The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems

Darwish, Ashraf; Hassanien, Aboul Ella; Elhoseny, Mohamed; Sangaiah, Arun Kumar; Muhammad, Khan

doi:10.1007/s12652-017-0659-1

Cited by 308 publications

(111 citation statements)

References 61 publications

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“…[26][27][28][29][30][31][32] ORCID Mohamed Elhoseny http://orcid.org/0000-0001-6347-8368 For identifying and visualizing the tumor in the MRI brain images, two important models such as Feature Selection (FS) and Machine Learning classification techniques were utilized.…”

Section: Resultsmentioning

confidence: 99%

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Optimal feature level fusion based ANFIS classifier for brain MRI image classification

Shankar

Elhoseny

Lakshmanaprabu

et al. 2018

Concurrency and Computation

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The cases identified with Brain tumor have increased with respect to time owing to various reasons. One of the major challenging issues can be defined by incorporating image processing along with data mining models as classification approach. There are various procedures as of now exhibited for segmentation of brain tumor effectively. In any case, it is as yet unequivocal to distinguish the brain tumor from MR images. In this new tumor classifying, considering two significant models, such as Feature Selection (FS) and Machine Learning classification techniques, are extremely valuable for distinguishing and visualizing the tumor in the MRI brain images; it is classified using Adaptive Neuro-Fuzzy Interface System (ANFIS). For better classification of image, Optimal Feature Level Fusion (OFLF) is considered to fuse low and high-level feature of brain image; from this analysis, the images are classifying as Benign or Malignant. From this implementation of medical images, the experiment results are evaluating performance metrics are compared existing classifiers. From the proposed MRI image classification process the accuracy as 96.23%, sensitivity as 92.3%, and specificity as 94.52%, compared to existing classifier. It is in the working platform of MATLAB that this proposed methodology is implemented.

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Section: Resultsmentioning

confidence: 99%

“…In the future work, we will apply the proposed model on different smart applications to improve its performance. [26][27][28][29][30][31][32] ORCID Mohamed Elhoseny http://orcid.org/0000-0001-6347-8368…”

Section: Resultsmentioning

confidence: 99%

Optimal feature level fusion based ANFIS classifier for brain MRI image classification

Shankar

Elhoseny

Lakshmanaprabu

et al. 2018

Concurrency and Computation

Self Cite

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“…A particular attention, in this perspective, should be also paid to the relationship among Ambient Intelligence, Assistive Technologies, Computer Assisted/ Aided Rehabilitation. Further studies and analysis should hence also be carried out in this direction, for there are a number of "opportunities, challenges and open problems" (Darwish et al 2019). In a connected world, the capability of smart environments (i.e.…”

Section: Discussionmentioning

confidence: 99%

From AAL to ambient assisted rehabilitation: a research pilot protocol based on smart objects and biofeedback

Meriggi

Mandalà

Brazzoli

et al. 2020

J Ambient Intell Human Comput

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The progressive miniaturization of electronic devices and their exponential increase in processing, storage and transmission capabilities, represent key factors of the current digital transformation, also sustaining the great development of Ambient Assisted Living (AAL) and the Internet of Things. Although most of the investigations in the recent years focused on remote monitoring and diagnostics, rehabilitation too could be positively affected by the widespread integrated use of these devices. Smart Objects in particular may be among the enablers to new quantitative approaches. In this paper, we present a proofof-concept and some preliminary results of an innovative pediatric rehabilitation protocol based on Smart Objects and biofeedback, which we administered to a sample of children with unilateral cerebral palsy. The novelty of the approach mainly consists in placing the sensing device into a common toy (a ball in our protocol) and using the information measured by the device to administer multimedia-enriched type of exercises, more engaging if compared to the usual rehabilitation activities used in clinical settings. We also introduce a couple of performance indexes, which could be helpful for a quantitative continuous evaluation of movements during the exercises. Even if the number of children involved and sessions performed are not suitable to assess any change in the subjects' abilities, nor to derive solid statistical inferences, the novel approach resulted very engaging and enjoyable by all the children participating in the study. Moreover, given the almost non-existent literature on the use of Smart Objects in pediatric rehabilitation, the few qualitative/quantitative results here reported may promote the scientific and clinical discussion regarding AAL solutions in a "Computer Assisted Rehabilitation" perspective, towards what can be defined "Pediatric Rehabilitation 2.0".

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“…Cloud computing (CC) for IoT has emerged as a new platform in the 21st century. Darwish et al 24 established a new concept between CC and IoT named as Cloud IoT-Health (CC-IoT) paradigm. The term CC-IoT and several key integration challenges are considered to show a practical vision that integrates current mechanisms of CC and IoT in healthcare applications.…”

Section: Related Workmentioning

confidence: 99%

Energy harvesting Internet of Things health-based paradigm: Towards outage probability reduction through inter–wireless body area network cooperation

Thabit

Mahmoud

Alkhayyat

et al. 2019

International Journal of Distributed Sensor Networks

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In today’s healthcare environment, the Internet of Things technology provides suitability among physicians and patients, as it is valuable in numerous medicinal fields. Wireless body sensor network technologies are essential technologies in the growth of Internet of Things healthcare paradigm, where every patient is monitored utilising small-powered and lightweight sensor nodes. A dual-hop, inter–wireless body sensor network cooperation and an incremental inter–wireless body sensor network cooperation with energy harvesting in the Internet of Things health-based paradigm have been investigated and designed in this work. The three protocols have been named and abbreviated as follows: energy harvesting–based dual-hop cooperation, energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based incremental inter–wireless body sensor network cooperation. Outage probabilities for the three designed protocols were investigated and inspected, and mathematical expressions of the outage probabilities were derived. The simulation and numerical results showed that the energy harvesting–based incremental inter–wireless body sensor network cooperation provided superior performance over the energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based dual-hop cooperation by 1.38 times and 5.72 times, respectively; while energy harvesting–based inter–wireless body sensor network cooperation achieved better performance over energy harvesting–based dual-hop cooperation by 1.87 times.

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The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems

Cited by 308 publications

References 61 publications

Optimal feature level fusion based ANFIS classifier for brain MRI image classification

Optimal feature level fusion based ANFIS classifier for brain MRI image classification

From AAL to ambient assisted rehabilitation: a research pilot protocol based on smart objects and biofeedback

Energy harvesting Internet of Things health-based paradigm: Towards outage probability reduction through inter–wireless body area network cooperation

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