Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.

This book seeks to generalize techniques and experiences in designing and analyzing cryptographic schemes for blockchain. It devotes three chapters to review the background and basic knowledge, four chapters to discuss specific types of cryptographic primitive design for blockchain, one chapter to discuss optimization tools and another chapter for blockchain regulation and economies. This book covers the systematic survey of research objects, as well as detailed reviews of cryptographic schemes, lectures and methodologies to practice cryptography. The main findings of this book are summarized as following, first, the practical design and analysis of cryptographic schemes for blockchain can address major problems in blockchain at algorithmic level. Then, some intrinsic deficiencies in some traditional cryptographic primitives, like centralized setup, impractical design, etc, prevent the successful application of these primitives in blockchain. However, huge efforts are being made to make these primitives practical and applicable for researchers. Finally, the formal and rigorous design and analysis of public key cryptographic algorithms is vital to blockchain. Design and Analysis of Cryptographic Algorithms in Blockchain is a useful textbook for graduate students and PhD students, or researches who wish to connect cryptography with blockchain for research and developing projects.

This book seeks to generalize techniques and experiences in designing and analyzing cryptographic schemes for blockchain. It devotes three chapters to review the background and basic knowledge, four chapters to discuss specific types of cryptographic primitive design for blockchain, one chapter to discuss optimization tools and another chapter for blockchain regulation and economies. This book covers the systematic survey of research objects, as well as detailed reviews of cryptographic schemes, lectures and methodologies to practice cryptography. The main findings of this book are summarized as following, first, the practical design and analysis of cryptographic schemes for blockchain can address major problems in blockchain at algorithmic level. Then, some intrinsic deficiencies in some traditional cryptographic primitives, like centralized setup, impractical design, etc, prevent the successful application of these primitives in blockchain. However, huge efforts are being made to make these primitives practical and applicable for researchers. Finally, the formal and rigorous design and analysis of public key cryptographic algorithms is vital to blockchain. Design and Analysis of Cryptographic Algorithms in Blockchain is a useful textbook for graduate students and PhD students, or researches who wish to connect cryptography with blockchain for research and developing projects.

Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

This book "Advanced Applications of Computational Mathematics" covers multidisciplinary studies containing advanced research in the field of computational and applied mathematics. The book includes research methodology, techniques, applications, and algorithms. The book will be very useful to advanced students, researchers and practitioners who are involved in the areas of computational and applied mathematics and engineering.

This book is the result of several decades of teaching experience in data structures and algorithms. It is self-contained but does assume some prior knowledge of data structures, and a grasp of basic programming and mathematics tools. Basic Concepts in Algorithms focuses on more advanced paradigms and methods combining basic programming constructs as building blocks and their usefulness in the derivation of algorithms. Its coverage includes the algorithms' design process and an analysis of their performance. It is primarily intended as a textbook for the teaching of Algorithms for second year undergraduate students in study fields related to computers and programming.Klein reproduces his oral teaching style in writing, with one topic leading to another, related one. Most of the classical and some more advanced subjects in the theory of algorithms are covered, though not in a comprehensive manner. The topics include Divide and Conquer, Dynamic Programming, Graph algorithms, probabilistic algorithms, data compression, numerical algorithms and intractability. Each chapter comes with its own set of exercises, and solutions to most of them are appended.Related Link(s)

This book presents watermarking algorithms derived from signal processing methods such as wavelet transform, matrix decomposition and cosine transform to address the limitations of current technologies. For each algorithm, mathematical foundations are explained with analysis conducted to evaluate performances on robotness and efficiency. Combining theories and practice, it is suitable for information security researchers and industrial engineers.

Soft computing techniques are no longer limited to the arena of computer science. The discipline has an exponentially growing demand in other branches of science and engineering and even into health and social science. This book contains theory and applications of soft computing in engineering, health, and social and applied sciences. Different soft computing techniques such as artificial neural networks, fuzzy systems, evolutionary algorithms and hybrid systems are discussed. It also contains important chapters in machine learning and clustering. This book presents a survey of the existing knowledge and also the current state of art development through original new contributions from the researchers. This book may be used as a one-stop reference book for a broad range of readers worldwide interested in soft computing. In each chapter, the preliminaries have been presented first and then the advanced discussion takes place. Learners and researchers from a wide variety of backgrounds will find several useful tools and techniques to develop their soft computing skills. This book is meant for graduate students, faculty and researchers willing to expand their knowledge in any branch of soft computing. The readers of this book will require minimum prerequisites of undergraduate studies in computation and mathematics.

