Computing and Networks

Generative AI for Multimedia Content Processing, Security and Privacy

Fri, 19 Dec 2025 09:00:35 GMT

Exploring the field of generative artificial intelligence (GenAI) and its use in the processing and security of multimedia content, this co-authored book addresses the critical needs and emerging challenges in the rapidly evolving intersection of artificial intelligence, multimedia content, and cybersecurity. The capabilities of sophisticated GenAI models in generating, improving, and manipulating multimedia information, including images, videos, and audio are thoroughly explored. Coverage also extends to technical innovations including advanced neural network topologies, novel training methods, and methods for boosting GenAI-generated content quality and authenticity.

The book's focus on security and privacy concerns and the weaknesses brought on by GenAI includes data privacy challenges, the development of unlicensed material, and the exploitation of AI for nefarious intentions. Furthermore, the ethical implications and legal obstacles involved in guaranteeing the secure and accountable implementation of generative AI in multimedia applications are examined and potential future directions identified.

Generative AI for Multimedia Content Processing, Security and Privacy: Fundamentals, advances and applications will be a useful reference for multimedia researchers and engineers working with GenAI, particularly those interested in security and privacy challenges. Cybersecurity researchers and engineers working on digital content security and synthetic media prevention, AI researchers and engineering and technology practitioners developing generative AI technologies, will all find relevant information in this volume. The book will also be of interest to regulatory and compliance experts and policy makers working in the field.

The Power of Large Language Models and AI in the Digital Age

Mon, 13 Oct 2025 08:00:49 GMT

Large language models (LLMs) represent a profound breakthrough in artificial intelligence. More than just statistical tools, these vast neural networks undergo an intensive training process that unlocks unexpected, emergent abilities. Models like ChatGPT are now demonstrating a surprising grasp of reasoning, semantics, and real-world concepts, leading many researchers to ask: are we witnessing the first sparks of Artificial General Intelligence (AGI)?

These models are doing more than just revolutionizing natural language processing; they are forcing us to reconsider the boundaries of machine intelligence. While their applications in chatbots, complex problem-solving, and content creation are already reshaping our digital world, their deeper implications point toward an entirely new era of AI.

To understand these emergent capabilities and confront the AGI question, a deep dive into the core technology is essential. This co-authored book provides that crucial foundation. The authors explore the significance and capabilities of LLMs, alongside the immense ethical, social, and security challenges that arise as these models grow more powerful.

For anyone seeking a comprehensive guide to navigate this new territory, The Power of Large Language Models and AI in the Digital Age: Technologies, applications, security and ethics is a valuable resource. It is essential reading for data scientists, researchers from academia and industry, lecturers, and advanced students in AI, computer science, and data science who will shape the future of intelligent systems.

Generative AI for Sign Language Recognition and Translation

Fri, 12 Sep 2025 14:39:53 GMT

Sign languages differ fundamentally from spoken and written languages, with their own grammar, syntax, and three-dimensional expression involving hand gestures, facial expressions, body movements, and spatial relationships. These non-manual elements are crucial in conveying grammatical structures, nuances, and emotional tones, making sign languages uniquely complex communication systems.

This book provides a comprehensive foundation for understanding the linguistic structures of sign languages and explores the application of artificial intelligence (AI) techniques - ranging from classical machine learning to deep learning and generative AI - for developing effective sign language translation systems. It offers an end-to-end overview, covering linguistic fundamentals, available datasets, text-to-sign and speech-to-sign translation, vision-based sign recognition, pose estimation, and video-based sign language generation.

Dedicated chapters focus on model architectures, dataset curation strategies, evaluation metrics, benchmarking tools, and human-centered design approaches for accessible communication systems. Ethical considerations and responsible AI practices are also discussed to promote the development of inclusive and equitable sign language technologies.

Complemented by Python code examples, downloadable resources, and implementation insights, this book serves as a practical guide for researchers, engineers, students, and technology professionals aiming to develop AI-powered sign language systems. The multidisciplinary content also supports linguists, accessibility advocates, and application developers working on inclusive language technologies.

With its broad coverage and practical orientation, this book is suited to academic and industry professionals in artificial intelligence, computer vision, natural language processing, human-computer interaction, speech technology, and accessibility research, as well as students and early-career researchers seeking a well-rounded introduction to AI-driven sign language translation.

By bridging AI methodologies with real-world sign language applications, this book promotes the development of inclusive AI systems supporting communication accessibility for diverse populations.

