Digital Image Processing
The book augurs to be a mix of theoretical and practical perceptions of the related concepts pertaining to image processing. The primary objectives orient to offer an overview to the elementary concepts and practices appropriate to DIP as well as to provide theoretical exposition.
Human Factors in Engineering
This book addresses aspects of human factors in engineering and provides a detailed discussion of novel approaches, systems engineering tools, artificial cognitive systems, and intelligent technologies and automation.
Intelligent Internet of Things for Smart Healthcare Systems
The book focuses on developments in artificial intelligence (AI) and internet of things (IoT) integration for smart healthcare, with an emphasis on current methodologies and frameworks for the design, growth, implementation, and creative use of such convergence technologies to provide insight into smart healthcare service demands. Concepts like signal recognition, computation, internet of health stuff, and so forth and their applications are covered. Development in connectivity and intelligent networks allowing for social adoption of ambient intelligence is also included.Features: -Introduces Intelligent IoT as applicable to the key areas of smart healthcare.-Discusses computational intelligence and IoT-based optimizations of smart healthcare systems-Explores effective management of healthcare systems using dedicated IoT-based infrastructures-Includes dedicated chapters on securing patient's confidential data -Reviews diagnosis of critical diseases from medical imaging using advanced deep learning-based approachesThis book is aimed at researchers, professionals, and graduate students in intelligent systems, big data, cloud computing, information security, and healthcare systems.
Computational Statistical Methodologies and Modeling for Artificial Intelligence
This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems.
Machine Learning
This book provides an introduction to the most popular methods in machine learning. It covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including CNNs, reinforcement learning, and unsupervised learning focused on clustering.
Generative Adversarial Networks and Deep Learning
This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. It concentrates on cutting-edge research in DL and GAN which includes creating new tools and methods for processing text, images, and audio.
Modern Time Series Forecasting with Python - Second Edition
Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architecturesKey Features: - Apply ML and global models to improve forecasting accuracy through practical examples- Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS- Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions- Purchase of the print or Kindle book includes a free eBook in PDF formatBook Description: Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you're working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you'll learn preprocessing, feature engineering, and model evaluation. As you progress, you'll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.What You Will Learn: - Build machine learning models for regression-based time series forecasting- Apply powerful feature engineering techniques to enhance prediction accuracy- Tackle common challenges like non-stationarity and seasonality- Combine multiple forecasts using ensembling and stacking for superior results- Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series- Evaluate and validate your forecasts using best practices and statistical metricsWho this book is for: This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.Table of Contents- Introducing Time Series- Acquiring and Processing Time Series Data- Analyzing and Visualizing Time Series Data- Setting a Strong Baseline Forecast - Time Series Forecasting as Regression - Feature Engineering for Time Series Forecasting- Target Transformations for Time Series Forecasting - Forecasting Time Series with Machine Learning Models - Ensembling and Stacking- Global Forecasting Models - Introduction to Deep Learning- Building Blocks of Deep Learning for Time Series- Common Modeling Patterns for Time Series- Attention and Transformers for Time Series(N.B. Please use the Read Sample option to see further chapters)
Data-Driven Intelligence in Wireless Networks
This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence. This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
Advances in Human-Machine Interaction, Artificial Intelligence, and Robotics
The aim of the following reprint is to explore and showcase the recent advances and interdisciplinary approaches within the field of human-robot interaction (HRI) and artificial intelligence (AI). This reprint features a selection of distinguished studies, ranging from the development of new methodologies in co-manipulation using electromyography to innovative uses of augmented reality in museums. It includes research that enhances human-AI communication, the adoption of Industry 4.0 technologies in education, and cutting-edge developments in defect detection in industrial settings. These contributions from global experts illuminate the multifaceted nature of technological progress in AI and robotics. By integrating diverse fields such as healthcare, manufacturing, and smart environments, this collection not only pushes the boundaries of current technology but also lays the groundwork for future innovations that could revolutionize our interaction with machines. Each study within this reprint provides a forward-looking perspective on how these technologies can be further refined and effectively integrated into society to improve everyday life and operational efficiency. This reprint is a journey into the potential of HRI and AI to transform our world, ensuring that technology serves as a bridge to more intuitive, inclusive, and sustainable human experiences.
Security, Privacy, and Trust in WBANs and E-Healthcare
Wireless Body Area Networks (WBANs) are vulnerable to cyberattacks and security breaches that could unlock the door for cybercriminals to penetrate hospital networks. This book covers the fundamental concepts of security and privacy in WBANs including security requirements, issues, and challenges.
Applications of Blockchain and Artificial Intelligence in Finance and Governance
In this book, the authors delve into the intricacies of this dynamic intersection, offering a comprehensive exploration of the transformative potential of these cutting-edge technologies.
