Please use this identifier to cite or link to this item:
https://dspace.chmnu.edu.ua/jspui/handle/123456789/3065Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Sidenko, I. | - |
| dc.contributor.author | Kondratenko, Y. | - |
| dc.contributor.author | Skarga-Bandurova, I. | - |
| dc.contributor.author | Saliutin, M. | - |
| dc.date.accessioned | 2026-01-09T13:13:17Z | - |
| dc.date.available | 2026-01-09T13:13:17Z | - |
| dc.date.issued | 2025 | - |
| dc.identifier.isbn | 978-874380096-5, 978-874380097-2 | - |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-105026200370&partnerID=40&md5=ef1480c67f6dd5eeb09a27cc13b640a2 | - |
| dc.identifier.uri | https://dspace.chmnu.edu.ua/jspui/handle/123456789/3065 | - |
| dc.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. | uk_UA |
| dc.description.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. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | River Publishers | uk_UA |
| dc.subject | Artificial intelligence | uk_UA |
| dc.subject | Building segmentation | uk_UA |
| dc.subject | Convolutional and recurrent neural networks | uk_UA |
| dc.subject | Landmines identification | uk_UA |
| dc.subject | Mask recognition | uk_UA |
| dc.subject | Military objects classification | uk_UA |
| dc.title | Artificial Intelligence Technologies for Efficient Solving of Recognition Tasks | uk_UA |
| dc.type | Book chapter | uk_UA |
| Appears in Collections: | Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Sidenko, I., Kondratenko, Y., Skarga-Bandurova, I., Zhukov, Y., & Saliutin, M.pdf | 97.29 kB | Adobe PDF | View/Open |
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