Advanced Analytics and Deep Learning Models

The book discusses the importance of advanced analytics and deep learning in real-world applications such as e-healthcare, transportation, agriculture, and the stock market. It combines machine learning, artificial intelligence, graphs, text mining, data mining, and semantic analysis to analyze data. Deep learning is also exploring large amounts of unsupervised data, making it beneficial for big data. The book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in various sectors. It is aimed at researchers, industry engineers, software engineers, and advanced students in artificial intelligence, big data, computer science, and electronic engineering.

Format: Hardback
Length: 432 pages
Publication date: 24 May 2022
Publisher: John Wiley & Sons Inc

The book delves into the realm of advanced analytics and deep learning models, offering readers a comprehensive grasp of the significance of these technologies in numerous practical real-world applications. These applications span diverse sectors, including e-healthcare, transportation, agriculture, and the stock market.

Advanced analytics is a multifaceted discipline that amalgamates machine learning, artificial intelligence, graphs, text mining, data mining, and semantic analysis. It represents a data analysis approach that extends beyond traditional business intelligence, employing a range of techniques and tools to conduct semi-autonomous and autonomous data analysis.

While deep learning and data analysis hold prominent positions within the field of data science, they are closely intertwined. Virtually all private and public organizations accumulate vast amounts of data, often referred to as domain-specific data. Numerous small and large companies are actively exploring vast datasets to harness the potential of existing and emerging technologies. Deep learning, in particular, has made significant strides in analyzing large volumes of unsupervised data, rendering it highly beneficial and effective for handling big data challenges.

Deep learning possesses the capability to address a wide array of problems and challenges, encompassing tasks such as collecting unlabeled and uncategorized raw data, extracting intricate patterns from substantial datasets, expediting information retrieval, and accurately tagging data. This book comprises 16 chapters dedicated to artificial intelligence, machine learning, deep learning, and their applications across various useful sectors. These sectors encompass stock market prediction, recommendation systems for enhanced service selection, e-healthcare, telemedicine, transportation, and innovations and future opportunities in the realms of fog computing/cloud computing and artificial intelligence.

The intended audience for this book encompasses researchers and practitioners in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers specializing in healthcare, telemedicine, and transportation. By exploring the intricacies of advanced analytics and deep learning models, readers will gain valuable insights into the transformative power of these technologies and their potential to drive innovation and progress in diverse industries.

Weight: 744g
Dimension: 161 x 240 x 28 (mm)
ISBN-13: 9781119791751