Futuristic Cyber-Twin Architecture for 6G Technology to Support Internet of Everything

Juneja, Sapna; Gahlan, Mamta; Kautish, Sandeep

doi:10.1155/2021/9101782

Cited by 21 publications

(10 citation statements)

References 26 publications

(18 reference statements)

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“…An experimental evaluation for the detection of WBC images using the DenseNet121 CNN model is implemented [ 32 ]. The CNN model was implemented using the blood cell images collected from the White Blood Cell Dataset.…”

Section: Feature Extraction Using Densenet121mentioning

confidence: 99%

Deep Learning Model for the Automatic Classification of White Blood Cells

Sharma

Gupta

et al. 2022

Computational Intelligence and Neuroscience

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145

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Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.

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Section: Feature Extraction Using Densenet121mentioning

confidence: 99%

Deep Learning Model for the Automatic Classification of White Blood Cells

Sharma

Gupta

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

145

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“…The IoT protocols used for the data transfer within the network are CoAP (Constrained Application Protocol) and STOMP (Simple Text Oriented Messaging Protocol). CoAP and STOMP can be used for data transfer in different applications, such as smart healthcare and smart education, due to the security offered wirelessly using other technologies such as artificial intelligence and fuzzy logic [73][74][75][76][77][78][79][80][81][82][83][84][85][86].…”

Section: Smart Hydroponic System For Saffron Cultivationmentioning

confidence: 99%

Smart-Hydroponic-Based Framework for Saffron Cultivation: A Precision Smart Agriculture Perspective

Kour

Gupta

et al. 2022

Sustainability

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Saffron, one of the most expensive crops on earth, having a vast domain of applications, has the potential to boost the economy of India. The cultivation of saffron has been immensely affected in the past few years due to the changing climate. Despite the use of different artificial methods for cultivation, hydroponic approaches using the IoT prove to give the best results. The presented study consists of potential artificial approaches used for cultivation and the selection of hydroponics as the best approach out of these based on different parameters. This paper also provides a comparative analysis of six present hydroponic approaches. The research work on different factors of saffron, such as the parameters responsible for growth, reasons for the decline in growth, and different agronomical variables, has been shown graphically. A smart hydroponic system for saffron cultivation has been proposed using the NFT (nutrient film technique) and renewable sources of energy.

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“…Likewise, the complexity of end-user requirements raises the difficulty of service allocation. Above all, excellent resource selection is critical to meeting end-user needs [ 8 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

Syed

Rani

Mohammad³

et al. 2022

Journal of Healthcare Engineering

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In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

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Futuristic Cyber-Twin Architecture for 6G Technology to Support Internet of Everything

Cited by 21 publications

References 26 publications

Deep Learning Model for the Automatic Classification of White Blood Cells

Deep Learning Model for the Automatic Classification of White Blood Cells

Smart-Hydroponic-Based Framework for Saffron Cultivation: A Precision Smart Agriculture Perspective

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

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