The area of Explainable Artificial Intelligence (XAI) is concerned with providing methods and tools to improve the interpretability of black-box learning models.
This book presents innovative applications of deep learning techniques in wireless ad-hoc networks, addressing critical challenges such as trust, routing, traffic management, and intrusion detection.
This book gathers outstanding research papers presented at the 8th International Joint Conference on Advances in Computational Intelligence (IJCACI 2024), held in hybrid mode at South Asian University, New Delhi, India, during October 5–6, 2024.
How we think and react has a direct impact on experience design, but often designers don't understand the "e;whys"e; behind their best practices, leaving them at risk for misusing or underutilizing those designs.
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable.
This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms.
This book contributes to the advancement of aerospace technology and the optimization of navigation systems, thereby fostering innovation and progress in the field.
Geospatial tools to Groundwater Resources explain the most recent methods in Geographic Information Systems (GIS) and geostatistics as they apply to groundwater through complete case studies that demonstrate actual remote sensing applications in this field.
This second edition updates and expands upon the first beginner-focused guide to Procedural Content Generation via Machine Learning (PCGML), which is the use of computers to generate new types of content for video games (game levels, quests, characters, etc.
This book offers innovative insights into the integration of machine learning and inverse problems, showcasing cutting-edge methodologies that enhance computational efficiency and accuracy.
Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key FeaturesLearn how to apply machine learning techniques in the field of data scienceUnderstand when to use different data mining techniques, how to set up different analyses, and how to interpret the resultsA step-by-step approach to improving model development and performanceBook DescriptionMachine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data.
