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Machine learning and cognitive computing for mobile communications and wireless networks /
"Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 008 | 200528t20202020njua ob 001 0 eng | ||
| 010 | |a 2020023353 | ||
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| 020 | |a 1119640555 |q electronic book | ||
| 020 | |a 9781119640578 |q (electronic bk.) | ||
| 020 | |z 9781119640363 |q hardcover | ||
| 020 | |z 1119640369 | ||
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| 050 | 0 | 4 | |a QA76.59 |b .M33 2020 |
| 082 | 0 | 0 | |a 006.3/1 |2 23 |
| 049 | |a UAMI | ||
| 245 | 0 | 0 | |a Machine learning and cognitive computing for mobile communications and wireless networks / |c edited by Krishna Kant Singh, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India, Akansha Singh, Department of CSE, ASET, Amity University Uttar Pradesh, Noida, India, Korhan Cengiz, Electrical-Electronics Engineering Department, Trakya University, Edirne, Turkey, and Dac-Nhuong Le, Faculty of Information Technology, Haiphong University, Vietnam. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, Inc., |c 2020. | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 online resource (xiv, 253 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems"-- |c Provided by publisher. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on July 21, 2020). | ||
| 505 | 0 | |a Preface xiii 1 Machine Learning Architecture and Framework 1; Nilanjana Pradhan and Ajay Shankar Singh 1.1 Introduction 2 1.2 Machine Learning Algorithms 3 1.2.1 Regression 3 1.2.2 Linear Regression 4 1.2.3 Support Vector Machine 4 1.2.4 Linear Classifiers 5 1.2.5 SVM Applications 8 1.2.6 Naïve Bayes Classification 8 1.2.7 Random Forest 9 1.2.8 K-Nearest Neighbor (KNN) 9 1.2.9 Principal Component Analysis (PCA) 9 1.2.10 K-Means Clustering 10 1.3 Business Use Cases 10 1.4 ML Architecture Data Acquisition 14 1.5 Latest Application of Machine Learning 15 1.5.1 Image Identification 16 1.5.2 Sentiment Analysis 16 1.5.3 News Classification 16 1.5.4 Spam Filtering and Email Classification 17 1.5.5 Speech Recognition 17 1.5.6 Detection of Cyber Crime 17 1.5.7 Classification 17 1.5.8 Author | |
| 505 | 0 | |a Identification and Prediction 18 1.5.9 Services of Social Media 18 1.5.10 Medical Services 18 1.5.11 Recommendation for Products and Services 18 1.5.11.1 Machine Learning in Education 19 1.5.11.2 Machine Learning in Search Engine 19 1.5.11.3 Machine Learning in Digital Marketing 19 1.5.11.4 Machine Learning in Healthcare 19 1.6 Future of Machine Learning 20 1.7 Conclusion 22 References 23 2 Cognitive Computing: Architecture, Technologies and Intelligent Applications 25; Nilanjana Pradhan, | |
| 505 | 0 | |a Ajay Shankar Singh and Akansha Singh 2.1 Introduction 26 2.1 The Components of a Cognitive Computing System 27 2.3 Subjective Computing Versus Computerized Reasoning 28 2.4 Cognitive Architectures 29 2.5 Subjective Architectures and HCI 31 2.6 Cognitive Design and Evaluation 32 2.6.1 Architectures Conceived in the 1940s Can't Handle the Data of 2020 41 2.7 Cognitive Technology Mines Wealth in Masses of Information 41 2.7.1 Technology is Only as Strong as Its Flexible, Secure Foundation 42 2.8 Cognitive Computing: Overview 43 2.9 The Future of Cognitive Computing 47 References 49 3 Deep Reinforcement Learning for Wireless Network 51; Bharti Sharma, R.K Saini, | |
| 505 | 0 | |a KNN and SVM Models for Wireless 60 3.4.2 Bayesian Learning for Cognitive Radio 60 3.4.3 Deep Learning in Wireless Network 61 3.4.4 Deep Reinforcement Learning in Wireless Network 62 3.4.5 Traffic Engineering and Routing 63 3.4.6 Resource Sharing and Scheduling 64 3.4.7 Power Control and Data Collection 64 3.5 Conclusion 65 References 66 4 Cognitive Computing for Smart Communication 73; Poonam Sharma, | |
