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              <text>A hybrid deep learning and quantum computing approach for optimized encryption algorithms in secure communications</text>
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              <text>Cybersecurity; Deep learning; Encryption algorithms; Hybrid approach; Neural networks; Quantum computing; Secure communications</text>
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              <text>As online dangers get worse, there is a greater need for strong encryption methods to protect private conversations. Utilizing the strengths of both deep learning and quantum computing, this study suggests a new mixed method for improving the security of communication systems by making encryption algorithms work better. When it comes to keeping up with new online threats, traditional security methods often fall behind. Deep learning techniques could be a good way to improve encryption algorithms because they let the system learn and change to new attack methods. In the meantime, quantum computing offers unmatched computing power that can completely change how cryptography works by using quantum events like superposition and entanglement. Our suggested method combines the flexibility of deep learning with the computing power of quantum computing to get around the problems with current encryption methods. This will make safe communication systems more resistant to attacks from smart people. Through tests and models, we show that our mixed approach works better and more effectively than current encryption methods. This shows that it has the ability to solve the growing safety problems in a world that is becoming more and more linked.  2024, Taru Publications. All rights reserved.</text>
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              <text>Al-Alshaikh H.A.; Poonia R.C.; Saudagar A.K.J.; Tripathi K.N.</text>
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              <text>Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 7, pp. 2101-2110.</text>
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              <text>Taru Publications</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.47974/JDMSC-2083" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.47974/JDMSC-2083&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210474016&amp;amp;doi=10.47974%2FJDMSC-2083&amp;amp;partnerID=40&amp;amp;md5=a5942e38fa836800749ac96e68aef827" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210474016&amp;amp;doi=10.47974%2fJDMSC-2083&amp;amp;partnerID=40&amp;amp;md5=a5942e38fa836800749ac96e68aef827&lt;/a&gt;</text>
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              <text>ISSN: 9720529</text>
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              <text>Al-Alshaikh H.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Uttar Pradesh, NCR, Delhi, 201003, India; Saudagar A.K.J., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Tripathi K.N., Department of Computer Engineering, ISBM College of Engineering, Maharashtra, Pune, 411042, India</text>
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