Please use this identifier to cite or link to this item: https://dspace.chmnu.edu.ua/jspui/handle/123456789/3065
Title: Artificial Intelligence Technologies for Efficient Solving of Recognition Tasks
Authors: Sidenko, I.
Kondratenko, Y.
Skarga-Bandurova, I.
Saliutin, M.
Keywords: Artificial intelligence
Building segmentation
Convolutional and recurrent neural networks
Landmines identification
Mask recognition
Military objects classification
Issue Date: 2025
Publisher: River Publishers
Abstract: This chapter investigates the practical application of artificial intelligence (AI) technologies in addressing various recognition challenges across multiply sectors. By focusing on convolutional and recurrent neural networks, we analyze their efficacy in tasks ranging from medical diagnosis and transportation logistics to military operations, and beyond. Through an analysis of successful implementations, this study highlights how AI enhances classification and recognition capabilities in real-world scenarios, specifically how AI is changing the future of security and remote sensing through the automation of recognition tasks. Additionally, we examine future prospects for AI development, identifying potential advancements and improvements to current technologies. This analysis contributes to the ongoing discourse on practical applications and future directions of AI technology, offering insights into how it can effectively solve complex recognition problems.
Description: Sidenko, I., Kondratenko, Y., Skarga-Bandurova, I., Zhukov, Y., & Saliutin, M. (2025). Artificial Intelligence Technologies for Efficient Solving of Recognition Tasks. Artificial Intelligence: Achievements and Recent Developments, 145–196.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105026200370&partnerID=40&md5=ef1480c67f6dd5eeb09a27cc13b640a2
https://dspace.chmnu.edu.ua/jspui/handle/123456789/3065
ISBN: 978-874380096-5, 978-874380097-2
Appears in Collections:Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus

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