Artificial Intelligence Advances




After decades of basic research and more promises than impressive applications, artificial intelligence (AI) is starting to deliver benefits. Organizations around the world have been realizing that there are substantial performance gains and increases in productivity for the use of AI and predictive analytics techniques. Their use is bringing a new era of breakthrough innovation and opportunities. Artificial Intelligence Advances In Research And Applications book compiles research insights and applications in diverse areas such as manufacturing, supply chain management, pricing, autonomous vehicles, healthcare, e-commerce, and aeronautics. Using classical and advanced tools in AI such as deep learning, particle swarm optimization, support vector machines and genetic programming among others.

This is a very distinctive book which discusses important applications using a variety of paradigms from AI and outlines some of the research to be performed. The work supersedes similar books that do not cover as diversified a set of sophisticated applications. The authors present a comprehensive and articulated view of recent developments, identifies the applications gap by quoting from the experience of experts, and details suggested research areas. Artificial Intelligence: Advances in Research and Applications guides the reader through an intuitive understanding of the methodologies and tools for building and modeling intelligent systems.

The book’s coverage is broad, starting with clustering techniques with unsupervised ensemble learning, where the optimal combination strategy of individual partitions is robust in comparison to the selection of an algorithmic clustering pool. The book discusses how the use of AI can allow for product development and other benefits not just for businesses, but also for economies. Finally, you can find an interesting investigation of the “transhuman” dimension of AI. (Nova)






Table of Contents

  • Chapter 1 Unsupervised Ensemble Learning
  • Ramazan Ünlü
  • Chapter 2 Using Deep Learning to Configure Parallel Distributed Discrete-Event Simulators
  • Edwin Cortes, Luis Rabelo, and Gene Lee
  • Chapter 3 Machine Learning Applied to Autonomous Vehicles
  • Olmer Garcia and Cesar Diaz
  • Chapter 4 Evolutionary Optimization of Support Vector Machines Using Genetic Algorithms
  • Fred K. Gruber
  • Chapter 5 Texture Descriptors for the Generic Pattern Classification Problem
  • Loris Nanni, Sheryl Brahnam, and Alessandra Lumini
  • Chapter 6 Simulation Optimization Using a Hybrid Scheme with Particle Swarm Optimization for a Manufacturing Supply Chai
  • Alfonso T. Sarmiento and Edgar Gutierrez
  • Chapter 7 The Estimation of Cutting Forces in the Turning of Inconel
  • Assisted with a High-Pressure Coolant Using Bio-Inspired
  • Artificial Neural Networks
  • Djordje Cica and Davorin Kramar
  • Chapter 8 Predictive Analytics using Genetic Programming
  • Luis Rabelo, Edgar Gutierrez, Sayli Bhide and Mario Marin
  • Chapter 9 Managing Overcrowding in Healthcare using Fuzzy Logic
  • Abdulrahman Albar, Ahmad Elshennawy, Mohammed Basingab and Haitham Bahaitham
  • Chapter 10 The Utilization of Case-Based Reasoning: A Case Study of the
  • Healthcare Sector Using Simulation Modeling
  • Khaled Alshareef, Ahmad Rahal, and Mohammed Basingab
  • Chapter 11 Agent-Based Modeling Simulation and Its Application to E-commerce
  • Oloruntomi Joledo, Edgar Gutierrez, and Hatim Bukhari
  • Chapter 12 Artificial Intelligence for the Modeling and Prediction of the Bioactivities of Complex Natural Products
  • Jose M. Prieto
  • Chapter 13 Predictive Analytics for Thermal Coal Prices Using Neural Networks and Regression Trees
  • Mayra Bornacelli, Edgar Gutierrez, and John Pastrana
  • Chapter 14 Explorations of the ‘Transhuman’ Dimension of Artificial Intelligence
  •  Bert Olivier
  • Index

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