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Laith Abualigah - Deep Learning Applications, Inbunden

Inbunden, Engelska, 2026

Deep Learning Applications

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Av Laith Abualigah

1875 kr

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Beskrivning
A comprehensive compilation that explores a wide range of applications where deep learning techniques have had impact and its practical uses. The book covers fields like healthcare, finance, autonomous systems, and more. The methodologies such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and reinforcement learning, have been included.In healthcare deep learning is used for medical image analysis, disease prediction, and personalized medicine. In the financial sector, aspects like fraud detection, algorithmic trading, and risk management are reviewed. Also autonomous systems, including self-driving cars and drones are explored.Each chapter includes detailed case studies, experimental results, and insights from leading experts, offering readers both foundational knowledge and advanced techniques. The book serves as a valuable resource for researchers, practitioners, and students seeking to understand the breadth and depth of deep learning applications.
Produktinformation
  • Utgivningsdatum: 2026-05-12
  • Mått: 156 x 234 x 14 mm
  • Vikt: 470 g
  • Format: Inbunden
  • Språk: Engelska
  • Antal sidor: 156
  • Förlag: Taylor & Francis Ltd
  • ISBN: 9781041105725
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Laith Abualigah is an Associate Professor at the Computer Science Department at Al Al-Bayt University, Jordan. He is also a distinguished researcher at many prestigious universities. He obtained a PhD from the School of Computer Science at Universiti Sains Malaysia (USM), Malaysia, in 2018. He is Clativate’s Highly Cited Researcher for the years 2021-2024 and a top 1% Influential Researcher, of Web of Science. He is also a top 2% scientist (Stanford University). He has published more than 650 journal papers and books, which collectively have been cited more than 33,000 times (H-index = 81). His main research interests are Artificial Intelligence, Meta-heuristic Modeling, and Optimization Algorithms, Evolutionary Computations, Information Retrieval, Text clustering, Feature Selection, Combinatorial Problems, Optimization, Advanced Machine Learning, Big data, and Natural Language Processing. He is an associate editor of many prestigious journals.

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