Metaheuristics for Enterprise Data Intelligence
CRC Press (Verlag)
978-1-032-68377-5 (ISBN)
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.
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.
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
| Erscheinungsdatum | 10.07.2024 |
|---|---|
| Reihe/Serie | Advances in Metaheuristics |
| Zusatzinfo | 1 Tables, color; 11 Tables, black and white; 18 Line drawings, color; 12 Line drawings, black and white; 17 Halftones, color; 4 Halftones, black and white; 35 Illustrations, color; 16 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 476 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-032-68377-5 / 1032683775 |
| ISBN-13 | 978-1-032-68377-5 / 9781032683775 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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