Hybrid Computational Intelligence: Challenges and Applications: Hybrid Computational Intelligence for Pattern Analysis and Understanding
Editat de Siddhartha Bhattacharyya, Vaclav Snasel, Deepak Gupta, Ashish Khannaen Limba Engleză Paperback – 6 mar 2020
Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.
- Provides insights into the latest research trends in hybrid intelligent algorithms and architectures
- Focuses on the application of hybrid intelligent techniques for pattern mining and recognition, in big data analytics, and in human-computer interaction
- Features hybrid intelligent applications in biomedical engineering and healthcare informatics
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Specificații
ISBN-13: 9780128186992
ISBN-10: 0128186992
Pagini: 250
Dimensiuni: 152 x 229 mm
Greutate: 0.34 kg
Editura: ELSEVIER SCIENCE
Seria Hybrid Computational Intelligence for Pattern Analysis and Understanding
ISBN-10: 0128186992
Pagini: 250
Dimensiuni: 152 x 229 mm
Greutate: 0.34 kg
Editura: ELSEVIER SCIENCE
Seria Hybrid Computational Intelligence for Pattern Analysis and Understanding
Public țintă
Researchers, professionals, and graduate students in computer science & engineering, and electrical engineeringCuprins
1. Application and Techniques of Opinion Mining 2. Influence of Big Data in Smart Tourism 3. Deep learning and its applications for Content-Based Video Retrieval 4. A Computationally Intelligent agent for detecting fake news using Generative Adversarial networks (GANs) 5. Detection of Primary Glaucoma using ANN with the help of Back Propagation algo in Bio-medical Image Processing 6. Hybrid Computational Intelligence with Applications to BioMedical Image and HealthCare 7. Application of Hybrid Computational Intelligence in Healthcare 8. Utility System for premature plant disease detection using machine learning 9. Artificial Intelligence-Based Computational Fluid Dynamics (CFD) Approaches 10. Real-Time Video Segmentation using a Vague Adaptive Threshold