Metaheuristics for Enterprise Data Intelligence

Sakhare, Kaustubh Vaman, Vyas, Vibha, Shastri, Apoorva S.

  • 出版商: CRC
  • 出版日期: 2024-08-07
  • 售價: $5,760
  • 貴賓價: 9.5$5,472
  • 語言: 英文
  • 頁數: 146
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032683775
  • ISBN-13: 9781032683775
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

隨著數據經濟的興起,資訊已成為商業卓越的核心。每個企業,不論其所屬的領域,日常活動中都會攜帶和處理大量數據。將龐大的數據集轉換為有洞察力的資訊在開發更好的商業解決方案中扮演著重要角色。數據智能及其分析在數據表示、知識系統構建、問題解決以及趨勢分析和決策的預測系統方面面臨多重挑戰。可用的數據可能來自任何模式,特別是當數據與醫療保健、生物醫學、金融、零售、網絡安全、網絡、供應鏈管理、製造等相關時。因此,優化這些系統對於獲得最佳結果和結論至關重要。為此,基於人工智慧的自然啟發優化方法或基於近似的優化方法正變得非常強大。值得注意的元啟發式演算法包括遺傳演算法、差分演化、蟻群優化、粒子群優化、人工蜜蜂群、灰狼優化器、政治優化器、群體智慧和聯賽冠軍演算法。本書系統性地討論了基於人工智慧的元啟發式演算法在廣泛領域中的應用,包括大數據智能和預測分析、企業分析、圖優化演算法、機器學習和集成學習、計算機視覺企業實踐及數據基準測試。

作者簡介

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.

作者簡介(中文翻譯)

Dr. Kaustubh Sakhare,資深數據科學家,系統工程與生產整合部(SEPI),約翰迪爾,印度浦那。
Dr. Vibha Vyas,副教授,電子與電信系,工程學院,印度浦那。
Dr. Apoorva S. Shastri,研究助理教授,人工智慧研究所,麻省理工世界和平大學,印度浦那。