| ISBN13: | 9781032737539 |
| ISBN10: | 10327375311 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 366 oldal |
| Méret: | 234x156 mm |
| Súly: | 834 g |
| Nyelv: | angol |
| Illusztrációk: | 111 Illustrations, black & white; 30 Halftones, black & white; 81 Line drawings, black & white |
| 691 |
Villamosmérnöki tudományok, híradástechnika, műszeripar
Energetika, energiaipar
Számítógép architektúrák, logikai tervezés
Szuperszámítógépek
Mesterséges intelligencia
Környezetmérnöki tudományok
Villamosmérnöki tudományok, híradástechnika, műszeripar (karitatív célú kampány)
Energetika, energiaipar (karitatív célú kampány)
Számítógép architektúrák, logikai tervezés (karitatív célú kampány)
Szuperszámítógépek (karitatív célú kampány)
Mesterséges intelligencia (karitatív célú kampány)
Környezetmérnöki tudományok (karitatív célú kampány)
Machine Learning Hybridization and Optimization for Intelligent Applications
GBP 150.00
Kattintson ide a feliratkozáshoz
A Prosperónál jelenleg nincsen raktáron.
This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.
This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.
Features:
- Focuses on hybridization and optimization of machine learning techniques
- Reviews supervised, unsupervised, and reinforcement learning using case study-based applications
- Covers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computing
- Explains computing models using real-world examples and dataset-based experiments
- Includes case study-based explanations and usage for machine learning technologies and applications
This book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.
1. Big Data Computing: Transforming From Cloud Computing to Edge Scheduling Perspectives Review. 2. Decision Making in the Field of Unmanned Aerial Vehicles: State-of-the-Art. 3. A Brief Study on Understanding and Handling COVID-19: Test Bed for Forecasting with Deep Learning and Machine Learning Algorithms. 4. AgTech: Using Sensors and Machine Learning to Revolutionize Farming Practices (IoT). 5. Developing an AI-based Multi-Task Transfer Learning Framework for Automating Judicial Contracts. 6. Analysis of Deep Learning Methodologies for Handling Non-Medical Big Data and Very Limited Medical Data with Feature Extraction and Annotation Techniques. 7. Introduction to Virtualization Security and Cloud Security. 8.Security Breaches in IoT Applications: An Extensive Study. 9.An Efficient and Accurate Classifcation Algorithm for ECG Signals Using PNN and KNN. 10. Big Data Analytics: The Classification of Remote Sensing Images Using Machine Learning Techniques. 11. Segmentation of Transmission Tower Components Based on Machine Learning. 12. A Systematic Analysis of Robot Path Planning and Optimization Techniques. 13.Pneumonia Prediction Model Using Deep Learning on Docker. 14. A Sequential Deep Learning Model Approach to OCR-Based Handwritten Digit Recognition for Physically Impaired People. 15. A Deep Learning Strategy for Sign Language Classification and Recognition for Hearing-Impaired People. 16. Non-fungible Tokens (NFT): The Design and Development of the "Obstacle Assault" Game and "Turtle Sidestep" Game. 17. Design and Development of 2D Space Shooter Game and Arcade Game Using Unity. 18. An Ensemble Technique Using Genetic Algorithm and Deep Learning for the Prediction of Rice Diseases. 19. History of Machine Learning. 20. Internet of Things Start-Ups: An Overview of the Privacy and Security in IoT Start-Ups.