Matrix Factorization for Multimedia Clustering

Thu, 21 Aug 2025 08:10:12 GMT

Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data may exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.

Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets.

The Digital Twin Handbook

Thu, 21 Aug 2025 08:09:16 GMT

A digital twin (DT) is a digital representation of a real-world physical product, system, or process. It is indistinguishable from its physical counterpart and used for simulation, integration, testing, monitoring and maintenance, often in real-time and in synchronization with the physical system. Digital twins offer the ability to gain a deep insight into the operating principles of any services, the interactions between different parts of the services and the behavioural aspects in a way that can be interactive and actionable for users and decision-makers.

DTs are now being implemented in many industries including the engineering design for smart cities, the built environment, manufacturing, robotics and healthcare. This enabling technology supports the development of connected environments in which sensing, monitoring, actuation and interventions are seamlessly choreographed to allow stakeholders to make data-driven, context-aware, and strategic decisions to better serve end users from the public and private and third sectors.

This edited book presents challenges, opportunities and solutions for the implementation of DT services along with their implementation experience. The book's main objective is to present a holistic view of the technological challenges, limitations and trends for enabling DT services. The authors will present challenging aspects of DT services that will identify the current and prospective potentials of DT application areas. This book will not only cover the technical aspects of DT services but also its benefits and future research directions and perspectives.

This comprehensive guide to the topic is intended to provide a holistic view for researchers, engineers and scientists in academia and industry working on digital twins and their application in smart cities, industry 4.0/5.0, and real-time monitoring. It will also be of interest to experts and developers who want to understand and realise the opportunities and challenges of using emerging techniques and algorithms for designing and developing digital twins.

Generative AI Unleashed

Fri, 14 Mar 2025 09:25:36 GMT

Today's generative AI has been marked by the advent of neural networks, inspired by the human brain, which are trained to recognize patterns in a dataset. Once the network is trained, it can make decisions or predictions without being programmed to perform tasks. Generative AI learns from a set of data without explicit instructions and can create and generate new digital content such as text, audio and art. Recent models are beginning to overcome challenges such as computational power, data quality and training stability.

This edited book on generative AI presents novel perspectives, approaches and methodologies, as well as security, ethical and legal considerations, and future trends. Topics and technologies covered include generative transformers and text generation models, generative models for human-like speech synthesis, generative AI for image synthesis, data synthesis for privacy protection, and exploration of the impact of generative AI in fields including industry 4.0, astronomy, and brain tumour detection. Two chapters offer perspectives on ethics and legality.

Generative AI Unleashed: Advancements, transformative applications and future frontiers will serve as a valuable resource for researchers, engineers, advanced students and lecturers operating in the domains that are significantly impacted by generative AI.

Advanced Networking Technologies

Thu, 13 Feb 2025 11:14:30 GMT

Advanced networking methods and technologies are emerging as critical components in enabling novel applications. They are supported by recent technological advances in wireless communications along with the integration of various functionalities such as sensing, communication and artificial intelligence. The main goal of advanced networking is to achieve more efficient, stable and secure networks in internet and mobile communication systems. To fulfil the increasing network demand from applications, advanced networking technologies such as load balancing, fault tolerance technology, encryption technology, virtualization technology and cloud computing are used to improve network speed, optimize network performance, enhance network security, and provide a better user experience.

This book covers innovative advances in networking technologies. The book is written in a tutorial style to benefit a broad advanced research audience in networking, communication, computer science, and security. The authors put a particular focus on cyber security for all advanced network concepts and technologies and have included useful case studies.

Energy Optimization and Security in Federated Learning for IoT Environments

Mon, 11 Nov 2024 08:46:54 GMT

Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable.

Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems.

In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure.

Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, R&D professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.

Engineering the Metaverse

Mon, 16 Sep 2024 09:14:13 GMT

The metaverse offers a new way of interacting with the world via immersive and interactive technologies. It has the potential to change the way we learn, work and play. 3D modelling, artificial intelligence, virtual, augmented, extended and mixed reality (VR/AR/XR/MR), the Internet of Things (IoT), Web 3.0, 5G and 6G communication, digital twins and simulation technologies, edge and cloud computing, blockchain and cybersecurity are seen as the key enabling and supporting technologies and tools for establishing the next generation metaverse environments. Industrial verticals are keen to embrace the flexible paradigm of the metaverse to be more resilient and relevant to their customers and consumers.

This book focuses on the contributing and enabling technologies and use cases for the metaverse including engineering techniques, deployment environments and integrated platforms for designing and developing metaverse applications.

