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The Internet of Educational Things - Enhancing Students’ Engagement and Learning Performance delves into the transformative potential of the Internet of Things (IoT) within education. This comprehensive guide explores how IoT technology can revolutionize traditional teaching methods and learning environments, fostering more interactive, adaptive, and data-driven experiences. The book covers a wide range of topics, including the development of IoT-enabled classrooms, intelligent tutoring systems, and online labs. By leveraging real-time data and advanced analytics, educators can personalize learning paths, enhance student engagement, and optimize resource allocation. Practical applications, real-world examples, and case studies illustrate the benefits and challenges of incorporating IoT in educational settings, making it a valuable resource for students, teachers, researchers, and policymakers. The book provides practical implementation strategies and addresses critical issues such as data privacy, cybersecurity, and ethical considerations. It thoroughly examines the latest technologies, including AI, AR, VR, and digital twins, and their integration with IoT to create futuristic learning environments. The book’s unique contribution lies in its emphasis on securing IoT systems and its recommendations for overcoming infrastructure readiness and staff training obstacles. By presenting a forward-looking perspective on the role of IoT in education, this book aims to equip stakeholders with the knowledge and tools necessary to create innovative, inclusive, and secure learning ecosystems that prepare students for the future.
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This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Topics and features: Describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing Presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding Provides detailed case studies on approaches to clustering, data classification and regression analysis Explains the process of creating a working recommender system using Scalding and Spark Supplies a complete list of supplementary source code and datasets at an associated website Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. K.G. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications. Anil Kumar Muppalla is also a researcher at MSRIT.
Computer Science. --- Computer Communication Networks. --- Programming Techniques. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence (incl. Robotics). --- Image Processing and Computer Vision. --- Computer science. --- Data mining. --- Artificial intelligence. --- Computer vision. --- Informatique --- Réseaux d'ordinateurs --- Exploration de données (Informatique) --- Intelligence artificielle --- Vision par ordinateur --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Telecommunications --- Computer communication systems. --- Computer programming. --- Image processing. --- Artificial Intelligence. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Apache Hadoop. --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Optical equipment --- Programming --- Distributed processing
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Educational evaluation. --- Educational tests and measurements. --- Learning. --- Avaluació educativa --- Tests i proves en educació --- Educational assessment --- Educational measurements --- Mental tests --- Tests and measurements in education --- Psychological tests for children --- Psychometrics --- Students --- Examinations --- Psychological tests --- Educational program evaluation --- Evaluation research in education --- Instructional systems analysis --- Program evaluation in education --- Self-evaluation in education --- Evaluation --- Learning process --- Comprehension --- Education --- Mesuraments en educació --- Mesuraments i proves educatives --- Proves en educació --- Tests en educació --- Psicometria --- Proves d'accés a la universitat --- Qualificacions (Ensenyament) --- Tests de lectura --- Avaluació dels alumnes --- Avaluació dels estudiants --- Tests --- Avaluació --- Educació --- Avaluació contínua --- Avaluació curricular --- Avaluació dels professors --- Avaluació formativa --- Avaluació de sistemes educatius --- Qualitat de l'ensenyament --- Rating of
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This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Topics and features: Describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing Presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding Provides detailed case studies on approaches to clustering, data classification and regression analysis Explains the process of creating a working recommender system using Scalding and Spark Supplies a complete list of supplementary source code and datasets at an associated website Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. K.G. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications. Anil Kumar Muppalla is also a researcher at MSRIT.
Programming --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- beeldverwerking --- datamining --- computers --- programmeren (informatica) --- KI (kunstmatige intelligentie) --- computernetwerken --- data acquisition
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This textbook tackles the matter of contemporary learners' needs, and introduces modern learning, teaching, and assessment methods. It provides a deeper understanding of these methods so that the students and teachers can create teaching and learning opportunities for themselves and others. It explores the meaning of 'pedagogy', why it is essential, and how pedagogy has evolved to take 21st-century skills and learning into account. This textbook showcases various modern learning, teaching, and assessment methods for contemporary learners in an increasingly digital environment. Each chapter presents insights and case studies that show how such modern methods can be applied to classrooms, and how they can support the existing curriculum. It shows students, educators, and researchers alike how to effectively make sense of and use modern learning, teaching, and assessment methods in everyday practice.
Teacher education. Teacher's profession --- Didactic evaluation --- Computer assisted instruction --- Audiovisual methods --- Teaching --- onderwijstechnologie --- assessments (onderwijs) --- evaluatie (onderwijs) --- onderwijs --- computerondersteund onderwijs --- lerarenopleiding --- lesgeven
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The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the fields of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow exponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is stored is growing at a phenomenal rate. As a result, traditional ad hoc mixtures of statistical techniques and data management tools are no longer adequate for analyzing this vast collection of data. Several domains where large volumes of data are stored in centralized or distributed databases includes applications like in electronic commerce, bioinformatics, computer security, Web intelligence, intelligent learning database systems, finance, marketing, healthcare, telecommunications, and other fields. With the importance of soft computing applied in data mining applications in recent years, this monograph gives a valuable research directions in the field of specialization. As the authors are well known writers in the field of Computer Science and Engineering, the book presents state of the art technology in data mining. The book is very useful to researchers in the field of data mining. - N R Shetty, President, ISTE, India.
Soft computing --- Data mining --- Applied Mathematics --- Civil Engineering --- Computer Science --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Soft computing. --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Computer science. --- Artificial intelligence. --- Computer-aided engineering. --- Applied mathematics. --- Engineering mathematics. --- Computer Science. --- Computer-Aided Engineering (CAD, CAE) and Design. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Engineering --- Engineering analysis --- Mathematical analysis --- CAE --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Mathematics --- Data processing --- Cognitive computing --- Computational intelligence --- Database searching --- Computer aided design. --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- CAD (Computer-aided design) --- Computer-assisted design --- Computer-aided engineering --- Design
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In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.
Electronic data processing --- Machine learning. --- Distributed processing. --- Computer science. --- Data mining. --- Artificial intelligence. --- Mathematics. --- Visualization. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Big Data. --- Artificial Intelligence (incl. Robotics). --- Learning, Machine --- Artificial intelligence --- Machine theory --- Distributed computer systems in electronic data processing --- Distributed computing --- Distributed processing in electronic data processing --- Computer networks --- Big data. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Visualisation --- Imagination --- Visual perception --- Imagery (Psychology) --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data sets, Large --- Large data sets --- Data sets --- Math --- Science
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This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.
Online social networks. --- Python (Computer program language) --- Scripting languages (Computer science) --- Electronic social networks --- Social networking Web sites --- Social media --- Social networks --- Sociotechnical systems --- Web sites --- Computer Communication Networks. --- Information Systems Applications (incl. Internet). --- Python (Computer program language). --- Python. --- Social sciences --- Network analysis. --- Network analysis (Social sciences) --- SNA (Social network analysis) --- Social network analysis --- System analysis --- Methodology --- Computer communication systems. --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Electronic data processing --- Network computers --- Distributed processing
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This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture. .
Computational biology. --- Biology --- Bioinformatics --- Computational intelligence. --- Bioinformatics. --- Machine learning. --- Computational Intelligence. --- Computational Biology/Bioinformatics. --- Machine Learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Bio-informatics --- Biological informatics --- Information science --- Computational biology --- Systems biology --- Intelligence, Computational --- Soft computing --- Data processing
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Artificial intelligence --- Medical applications. --- Medicine --- Data processing
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