Deep Learning Applications

Select Topics
1. Auflage 2026
ISBN: 978-1-041-10572-5
Verlag: CRC Press

Buch, Englisch, 168 Seiten, Format (B × H): 156 mm x 234 mm

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.

Zielgruppe

Academic and Professional Practice & Development

Autoren/Hrsg.

Abualigah, Laith

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Preface. Smart Healthcare Solutions: The Role of Artificial Intelligence in Diagnosing and Treating Patients. Artificial Intelligence for Financial Market Forecasting: A Machine Learning Approach. Artificial Intelligence for Autonomous Systems: Innovations in Self-Driving Cars and Drones. The Integration of Artificial Intelligence and Robotics for Autonomous Systems. DD-SSD: Deep Detector for Strip Steel Defects. A Prospective study of Indoor Air Quality Monitoring System using IoT. Autonomous Plant Monitoring and Maintaining Robot to Identify Plant Diseases and Nutrient Deficiencies. Determination and Diagnosis of Diabetes Mellitus. Inflated 3D Convnet for Detection of Sign Language. Flora and Fauna Identification using YOLOv3 Algorithm. Thyroid Cancer Reoccurrence Prediction Using Machine Learning. Recommendation System for Social Media Using Graph and Web Analyti. Rfid Car Using Arduino Mega 2560 by Dijkstra’s Algorithm. Stock Market Analysis Using Ensemble Learning Approach Deep Learning Applications: Select Topics. Detecting COVID-19 with Chest X-ray using PyTorch. Personalized News Recommendations System Based on Hybrid Filtering Techniques: a case study on user comments and readership history. Multilabel Classification of Arabic Articles Based on Mawjaz Topics Using Deep Learning Approaches. Data mining in health care sector: A review. Translation from Spoken Arabic Digits to Sign Language based on Deep Learning. Future Directions in Deep Learning: Challenges and Opportunities. Index.

Über Autor(innen)

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.