Engineering the Metaverse: Enabling technologies, platforms and use cases is aimed at industry and academic researchers, scientists, engineers, architects and programmers working in the fields of information and communication technologies, 5G and 6G communication, AI, data science, IoT, edge and cloud computing, cybersecurity and automation with a focus on immersive technologies. It will also be a useful reference for lecturers and advanced students, and product and project managers as well as developers in the field of the metaverse.

Federated Learning for Multimedia Data Processing and Security in Industry 5.0

Mon, 16 Sep 2024 09:12:18 GMT

Industry 5.0 is the upcoming industrial revolution where people will be working together with smart machines and robots, thereby bringing human touch and intelligence back to the decision-making process. Challenges include the security and privacy of sensitive multimedia data and near zero latency for mission critical applications.

Federated learning is a machine learning technique that trains algorithms across multiple decentralized edge devices or servers by holding local data samples without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all local datasets are uploaded to one server. This method enables multiple actors to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights and access to heterogeneous data.

The objective of this book is to show how federated learning can solve multimedia data processing and security challenges in Industry 5.0. The book introduces new research paradigms for the security and privacy preservation of multimedia data. It provides a detailed discussion on how federated learning can be used to handle big data, preserve privacy, reduce computational and communication costs; and shows how to integrate federated learning with other disruptive technologies including blockchain, digital twins and 5G and beyond.

Federated Learning for Multimedia Data Processing and Security in Industry 5.0 is an essential reference for advanced students, lecturers, and academic and industry researchers working in the fields of machine learning federated learning, computer and network security, data science, multimedia, computer vision and Industry 5.0 applications.

Explainable Artificial Intelligence for Trustworthy Internet of Things

Thu, 08 Feb 2024 09:44:02 GMT

A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data.

Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks.

Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation.

This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence.

Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.

Managing Internet of Things Applications across Edge and Cloud Data Centres

Tue, 03 Oct 2023 09:39:01 GMT

Cloud computing has been a game changer for internet-based applications such as content delivery networks, social networking and multi-tier enterprise applications. However, the requirements for low-latency data access, security, bandwidth, mobility, and cost have challenged centralized data center-based cloud computing models, which is driving the need for the novel computing paradigms of edge and fog computing. The internet of things (IoT) focuses on discovery, aggregation, management, and acting on data originating from internet-connected devices via programmable sensors, actuators, mobile phones, surveillance cameras, routers, gateways and switches. But the aggregation of this data is expensive and can be time consuming.

Traditional cloud-centric resource management models need to move towards more distributed and decentralized models so that they can cope with the challenges posed by the evolution of IoT smart devices and network solutions. However, supporting IoT data processing across cloud and edge data centers is not a trivial challenge. IoT sensing devices must be configured as a collection of data-analytics driven workflows where each node in the process can essentially run on multiple heterogeneous cloud and edge data centers.

This book presents state-of-the-art interdisciplinary computing research in the application lifecycle management for internet of things in edge and cloud computing. The book addresses challenges from a distributed system perspective that includes both cyber and physical aspects. The authors aim to bring together the four paradigms of cloud and edge computing, cyber physical systems, internet of things and big data for future ICT systems.

Written and edited by an international team of experts in the field, this book offers key insights to researchers, engineers, IT professionals, advanced students, postgraduate students and lecturers working in the fields of parallel and distributed computing, data mining, information retrieval, cloud, edge and fog computing, and the IoT.

Enabling Technologies for Smart Fog Computing

Thu, 07 Sep 2023 09:23:35 GMT

Fog computing is a decentralized computing infrastructure in which computing resources are located between the data source and the cloud or any other data centers. The word "fog" refers to its cloud-like properties, which are closer to the "ground", using edge devices that carry out locally computation, storage and communication tasks. An additional benefit is that the processed data is likely to be needed by the same devices that generated the data. By processing locally rather than remotely, the latency between input and response are minimized. This technology has countless application domains such as industrial process control, smart cities, transportation, healthcare and agriculture.

In this book, all the important topics in fog computing systems are covered, including energy efficiency, quality of service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling. Special attention is devoted to emerging trends and industry needs associated with utilizing mobile edge computing, internet of things (IoT), resource estimation as well as virtualization in the fog computing environment. Current research on automation, robotics, data privacy, security and trust in fog computing is explored in depth. The book also discusses emerging techniques including deep learning, mobile edge computing, smart grid and intelligent transportation systems beyond theoretical and foundational concepts for smart applications including real time traffic surveillance, interoperability of fog computing architecture and smart homes and smart cities.

