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Inventive communication and computational technologies ; Proceedings of ICICCT 2020
This book gathers selected papers presented at the 4th International Conference on Inventive Communication and Computational Technologies (ICICCT 2020), held on 28–29 May 2020 at Gnanamani College of Technology, Tamil Nadu, India. The respective contributions highlight recent research efforts and advances in a new paradigm called ISMAC (IoT in Social, Mobile, Analytics and Cloud contexts). The topics covered include the Internet of Things, Social Networks, Mobile Communications, Big Data Analytics, Bio-inspired Computing and Cloud Computing. Given its scope, the book is chiefly intended for academics and practitioners working to resolve practical issues in this area.
Intrusion Detection Systems
Sٍheds new light on defense alert systems against computer and network intrusions. It also covers integrating intrusion alerts within security policy framework for intrusion response, related case studies and much more. This volume is presented in an easy-to-follow style while including a rigorous treatment of the issues, solutions, and technologies tied to the field.
Intrusion and Malware Detection and Vulnerability Assessment 2nd International Conference, DIMVA 2005, Vienna, Austria, July 7-8, 2005, Proceedings
Represents an increase of approximately 25% compared with the n- ber of submissions last year. All submissions were carefully reviewed by at least three Program Committee members or external experts according to the cri- ria of scienti?c novelty, importance to the ?eld, and technical quality. The ?nal selection took place at a meeting held on March 18, 2005, in Zurich, Switz- land. Fourteen full papers were selected for presentation and publication in the conference proceedings. In addition, three papers were selected for presentation in the industry track of the conference. The program featured both theoretical and practical research results, which were grouped into six sessions. Philip Att?eld from the Northwest Security Institute gave the opening keynote speech. The slides presented by the authors are available on the DIMVA 2005 Web site at http://www.dimva.org/dimva2005 We sincerely thank all those who submitted papers as well as the Program Committee members and the external reviewers for their valuable contributions.
Introductory Statistics with R
R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.
Introduction to Video Search Engines
Their book has a practical emphasis with the goal of bringing readers up to date on the state of the art in multimedia search technologies and systems. It explains the overall process of video content acquisition, indexing and retrieval with browsing, it provides overviews of constituent technologies such as information retrieval, Internet video systems, video and multimedia processing to extract index data, and it gives examples of research prototypes and existing commercial systems and describes their features.
Introduction to Variance Estimation
The book provides instruction on the methods that are vital to data-driven decision making in business, government, and academe. It will appeal to survey statisticians and other scientists engaged in the planning and conduct of survey research, and to those analyzing survey data and charged with extracting compelling information from such data. It will appeal to graduate students and university faculty who are focused on the development of new theory and methods and on the evaluation of alternative methods. Software developers concerned with creating the computer tools necessary to enable sound decision-making will find it essential.
Introduction to Scientific Visualization
Scientific visualization is recognised as important for understanding data, whether measured, sensed remotely or calculated. Introduction to Scientific Visualization is aimed at readers who are new to the subject, either students taking an advanced option at undergraduate level or postgraduates wishing to visualize some specific data.
Introduction to Scientific Programming with Python
This book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming.
Introduction to Programming with Fortran : with coverage of Fortran 90, 95, 2003 and 77
Introduction to Programming with Fortran contains: lots of clear and simple examples highlighting the key language features of the most recent versions of Fortran – Fortran 2003, 95 and 90. practical examples based on ISO TR 15580 and ISO TR 15581 which are widely supported and cover the ISO TR on Enhanced Modules – particularly important to large code suites common problems that occur when programming which are highlighted via clear examples and solutions Introduction to Programming with Fortran is an essential introduction for beginners as well as a concise reference for professionals. Overall the book gives a very effective hands-on coverage of Fortran, valuable to students and practitioners alike.
Introduction to PHP for Scientists and Engineers : Beyond JavaScript
This text presents key information needed to write your own online science and engineering applications, including reading, creating and manipulating data files stored as text on a server, thereby overcoming the limitations of a client-side language.
