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Achieving Sustainable Business through AI, Technology Education and Computer Science : Volume 1: Computer Science, Business Sustainability, and Competitive Advantage / / edited by Allam Hamdan



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Titolo: Achieving Sustainable Business through AI, Technology Education and Computer Science : Volume 1: Computer Science, Business Sustainability, and Competitive Advantage / / edited by Allam Hamdan Visualizza cluster
Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Edizione: 1st ed. 2025.
Descrizione fisica: 1 online resource (707 pages)
Disciplina: 006.3
Soggetto topico: Engineering - Data processing
Computational intelligence
Artificial intelligence
Data Engineering
Computational Intelligence
Artificial Intelligence
Persona (resp. second.): HamdanAllam
Nota di bibliografia: Includes bibliographical references.
Nota di contenuto: Artificial Intelligence Machine Learning and Blockchain Applications in Business -- Integrating Perceived Organizational Support with CMMI for IT Outsourcing Success in the UAE.-Digital Payment Systems and Business Development of Small and Petty Traders in India A Monetary Analysis -- Understanding the impact of Artificial Intelligence Applications on Indian higher Education sector -- Technology Integration in Multidisciplinary Business Education A Faculty Driven Qualitative Study.
Sommario/riassunto: This book aims to explore the intersection of AI, technology education, and computer science with sustainable business practices. It delves into the application of cutting-edge technologies such as artificial intelligence, machine learning, and blockchain in various business domains, including healthcare, education, government services, and digital transformation.
Titolo autorizzato: Achieving Sustainable Business Through AI, Technology Education and Computer Science  Visualizza cluster
ISBN: 9783031708558
3031708555
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910983343403321
Lo trovi qui: Univ. Federico II
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Serie: Studies in Big Data, . 2197-6511 ; ; 158