Machine Learning Techniques for Analysis of Mars Weather Data

Pant, Piyush; Rajawat, Anand Singh; Goyal, S. B.; Kemat, Baharu Bin; Mihălţan, Traian Candin; Verma, Chaman; Răboacă, Maria Simona

doi:10.1109/ecai58194.2023.10194233

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…In light of this fact [17], the approach that has been presented makes use of machine learning in order to identify the factors that are primarily [18] responsible for the failure of root canal therapy, such as a broken instrument, an overfilled cavity, a perforated root, or an underfilled cavity [4,6,7]. This paper also provides insights into the importance of [19][20][21] these variables for determining the tooth's survival time after root canal therapy (treatment longevity detection), which is not revealed in many studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Longevity Recommendation for Root Canal Treatment Using Machine Learning

Choudhari,

Rajawat,

Goyal

2024

RAiSE-2023

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Section: Literature Reviewmentioning

confidence: 99%

Longevity Recommendation for Root Canal Treatment Using Machine Learning

Choudhari,

Rajawat,

Goyal

2024

RAiSE-2023

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“…You need to use metrics like accuracy, precision, recall, and so on to evaluate your QDNN's performance on the testing set. The QDNN should be set up and monitored regularly [20]. Deploying your QDNN on a quantum computer or simulator is necessary for classifying new attack vectors on the Ethereum blockchain…”

Section: Gas Limit Attacksmentioning

confidence: 99%

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

Rajawat,

Goyal,

Kumar

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

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“…AI-powered verification processes can quickly and accurately verify customer identities, reducing the time and cost involved in the onboarding process [11].…”

Section: Automated Verificationmentioning

confidence: 99%