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
Introduction to Intelligent Construction Technology of Transportation Infrastructure
Expounds on the related technologies of intelligent transportation infrastructure construction. Based on the essential characteristics of intelligent construction, "perception, analysis, decision-making, and execution," the basic structure of intelligent construction technology (ICT) is established. With the integration of engineering construction technologies, the analyses of the essence of intelligent algorithms and the feasibility of Artificial Intelligence (AI) are provided. The book introduces the essential characteristics of Big Data and the Internet of Things and their relationship with engineering construction. On this basis, the feasibility and implementation plan of intelligent technology applications in design, construction, and maintenance are analyzed and demonstrated with engineering examples.
Introduction to Genetic Algorithms
This book is designed to provide an in-depth knowledge on the basic operational features and characteristics of Genetic Algorithms. The various operators and techniques given in the book are pertinent to carry out Genetic Algorithm Research Projects. The book also explores the different types are Genetic Algorithms available with their importance. Implementation of Genetic Algorithm concept has been performed using the universal language C/C++ and the discussion also extends to Genetic Algorithm MATLAB Toolbox. Few Genetic Algorithm problems are programmed using MATLAB and the simulated results are given for the ready reference of the reader. The applications of Genetic Algorithms in Machine learning, Mechanical Engineering, Electrical Engineering, Civil Engineering, Data Mining, Image Processing, and VLSI are dealt to make the readers understand where the concept can be applied.
Introduction to data systems : Building from Python
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.
Introduction to Data Mining and its Applications
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.
Introduction to Data Envelopment Analysis and Its Uses : With DEA-Solver Software and References
Recent years have seen a great variety of applications of DEA (Data Envelopment Analysis) for use in evaluating the performances of many different kinds of entities engaged in many different activities in many different contexts in many different countries. One reason is that DEA has opened up possibilities for use in cases which have been resistant to other approaches because of the complex (often unknown) nature of the relations between multiple inputs and multiple outputs involved in many of these activities (which are often reported in non-commeasurable units). It provide a systematic introduction to DEA and its uses as a multifaceted tool for evaluating problems in a variety of contexts.
Introduction to Computational Biology : An Evolutionary Approach
Molecular biology has changed dramatically over the past two decades. Until the early 1990s genes were studied one at a time by small teams of researchers; today entire genomes are sequenced by internationally collaborating laboratories. In the bygone gene-centered era the accumulation of data was the rate-limiting step in research. Now that step is often data interpretation. This is increasingly dependent on computational methods and as a consequence, computational biology has emerged in the past decade as a new subdiscipline of biology. This introduction to computational biology is centered on the analysis of molecular sequence data. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors.
Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing
Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information.
Introduction to Algorithms
Combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming.
Introduction pratique aux bases de données relationnelles = A practical introduction to relational databases
Cet ouvrage introduit le lecteur dans le domaine des bases de données relationnelles en présentant une vaste sélection de sujets portant sur la modélisation des données, les langages de base de données, l'architecture des systèmes et l'évolution post-relationnelle. - Notions fondamentales: le modèle relationnel, les composants d’un système de gestion de bases de données, l’organisation de la mise en œuvre d’une base de données, les tâches de gestion des données. - De l'analyse à la base de données : le modèle entité association, la généralisation et l’agrégation, les dépendances et les formes normales,les contraintes d’intégrité. - Aperçu des langages de requête et de manipulation des données: l’algèbre relationnelle, le calcul des prédicats, SQL, QUEL, QBE, le traitement des valeurs nulles, la protection des données. - Les composants de l'architecture d'un système de bases de données : la compilation, l’interprétation et l’optimisation des requêtes, l’environnement multiutilisateur, le concept de transaction et la sérialisation, les méthodes optimiste et pessimiste, les structures de stockage et les méthodes d’accès. L’intégration et la migration des bases de données: l’exploitation des bases de données hétérogènes, les bases de données sur le Web, les règles de conversion pour effectuer l’intégration et la migration, les variantes de migration des bases de données hétérogènes, la planification de l’intégration et de la migration.
