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The amalgamation of artificial intelligence (AI), optimization techniques, and blockchain is revolutionizing how to conceptualize, design, and operate aerospace systems. While optimization techniques are pivotal in streamlining aerospace processes, security challenges have recently surfaced. AI and Blockchain Optimization Techniques in Aerospace Engineering delves into the transformative impact of technologies on various facets of the aerospace industry, offering a multidimensional solution to overcome security concerns and enhance the overall efficiency of aerospace systems The book explores how machine learning reshapes aerospace systems by automating complex tasks through self/reinforced learning methods. From air traffic data analysis to flight scheduling, geographical information, and navigation, machine learning has become an indispensable tool, offering valuable insights that enhance aerospace operations. Simultaneously, blockchain technology, with its inherent characteristics of decentralization and tamper-proof ledgers, ensures transparency, accountability, and security in transactions, providing an innovative approach to data integrity and system resilience. Designed for technology development professionals, academicians, data scientists, industrial experts, researchers, and students, the book offers a panoramic view of the latest innovations in the field. The content spans various critical areas, including aerodynamics, flight mechanics, gas dynamic medical imaging, analytics and diagnostics, image segmentation, deep learning-based computer-aided diagnosis systems, natural language processing, robotics-assisted systems, and bio-inspired algorithms for aerospace applications.
This is a digital product.
AI and Blockchain Optimization Techniques in Aerospace Engineering is written by Vignesh U. and published by Engineering Science Reference. The Digital and eTextbook ISBNs for AI and Blockchain Optimization Techniques in Aerospace Engineering are 9798369314937, and the print ISBNs are 9798369314913, . Additional ISBNs for this eTextbook include 9798369314920.


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