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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

BooksJournals

    • A Machine-Learning Approach to Phishing Detection and Defense

      • 1st Edition
      • December 5, 2014
      • O.A. Akanbi + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 2 9 2 7 5
      • eBook
        9 7 8 0 1 2 8 0 2 9 4 6 6
      Phishing is one of the most widely-perpetrated forms of cyber attack, used to gather sensitive information such as credit card numbers, bank account numbers, and user logins and passwords, as well as other information entered via a web site. The authors of A Machine-Learning Approach to Phishing Detetion and Defense have conducted research to demonstrate how a machine learning algorithm can be used as an effective and efficient tool in detecting phishing websites and designating them as information security threats. This methodology can prove useful to a wide variety of businesses and organizations who are seeking solutions to this long-standing threat. A Machine-Learning Approach to Phishing Detetion and Defense also provides information security researchers with a starting point for leveraging the machine algorithm approach as a solution to other information security threats.

    • Introduction to Machine Learning

      • 1st Edition
      • June 28, 2014
      • Yves Kodratoff
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 0 3 7 9
      • eBook
        9 7 8 0 0 8 0 5 0 9 3 0 3
      A textbook suitable for undergraduate courses in machine learningand related topics, this book provides a broad survey of the field.Generous exercises and examples give students a firm grasp of theconcepts and techniques of this rapidly developing, challenging subject.Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginnin... with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog.Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.

    • Systems Approach to Appropriate Technology Transfer

      • 1st Edition
      • May 17, 2014
      • P. Fleissner
      • English
      • Paperback
        9 7 8 1 4 8 3 1 1 4 5 2 1
      • eBook
        9 7 8 1 4 8 3 1 4 6 9 4 2
      Systems Approach to Appropriate Technology Transfer is a collection of selected papers presented at the International Federation of Automatic Control (IFAC) Symposium, held in Vienna, Austria. The objective of the symposium is to analyze the transfer process of technologies by using the systems approach and gather insights that can be used for the enhancement of future transfer programs. The book is a rich presentation of articles and research papers from scientists and engineers from all over the world, and is composed of introductory, technical discussion, and round table discussion papers. The introductory papers give insights to the concepts of technology transfer, systems approach, and use of appropriate technologies. The technical discussions touch on technology transfer in selected fields, energy technologies, flexible manufacturing systems, information and communication, social and educational aspects, and case studies. The four round table discussions focus on the application of technologies to support small-scale enterprises and users’ participation; appropriate technology transfer on microelectronics; policies and strategies for appropriate technology transfer; and the impact of informatics on technology transfer. The text will appeal to computer scientists, engineers, policymakers, and students of information technology.

    • Advances in Software Science and Technology

      • 1st Edition
      • December 1, 2014
      • Teruo Hikita + 2 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 2 2 9 7
      • eBook
        9 7 8 1 4 8 3 2 1 5 7 2 3
      Advances in Software Science and Technology, Volume 4 provides information pertinent to the advancement of the science and technology of computer software. This book discusses the various applications for computer systems. Organized into two parts encompassing 10 chapters, this volume begins with an overview of the historical survey of programming languages for vector/parallel computers in Japan and describes compiling methods for supercomputers in Japan. This text then explains the model of a Japanese software factory, which is presented by the logical configuration that has been satisfied by the semantics of software engineering. Other chapters consider fluent joint as an algorithm that operates on relations organized as multidimensional linear hash files. The final chapter deals with the rules for submission of English papers that will be published, which includes papers that are reports of academic research by members of the Society. This book is a valuable resource for scientists, software engineers, and research workers.

    • Data Architecture: A Primer for the Data Scientist

      • 1st Edition
      • November 26, 2014
      • W.H. Inmon + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 2 0 4 4 9
      • eBook
        9 7 8 0 1 2 8 0 2 0 9 1 3
      Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data

    • Software and System Development using Virtual Platforms

      • 1st Edition
      • September 15, 2014
      • Daniel Aarno + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 0 7 2 5 9
      • eBook
        9 7 8 0 1 2 8 0 0 8 1 3 3
      Virtual platforms are finding widespread use in both pre- and post-silicon computer software and system development. They reduce time to market, improve system quality, make development more efficient, and enable truly concurrent hardware/software design and bring-up. Virtual platforms increase productivity with unparalleled inspection, configuration, and injection capabilities. In combination with other types of simulators, they provide full-system simulations where computer systems can be tested together with the environment in which they operate. This book is not only about what simulation is and why it is important, it will also cover the methods of building and using simulators for computer-based systems. Inside you’ll find a comprehensive book about simulation best practice and design patterns, using Simics as its base along with real-life examples to get the most out of your Simics implementation. You’ll learn about: Simics architecture, model-driven development, virtual platform modelling, networking, contiguous integration, debugging, reverse execution, simulator integration, workflow optimization, tool automation, and much more.

    • The Complete Business Process Handbook

      • 1st Edition
      • December 6, 2014
      • Mark Von Rosing + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 7 9 9 9 5 9 3
      • eBook
        9 7 8 0 1 2 8 0 0 4 7 2 2
      The Complete Business Process Handbook is the most comprehensive body of knowledge on business processes with revealing new research. Written as a practical guide for Executives, Practitioners, Managers and Students by the authorities that have shaped the way we think and work with process today. It stands out as a masterpiece, being part of the BPM bachelor and master degree curriculum at universities around the world, with revealing academic research and insight from the leaders in the market. This book provides everything you need to know about the processes and frameworks, methods, and approaches to implement BPM. Through real-world examples, best practices, LEADing practices and advice from experts, readers will understand how BPM works and how to best use it to their advantage. Cases from industry leaders and innovators show how early adopters of LEADing Practices improved their businesses by using BPM technology and methodology. As the first of three volumes, this book represents the most comprehensive body of knowledge published on business process. Following closely behind, the second volume uniquely bridges theory with how BPM is applied today with the most extensive information on extended BPM. The third volume will explore award winning real-life examples of leading business process practices and how it can be replaced to your advantage.

    • Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

      • 1st Edition
      • December 9, 2014
      • Leonidas Deligiannidis + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 2 0 4 5 6
      • eBook
        9 7 8 0 1 2 8 0 2 0 9 2 0
      Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors’ Leonidas Deligiannidis and Hamid Arabnia cover; Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. How to use image processing and visualization to analyze big data.

    • Detecting and Combating Malicious Email

      • 1st Edition
      • October 7, 2014
      • Julie JCH Ryan + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 0 1 1 0 3
      • eBook
        9 7 8 0 1 2 8 0 0 5 4 6 0
      Malicious email is, simply put, email with a malicious purpose. The malicious purpose could be fraud, theft, espionage, or malware injection. The processes by which email execute the malicious activity vary widely, from fully manual (e.g. human-directed) to fully automated. One example of a malicious email is one that contains an attachment which the recipient is directed to open. When the attachment is opened, malicious software is installed on the recipient’s computer. Because malicious email can vary so broadly in form and function, automated detection is only marginally helpful. The education of all users to detect potential malicious email is important to containing the threat and limiting the damage. It is increasingly necessary for all email users to understand how to recognize and combat malicious email. Detecting and Combating Malicious Email describes the different types of malicious email, shows how to differentiate malicious email from benign email, and suggest protective strategies for both personal and enterprise email environments.

    • Machine Learning Proceedings 1989

      • 1st Edition
      • June 28, 2014
      • Alberto Maria Segre
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 0 3 6 2
      • eBook
        9 7 8 1 4 8 3 2 9 7 4 0 8
      Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

Information systems

Internet and web technology

Software

Theory and mathematics