Muutke küpsiste eelistusi
  • Formaat - EPUB+DRM
  • Hind: 64,99 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.



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 decision making. The data available could be of any modality, especially when data is associated with healthcare, biomedical, finance, retail, cyber security, networking, supply chain management, manufacturing and so on. 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, League Championship Algorithm, and many more. 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.

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

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.