Titlebook: Intelligent Informatics; Proceedings of Eight Sankar K. Pal,Sabu M. Thampi,Ajith Abraham Conference proceedings 2025 The Editor(s) (if appl

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书目名称Intelligent Informatics影响因子(影响力)




书目名称Intelligent Informatics影响因子(影响力)学科排名




书目名称Intelligent Informatics网络公开度




书目名称Intelligent Informatics网络公开度学科排名




书目名称Intelligent Informatics被引频次




书目名称Intelligent Informatics被引频次学科排名




书目名称Intelligent Informatics年度引用




书目名称Intelligent Informatics年度引用学科排名




书目名称Intelligent Informatics读者反馈




书目名称Intelligent Informatics读者反馈学科排名

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A Handy Simulated Radar Interface for Black Flight Identification System. By understanding the working mechanism in recognition and identification, a User-Case Diagram has been successfully designed that shows the working mechanism of the interface between these systems which is implemented to support the exchange of information between the Radar system and the AI-based recognition and identification system.

Abnormal

Conference proceedings 2025ber 18–20, 2023, Bangalore, India. The revised papers presented were carefully reviewed and selected from several initial submissions. The scope of the symposium includes AI, machine learning, cognitive computing, soft computing, security informatics, data science, computer vision, pattern recogniti

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Comprehensive Survey of Audio-to-Text Conversionthe same process. This review further dissects the real-world applications of audio-to-text conversion, domains such as transcription services, voice assistants, and accessibility technologies. It explores the challenges and open research questions on this topic.

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ULCER

Conference proceedings 2025on, intelligent software engineering, intelligent networked systems, IoT, cyber-physical systems, and NLP. The book is directed to the researchers and scientists engaged in various fields of computing and network communication domains..

性别

Identification of Taurine Cattle Breed Based on Convolutional Neural Networknoresistance capacity. In this study, a computer vision solution is proposed for lobi taurine cattle classification. We implemented a customized Convolutional Neural Network (CNN) on a dataset containing 2379 images taken from three angles: front, side, and rear. The CNN was trained on four subsets