Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks

Quintana, Guillem; Rudolf, Thomas; Ciurana, Joaquim; Brecher, Christian

doi:10.1007/s12206-011-0806-0

Cited by 13 publications

(2 citation statements)

References 24 publications

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“…In single layer architecture, we used the tansig transfer function in the hidden layer and the purelin transfer function in the output layer. Alomari et al (2018) and Quintana et al (2011) say that this network can ballpark any function with a limited number of discontinuities if the appropriate number of neurons are provided.…”

Section: Figure 6 Different Transfer Functionmentioning

confidence: 99%

Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem

Jain¹,

Singh²,

Sharma

2021

IJIKM

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Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

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Section: Figure 6 Different Transfer Functionmentioning

confidence: 99%

Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem

Jain¹,

Singh²,

Sharma

2021

IJIKM

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“…Values of the surface roughness and the crest height can be obtained through these equations that have been applied in several research works [14,15] following the same methodology. The theoretical values obtained give to the user an approximation to the real values which will be obtained once the machining is performed.…”

Section: New Frontiers In Manufacturing Engineering and Materials Pro...mentioning

confidence: 99%

Experimental Introduction to Surface Roughness Parameters Measurement

Teixidor

Quintana

Ciurana

2013

MSF

Self Cite

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Surface roughness influences the performance of a finished part. In machining operations, the surface roughness generated is influenced by an enormous set of factors. In ball end milling operations, the geometric characteristics of the cut clearly affect the surface crests generated. This paper presents an experimental methodology that permits engineering students to identify and analyze the surface roughness. The methodology is applicable to training courses and surface texture generation as well.

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Application of Artificial Neural Network for Modeling Surface Roughness in Machining Process—A Review

Pahuja

Kant

Jawalkar

et al. 2020

Lecture Notes on Multidisciplinary Industrial Engineering

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Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks

Cited by 13 publications

References 24 publications

Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem

Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem

Experimental Introduction to Surface Roughness Parameters Measurement

Application of Artificial Neural Network for Modeling Surface Roughness in Machining Process—A Review

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