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  • Sách/Book

  • System Design for Epidemics Using Machine Learning and Deep Learning

    • Authors: G. R. Kanagachidambaresan (2023)

  • This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis.
  • Sách/Book

  • AI for scientific discovery

    • Authors: Janna Hastings (2023)

  • AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines. Artificial intelligence technologies support discovery science in multiple different ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation - in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research, by underpinning the discovery process with AI-enabled standards and pipelines, support the management of large-scale data and...
  • Sách/Book

  • Explainable deep learning AI : methods and challenges

    • Authors: Jenny Benois-Pineau (2023)

  • The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches.
  • Sách/Book

  • Deep learning and scientific computing with R torch

    • Authors: Sigrid Keydana (2023)

  • This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold: Provide a thorough introduction to torch basics - both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch. Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classification.
  • Sách/Book

  • Classification applications with deep learning and machine learning technologies

    • Authors: Laith Abualigah (2023)

  • This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies image and data classifications.
  • Sách/Book

  • Deep learning in cancer diagnostics : a feature-based transfer learning evaluation

    • Authors: Mohd Hafiz Arzmi (2023)

  • In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer
  • Sách/Book

  • Time Series Algorithms Recipes Implement Machine Learning and Deep Learning Techniques with Python

    • Authors: Akshay R. Kulkarni (2023)

  • This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average).