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              <text>Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm</text>
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          <name>Subject</name>
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              <text>catastrophe relief; CNN; Modified smell bees optimization (MSBO); optimization algorithms; routing protocols; UAV networks</text>
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              <text>UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This studys principal objective is to further develop UAV network routing decisions by using CNNs ability for design recognition and the Modified SBOs optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAVs, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms.  2024 River Publishers.</text>
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              <text>Gupta S.K.; Ul Din S.M.; Upreti K.; Mahajan S.; Date S.S.</text>
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              <text>Journal of Mobile Multimedia, Vol-20, No. 5, pp. 1117-1152.</text>
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          <name>Publisher</name>
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              <text>River Publishers</text>
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          <name>Date</name>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.13052/jmm1550-4646.2056" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.13052/jmm1550-4646.2056&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214506466&amp;amp;doi=10.13052%2Fjmm1550-4646.2056&amp;amp;partnerID=40&amp;amp;md5=e0f9f9d5b6979a9801ebbae3839eb4ad" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214506466&amp;amp;doi=10.13052%2fjmm1550-4646.2056&amp;amp;partnerID=40&amp;amp;md5=e0f9f9d5b6979a9801ebbae3839eb4ad&lt;/a&gt;</text>
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 15504646</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Gupta S.K., Department of Electronics and Communication Engineering, Central University of Jammu, Jammu, Samba, 181143, India; Ul Din S.M., Department of Electronics and Communication Engineering, Indian Institute of Technology, Chhattisgarh, Bhilai, 491002, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR, 201003, India; Mahajan S., Amity School of Engineering and Technology (ASET), Amity University, Gurugram, Haryana, Panchgaon, 122412, India; Date S.S., Department of Artificial Intelligence and Data Science, CSMSS Chh, Shahu College of Engineering, Maharashtra, Chh. Sambhajinagar, India</text>
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