Daniel Minoli, Benedict Occhiogrosso

AI Applications to Communications and Information Technologies (eBook, ePUB)

The Role of Ultra Deep Neural Networks

• Format: ePub

Produktbeschreibung

AI Applications to Communications and Information Technologies Apply the technology of the future to networking and communications. Artificial intelligence, which enables computers or computer-controlled systems to perform tasks which ordinarily require human-like intelligence and decision-making, has revolutionized computing and digital industries like few other developments in recent history. Tools like artificial neural networks, large language models, and deep learning have quickly become integral aspects of modern life. With research and development into AI technologies proceeding at lightning speeds, the potential applications of these new technologies are all but limitless. AI Applications to Communications and Information Technologies offers a cutting-edge introduction to AI applications in one particular set of disciplines. Beginning with an overview of foundational concepts in AI, it then moves through numerous possible extensions of this technology into networking and telecommunications. The result is an essential introduction for researchers and for technology undergrad/grad student alike. AI Applications to Communications and Information Technologies readers will also find: * In-depth analysis of both current and evolving applications * Detailed discussion of topics including generative AI, chatbots, automatic speech recognition, image classification and recognition, IoT, smart buildings, network management, network security, and more * An authorial team with immense experience in both research and industry AI Applications to Communications and Information Technologies is ideal for researchers, industry observers, investors, and advanced students of network communications and related fields.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 496
• Erscheinungstermin: 8. November 2023
• Englisch
• ISBN-13: 9781394190010
• Artikelnr.: 69438290

Autorenporträt

Daniel Minoli is Principal Consultant for DVI Communications, New York, USA, and a longtime Expert Witness and Testifying Expert in networking, wireless, video, IoT, and VoIP. In addition to working as Director of Engineering for gamut of premiere high-tech firms, he has acted as Adjunct Instructor at New York University and Stevens Institute of Technology, USA for twenty years. He has published extensively on networks, IP/IPv6, video, wireless communications, and related subjects. Benedict Occhiogrosso is Co-Founder of DVI Communications, New York, USA, with extensive experience as a technology engineer, manager and executive. He is a subject matter expert in several disciplines now enhanced by artificial intelligence including telecommunications networking, speech recognition, image processing and building management systems. He has also served as a testifying expert witness and advisor on patent portfolios.