The opportunistic network is an emerging and recent area of research. To make this research area more adaptable for practical and industrial use, there is a need to further investigate several research challenges in all aspects of opportunistic networks. Therefore, Opportunistic Networks: Fundamentals, Applications and Emerging Trends provides theoretical, algorithmic, simulation, and implementation-based research developments related to fundamentals, applications, and emerging research trends in opportunistic networks. The book follows a theoretical approach to describe fundamentals to beginners and incorporates a practical approach depicting the implementation of real-life applications to intermediate and advanced readers. This book is beneficial for academicians, researchers, developers, and engineers who work in or are interested in the fields related to opportunistic networks, delay tolerant networks, and intermittently connected ad hoc networks. This book also serves as a reference book for graduate and postgraduate courses in computer science, computer engineering, and information technology streams.

Based on the practical experiences of its authors, who collectively have spent several decades teaching software skills to scientists. Readers only need a basic understanding of Python includes over a hundred exercises to allow readers to practice their skills

In this multidisciplinary book, experts from around the globe examine how data-driven political campaigning works, what challenges it poses for personal privacy and democracy, and how emerging practices should be regulated. The rise of big data analytics in the political process has triggered official investigations in many countries around the world, and become the subject of broad and intense debate. Political parties increasingly rely on data analytics to profile the electorate and to target specific voter groups with individualised messages based on their demographic attributes. Political micro-targeting has become a major factor in modern campaigning, because of its potential to influence opinions, to mobilise supporters and to get out votes. The book explores the legal, philosophical and political dimensions of big data analytics in the electoral process. It demonstrates that the unregulated use of big personal data for political purposes not only infringes voters' privacy rights, but also has the potential to jeopardise the future of the democratic process, and proposes reforms to address the key regulatory and ethical questions arising from the mining, use and storage of massive amounts of voter data. Providing an interdisciplinary assessment of the use and regulation of big data in the political process, this book will appeal to scholars from law, political science, political philosophy and media studies, policy makers and anyone who cares about democracy in the age of data-driven political campaigning.

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.

This book addresses the issue of improving the accuracy in exon prediction in DNA sequences using various adaptive techniques based on different performance measures that are crucial in disease diagnosis and therapy. First, the authors present an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods, followed by a review of literature starting with the biological background of genomic sequence analysis. Next, they cover various theoretical considerations of adaptive filtering techniques used for DNA analysis, with an introduction to adaptive filtering, properties of adaptive algorithms, and the need for development of adaptive exon predictors (AEPs) and structure of AEP used for DNA analysis. Then, they extend the approach of least mean squares (LMS) algorithm and its sign-based realizations with normalization factor for DNA analysis. They also present the normalized logarithmic-based realizations of least mean logarithmic squares (LMLS) and least logarithmic absolute difference (LLAD) adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants. This book ends with an overview of the goals achieved and highlights the primary achievements using all proposed techniques. This book is intended to provide rigorous use of adaptive signal processing algorithms for genetic engineering, biomedical engineering, and bioinformatics and is useful for undergraduate and postgraduate students. This will also serve as a practical guide for Ph.D. students and researchers and will provide a number of research directions for further work. Features Presents an overview of genomics engineering, structure of DNA sequence and its building blocks, genetic information flow in a cell, gene prediction along with its significance, and various types of gene prediction methods Covers various theoretical considerations of adaptive filtering techniques used for DNA analysis, introduction to adaptive filtering, properties of adaptive algorithms, need for development of adaptive exon predictors (AEPs), and structure of AEP used for DNA analysis Extends the approach of LMS algorithm and its sign-based realizations with normalization factor for DNA analysis Presents the normalized logarithmic-based realizations of LMLS and LLAD adaptive algorithms that include normalized LMLS (NLMLS) algorithm, normalized LLAD (NLLAD) algorithm, and their signed variants Provides an overview of the goals achieved and highlights the primary achievements using all proposed techniques Dr. Md. Zia Ur Rahman is a professor in the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His current research interests include adaptive signal processing, biomedical signal processing, genetic engineering, medical imaging, array signal processing, medical telemetry, and nanophotonics. Dr. Srinivasareddy Putluri is currently a Software Engineer at Tata Consultancy Services Ltd., Hyderabad. He received his Ph.D. degree (Genomic Signal Processing using Adaptive Signal Processing algorithms) from the Department of Electronics and Communication Engineering at Koneru Lakshmaiah Educational Foundation (K. L. University), Guntur, India. His research interests include genomic signal processing and adaptive signal processing. He has published 15 research papers in various journals and proceedings. He is currently a reviewer of publishers like the IEEE Access and IGI.

