Resource Management Framework Using Deep Neural Networks in Multi-Cloud Environment

Sangeetha, S. Brilly; Sabitha, R.; Dhiyanesh, B.; Kiruthiga, G.; Yuvaraj, N.; Raja, R. Arshath

doi:10.1007/978-3-030-74402-1_5

Cited by 30 publications

(7 citation statements)

References 17 publications

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“…e Internet of ings refers to a network of physical objects that provide the IP address for an Internet connection. e Internet of ings is defined as an invisible and intelligent network that communicates directly or indirectly with each other [8]. e Internet of ings enables communication between physical objects and other systems and devices powered by the Internet.…”

Section: Introductionmentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients’ health report and provides better treatment.

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Section: Introductionmentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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“…e next step is to bring its solutions to major cancer hospitals within a year. Researchers were also provided with information on how to retrieve data on the health sector over performing several diagnostic approaches [20][21][22], as well as how to ensure total retrievability. e visual image frames are segmented with the different modules in a visual element.…”

Section: Introductionmentioning

confidence: 99%

An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence

Arivazhagan

Venkatesh²,

Somasundaram

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors’ time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.

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“…However, the approach presented in [24] follows an edge computing paradigm focused on ad-hoc networks. S. B. Sangeetha et al address the increase in demand for cloud resources from media content [25]. They express the significance of cloud resource management to maintain the QoS.…”

Section: Literature Reviewmentioning

confidence: 99%

Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

Achar

2023

IEEE Access

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The age of the Fourth Industrial Revolution (4IR) is the era of smart technologies and services. The Internet of Things (IoT) is at the heart of these smart services. The IoTs are resource-constrain devices. They act as middleware in intelligent systems and maintain communications between cloud servers and smart services. Processing related to intelligent decision-making, including data processing, cleaning, feature extraction, and analysis, is performed on the cloud servers. The IoT devices respond according to the decisions the applications run on the cloud servers make. The massive number of internet-connected devices is increasing by 8% per year. The cloud infrastructure backing these enormous numbers of IoT devices must be scheduled efficiently to maintain Quality of Service (QoS). An optimized scheduling scheme is essential to minimize the cost and enhance scalability. This paper proposes an innovative and novel algorithm, Neural-Hill, which combines the Deep Neural Network (DNN) and Random Restart variant of the Hill Climbing algorithm to schedule IoT-Cloud resources efficiently and ensure scalability. It is a preemptive scheduling algorithm designed to operate in dynamic task scheduling. The performance of the Neural-Hill algorithm has been evaluated in terms of optimal solution-finding time, execution time, routing overhead, and throughput. The experimental results demonstrate the significant quality of service improvement with the assurance of better scalability.

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Resource Management Framework Using Deep Neural Networks in Multi-Cloud Environment

Cited by 30 publications

References 17 publications

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence

Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

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