Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

Bangash, Javed Iqbal; Khan, Abdul Rahim; Khan, Ashiq; Khan, Atif; Uddin, M. Irfan; Hua, Qiaozhi

doi:10.1155/2021/5560809

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…Despite melanoma causes a majority of deaths related to skin cancer, the survival rates are promising if it can be diagnosed and treated correctly at the earliest stage [12][13][14][15][16][17]. Otherwise, the estimated 5-year survival rate of a patient will be greatly reduced [18][19][20][21][22][23][24][25]. As melanoma is visible on the skin at its initial phase, early detection of melanoma is a crucial issue to reduce mortality and morbidity.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

Shixiang

Huang²,

et al. 2021

Mobile Information Systems

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Melanoma is considered to be one of the most dangerous human malignancy, which is diagnosed visually or by dermoscopic analysis and histopathological examination. However, as these traditional methods are based on human experience and implemented manually, there have been great limitations for general usability in current clinical practice. In this paper, a novel hybrid machine learning approach is proposed to identify melanoma for skin healthcare in various cases. The proposed approach consists of classic machine learning methods, including convolutional neural networks (CNNs), EfficientNet, and XGBoost supervised machine learning. In the proposed approach, a deep learning model is trained directly from raw pixels and image labels for classification of skin lesions. Then, solely based on modeling of various features from patients, an XGBoost model is adopted to predict skin cancer. Following that, a diagnostic system which composed of the deep learning model and XGBoost model is developed to further improve the prediction efficiency and accuracy. Different from experience-based methods and solely image-based machine learning methods, the proposed approach is developed based on the theory of deep learning and feature engineering. Experiments show that the hybrid model outperforms single model like the traditional deep learning model or XGBoost model. Moreover, the data-driven-based characteristics can help the proposed approach develop a guideline for image analysis in other medical applications.

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Section: Introductionmentioning

confidence: 99%

Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

Shixiang

Huang²,

et al. 2021

Mobile Information Systems

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“…The quality of link is measured using the packet reception rate [8]. In [27] author address the above mentioned QoS issues by presenting multiconstraint-aware routing mechanism (modular-based) approach with two types of frame work with and without relay nodes. It classifies the generated data packets into four categories critical data (CD), reliability-conscious data (RCD), delayconscious data (DCD), and non-conscious data (NCD).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Where total number of received packets (𝑇𝑅𝑃 𝑖,𝑗 ) is the total number successfully transmitted packets via the link between node 𝑆 𝑖 and node 𝑆 𝑗 . Similarly, Total number of packets forwarded (𝑇𝑃𝐹 𝑖,𝑗) is total number of attempts for both transmission as well as retransmission of all packets and γ is the average weighting factor [25], [27] as described in Equation ( 6). The link having maximum value of 𝑙𝑖𝑛𝑘𝑅 𝑖,𝑗 will be considered in route cast function.…”

Section: Proposed Routing Schemementioning

confidence: 99%

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A Multi-Hop QoS-Aware and Predicting Link Quality Estimation (PLQE) Routing Protocol for Reliable WBSN

et al. 2023

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Technological advancements and miniaturization of sensor have made it possible to use tiny size sensors nodes to monitor patients' physiological data in real-time at very low cost. The network of such small size sensor nodes is often called wireless body sensor networks (WBSNs). The patient's data is highly sensitive which required to reach reliably and timely at the destination node. However, short range wireless nodes and postural body movements cause frequent topological changes and recurrent network partitioning. The network partitioning leads to delayed and significant loss of critical patient's data such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG). Therefore, QoS requirement of WBSNs in terms of reliability, end-to-end delay and throughput are highly compromise. We in this paper propose a Predicting routing protocol PLQE to address reliable data transmission and network partitioning due to node mobility. The PLQE dynamically determines the efficient links by using beta probability density function, link quality and link delay estimation (LDE). The PLQE is composed of link reliability factor (LRF) and expected probability Indicator (EPI) to obtain most often updated status of the links to figure out stable and reliable end to end route to the destination node. The simulation results confirm the higher performance of the proposed scheme against state of the art routing protocols in terms of packet delivery ratio, end to end delay, throughput and normalized routing load.

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“…Colonoscopy images have been used to classify colitis by extracting texture features [12,13]. However, the challenges in extracting the features of gastrointestinal images limit the diagnostic accuracy [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

et al. 2021

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Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

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Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

Cited by 9 publications

References 40 publications

Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

A Multi-Hop QoS-Aware and Predicting Link Quality Estimation (PLQE) Routing Protocol for Reliable WBSN

Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

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