Inhaltsangabe

Ch 1: Overview 1.1 Introduction and Basic Concepts 1.1.1 Machine Learning
1.1.2 Deep Learning 1.1.3 Activation Functions 1.1.4 MLPs 1.1.5 RNNs 1.1.6
CNNs 1.1.7 Comparison 1.2 Learning Methods 1.3 Areas of applicability 1.4
Scope of this text References Glossary Ch 2: Current and evolving
applications to Natural Language Processing 2.1 Scope 2.2 Introduction 2.3
Overview of Natural Language Processing and Speech Processing 2.3.1
Feed-forward NN 2.3.2 RNNs 2.3.3 LSTM 2.3.4 Attention 2.3.5 Transformer 2.4
NLP/NLU Basics 2.4.1 Pre-training 2.4.2 NLP/NLG Architectures 2.4.3
Encoder-Decoder Methods 2.4.4 Application of Transformer 2.4.5 Other
Approaches 2.5 NLG Basics 2.6 Chatbots References Glossary Chapter 3:
Current and evolving applications to Speech Processing 3.1 Scope 3.2
Overview 3.2.1 Traditional Approaches 3.2.2 DNN-based feature extraction
3.3 Noise Cancellation 3.3.1 Approaches 3.3.2 Example of a system
supporting noise cancellation 3.4 Training 3.5 Applications to voice
interfaces used to control home devices & Digital Assistant Applications
3.6 Attention-based models 3.7 Sentiment Extraction 3.8 End-to-end learning
3.9 Speech Synthesis 3.10 Zero-shot TTS 3.11 VALL-E: Unseen speaker as an
acoustic prompt References Glossary Ch 4: Current and evolving applications
to Video and Imaging 4.1 Overview 4.2 Convolution process 4.3 CNNs 4.3.1
Nomenclature 4.3.2 Basic Formulation of the CNN layers 4.3.3 Fully
convolutional networks (FCN) 4.3.4 Convolutional Autoencoders 4.3.5 R-CNNs,
Fast R-CNN, Faster R-CNN 4.4 Imaging Applications 4.4.1 Basic Image
Management 4.4.2 Image segmentation and classification 4.4.3 Illustrative
examples of a DNN/CNN 4.4.4 Well-known Image classification networks 4.5
Specific application Examples 4.5.1 Semantic segmentation and semantic edge
detection 4.5.2 CNN Filtering Process For Video Coding 4.5.3 Virtual
clothing 4.5.5 Object Detection Applications 4.5.6 Classifying video data
4.5.7 Example of Training 4.5.8 Example: Image reconstruction is used to
remove artifacts 4.5.9 Example: Video Transcoding/Resolution-enhancement
4.5.10 Facial expression recognition 4.5.11 Transformer Architecture for
image processing 4.5.12 Example: A GAN Approach/Synthetic Photo 4.5.13
Situational Awareness 4.6 Other models: Diffusion and Consistency Models
References Glossary Ch 5: Current and evolving applications to IoT and
applications to Smart buildings and energy management 5.1 Introduction
5.1.1 IoT Applications 5.1.2 Smart Cities 5.2 Smart Building ML
Applications 5.2.1 Basic Building Elements 5.2.2 Particle Swarm
Optimization 5.2.3 Specific ML Example - Qin Model 5.2.3.1 EnergyPlus(TM)
5.3.3.2 Modeling and Simulation 5.2.3.3 Energy Audit Stage 5.2.3.4
Optimization Stage 5.2.3.5 Model Construction 5.2.3.6 EnergyPlus Models
5.2.3.7 Real-Time Control Parameters 5.2.3.8 Neural Networks in the Qin
Model (DNN, RNN, CNN) 5.2.3.9 Finding Inefficiency Measures 5.2.3.10
Particle Swarm Optimizer 5.2.3.11 Integration of Particle Swarm
Optimization with Neural Networks 5.2.3.12 Deep Reinforcement Learning
5.2.3.13 Deployments 5.3 Example of a Commercial Product - BrainBox 5.3.1
Overview 5.3.2 LSTM Application - technical background 5.3.3 BrainBox
Energy Optimization system References Glossary Ch 6: Current and evolving
applications to Network Cybersecurity 6.1 Overview 6.2 General Security
Requirements 6.3 Corporate resources/intranet Security Requirements 6.3.1
Network And Endsystem Security Testing 6.3.2 Application Security Testing
6.3.3 Compliance Testing 6.4 IoT Security (IoTSec) 6.5 Blockchains 6.6 Zero
Trust Environments 6.7 Areas of ML applicability 6.7.1 Example of
cyberintrusion detector 6.7.2 Example of Hidden Markov Model (HMM) for
intrusion detection 6.7.3 Anomaly Detection Example 6.7.4 Phishing
Detection Emails Using Feature Extraction 6.7.5 Example of classifier
engine to identify phishing websites 6.7.6 Example of system for data
protection 6.7.7 Example of an integrated cybersecurity threat management
6.7.8 Example of a Vulnerability Lifecycle Management System References
Glossary Ch 7: Current and evolving applications to Network Management 7.1
Overview 7.2 Examples of Neural Network-Assisted Network Management 7.2.1
Example of NN-based Network Management system (FM) 7.2.2 Example of a model
for predictions related to the operation of a telecommunication network
(FM) 7.2.3 Prioritizing Network Monitoring Alerts (FM, PM) 7.2.4 System for
Recognizing And Addressing Network Alarms (FM) 7.2.5 Load Control Of An
Enterprise Network (PM) 7.2.6 Data Reduction To Accelerate Machine Learning
For Networking (FM, PM) 7.2.7 Compressing Network Data (PM) 7.2.8 ML
Predictor For A Remote Network Management Platform (FM, PM, CM, AM) 7.2.9
Cable Television (CATV) Performance Management system (PM) References
Glossary