Metaheuristics For Enterprise Data Intelligence

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Metaheuristics for Enterprise Data Intelligence

Metaheuristics for Enterprise Data Intelligence
Author :
Publisher : CRC Press
Total Pages : 159
Release :
ISBN-10 : 9781040096475
ISBN-13 : 1040096476
Rating : 4/5 (476 Downloads)

Book Synopsis Metaheuristics for Enterprise Data Intelligence by : Kaustubh Vaman Sakhare

Download or read book Metaheuristics for Enterprise Data Intelligence written by Kaustubh Vaman Sakhare and published by CRC Press. This book was released on 2024-08-07 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the emergence of the data economy, information has become integral to business excellence. Every enterprise, irrespective of its domain of interest, carries and processes a lot of data in their day-to-day activities. Converting massive datasets into insightful information plays an important role in developing better business solutions. Data intelligence and its analysis pose several challenges in data representation, building knowledge systems, issue resolution and predictive systems for trend analysis and decisionmaking. The data available could be of any modality, especially when data is associated with healthcare, biomedical, finance, retail, cybersecurity, networking, supply chain management, manufacturing, etc. The optimization of such systems is therefore crucial to leveraging the best outcomes and conclusions. To this end, AI-based nature-inspired optimization methods or approximation-based optimization methods are becoming very powerful. Notable metaheuristics include genetic algorithms, differential evolution, ant colony optimization, particle swarm optimization, artificial bee colony, grey wolf optimizer, political optimizer, cohort intelligence and league championship algorithm. This book provides a systematic discussion of AI-based metaheuristics application in a wide range of areas, including big data intelligence and predictive analytics, enterprise analytics, graph optimization algorithms, machine learning and ensemble learning, computer vision enterprise practices and data benchmarking.


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