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[null,"1.","Record Nr.",null,"UNINA9911019341803321"]
[null,null,"Autore",null,"Minoli Daniel"]
[null,null,"Titolo",null,"AI Applications to Communications and Information Technologies : The Role of Ultra Deep Neural Networks"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2023"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9781394190010"]
[{"$nulls":4},"1394190018"]
[{"$nulls":4},"9781394190027"]
[{"$nulls":4},"1394190026"]
[{"$nulls":4},"9781394190034"]
[{"$nulls":4},"1394190034"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (493 pages)"]
[null,null,"Disciplina",null,"006.32"]
[null,null,"Soggetti",null,"Deep learning (Machine learning)"]
[{"$nulls":4},"Neural networks (Computer science)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright Page -- Contents -- About the Authors -- Preface -- Chapter 1 Overview -- 1.1 Introduction and Basic Concepts -- 1.1.1 Machine Learning -- 1.1.2 Deep Learning -- 1.1.3 Activation Functions -- 1.1.4 Multi-layer Perceptrons -- 1.1.5 Recurrent Neural Networks -- 1.1.6 Convolutional Neural Networks -- 1.1.7 Comparison -- 1.2 Learning Methods -- 1.3 Areas of Applicability -- 1.4 Scope of this Text -- A. Basic Glossary of Key AI Terms and Concepts -- References -- Chapter 2 Current and Evolving Applications to Natural Language Processing -- 2.1 Scope -- 2.2 Introduction -- 2.3 Overview of Natural Language Processing and Speech Processing -- 2.3.1 Feed-forward NNs -- 2.3.2 Recurrent Neural Networks -- 2.3.3 Long Short-Term Memory -- 2.3.4 Attention -- 2.3.5 Transformer -- 2.4 Natural Language Processing/Natural Language Understanding Basics -- 2.4.1 Pre-training -- 2.4.2 Natural Language Processing/Natural Language Generation Architectures -- 2.4.3 Encoder-Decoder Methods -- 2.4.4 Application of Transformer -- 2.4.5 Other Approaches -- 2.5 Natural Language Generation Basics -- 2.6 Chatbots -- 2.7 Generative AI -- A. Basic Glossary of Key AI Terms and Concepts Related to Natural Language Processing -- "]
[{"$nulls":4},"References -- 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 Specific Example of a System Supporting Noise Cancellation -- 3.4 Training -- 3.5 Applications to Voice Interfaces Used to Control Home Devices and 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."]
[{"$nulls":4},"A. Basic Glossary of Key AI Terms and Concepts -- References -- Chapter 4 Current and Evolving Applications to Video and Imaging -- 4.1 Overview and Background -- 4.2 Convolution Process -- 4.3 CNNs -- 4.3.1 Nomenclature -- 4.3.2 Basic Formulation of the CNN Layers and Operation -- 4.3.3 Fully Convolutional Networks (FCN) -- 4.3.4 Convolutional Autoencoders -- 4.3.5 R-CNNs, Fast R-CNN, Faster R-CNN -- 4.4 Imaging Applications -- 4.4.1 Basic Image Management -- 4.4.2 Image Segmentation and Image Classification -- 4.4.3 Illustrative Examples of a Classification DNN/CNN -- 4.4.4 Well-Known Image Classification Networks -- 4.5 Specific Application Examples -- 4.5.1 Semantic Segmentation and Semantic Edge Detection -- 4.5.2 CNN Filtering Process for Video Coding -- 4.5.3 Virtual Clothing -- 4.5.4 Example of Unmanned Underwater Vehicles/Unmanned Aerial Vehicles -- 4.5.5 Object Detection Applications -- 4.5.6 Classifying Video Data -- 4.5.7 Example of Training -- 4.5.8 Example: Image Reconstruction is Used to Remove Artifacts -- 4.5.9 Example: Video Transcoding/Resolution-enhancement -- 4.5.10 Facial Expression Recognition -- 4.5.11 Transformer Architecture for Image Processing -- 4.5.12 Example: A GAN Approach/Synthetic Photo -- 4.5.13 Situational Awareness -- 4.6 Other Models: Diffusion and Consistency Models -- A. Basic Glossary of Key AI Terms and Concepts -- B. Examples of Convolutions -- References -- Chapter 5 Current and Evolving Applications to IoT and Applications to Smart Buildings and Energy Management -- 5.1 Introduction -- 5.1.1 IoT Applications -- 5.1.2 Smart Cities -- 5.2 Smart Building ML Applications -- 5.2.1 Basic Building Elements -- 5.2.2 Particle Swarm Optimization -- 5.2.3 Specific ML Example - Qin Model -- 5.3 Example of a Commercial Product - BrainBox AI -- 5.3.1 Overview -- 5.3.2 LSTM Application - Technical Background."]
[{"$nulls":4},"5.3.3 BrainBox AI Commercial Energy Optimization System -- A. Basic Glossary of Key IoT (Smart Building) Terms and Concepts -- References -- Chapter 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 End System 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 -- A. Basic Glossary of Key Security Terms and Concepts -- References -- Chapter 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 "]
[{"$nulls":4},"Management System (Case of FM) -- 7.2.2 Example of a Model for Predictions Related to the Operation of a Telecommunication Network (Case of FM) -- 7.2.3 Prioritizing Network Monitoring Alerts (Case of FM and PM) -- 7.2.4 System for Recognizing and Addressing Network Alarms (Case of FM) -- 7.2.5 Load Control of an Enterprise Network (Case of PM) -- 7.2.6 Data Reduction to Accelerate Machine Learning for Networking (Case of FM and PM) -- 7.2.7 Compressing Network Data (Case of PM) -- 7.2.8 ML Predictor for a Remote Network Management Platform (Case of FM, PM, CM, AM)."]
[{"$nulls":4},"7.2.9 Cable Television (CATV) Performance Management System (Case of PM) -- A. Short Glossary of Network Management Concepts -- References -- Super Glossary -- Index -- EULA."]
[null,null,null,"Sommario/riassunto","This book, authored by Daniel Minoli and Benedict Occhiogrosso, explores the applications of artificial intelligence in the realm of information technologies with a specific focus on ultra-deep neural networks. It covers fundamental concepts of machine learning and deep learning, including activation functions, multi-layer perceptrons, and various types of neural networks such as recurrent and convolutional networks. The book delves into the application of these technologies in natural language processing, speech processing, video and imaging, IoT, smart buildings, and cybersecurity. It is intended for professionals and researchers in the field of AI and technology, providing insights into evolving methods and their practical applications."]