An Intelligent Health Care System in Fog Platform with Optimized Performance

Tripathy, Subhranshu Sekhar; Rath, Mamata; Tripathy, Niva; Roy, Diptendu Sinha; Francis, John Sharmila Anand; Bebortta, Sujit

doi:10.3390/su15031862

Cited by 16 publications

(5 citation statements)

References 38 publications

(60 reference statements)

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“…We compare our method to the existing methodologies (viz., QRL AND DRL) using the performance metrics like precision, recall, f-measure, and prediction accuracy for determining the prevalence of heart disease. The precision, recall, f-measure and accuracy can be mathematically represented as [41][42][43][44] × 100 (23) where 𝑇 𝑝 , 𝑇 𝑛 , 𝐹 𝑝 , 𝐹 𝑛 denote the true positive, true negative, false positive, and false negative respectively. The notations 𝑃𝑟, 𝑅, 𝑓 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒, and 𝐴 denote the precision, recall, f-measure and accuracy of the model respectively.…”

Section: B Resultsmentioning

confidence: 99%

See 1 more Smart Citation

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Bebortta

Tripathy²,

Basheer

et al. 2023

IEEE Access

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The prevalence of heart disease has remained a major cause of mortalities across the world and has been challenging for healthcare providers to detect early symptoms of cardiac patients. To this end, several conventional machine learning models have gained popularity in providing precise prediction of heart diseases by taking into account the underlying conditions of patients. The drawbacks associated with these methods are a lack of generalization and the convergence rate of these methods being much slower. As the healthcare data associated with these systems scale up leading to healthcare big data issues, a Cloud-Fog computing-based paradigm is necessary to facilitate low-latency and energy-efficient computation of the healthcare data. In this paper, a DeepMist framework is suggested which exploits Deep Learning models operating over Mist Computing infrastructure to leverage fast predictive convergence, low-latency, and energy efficiency for smart healthcare systems. We exploit the Deep Q Network (DQN) algorithm for building the prediction model for identifying heart diseases over the Mist computing layer. Different performance evaluation metrics, like precision, recall, f-measure, accuracy, energy consumption, and delay, are used to assess the proposed DeepMist framework. It provided an overall prediction accuracy of 97.6714 % and loss value of 0.3841, along with energy consumption and delay of 32.1002 mJ and 2.8002 ms respectively. To validate the efficacy of DeepMist, we compare its outcomes over the heart disease dataset in convergence with other benchmark models like Q-Reinforcement Learning (QRL) and Deep Reinforcement Learning (DRL) algorithms and observe that the proposed scheme outperforms all others.

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Section: B Resultsmentioning

confidence: 99%

“…Following [42], we can compute the energy consumption by the local computing IoT devices to offload the captured IoT data can be given as, ℰ 𝑖 𝑙𝑜𝑐 = 𝜌(𝑓 𝑖 ) 2 𝑝 𝑡𝑎𝑠𝑘𝑠 (17) From the above Eq. ( 17), the power coefficient pertaining to the architecture of the chip is denoted as 𝜌.…”

Section: E Energy Modelmentioning

confidence: 99%

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Bebortta

Tripathy²,

Basheer

et al. 2023

IEEE Access

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“…Following [ [32] , [33] , [34] ], we assume that the task arrivals to a certain fog node

within set

follow a Poisson process, which is represented by the parameter

, which is the sum of all client processes with the fog node in the federated framework and can be given as Eq. (10) below:

Furthermore, we model every fog node in set

using an M/M/1 queuing model, and the resulting queuing delay,

, is calculated as Eq.…”

Section: System Modelmentioning

confidence: 99%

FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform

Tripathy,

Bebortta,

Chowdhary

et al. 2024

Heliyon

Self Cite

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“…Smart cities have developed as centers of innovation that harness new technologies to address public health concerns. Specifically, the Artificial Intelligence of Things (AIoT) technology has developed as the result of the convergence between artificial intelligence (AI) and the Internet of Things (IoT) to offer a new paradigm to control pandemic diseases based on the data distributed across different geographical locations [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Smart healthcare systems usually focus on collecting medical imaging datasets to be used to build AI solutions for pandemic diseases during outbreaks. This data is usually sourced from varied imaging modalities and acquired from different healthcare institutions in the same smart city or even different cities, leading to inherent variability and heterogeneity across domains of data [ 6 ]. This cross-domain/multi-site bias arises as a possible consequence of variability in the specifications of equipment, scanning protocols, patient demographics, disease manifestations, and other facets.…”

Section: Introductionmentioning

confidence: 99%

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

Alrashdi

2024

BMC Med Imaging

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The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

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An Intelligent Health Care System in Fog Platform with Optimized Performance

Cited by 16 publications

References 38 publications

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform

Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies

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