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  • Sách/Book


  • Authors: Huimin Lu (2021)

  • This book provides insights into research in the field of artificial intelligence in combination with robotics technologies. The integration of artificial intelligence and robotic technologies is a highly topical area for researchers and developers from academia and industry around the globe, and it is likely that artificial intelligence will become the main approach for the next generation of robotics research.

  • Sách/Book


  • Authors: Bijoy Chand Chatterjee (2020)

  • The book explains the fragmentation problem in EONs, discusses, and analyzes the major conventional spectrum allocation policies in terms of the fragmentation effect in a network. The taxonomies of the fragmentation management approaches are presented along with different node architectures. State-of-the-art fragmentation management approaches are looked at.

  • Sách/Book


  • Authors: Sammie Bae (2019)

  • This book covers the practical applications of data structures and algorithms to encryption, searching, sorting, and pattern matching.

  • Sách/Book


  • Authors: Roger S. Pressman. (2010)

  • The seventh edition of Software Engineering: A Practitioner's Approach has been designed to consolidate and restructure the content introduced over the past two editions of the book. The chapter structure will return to a more linear presentation of software engineering topics with a direct emphasis on the major activities that are part of a generic software process.

  • Sách/Book


  • Authors: David L. Poole (2018)

  • The new edition also features expanded coverage on machine learning material, as well as on the social and ethical consequences of AI and ML. The book balances theory and experiment, showing how to link them together, and develops the science of AI together with its engineering applications.

  • Sách/Book


  • Authors: Markus Hofmann (2013)

  • Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics.

  • Sách/Book


  • Authors: Stanley Cohen (2021)

  • Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning.

  • Sách/Book


  • Authors: Norman Matloff (2019)

  • Probability and Statistics for Data Science: Math + R + Data covers "math stat"-distributions, expected value, estimation etc.-but takes the phrase "Data Science" in the title quite seriously: Real datasets are used extensively. All data analysis is supported by R coding. Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.

  • Sách/Book


  • Authors: D. Jude Hemanth (2021)

  • The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind