Metaheuristics for Enterprise Data Intelligence

1. Auflage 2026
ISBN: 978-1-032-69979-0
Verlag: Taylor & Francis Ltd

Buch, Englisch, 158 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Advances in Metaheuristics

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.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Sakhare, Kaustubh Vaman

Vyas, Vibha

Shastri, Apoorva S

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Über Autor(innen)

Dr Kaustubh Sakhare, Sr. Data Scientist, System Engineering & Production Integration (SEPI), John Deer, Pune, India.

Dr Vibha Vyas, Associate Professor, Department of Electronics and Telecommunication, College of Engineering, Pune, India.

Dr Apoorva S. Shastri, Research Assistant Professor, Institute of Artificial Intelligence, MIT World Peace University, Pune, India.