IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

Dhillon, Arwinder; Singh, Ashima; Vohra, Harpreet; Ellis, Caroline; Varghese, Blesson; Gill, Sukhpal Singh

doi:10.1080/17517575.2020.1820583

Cited by 27 publications

(22 citation statements)

References 69 publications

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“…Figure1: Fog Computing Architecture and Class of Service [21] Another example of layered approach for implementing CPU-bound, real-time Fog paradigm exists in IoTPulse where Fog devices play major role in health monitoring sector [22] . The system comprised of four layers: sensing layer, network layer, servicing layer and interfacing layer.…”

Section: Fog Architecturementioning

confidence: 99%

“…They utilize stacks of multi-layer neurons to efficiently represent complex relationships among data elements [2]. Swift, parallel and distributed computations are key features of Neural Networks [22]. Figure 4 C. Support Vector Machines (SVMs) These machines work under the principle of boundary consideration.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

“…These networks utilize conditional probability to model the system behavior [22]. These networks apply statistical techniques to describe probabilityrelationship among different elements.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…These networks apply statistical techniques to describe probabilityrelationship among different elements. Bayesian networks indicate the probability of occurrence of an event given that the prerequisite event has already taken place [22]. The method works on directed acyclic graphs.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…The second point is selected from the given dataset and previous classifications. The algorithm calculates the Euclidian distance of the given data points to predict where an unknown point must lie [22].…”

Section: Iknnmentioning

confidence: 99%

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A Comparative Study of Various Machine Learning Algorithms in Fog Computing

2021

IJATCSE

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Internet of Things is the reality of the future. It encompasses a vast prototype. The applications are limitless and the research potential is huge. Several unanswered questions still stand in the way. One of these challenging questions is the Network layer implementation of IoT. Cloud offers one perspective solution. Cloud services face the down side of connectivity and latency. Fog computing, as a complementing technology for Cloud answers the issues of latency and connectivity in the Cloud. Being novel idea in itself, Fog computing offers promising future growth. A major concept in Fog networks is its ability to learn and adopt to changing environment. This ability of the Fog devices to self-learn and improve itself is the core of machine-learning. Hence, machine-learning algorithms occupy central place in visualizing robust and effective Fog networks. This paper presents a comparative study of various machine-learning algorithms and their applications in the Fog network. The paper also projects the undiscovered research paradigms and future directions

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Section: Fog Architecturementioning

confidence: 99%

Section: B Artificial Neural Networkmentioning

confidence: 99%

“…These networks utilize conditional probability to model the system behavior [22]. These networks apply statistical techniques to describe probabilityrelationship among different elements.…”

Section: Bayesian Networkmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

Section: Iknnmentioning

confidence: 99%

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A Comparative Study of Various Machine Learning Algorithms in Fog Computing

2021

IJATCSE

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Software system to predict the infection in COVID‐19 patients using deep learning and web of things

et al. 2021

Self Cite

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Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F ‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

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Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

Cited by 27 publications

References 69 publications

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

Software system to predict the infection in COVID‐19 patients using deep learning and web of things

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

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