A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning

Kishor, Amit; Chakraborty, Chinmay; Jeberson, Wilson

doi:10.9781/ijimai.2020.12.004

Cited by 50 publications

(25 citation statements)

References 28 publications

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“…It can be seen that the false positive rate of Sobel, Canny, Prewitt, and Robert algorithms increases with the decrease of image contrast [33]. However, the self‐adaptive threshold algorithm entropy based on Shannon entropy proposed in this paper has little sensitivity to gray scale changes, because the self‐adaptive gray scale threshold selection can automatically adjust the threshold of feature segmentation according to the image contrast, thus reducing or even avoiding the interference of image contrast changes [34].…”

Section: Results and Analysismentioning

confidence: 99%

Feature recognition of state signal of electromechanical integration railway turnout over health parameters using CMOS area array technology

Zhou

Sharma

Shabaz

2022

The Journal of Engineering

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Railway turnout is the most important line connection equipment of high‐speed railway system. In order to improve the monitoring and maintenance of high‐speed railway turnout gap, we avoid the large impact of train operations on the turnout. With the increase of train speed, the impact of train operation on turnout is increasing. Therefore, the requirements for monitoring, maintenance, and adjustment of high‐speed railway turnout gap are also gradually improved. In this paper, the feature recognition research of mechatronic railway turnout state signal based on CMOS area array technology is proposed. First, it introduces the collection of turnout health parameters, the collection and processing of gap data in turnout based on CMOS, and the image feature recognition algorithm of adaptive threshold. The problem of inconvenient collecting the bifurcation image showing the gap is solved, and the pollution affects the accuracy. The experimental results show that, according to the fault mechanism of turnout system, turnout faults are mainly divided into electrical faults and mechanical faults. On‐site maintenance: the records show that the mechanical failure accounts for more than 90% of turnout failures. Fault category is the database of fault diagnosis model training. According to the structural principle and fault record description of turnout, there are 12 categories of turnout faults. The feature recognition of mechatronic railway turnout state signal based on CMOS area array technology proposed in this paper has certain validity and accuracy.

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Section: Results and Analysismentioning

confidence: 99%

Feature recognition of state signal of electromechanical integration railway turnout over health parameters using CMOS area array technology

Zhou

Sharma

Shabaz

2022

The Journal of Engineering

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“…To this end, in recent years, several research works have attempted to build more sophisticated failure detection and prediction models. DL models are incredibly successful in contexts where anomalous behaviours might arise in a wide variety of domains [26], [27], [28]. These problems are generally framed as supervised learning tasks and trained to distinguish between abnormal and normal points.…”

Section: Related Workmentioning

confidence: 99%

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

Borghesi¹,

Burrello²,

Bartolini³

2023

IEEE Internet Things J.

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In recent years, the Industrial Internet of Things (IIoT) has led to significant steps forward in many industries, thanks to the exploitation of several technologies, ranging from Big Data processing to Artificial Intelligence (AI). Among the various IIoT scenarios, large-scale data centers can reap significant benefits from adopting Big Data analytics and AIboosted approaches since these technologies can allow effective predictive maintenance. However, most of the off-the-shelf currently available solutions are not ideally suited to the HPC context, e.g., they do not sufficiently take into account the very heterogeneous data sources and the privacy issues which hinder the adoption of the cloud solution, or they do not fully exploit the computing capabilities available in loco in a supercomputing facility. In this paper, we tackle this issue, and we propose an IIoT holistic and vertical framework for predictive maintenance in supercomputers. The framework is based on a big lightweight data monitoring infrastructure, specialized databases suited for heterogeneous data, and a set of high-level AI-based functionalities tailored to HPC actors' specific needs. We present the deployment and assess the usage of this framework in several in-production HPC systems.

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“…A fuzzy rule is an expression of "if-then" statements, which indicates the dependencies between the given variables [27]. As the metrics taken has a great impact on the software reliability.…”

Section: Fuzzy Rulesmentioning

confidence: 99%

Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

Gandhi¹,

Khan²,

Sharma³

et al. 2022

Computer Systems Science and Engineering

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A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning

Cited by 50 publications

References 28 publications

Feature recognition of state signal of electromechanical integration railway turnout over health parameters using CMOS area array technology

Feature recognition of state signal of electromechanical integration railway turnout over health parameters using CMOS area array technology

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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