A hybrid model of Internet of Things and cloud computing to manage big data in health services applications

Elhoseny, Mohamed; Abdelaziz, Ahmed; Salama, Ahmed S.; Riad, A. M.; Muhammad, Khan; Sangaiah, Arun Kumar

doi:10.1016/j.future.2018.03.005

Cited by 297 publications

(132 citation statements)

References 18 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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“…This type delivers the applications to a user and frees a user from the burden of software maintenance . Task scheduling is considered one of the key challenges and experiments of the cloud computing environment . Many research and studies have presented to find optimal solutions for task scheduling in the cloud environment as the NP problem .…”

Section: Introductionmentioning

confidence: 99%

“…8 Task scheduling is considered one of the key challenges and experiments of the cloud computing environment. 9 Many research and studies have presented to find optimal solutions for task scheduling in the cloud environment as the NP problem. 10 In the cloud layers, user and service providers have an interactive communication between requested tasks and a set of virtual hardware resources to cover the optimization of resource management.…”

Section: Introductionmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.

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“…[5][6][7] The processing of medical big data [8][9][10] is also important in helping to discover more information about the disease. Due to the paramagnetic property of Hemosiderin, CMB can be visually inspected when a magnetic field is applied, such as the T2-gradient recalled echo imaging, 11 three-dimensional T2-GRE, and susceptibility weighted imaging (SWI) that is the most sensitive one to date. 12 Considering that the manual detection of CMB is time consuming and prone to errors because of the complex morphological nature of CMB.…”

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confidence: 99%

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Wang

Sun

Mehmood

et al. 2019

Concurrency and Computation

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Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. KEYWORDScerebral micro-bleeding, convolution neural network, detection, intra-cerebral hemorrhage, stochastic pooling, stroke INTRODUCTIONCerebral micro-bleedings (CMBs) are small chronic brain hemorrhages, which can be caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke and memory clinics that is initially set up for research purposes. 1 CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. 2 Previous studies show that, in a range of 18% to 68%, patients with ischemic stroke/transient ischemic attack have a high prevalence of CMBs. There are 23% of prevalence in patients with first-ever ischemic stroke and 44% in patients with recurrent ischemic stroke reported by a previous systematic review. 3 The strong relationship with CMBs and increased risk of ICH suggests that the antithrombotic drugs Concurrency Computat Pract Exper. 2020;32:e5130. wileyonlinelibrary.com/journal/cpe to test the performance of the proposed structure of CNN based on the stochastic pooling, we compared different pooling methods, including average pooling, max pooling, and stochastic pooling. Meanwhile, we compared CNN with different convolution number and the state-of-the-art method for CMB detection. The experiment result shows that our designed structure of CNN based on the stochastic pooling achieved the best performance in sensitivity as 97.22%, specificity as 97.35%, accuracy as 97.28%, and precision as 97.35%.The limitation of our work is that we may need to move the window from top to bottom and from left to right. This is time-consuming. It is better to use concurrent convolution in the future studies, and we may need to replace the fully connected layers with conv layers. Another limitation is that we have two labels (CMB and ...

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A hybrid model of Internet of Things and cloud computing to manage big data in health services applications

Cited by 297 publications

References 18 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

Task scheduling mechanisms in cloud computing: A systematic review

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

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