Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning

  • 11h 43m
  • Maki K. Habib
  • IGI Global
  • 2022

As technology spreads globally, researchers and scientists continue to develop and study the strategy behind creating artificial life. This research field is ever expanding, and it is essential to stay current in the contemporary trends in artificial life, artificial intelligence, and machine learning. This an important topic for researchers and scientists in the field as well as industry leaders who may adapt this technology.

The Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning provides concepts, theories, systems, technologies, and procedures that exhibit properties, phenomena, or abilities of any living system or human. This major reference work includes the most up-to-date research on techniques and technologies supporting AI and machine learning. Covering topics such as behavior classification, quality control, and smart medical devices, it serves as an essential resource for graduate students, academicians, stakeholders, practitioners, and researchers and scientists studying artificial life, cognition, AI, biological inspiration, machine learning, and more.

About the Author

Maki K. Habib is a Professor at the Mechanical Engineering Department, School of Sciences and Engineering, American University in Cairo, Egypt. His main areas of research are focused on human adaptive and friendly mechatronics, autonomous navigation, service robots and humanitarian demining, telecooperation, distributed teleoperation and collaborative control, flexible automation, wireless sensor networks and ambient intelligence, biomimetic and biomedical robots.

In this Book

  • Development of a Receptor Cell Model for Artificial Life
  • Quadruped Robots With Bio-Inspired Gait Generation Methods Using Sole Pressure Sensory Feedback
  • Deep Learning Models for Physiological Data Classification of Children During Computerized Auditory Tests—Deep Learning-Based Emotion Recognition in Child-Computer Interaction
  • Behavior Classification of Egyptian Fruit Bat (Rousettus aegyptiacus) from Calls With Deep Learning
  • Visual Feedback Control Through Real-Time Movie Frames for Quadcopter With Object Count Function and Pick-and-Place Robot With Orientation Estimator
  • Digital Charge Estimation for Piezoelectric Actuators—An Artificial Intelligence Approach
  • A Fog-Based Threat Detection for Telemetry Smart Medical Devices Using a Real-Time and Lightweight Incremental Learning Method
  • Joint Use of Fuzzy Entropy and Divergence as a Distance Measurement for Image Edge Detection
  • Neuroscience and Artificial Intelligence
  • Applications of the Bees Algorithm—Nature-Inspired Optimisation of Manufacturing Processes
  • Model Optimisation Techniques for Convolutional Neural Networks
  • Formalizing Model-Based Multi-Objective Reinforcement Learning With a Reward Occurrence Probability Vector
  • Entropy, Chaos, and Language
  • Improve Imbalanced Multiclass Classification Based on Modified SMOTE and Feature Selection for Student Grade Prediction
  • Contextualising Computational Thinking Disposition Framework from an Affective Perspective
  • Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques
  • Hyperspectral/Multispectral Imaging Methods for Quality Control
  • Inspection of Power Line Insulators—State of the Art, Challenges, and Open Issues
  • Compilation of References
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