Smart Distributed Embedded Systems for Healthcare Applications
The text covers applications, algorithms, and tools of distributed information processing in the healthcare sector. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.
Deep Learning in Gaming and Animations
The text discusses the core concepts and principles of deep learning in gaming and animation with applications in a single volume. It will be a useful reference text for graduate students, and professionals in diverse areas such as electrical engineering, electronics and communication engineering, computer science, gaming and animation.
Handbook of Iot and Blockchain
Handbook of IoT and Blockchain: Methods, solutions, and Recent Advancements includes contributions from around the globe on recent advances and findings in the domain of Internet of Things (IoT) and Blockchain. Chapters include theoretical analysis, practical implications, and extensive surveys with analysis on methods, algorithms, and processes for new product development.IoT and Blockchain are the emerging topics in the current manufacturing scenario.This handbook includes recent advances; showcases the work of research around the globe; offers theoretical analysis and practical implications; presents extensive surveys with analysis, new contributions, and proposals on methods, algorithms, and processes; and also covers recent advances from quantitative and qualitative articles, case studies, conceptual works, and theoretical backing.This handbook will be of interest to graduate students, researchers, academicians, institutions, and professionals that are interested in exploring the areas of IoT and Blockchain.
Next Generation Communication Networks for Industrial Internet of Things Systems
The text presents concepts and research challenges in communication networking for Industrial internet of things systems. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in diverse engineering domains including electrical, electronics and communications, and computer science.
Introduction to the Cyber Ranges
With the rising cybercrimes, a well-trained cybersecurity workforce in an organization has become a necessity. This book aims to provide substantial theoretical knowhow on cyber ranges, their architectural design, along with a case study of existing cyber ranges in leading urban sectors like military, academic and commercial.
Security Analytics
This book discusses a range of topics from information security to cryptography, mobile application attacks to Digital forensics and from Cybersecurity to Blockchain. The goal of the book is to provide a 360-degree view of cybersecurity to the readers with demonstration of practical applications.
Cognitive IoT
This book contains different research area of Cognitive IoT and explains how Machine learning algorithms can be applied for Cognitive IoT with applications (Covid-19), student performance evaluation, human healthcare for chronic disease prediction, wearable sensors energy optimization, and in farming.
Computer Applications in Engineering and Management
This book fundamental research contributions from the application of design in to civil, electronic, electrical and management to provide sustainable solutions for the future. It is primarily for graduates in computer science, information technology, civil engineering, electrical engineering, management and research scholars.
Deep Learning
Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding - and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively.Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader's understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.
Cyber-Physical Systems
The proposed book aims to present the emergence of Cyber Physical Systems in response to revolutionary advancements in IoT. The primary audience of this book are undergraduates, graduates and researchers of computer science and IT. Engineers and IT professionals would also find it useful.
Disruptive Developments in Biomedical Applications
This book covers advancements and future challenges in biomedical application development using disruptive technologies like AI, IoT and Signal Processing. Book is divided into 4 main sections namely medical image processing using AI, IoT and biomedical devices, signal processing and electronic health records, and advances with case studies.
Cloud Security
This book is written with an intension to provide a thorough and detailed discussion on various fundamental aspects of cloud security. It provides an insight in to cloud computing including definition, history, characteristics, service models, cloud deployment models, key technologies, service platforms and challenges.
Object Detection with Deep Learning Models
The book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision.
Bioinformatics Tools and Big Data Analytics for Patient Care
This book describe the applications of bioinformatics, data management and computational techniques in clinical studies and drug discovery for patient care. It gives details about the recent developments in the fields of artificial intelligence, cloud computing and data analytics for improved patient care.
Big Data Analysis for Green Computing
This book focuses on big data in business intelligence, data management, machine learning, cloud computing, and smart cities. It also provides an interdisciplinary platform to present and discuss recent innovations, trends, and concerns in the fields of big data and analytics.
Integration of Wsns Into Internet of Things
The internet has gone from an internet of people, to an Internet of Things. This has brought forth strong levels of complexity in handling interoperability that involves the integrating of WSNs into IoT.
Internet of Energy for Smart Cities
The focus of this book is on using the machine learning approaches to present various solutions for IoE network in smart cities to solve various research gaps such as demand response management, resource management and effective utilization of the underlying ICT network. It helps build the technical understanding for the reader.
Green Computing and Predictive Analytics for Healthcare
The emergent trends in Green Cloud Computing lead to new developments in various application domains, mainly in healthcare. The aim of this book is to collect innovative and high-quality research contributions related to the advances in the energy-aware cloud-enabled healthcare domain.
Iot in Healthcare Systems
Implementing new information technologies into the healthcare sector can provide alternatives to managing patients' health records, systems, and improving the quality of care received. This book provides an overview of IoT technologies related to the healthcare field and covers the main advantages and disadvantages along with case studies.
