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    Metaheuristics for Enterprise Data Intelligence

    Metaheuristics for Enterprise Data Intelligence by Sakhare, Kaustubh Vaman; Vyas, Vibha; Shastri, Apoorva S;

    Sorozatcím: Advances in Metaheuristics;

      • 10% KEDVEZMÉNY?

      • A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
      • Kiadói listaár GBP 140.00
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        70 854 Ft (67 480 Ft + 5% áfa)
      • Kedvezmény(ek) 10% (cc. 7 085 Ft off)
      • Discounted price 63 769 Ft (60 732 Ft + 5% áfa)

    Beszerezhetőség

    Becsült beszerzési idő: A Prosperónál jelenleg nincsen raktáron, de a kiadónál igen. Beszerzés kb. 3-5 hét..
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    Rövid leírás:

    This book provides a systematic discussion of AI-based Metaheuristics application in a wide range of areas including Big Data Intelligence, Predictive Analytics, Enterprise Analytics, Graph Optimization Algorithms, Machine Learning and Ensemble Learning, Computer Vision Enterprise Practices, Data Benchmarking and more.

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    Hosszú leírás:

    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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    Tartalomjegyzék:

    Chapter 1 ? Terror Attacks Forecast Using Machine Learning and Neo4j Sandbox: A Review


    Sagar Shinde, Suchitra Khoje, Ankit Raj and Lalitkumar Wadhwa


    Chapter 2 ? 5G Evolution and Revolution: A Study


    Namita K. Shinde, Chetan More, Payal Kadam and Vinod Patil


    Chapter 3 ? Metaheuristic Algorithms and Its Application in Enterprise Data


    Radhika D. Joshi, Sheetal Waghchaware and Rushikesh Dudhani


    Chapter 4 ? Petrographic Image Classification Accuracy Improvement Using Improved Learning


    Ashutosh Marathe, Tanuja Tewari and Falguni Vyas


    Chapter 5 ? Data Visualization and Dashboard Design for Enterprise Intelligence


    Nishikant Bhaskar Surwade, Bahubali Shiragapur and Anwar Hussain


    Chapter 6 ? Beyond the Hype: Understanding the Potential of ChatGPT in the Articulation of Technical Papers


    Neha Shaah


    Chapter 7 ? Metaheuristics and Deep Learning in Lung Nodule Detection and Classification


    Rama Vaibhav Kaulgud and Mandar Saundattikar


    Chapter 8 ? An Improved Face Recognition Method Using Canonical Correlation Analysis


    Ganesh D. Jadhav, Suhas Patil, Bhushan M. Borhade and Yogesh Shinde


    Chapter 9 ? Guesswork to Results: How ML-Based A/B Testing Is Changing the Game


    Namita K. Shinde, Payal Kadam, Aditya Choudhary, Bhavay Chopra and Krishnansh Awasthi

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