Intended for an audience of researchers from academia and industry, as well as lecturers, engineers and advanced students, Enabling Technologies for Smart Fog Computing offers valuable insights for those with an interest in the field.

Access Control and Security Monitoring of Multimedia Information Processing and Transmission

Thu, 10 Aug 2023 13:50:04 GMT

In the era of big data and multi-connectivity via IoTs, protecting and securing multimedia data has become a real necessity and priority for organizations and businesses, but this can be a rather difficult task due to the heterogeneous nature of platforms and data sets. It is therefore essential to improve the security level of multimedia information by developing core technologies to prevent the loss and damage of information during processing and transmission.

This book covers innovations and future perspectives in access control and security monitoring of multimedia Information processing and transmission. The authors present cybersecurity, privacy and control methods and technologies integrated with blockchain and multimedia AI, including encryption and watermarking techniques, wearable-based IoT security methods, multimedia data forensics and deepfake video security monitoring.

This will be a useful reference for researchers, engineers and scientists in both academia and industry as well as lecturers and advanced students for developing efficient methods, frameworks and techniques for multimedia information processing security and privacy. It will also be of interest to multimedia platform and system developers and designers.

Intelligent Multimedia Processing and Computer Vision

Fri, 07 Jul 2023 12:51:55 GMT

Intelligent multimedia involves the computer processing and understanding of perceptual input from speech, text, videos and images. Reacting to these inputs is complex and involves research from engineering, computer science and cognitive science. Intelligent multimedia processing deals with the analysis of images and videos to extract useful information for numerous applications including medical imaging, robotics, remote sensing, autonomous driving, AR/VR, law enforcement, biometrics, multimedia enhancement and reconstruction, agriculture, and security. Intelligent multimedia processing and computer vision have seen an upsurge over the last few years. With the increasing use of intelligent multimedia processing techniques in various sectors, the requirement for fast and reliable techniques to analyse and process multimedia content is increasing day by day.

Intelligent Multimedia Processing and Computer Vision: Techniques and applications reviews cutting edge research in the areas of intelligent multimedia processing and computer vision techniques and applications with a particular emphasis on interdisciplinary approaches and novel solutions. The book is aimed at practicing engineers, scientists, technology professionals, researchers and advanced students in the fields of multimedia processing and security, image processing, computer vision, biometrics, intelligent and smart technologies, machine learning and deep learning, and autonomous systems.

Explainable Artificial Intelligence (XAI)

Fri, 07 Jul 2023 12:51:05 GMT

The world is keen to leverage multi-faceted AI techniques and tools to deploy and deliver the next generation of business and IT applications. Resource-intensive gadgets, machines, instruments, appliances, and equipment spread across a variety of environments are empowered with AI competencies. Connected products are collectively or individually enabled to be intelligent in their operations, offering and output.

AI is being touted as the next-generation technology to visualize and realize a bevy of intelligent systems, networks and environments. However, there are challenges associated with the huge adoption of AI methods. As we give full control to AI systems, we need to know how these AI models reach their decisions. Trust and transparency of AI systems are being seen as a critical challenge. Building knowledge graphs and linking them with AI systems are being recommended as a viable solution for overcoming this trust issue and the way forward to fulfil the ideals of explainable AI.

The authors focus on explainable AI concepts, tools, frameworks and techniques. To make the working of AI more transparent, they introduce knowledge graphs (KG) to support the need for trust and transparency into the functioning of AI systems. They show how these technologies can be used towards explaining data fabric solutions and how intelligent applications can be used to greater effect in finance and healthcare.

Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications is aimed primarily at industry and academic researchers, scientists, engineers, lecturers and advanced students in the fields of IT and computer science, soft computing, AI/ML/DL, data science, semantic web, knowledge engineering and IoT. It will also prove a useful resource for software, product and project managers and developers in these fields.

Personal Knowledge Graphs (PKGs)

Mon, 12 Jun 2023 07:44:34 GMT

Since the development of the semantic web, knowledge graphs (KGs) have been used by search engines, knowledge-engines and question-answering services as well as social networks. A knowledge graph, also known as a semantic network, represents and illustrates a network of real-world entities such as objects, events, situations, or concepts and the relationships between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term "knowledge graph". Knowledge graphs structure the information of entities, their properties and the relation between them.