This book meant for those who seek to apply evolutionary algorithms to problems in engineering and science. To this end, it provides the theoretical background necessary to the understanding of the presented evolutionary algorithms and their shortcomings, while also discussing themes that are pivotal to the successful application of evolutionary algorithms to real-world problems. The theoretical descriptions are illustrated with didactical Python implementations of the algorithms, which not only allow readers to consolidate their understanding, but also provide a sound starting point for those intending to apply evolutionary algorithms to optimization problems in their working fields. Python has been chosen due to its widespread adoption in the Artificial Intelligence community. Those familiar with high level languages such as MATLAB (TM) will not have any difficulty in reading the Python implementations of the evolutionary algorithms provided in the book. Instead of attempting to encompass most of the existing evolutionary algorithms, past and present, the book focuses on those algorithms that researchers have recently applied to difficult optimization problems, such as control problems with continuous action spaces and the training of high-dimensional convolutional neural-networks. The basic characteristics of real-world optimization problems are presented, together with recommendations on its proper application to evolutionary algorithms. The applied nature of the book is reinforced by the presentation of successful cases of the application of evolutionary algorithms to optimization problems. This is complemented by Python source codes, giving users an insight into the idiosyncrasies of the practical application of evolutionary algorithms.

Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.

High quality meshes play a key role in many applications based on digital modeling and simulation. The finite element method is a paragon for such an approach and it is well known that quality meshes can significantly improve computational efficiency and solution accuracy of this method. Therefore, a lot of effort has been put in methods for improving mesh quality. These range from simple geometric approaches, like Laplacian smoothing, with a high computational efficiency but possible low resulting mesh quality, to global optimization-based methods, resulting in an excellent mesh quality at the cost of an increased computational and implementational complexity. The geometric element transformation method (GETMe) aims to fill the gap between these two approaches. It is based on geometric mesh element transformations, which iteratively transform polygonal and polyhedral elements into their regular counterparts or into elements with a prescribed shape. GETMe combines a Laplacian smoothing-like computational efficiency with a global optimization-like effectiveness. The method is straightforward to implement and its variants can also be used to improve tangled and anisotropic meshes. This book describes the mathematical theory of geometric element transformations as foundation for mesh smoothing. It gives a thorough introduction to GETMe-based mesh smoothing and its algorithms providing a framework to focus on effectively improving key mesh quality aspects. It addresses the improvement of planar, surface, volumetric, mixed, isotropic, and anisotropic meshes and addresses aspects of combining mesh smoothing with topological mesh modification. The advantages of GETMe-based mesh smoothing are demonstrated by the example of various numerical tests. These include smoothing of real world meshes from engineering applications as well as smoothing of synthetic meshes for demonstrating key aspects of GETMe-based mesh improvement. Results are compared with those of other smoothing methods in terms of runtime behavior, mesh quality, and resulting finite element solution efficiency and accuracy. Features: * Helps to improve finite element mesh quality by applying geometry-driven mesh smoothing approaches. * Supports the reader in understanding and implementing GETMe-based mesh smoothing. * Discusses aspects and properties of GETMe smoothing variants and thus provides guidance for choosing the appropriate mesh improvement algorithm. * Addresses smoothing of various mesh types: planar, surface, volumetric, isotropic, anisotropic, non-mixed, and mixed. * Provides and analyzes geometric element transformations for polygonal and polyhedral elements with regular and non-regular limits. * Includes a broad range of numerical examples and compares results with those of other smoothing methods.

