Детальная информация

In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI.

• Cover
• Title Page
• Copyright Page
• Book Series
• Table of Contents
• Detailed Table of Contents
• Preface
• Chapter 1: Review and Applications of Machine Learning and Artificial Intelligence in Engineering
• Chapter 2: Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems
• Chapter 3: A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks
• Chapter 4: Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons
• Chapter 5: Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods
• Chapter 6: A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications
• Chapter 7: High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods
• Chapter 8: Artificial Intelligence Towards Water Conservation
• Chapter 9: Analysis of Ground Water Quality Using Statistical Techniques
• Chapter 10: Probe People and Vehicle-Based Data Sources Application in Smart Transportation
• Chapter 11: Application of Machine Learning Methods for Passenger Demand Prediction in Transfer Stations of Istanbul's Public Transportation System
• Chapter 12: Metaheuristics Approaches to Solve the Employee Bus Routing Problem With Clustering-Based Bus Stop Selection
• Chapter 13: An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks
• Chapter 14: An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics
• Compilation of References
• About the Contributors
• Index