| 505 | 0 | |a Akansha Singh and Aman Jatain 4.1 Introduction 74 4.2 Cognitive Computing Evolution 75 4.3 Characteristics of Cognitive Computing 76 4.4 Basic Architecture 77 4.4.1 Cognitive Computing and Communication 77 4.5 Resource Management Based on Cognitive Radios 78 4.6 Designing 5G Smart Communication with Cognitive Computing and AI 80 4.6.1 Physical Layer Design Based on Reinforcement Learning 82 4.7 Advanced Wireless Signal Processing Based on Deep Learning 84 4.7.1 Modulation 85 4.7.2 Deep Learning for Channel Decoding 86 4.7.3 Detection Using Deep Learning 87 4.8 Applications of Cognition-Based Wireless Communication 87 4.8.1 Smart Surveillance Networks for Public Safety 88 4.8.2 Cognitive Health Care Systems 88 4.9 Conclusion 89 References 89 5 Spectrum Sensing and Allocation Schemes for Cognitive Radio 91; Amrita Rai, Amit Sehgal, T.L. | |
| 505 | 0 | |a Singal and Rajeev Agrawal 5.1 Foundation and Principle of Cognitive Radio 92 5.2 Spectrum Sensing for Cognitive Radio Networks 94 5.3 Classification of Spectrum Sensing Techniques 95 5.4 Energy Detection 97 5.5 Matched Filter Detection 100 5.6 Cyclo-Stationary Detection 103 5.7 Euclidean Distance-Based Detection 107 5.8 Spectrum Allocation for Cognitive Radio Networks 108 5.9 Challenges in Spectrum Allocation 118 5.9.1 Spectrum and Network Heterogeneity 119 5.9.2 Issues and Challenges 120 5.10 Future Scope in Spectrum Allocation 122 References 123 6 Significance of Wireless Technology in Internet of Things (IoT) 131; Ashish Tripathi, Arun Kumar Singh, Pushpa Choudhary, Prem Chand Vashist and K. K. | |
| 505 | 0 | |a 6.3.3 IoT Connections 144 6.3.3.1 Device-to-Device (D2D)/Machine-to-Machine (M2M) 144 6.3.3.2 Machine-to-Gateway/Router (M2G/R) 145 6.3.3.3 Gateway/Router-to-Data System (G/R2DS) 145 6.3.3.4 Data System to Data System (DS2DS) 145 6.3.4 IoT Protocols/Standards 145 6.3.4.1 Network Protocols for IoT 146 6.3.4.2 Data Protocols for IoT 148 6.4 Conclusion 150 References 150 7 Architectures and Protocols for Next-Generation Cognitive Networking 155; R. Ganesh Babu, V. Amudha and P. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Mobile computing |x Technological innovations. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Soft computing. | |
| 650 | 6 | |a Informatique mobile |x Innovations. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Informatique douce. | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Soft computing |2 fast | |
| 700 | 1 | |a Singh, Krishna Kant |q (Telecommunications professor), |e editor. | |
| 700 | 1 | |a Singh, Akansha, |e editor | |
| 700 | 1 | |a Cengiz, Korhan, |e editor. | |
| 700 | 1 | |a Le, Dac-Nhuong, |e editor | |
| 758 | |i has work: |a Machine learning and cognitive computing for mobile communications and wireless networks (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGD6JgH3YWxxjWHxpq87QC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Machine learning and cognitive computing for mobile communications and wireless networks |d Hoboken, NJ, USA : Wiley-Scrivener, 2020. |z 9781119640363 |w (DLC) 2020023352 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6214901 |z Texto completo |
| 938 | |a YBP Library Services |b YANK |n 16793099 | ||
| 938 | |a Askews and Holts Library Services |b ASKH |n AH36662243 | ||
| 938 | |a EBSCOhost |b EBSC |n 2492132 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6214901 | ||
| 994 | |a 92 |b IZTAP | ||