This book focuses on the fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). It discusses routing protocols from a practicing engineer's perspective, linking theory and fundamental concepts to common practices and everyday examples. The book benefits and reflects the author's more than 22 years of designing and working with IP routing devices and protocols (and Telecoms systems, in general). Every aspect of the book is written to reflect current best practices using real-world examples. This book describes the various methods used by routers to learn routing information. The author includes discussion of the characteristics of the different dynamic routing protocols, and how they differ in design and operation. He explains the processing steps involved in forwarding IP packets through an IP router to their destination and discusses the various mechanisms IP routers use for controlling routing in networks. The discussion is presented in a simple style to make it comprehensible and appealing to undergraduate and graduate level students, research and practicing engineers, scientists, IT personnel, and network engineers. It is geared toward readers who want to understand the concepts and theory of IP routing protocols, through real-world example systems and networks. Focuses on the fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). Describes the various methods used by routers to learn routing information. Includes discussion of the characteristics of the different dynamic routing protocols, and how they differ in design and operation. Provides detailed descriptions of the most common distance-vector routing protocols RIPv2 and EIGRP. Discusses the various mechanisms IP routers use for controlling routing in networks. James Aweya, PhD, is a chief research scientist at the Etisalat British Telecom Innovation Center (EBTIC), Khalifa University, Abu Dhabi, UAE. He has authored four books including this book and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).

This book discusses link-state routing protocols (OSPF and IS-IS), and the path-vector routing protocol (BGP). It covers their most identifying characteristics, operations, and the databases they maintain. Material is presented from a practicing engineer's perspective, linking theory and fundamental concepts to common practices and real-world examples. Every aspect of the book is written to reflect current best practices using real-world examples. The book begins with a detailed description of the OSPF area types and hierarchical routing, and the different types of routers used in an OSPF autonomous system. The author goes on to describe in detail the different OSPF packet types, and inbound and outbound processing of OSPF link-state advertisements (LSAs). Next, the book gives an overview of the main features of IS-IS. The author then discusses the two-level routing hierarchy for controlling the distribution of intra-domain (Level 1) and inter-domain (Level 2) routing information within an IS-IS routing domain. He then describes in detail IS-IS network address formats, IS-IS routing metrics, IS-IS packet types, IS-IS network types and adjacency formation, IS-IS LSDB and synchronization, and IS-IS authentication. The book then reviews the main concepts of path-vector routing protocols, and describes BGP packet types, BGP session states and Finite State Machine, BGP path attributes types, and BGP Autonomous System Numbers (ASNs). Focuses solely on link-state routing protocols (OSPF and IS-IS), and the only path-vector routing protocol in use today (BGP). Reviews the basic concepts underlying the design of IS-IS and provides a detailed description of IS-IS area types and hierarchical routing, and the different types of routers used by IS-IS. Discusses the two-level routing hierarchy for controlling the distribution of intra-domain (Level 1) and inter-domain (Level 2) routing information within an IS-IS routing domain. Describes in detail BGP packet types, BGP session states and Finite State Machine, BGP path attributes types, and BGP ASNs, includes a high-level view of the typical BGP router and its components, and inbound and outbound message processing. James Aweya, PhD, is a chief research scientist at the Etisalat British Telecom Innovation Center (EBTIC), Khalifa University, Abu Dhabi, UAE. He has authored four books including this book and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).

