A Building Topical 2-Gram Model: Discovering and Visualizing the Topics Using Frequent Pattern Mining

Patil, Aboli H.; Mahalle, Parikshit N.

doi:10.1007/978-981-33-4073-2_2

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…ML-enhanced quantum cryptography is hampered by a number of issues. First off, because typical ML techniques are not naturally suited for quantum data, integrating machine learning with quantum systems necessitates careful consideration of hardware limitations and quantum noise [20]. Additionally, as ML techniques advance, adversarial attacks on quantum cryptography systems get more complex, either exploiting flaws in the learning models themselves or hostile perturbations on quantum states.…”

Section: Adversarial Attacks On Quantum Cryptographic Systemsmentioning

confidence: 99%

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Chandre,

Shendkar,

Deshmukh

et al. 2023

IJRITCC

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Quantum cryptography has emerged as a promising paradigm for secure communication, leveraging the fundamental principles of quantum mechanics to guarantee information confidentiality and integrity. In recent years, the field of quantum cryptography has witnessed remarkable advancements, and the integration of machine learning techniques has further accelerated its progress. This research paper presents a comprehensive review of the latest developments in quantum cryptography, with a specific focus on the utilization of machine learning algorithms to enhance its capabilities. The paper begins by providing an overview of the principles underlying quantum cryptography, such as quantum key distribution (QKD) and quantum secure direct communication (QSDC). Subsequently, it highlights the limitations of traditional quantum cryptographic schemes and introduces how machine learning approaches address these challenges, leading to improved performance and security. To illustrate the synergy between quantum cryptography and machine learning, several case studies are presented, showcasing successful applications of machine learning in optimizing key aspects of quantum cryptographic protocols. These applicatiocns encompass various tasks, including error correction, key rate optimization, protocol efficiency enhancement, and adaptive protocol selection. Furthermore, the paper delves into the potential risks and vulnerabilities introduced by integrating machine learning with quantum cryptography. The discussion revolves around adversarial attacks, model vulnerabilities, and potential countermeasures to bolster the robustness of machine learning-based quantum cryptographic systems. The future prospects of this combined field are also examined, highlighting potential avenues for further research and development. These include exploring novel machine learning architectures tailored for quantum cryptographic applications, investigating the interplay between quantum computing and machine learning in cryptographic protocols, and devising hybrid approaches that synergistically harness the strengths of both fields. In conclusion, this research paper emphasizes the significance of machine learning-enhanced advancements in quantum cryptography as a transformative force in securing future communication systems. The paper serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art in this multidisciplinary domain and charting the course for its future advancements.

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Section: Adversarial Attacks On Quantum Cryptographic Systemsmentioning

confidence: 99%

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Chandre,

Shendkar,

Deshmukh

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…For implementation, the book review is transformed by a 2-gram (or bi-gram) model that is suitable for the short and middle texts [18][19]. In short, texts are split into two-word sequences, and the vector representations of texts are conducted from the frequency of split strings.…”

Section: Stacked Denoising Autoencodersmentioning

confidence: 99%

Utilize Deep learning to increase the performance of a Book recommender system using the Item-based Collaborative Filtering

Giap¹,

Dieu²,

Lan³

et al. 2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

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Collaborative Filtering is a common and efficient approach for recommendation problems. In this study, we have investigated the power of deep learning in textual feature extraction and applied this advantage to a highperformance item-based collaborative filtering recommender system. The proposed approach has been experienced on book datasets added by texts collected from famous book review sites. The experiment proves that the proposed model has better performance thanks to the contribution of the new item profile process method based on Deep Learning.

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A Building Topical 2-Gram Model: Discovering and Visualizing the Topics Using Frequent Pattern Mining

Cited by 2 publications

References 15 publications

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Utilize Deep learning to increase the performance of a Book recommender system using the Item-based Collaborative Filtering

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