Efficient Evolutionary Techniques for Wireless Body Area Using Cognitive Radio Networks

Suriya, M.; Sumithra, M. G.

doi:10.1007/978-3-030-02674-5_4

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…[81] suggested a particle swarm optimization approach for essential medical wireless application networks employing cognitive radio-enabled WBAN for effective battery use and uninterrupted data transfer in BAN. For the conveyance of patient data inside hospitals and telemedicine, [82] suggests an overlay cognitive radio method. In this case, the primary user is a set of telemedicine transmitters and receivers, and the secondary user is an in-hospital patient monitor.…”

Section: Designing a Telemedicine Networkmentioning

confidence: 99%

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

AfricArXiv

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Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software-Defined Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. The first section of the article goes over the history of traffic and capacity expansion, as well as future projections. By introducing a medical and image data communication protocol for telemedicine, the second section examines the technological constraints of expanding capacity in the era of UAV & software defined networking. The third section discusses ways to maximize network capacity by considering quality of service (QoS) capacity issues. Finally, the article explores how to construct a telemedicine network that can provide performance, customization, and capabilities to keep up with increased traffic in the coming decades. Research gaps and future directions were presented in the last section.

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Section: Designing a Telemedicine Networkmentioning

confidence: 99%

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

AfricArXiv

View full text Add to dashboard Cite

show abstract

Section: Cognitive Radio-based Telemedicine Network Designmentioning

confidence: 99%

Maximizing network capacity, control and management in designing a telemedicine network: A review and recent challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

Nig. J. Tech.

View full text Add to dashboard Cite

Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software-Defined Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. The first section of the article goes over the history of traffic and capacity expansion, as well as future projections. By introducing a medical and image data communication protocol for telemedicine, the second section examines the technological constraints of expanding capacity in the era of UAV & softwaredefined networking. The third section discusses ways to maximize network capacity by considering quality of service (QoS) capacity issues. Finally, the article explores how to construct a telemedicine network that can provide performance, customization, and capabilities to keep up with increased traffic in the coming decades. Research gaps and future directions were presented in the last section.

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“…A qualitative analysis of two hundred patients' images was achieved using this method [ 2 ]. Suriya et al worked on the color information-based pattern segmentation by considering 75 patients' image samples and achieved around 90% of detection [ 3 ]. Liang et al, according to their study, could detect 90% of the speckle noise images using a median filter based on histogram and morphological operations [ 4 ].…”

Section: Related Workmentioning

confidence: 99%

“…As a result, the WHO supports the advancement of rapid and inexpensive diagnostic testing that aids incorrect treatment method identification. In 2019, there has been an estimated 229 million cases of malaria worldwide [ 2 , 3 ]. The rate of death totaled 409,000 cases of malaria, as measured by an annual survey [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models

Alharbi

Aravinda²,

Meng

et al. 2022

Computational Intelligence and Neuroscience

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Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.

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Efficient Evolutionary Techniques for Wireless Body Area Using Cognitive Radio Networks

Cited by 9 publications

References 8 publications

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Maximizing network capacity, control and management in designing a telemedicine network: A review and recent challenges

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models

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