This book discusses link-state routing protocols (OSPF and IS-IS), and the path-vector routing protocol (BGP). It covers their most identifying characteristics, operations, and the databases they maintain. Material is presented from a practicing engineer's perspective, linking theory and fundamental concepts to common practices and real-world examples. Every aspect of the book is written to reflect current best practices using real-world examples. The book begins with a detailed description of the OSPF area types and hierarchical routing, and the different types of routers used in an OSPF autonomous system. The author goes on to describe in detail the different OSPF packet types, and inbound and outbound processing of OSPF link-state advertisements (LSAs). Next, the book gives an overview of the main features of IS-IS. The author then discusses the two-level routing hierarchy for controlling the distribution of intra-domain (Level 1) and inter-domain (Level 2) routing information within an IS-IS routing domain. He then describes in detail IS-IS network address formats, IS-IS routing metrics, IS-IS packet types, IS-IS network types and adjacency formation, IS-IS LSDB and synchronization, and IS-IS authentication. The book then reviews the main concepts of path-vector routing protocols, and describes BGP packet types, BGP session states and Finite State Machine, BGP path attributes types, and BGP Autonomous System Numbers (ASNs). Focuses solely on link-state routing protocols (OSPF and IS-IS), and the only path-vector routing protocol in use today (BGP). Reviews the basic concepts underlying the design of IS-IS and provides a detailed description of IS-IS area types and hierarchical routing, and the different types of routers used by IS-IS. Discusses the two-level routing hierarchy for controlling the distribution of intra-domain (Level 1) and inter-domain (Level 2) routing information within an IS-IS routing domain. Describes in detail BGP packet types, BGP session states and Finite State Machine, BGP path attributes types, and BGP ASNs, includes a high-level view of the typical BGP router and its components, and inbound and outbound message processing. James Aweya, PhD, is a chief research scientist at the Etisalat British Telecom Innovation Center (EBTIC), Khalifa University, Abu Dhabi, UAE. He has authored four books including this book and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).

This book focuses on the fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). It discusses routing protocols from a practicing engineer's perspective, linking theory and fundamental concepts to common practices and everyday examples. The book benefits and reflects the author's more than 22 years of designing and working with IP routing devices and protocols (and Telecoms systems, in general). Every aspect of the book is written to reflect current best practices using real-world examples. This book describes the various methods used by routers to learn routing information. The author includes discussion of the characteristics of the different dynamic routing protocols, and how they differ in design and operation. He explains the processing steps involved in forwarding IP packets through an IP router to their destination and discusses the various mechanisms IP routers use for controlling routing in networks. The discussion is presented in a simple style to make it comprehensible and appealing to undergraduate and graduate level students, research and practicing engineers, scientists, IT personnel, and network engineers. It is geared toward readers who want to understand the concepts and theory of IP routing protocols, through real-world example systems and networks. Focuses on the fundamental concepts of IP routing and distance-vector routing protocols (RIPv2 and EIGRP). Describes the various methods used by routers to learn routing information. Includes discussion of the characteristics of the different dynamic routing protocols, and how they differ in design and operation. Provides detailed descriptions of the most common distance-vector routing protocols RIPv2 and EIGRP. Discusses the various mechanisms IP routers use for controlling routing in networks. James Aweya, PhD, is a chief research scientist at the Etisalat British Telecom Innovation Center (EBTIC), Khalifa University, Abu Dhabi, UAE. He has authored four books including this book and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE).

This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.

Originally published in 1986 and a major contribution towards improving operations within transportation systems, this book provides detailed coverage of the theory of transportation networks as a general traffic and transportation discipline. It examines some of the daily difficulties encountered by traffic and transportation experts, uncovering the intricacies of vehicle routing and scheduling, crew planning and facilities placement. This problem-solving approach uses multiple numerical examples and simple mathematical methods to enable newcomers to apply the book's solutions to the situations they encounter on the job.

Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.

This book discusses the security issues in a wide range of wireless devices and systems, such as RFID, Bluetooth, ZigBee, GSM, LTE, and GPS. It collects the findings of recent research by the UnicornTeam at 360 Technology, and reviews the state-of-the-art literature on wireless security. The book also offers detailed case studies and theoretical treatments - specifically it lists numerous laboratory procedures, results, plots, commands and screenshots from real-world experiments. It is a valuable reference guide for practitioners and researchers who want to learn more about the advanced research findings and use the off-the-shelf tools to explore the wireless world.

"High Performance Deformable Image Registration Algorithms for Manycore Processors" develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans.

This book demonstrates:

How to redesign widely used image registration algorithms so as to best expose the underlying parallelism available in these algorithmsHow to pose and implement the parallel versions of the algorithms within the single instruction, multiple data (SIMD) model supported by GPUsProgramming "tricks" that can help readers develop other image processing algorithms, including registration algorithms for the GPU