Buch, Englisch, 515 Seiten, Format (B × H): 155 mm x 235 mm
Buch, Englisch, 515 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-3-032-35458-7
Verlag: Springer
This book is about techniques that can make AI more explainable.
While AI techniques have been spectacularly successful, they are not perfect: their answers are often wrong. To distinguish between great and flawed recommendations, it is desirable to make AI systems explain their recommendations.
This means translating numerical computations—that AI systems perform—into human-understandable natural-language form. It thus makes sense to use existing techniques—e.g., fuzzy—that connect numerical computations with natural language.
The use of fuzzy techniques to make AI explainable is the book's focus. This book contains both theoretical results and applications—to aerospace engineering, agriculture, biology, digital twins, education, law enforcement, medicine, etc. It can be recommended to students and practitioners interested in the state-of-the-art fuzzy-related explainable AI, and to researchers focused on remaining challenges.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Modified Hargreaves Equation Using Interval Type 2 Fuzzy Logic System for Predication of Reference Evapotranspiration Case Study for Arid.- From Fuzzy to Clear Visualizing System through Heatmaps Derived from Approximate Inverse Model Explanations and Membership Function Activity.- Evolutionary Optimization of 1D CNN for Non contact Respiration Pattern Classification.- On the fuzzy Ishigami function.
