Critical Condition Detection Using Lion Hunting Optimizer and SVM Classifier in a Healthcare WBAN

Kathuria, Madhumita; Gambhir, Sapna

doi:10.4018/ijehmc.2020010104

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…However, the challenges that need to be addressed while using machine learning in WBAN are non-perfect predictions (cannot be 100% accurate), shortage of available datasets, and attacks by adversaries (adding noise in the input model to mislead the classifier). [192][193][194][195][196] Cognitive radio in WBAN: Cognitive radio (CR) is a form of radio or wireless communication system that can be configured to detect unoccupied and occupied channels in a smart manner for an unlicensed user while avoiding user interference. The system assists in the increased utilization of unused or less used channels in an intelligent manner through its unique features namely "learn," "sense," and "adapt."…”

Section: Challenges In Routing Protocolsmentioning

confidence: 99%

“…At different stages of WBAN processes, machine learning techniques can help in optimizing the network performance and processing of sensed collected data. However, the challenges that need to be addressed while using machine learning in WBAN are non‐perfect predictions (cannot be 100% accurate), shortage of available datasets, and attacks by adversaries (adding noise in the input model to mislead the classifier) 192–196 Cognitive radio in WBAN: Cognitive radio (CR) is a form of radio or wireless communication system that can be configured to detect unoccupied and occupied channels in a smart manner for an unlicensed user while avoiding user interference.…”

Section: Challenges and Open Research Issues For Routing In Wbanmentioning

confidence: 99%

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Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Narwal,

Malik,

Mohapatra

et al. 2023

Int J Communication

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SummaryIn the present scenario, wireless body area network (WBAN) use case has gone beyond medical monitoring (e.g., E‐Healthcare) and has become a vital part of our daily lives. WBANs, applied in E‐Healthcare systems, extract the physiological parameters through the implanted (in‐body) and/or wearable (on‐body) sensors applied to the human body and route them to the remote server in a systematized and well‐grounded manner. Thus, routing is the most primitive and non‐trivial task in WBANs for prompt and proper delivery of sensed data. The design of routing protocol is crucial for efficacious communication and high network performance. Therefore, the target of this paper is to present an all‐encompassing taxonomy of the state‐of‐the‐art routing protocols in WBANs. Firstly, this paper provides an insight into WBANs and their related projects. Next, WBAN routing challenges are explored, and after clustering these challenges based on their operating mechanism, the routing protocols are categorized into eight categories, which correspond to cluster‐ and tree‐based, cross‐layer‐based, fuzzy‐based, interference‐based, posture‐based, QoS‐based, SDN‐based, and temperature‐based routing protocols. Furthermore, comparison tables highlighting the goals, strengths, limitations, and novelty of each protocol, along with a qualitative comparison table of the routing protocols for each category based on their features are furnished. This exhaustive survey can serve as a starting point for WBAN researchers, as open research issues have been highlighted in the discussion section.

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Section: Challenges In Routing Protocolsmentioning

confidence: 99%

Section: Challenges and Open Research Issues For Routing In Wbanmentioning

confidence: 99%

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Narwal,

Malik,

Mohapatra

et al. 2023

Int J Communication

View full text Add to dashboard Cite

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“…Whenever an abnormal class is detected, the linear regression is invoked to predict the value being compared with the actual one. In the work [ 11 ], a hybrid solution was proposed between a machine learning SVM classifier and a nature-inspired optimization named lion hunting algorithm for fault detection in WBANs. In [ 21 ], the authors considered three body sensors—HR, BP, and SpO2—and they used a Bayesian network to estimate the conditional probability; whenever the value of this probability is greater than a specific threshold, then the sensor reading is diagnosed as correct.…”

Section: Related Workmentioning

confidence: 99%

“…As for machine learning approaches, they involve complex computation, which leads to high energy consumption and high storage capacity to store the learning data [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

Bahache

Tahari

Herrera-Tapia

et al. 2022

Sensors

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Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices’ resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions.

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“…Among these algorithms, the SVM has been widely used recently. To mention a few, finance [4], [5], engineering [6], healthcare [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

An empirical assessment of different kernel functions on the performance of support vector machines

et al. 2021

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Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.

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Critical Condition Detection Using Lion Hunting Optimizer and SVM Classifier in a Healthcare WBAN

Cited by 6 publications

References 23 publications

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

An empirical assessment of different kernel functions on the performance of support vector machines

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