is advantageous because of its high service reliability.

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Privacy-Preserving Authentication and Key-Management Protocol for Health Information Systems

Cited by 50 publications

References 0 publications

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network

COVID-19 prediction through X-ray images using various layers in convolutional neural network

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