Using IoT and Semantic Web Technologies for Healthcare and Medical Sector

Malik, Nikita; Malik, Sanjay Kumar

doi:10.1002/9781119641391.ch5

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…e problem formulation [25] is defined with the following assumptions that includes past computational time over each executing task and the knowledge of computational overhead for scheduled task on the IoHT-cloud model in e-healthcare systems.…”

Section: Problem Formulationmentioning

confidence: 99%

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Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Arivazhagan

Somasundaram²,

Babu

et al. 2022

Scientific Programming

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Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

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Section: Problem Formulationmentioning

confidence: 99%

“…IoT jobs are not always tolerant of delays, despite the fact that task scheduling is notoriously difficult. So, the task scheduling architecture should allow IoT applications to be handled quickly while avoiding the complexity of the IoT infrastructure [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Arivazhagan

Somasundaram²,

Babu

et al. 2022

Scientific Programming

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“…ere have been significant attempts to incorporate the various aspects of IoT resources into ontology creation, such as connectivity, virtualization, mobility, energy, or life cycle [108,109].…”

Section: Scope Of Frontier Technology Research Even Though Cutting-ed...mentioning

confidence: 99%

“…Earlier studies have apparently failed to solve this problem. There have been significant attempts to incorporate the various aspects of IoT resources into ontology creation, such as connectivity, virtualization, mobility, energy, or life cycle [ 108 , 109 ]. The authors attempted to enhance the computerization of the health and medical industry by utilizing the Internet of Things (IoT) and Semantic Web technologies (SWTs), which are two key emerging technologies that play a significant role in overcoming the challenges of handling and presenting data searches in hospitals, clinics, and other medical establishments on a regular basis.…”

Section: Research Gapsmentioning

confidence: 99%

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

Haque

Arifuzzaman

Siddik

et al. 2022

International Journal of Clinical Practice

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Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems’ progress.

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“…An accuracy of more than 85% is achieved with the help of this deep learning model. A detailed application of such a deep learning system to the healthcare system can be observed from Jain et al 30 and Malik and Malik, 31 wherein different formulations and datasets for representation and retrieval of healthcare data are done to provide the best results in terms of document retrieval efficiency. Deep learning crawlers can also be useful for data retrieval.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel semantic representation approach for web documents using deep entity linking and multidocument support

Urkude

Pandey

2022

Int J Communication

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Summary Representing semantic web information in the form of existing formats like extensible mark‐up language (XML), resource description framework (RDF) or web ontology language (OWL) is limited by the inability to maintain intradocument relationships between different semantic entities. Moreover, these standards are built for generic data representation and thus do not have inherent support for the semantic description of the document and, consequently, do not support features like multidocument linkages and context‐sensitivity. In order to remove these drawbacks, a semantic web‐specific document representation approach, namely, deep semantic XML (DS‐XML) using deep linking and machine learning, is presented in this paper. The DS‐XML approach facilitates the use of semantics with the help of a novel document structure that focuses on defining any web document in a semantic form. It includes ontologies and linkage between different vocabularies, which assists in achieving semantic interoperability. In order to make this approach future proof, a novel deep linking approach is integrated at the document level, which allows DS‐XML to understand more about the contents of the document and represent information in an aggregated manner. The deep linking layer is combined with the machine learning‐based classification layer that allows the document information to be represented in an interactive manner. The proposed DS‐XML approach is tested on different document retrieval applications, including a few Internet of Things (IoT) applications, and the representation performance is found to be superior to the existing semantic approach (XML, RDF and OWL).

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Using IoT and Semantic Web Technologies for Healthcare and Medical Sector

Cited by 8 publications

References 24 publications

Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

A novel semantic representation approach for web documents using deep entity linking and multidocument support

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