- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
This book explores deep learning architectures such as convolutional neural networks and recurrent neural networks for tasks like image analysis, speech recognition, and natural language processing within network paradigms. It uses machine learning algorithms such as neural networks, support vector machines, and decision trees for data analysis and prediction tasks.
This book:
Covers a wide range of topics within network paradigms, including intelligence modeling, sustainability, quantum computing, and network security.
Utilizes various machine learning algorithms such as neural networks, support vector machines, and decision trees for data analysis, and prediction tasks.
Addresses contemporary issues like fake news detection, social media analysis, and cybersecurity.
Employs network analysis techniques to understand the structure and dynamics of complex systems, including social networks, communication networks, and biological networks.
Explores the integration of quantum computing principles and algorithms to solve computational intelligence tasks efficiently, especially in quantum-based network paradigms.
It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics and communications engineering, computer engineering, and information technology.
Contents
1. Enhancing ECG Analysis Through Parametric Quartic Spline Modeling and Machine Learning Classification. 2. Quantum Networking Paradigm. 3. Genetic Algorithm-Based Framework for Optimizing Image Enhancement. 4. Machine Learning Security on Drones or UAV. 5. Image Forgery Detection. 6. The Future of Road Safety Integrating Computational Intelligence with Network Paradigms and AI Innovations. 7. Document Classification Engine to Segregate Multi-lingual PDF Documents. 8. FNDetector: Fake News Detection using Combinations of Various Features. 9. Survey of Visual Deepfake Detection Methods. 10. Empowering Educators: Leveraging Large Language Models for Lecture Preparation Material Development.
