Development and Verification of Material Plasma Exposure Concepts

Pawar, Avinash M.

doi:10.17762/ijnpme.v9i03.90

Cited by 7 publications

(6 citation statements)

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“…Nanoparticles are also observed to be reactive in nature and prove toxic, besides being expensive to prepare. Additional findings tend to suggest that for larger-sized particles, their clogging of flow channels accounts for device pressure losses as viscosity increases marginally [3]. The mixed observations above point to the need for a holistic analysis of how the properties of nanolubricants tend to shape the heat transfer characteristics of materials or devices.…”

Section: Figure 1: (A) Thermal Conductivity Materials (B) Thermal Con...mentioning

confidence: 98%

“…Here, it has been avowed that upon dispersion in fluids, nanoparticles exhibit some notable degree of stability. Also, they have been documented to be able to steer improvements in the fluids' thermal properties [3]. The adequacy of nanolubricants that makes them ideal for use as heat transfer fluids has also been linked to their promising features such as reduced pump power (outperforming pure liquids and ensuring that the intensified heat transfer is realized), particle/fluid nanolayer existence, and the particles' state of Brownian motion [4].…”

Section: Introductionmentioning

confidence: 99%

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Thermal Property Investigation in Nanolubricants via Nano- Scaled Particle Addition

Sherje¹

2021

IJNPME

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In nanolubricants, the increase in scholarly attention has been attributed to the affirmation that they exhibit enhanced thermo-physical features and that they can also be used in various thermal applications. Some of these applications where they could be incorporated include solar energy harvesting, industrial applications, and heat exchanger effectiveness enhancement. Recently, various approaches have been employed to enhance the coefficient of heat transfer, especially between the fluid contact surfaces and the working fluids. When it comes to conventional fluids of heat transfer, examples being ethylene glycol/water, thermal oils, and water, some studies document that they exhibit limitations. For instance, these fluids exhibit low thermal properties when compared to the solids with which they interact. To respond to this dilemma, there have been efforts in this study to have the fluids’ thermal properties improved via nano-scaled particle addition, causing marked evolutions in the evaluations of the behavior of fluids of heat transfer. Indeed, findings suggest that in base fluids, when the solid particles are suspended, there tends to be an enhancement in the fluid’s energy transmission; hence, notable improvements in material thermal conductivity properties, besides the betterment of material heat transfer characteristics.

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Section: Figure 1: (A) Thermal Conductivity Materials (B) Thermal Con...mentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Thermal Property Investigation in Nanolubricants via Nano- Scaled Particle Addition

Sherje¹

2021

IJNPME

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“…A collection of mathematical theories and methods known as machine learning algorithms enable computers to learn from experience and advance without explicit programming 9. These algorithms make up the foundation of artificial intelligence (AI) and are utilized in a variety of tasks [10], including fraud detection, natural language processing, image identification, and image recognition.…”

Section: E Machine Learningmentioning

confidence: 99%

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Al-Abadi,

Mohamed,

Fakhfakh

2023

IJRITCC

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Internet of Medical Things (IoMT ) refers to the network of medical devices and healthcare systems that are connected to the internet. However, this connectivity also makes IoMT vulnerable to cyberattacks such as DoS and Delay attacks , posing risks to patient safety, data security, and public trust. Early detection of these attacks is crucial to prevent harm to patients and system malfunctions. In this paper, we address the detection and mitigation of DoS and Delay attacks in the IoMT using machine learning techniques. To achieve this objective, we constructed an IoMT network scenario using Omnet++ and recorded network traffic data. Subsequently, we utilized this data to train a set of common machine learning algorithms. Additionally, we proposed an Enhanced Random Forest Classifier for Achieving the Best Execution Time (ERF-ABE), which aims to achieve high accuracy and sensitivity as well as low execution time for detecting these types of attacks in IoMT networks. This classifier combines the strengths of random forests with optimization techniques to enhance performance. Based on the results, the execution time has been reduced by implementing ERF-ABE, while maintaining high levels of accuracy and sensitivity.

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“…Reference [14] introduced an enriched IDS. For selecting the features as of the dataset, the Grey Wolf Optimizer (GWO) algorithm was utilized.…”

Section: Literature Surveymentioning

confidence: 99%

“…Here, with DNN [8], SVM [14], and Weighted SVM (WSVM) [5], the detection rate of the proposed model is assessed and compared. Discussion: Grounded on the detection rate, the performance analysis is depicted in figure 6.When contrasted with the prevailing systems, the proposed technique attains a better detection rate (97%).…”

Section: Performance Analysis Using the Nsl-kdd Datasetmentioning

confidence: 99%

3-WAY Secured WSN with CSDSM-DNN based Intrusion Detection Model

Santhi,

Sowmiya,

Shalinirajan

et al. 2023

IJRITCC

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In Wireless Sensor Networks (WSNs), intrusion aims indegrading or even eliminating the capacity of these networks for providing their functions. Thus, in recent years, several ideas are brought and employed. However, these techniques still did not fulfill their requirements in attaining better classification accuracy. This paper proposes a novel Cosine Similarity Distance integrated Sammon Mapping learning layer-Deep Neural Network (CSDSM-DNN)-centricIntrusion Detection Model (IDM) in WSNfor attaining better outcomes. Initially, the nodes are clustered; after that, utilizing Binomial Distribution based Dwarf Mongoose Optimization (BD-DMO), the cluster heads are selected. Then, theIdentity Matrix Function-Kalman Filter (IMF-KF) identified the optimal route. Subsequently, the data is transferred via the secured route. The transferred data is pre-processed and then, the important features are selected. Lastly, to classify whether the data is attacked or non-attacked, the selected features are given into the CSDSM-DNN. Therefore, with the prevailing approaches, the experiential outcomes are evaluated and analogized and it exhibits the proposed model’s higher reliability and efficacy.

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Development and Verification of Material Plasma Exposure Concepts

Cited by 7 publications

References 0 publications

Thermal Property Investigation in Nanolubricants via Nano- Scaled Particle Addition

Thermal Property Investigation in Nanolubricants via Nano- Scaled Particle Addition

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

3-WAY Secured WSN with CSDSM-DNN based Intrusion Detection Model

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