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Titlebook: Soft Computing and Signal Processing; Proceedings of 5th I V. Sivakumar Reddy,V. Kamakshi Prasad,K. T. V. Red Conference proceedings 2023 T

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G. Revathy,Pokkuluri Kiran Sree,S. Sasikala Devi,R. Karunamoorthi,S. Senthil Vadivuwill have the ability to extend your project into the future as Apple sees it. Make no mistake—the path for a modular and flexible project is not easy. With the help of Swift 978-1-4842-7048-6978-1-4842-7049-3
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B. S. S. Sowjanya Lakshmi,Ravi Kiran Varma Pwill have the ability to extend your project into the future as Apple sees it. Make no mistake—the path for a modular and flexible project is not easy. With the help of Swift 978-1-4842-7048-6978-1-4842-7049-3
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Soumen Kanrarwill have the ability to extend your project into the future as Apple sees it. Make no mistake—the path for a modular and flexible project is not easy. With the help of Swift 978-1-4842-7048-6978-1-4842-7049-3
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S. Sneha Latha,D. Lathika,T. Srehari,P. Yaswanthram,B. A. Sabarishwill have the ability to extend your project into the future as Apple sees it. Make no mistake—the path for a modular and flexible project is not easy. With the help of Swift 978-1-4842-7048-6978-1-4842-7049-3
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S. Muthuselvan,S. Rajaprakash,H. Lakshmi Priya,Albin Davis,Ameer Ali,V. AshikSpring andthe 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research.  Overall, this book offers a timely, practice-oriented reference guide to graduate 978-3-030-78356-3978-3-030-78354-9Series ISSN 2195-9862 Series E-ISSN 2195-9870
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Decentralized Payment Architecture for E-Commerce and Utility Transactions with Government Verifiedecurity and efficiency. The paper focuses on two essential aspects of the design: how to verify the identities of individuals in transactions and how to maximize efficiency of this architecture while promising high security. In addition, the paper will also explore the potential challenges associate
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Visual Learning with Dynamic Recall,ct the necessary data for every single object category. This project is an attempt to study and implement a few-shot learning algorithm that extends an object recognition system with a few-shot classification weight generator using knowledge about the base categories to generate weights for the nove
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