Machine Learning in Healthcare
Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields.
Advanced Computational Methods in Mechanical and Materials Engineering
This book provides in-depth knowledge to solve engineering, geometrical, mathematical, and scientific problems with the help of advanced computational methods with focus on mechanical and materials engineering. It covers pertinent topics under sections on design and fluids, thermal engineering and materials engineering.
Hybrid Image Processing Methods for Medical Image Examination
This book focuses on implementation of a possible Hybrid Image Examination technique for medical images describing various image thresholding and segmentation methods along with essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, supported by pertinent case studies.
AI and Deep Learning in Biometric Security
This book focuses on artificial intelligence and deep learning approaches, with case studies to solve problems associated with biometric security such as authentication, spoofing attack detection, and object detection.
Blockchain for IoT
This book provides the basic concepts of blockchain technology and its applications to varied domains catering to socio-technical fields. It also introduces intelligent blockchain platforms by way of infusing elements of computational intelligence into blockchain technology.
Computational Science and its Applications
Computational science seeks to gain understanding of science through the use and analysis of mathematical models on high performance computers. The topics covered are gravitational waves, applications of wavelet and fractals, modeling by partial differential equations on flat structure as, production of natural calamities and diseases, etc
Transformation in Healthcare with Emerging Technologies
This book presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain.
Disruptive Trends in Computer Aided Diagnosis
This book is an attempt to collate novel techniques and methodologies in the domain of content- based image classification and deep learning/machine learning techniques to design efficient computer aided diagnosis architecture. It is aimed to highlight new challenges and probable solutions.
Fundamentals of Internet of Things
The book emphasizes on building fundamentals of IoT networks by leveraging important and relevant concepts from Signal Processing, Communications, Networks and Machine learning. It focuses on IoT protocols, energy harvesting, control optimization, clustering, data fusion, data analytics, localization, fog computing, etc.
Advances in Mathematical and Computational Modeling of Engineering Systems
The text focuses on the recent research developments in the mathematical modeling and scientific computing of biological and engineering systems. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in diverse fields including computer engineering, electrical engineering etc.
Smart and Secure Internet of Healthcare Things
This book cover various research issues of smart and secure IoHT, aimed at providing solutions for remote healthcare monitoring using pertinent techniques. Applications of machine learning techniques and data analytics in IoHT along with latest communication and networking technologies, and cloud computing are also discussed.
Energy Harvesting
Energy Harvesting: Enabling IoT Transformations gives insight into the emergence of energy harvesting technology and its integration with IoT-based applications. The book educates the reader on how energy is harvested from different sources, increasing the effectiveness, efficiency and lifetime of IoT devices.- Discusses the technology and practices involved in energy harvesting for biomedical, agriculture and automobile industries- Compares the performance of IoT-based devices with and without energy harvesting for different applications- Studies the challenges and issues in the implementation of EH-IoT- Includes case studies on energy-harvesting approach for solar, thermal and RF sources- Analyzes the market and business opportunities for entrepreneurs in the field of EH-IoT.This book is primarily aimed at graduates and research scholars in wireless sensor networks. Scientists and R&D workers in industry will also find this book useful.
Pde Toolbox Primer for Engineering Applications with Matlab(r) Basics
This book introduces MATLAB(R) as an essential foundation for Partial differential equations (PDEs), and modeler of the PDE toolbox with appropriate explanatory solutions as applied to engineering problems. The appendix contains collections of commands and functions used to solve actual problems of engineering.
Unleashing the Art of Digital Forensics
This book is intended to make the reader aware of the constraints and considerations that apply during a forensic examination in law enforcement and in the private sector. It is aimed primarily at graduates, postgraduates and researchers in digital forensics and cybersecurity.
Spaceborne Synthetic Aperture Radar Remote Sensing
This book provides basic and advanced concepts of the SAR, PolSAR, InSAR, PolInSAR, and all the necessary information on various applications and analysis of data of multiple sensors. It includes information on SAR remote sensing, data processing, and separate applications of SAR technology, compiled in one place.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
This book presents recent advancements in research, new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models. The book undertakes to stimulate scientific exchange, ideas, and experiences in the field of DSS applications.
Prediction and Analysis for Knowledge Representation and Machine Learning
This book illustrates different techniques and structures that are used in knowledge representation and machine learning. The aim of this book is to draw the attention of graduates, researchers and practitioners working in field of information technology and computer science (in knowledge representation in machine learning).
Swarm Intelligence and Evolutionary Computation
This book aims at providing theoretical knowledge in the application of swarm intelligence and evolutionary computation including several recent meta-heuristic algorithms and also providing practical emerging applications in machine learning and deep learning.