Personal knowledge graphs (PKG) encode the same information at an individual level and therefore vary widely. PKGs require the processing of each person's individual information and is constructed in an automated fashion. Once a PKG is constructed, it will be integrated in broader purpose KGs. A PKG is a representation of all relevant common-sense knowledge and personal data for a user and can support the development of innovative applications such as a digitalized personalized coach. It empowers stakeholders to make more effective decisions.

This book explores in a structured manner the global advanced research around PKGs to support the development of innovative digitalized personalized applications such as personal banking, personalized book-keeping, daily health-related activities monitoring and goal management tracking. The authors present methodologies, tools and applications including innovative topics tailored for PKGs such as named entity recognition and linking, construction approaches, modelling of personalization and context-awareness, evaluation approaches, relation extraction techniques, query answering in user specific knowledge graphs, knowledge representation and reasoning (KRR), visualization tools, integration tools and techniques, and fact summarization.

The book provides systematic coverage of this complex topic for researchers, scientists and engineers in both industry and academia working in data science, ICTs, knowledge engineering, semantic web, reasoning, information retrieval, and machine and deep learning with a focus on knowledge graphs. Advanced students with an interest in the field will also find this to be a useful resource.

Nature-inspired Optimization Algorithms and Soft Computing

Mon, 12 Jun 2023 07:43:33 GMT

We have witnessed an explosion of research activity around nature-inspired computing and bio-inspired optimization techniques, which can provide powerful tools for solving learning problems and data analysis in very large data sets. To design and implement optimization algorithms, several methods are used that bring superior performance. However, in some applications, the search space increases exponentially with the problem size. To overcome these limitations and to solve efficiently large scale combinatorial and highly nonlinear optimization problems, more flexible and adaptable algorithms are necessary.

Nature-inspired computing is oriented towards the application of outstanding information-processing aptitudes of the natural realm to the computational domain. The discipline of nature-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization. Metaheuristic search algorithms with population-based frameworks are capable of handling optimization in high-dimensional real-world problems for several domains including imaging, IoT, smart manufacturing, and healthcare. The integration of intelligence with smart technology enhances accuracy and efficiency. Smart devices and systems are revolutionizing the world by linking innovative thinking with innovative action and innovative implementation.

The aim of this edited book is to review the intertwining disciplines of nature-inspired computing and bio-inspired soft-computing (BISC) and their applications to real world challenges. The contributors cover the interaction between metaheuristics, such as evolutionary algorithms and swarm intelligence, with complex systems. They explain how to better handle different kinds of uncertainties in real-life problems using state-of-art of machine learning algorithms. They also explore future research perspectives to bridge the gap between theory and real-life day-to-day challenges for diverse domains of engineering.

The book will offer valuable insights to researchers and scientists from academia and industry in ICTs, IT and computer science, data science, AI and machine learning, swarm intelligence and complex systems. It is also a useful resource for professionals in related fields, and for advanced students with an interest in optimization and IoT applications.

Intelligent Multimedia Technologies for Financial Risk Management

Fri, 03 Mar 2023 14:10:15 GMT

Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support their activities and strategic goals.

This volume provides an overview of multimedia technologies in finance and banking, introduces suitable machine learning and deep learning techniques for financial data analysis, discusses fraud and cyber operation countermeasures for multimedia in financial services, presents concrete applications of natural language processing (NPR) for financial data, introduces robotic process automation technology from the financial market to technology implementation, explains how self-supervised, unsupervised and semi-supervised learning are driving the financial market revolution, and unlocks real-world case studies in multimedia banking across the globe.

The book is intended for professionals involved in multimedia systems and technology design and applications. It can also be used as an advanced text for courses on multimedia.

Earth Observation Data Analytics Using Machine and Deep Learning

Fri, 03 Mar 2023 14:09:07 GMT

Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and multi-temporal observations.

Approaches for applying pre-processing methods and deep learning techniques to satellite images for various applications - such as identifying land cover features, object detection, crop classification, target recognition, and the monitoring of earth resources - are described. Cost-efficient resource allocation solutions are provided, which are robust against common uncertainties that occur in annotating and extracting features on satellite images.

This book is a valuable resource for engineers and researchers in academia and industry working on AI, machine and deep learning, data science, remote sensing, GIS, SAR, satellite communications, space science, image processing and computer vision. It will also be of interest to staff at research agencies, lecturers and advanced students in related fields. Readers will need a basic understanding of computing, remote sensing, GIS and image interpretation.