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Development and future of internet of drones (IoD) : insights, trends and road ahead / / Rajalakshmi Krishnamurthi, Anand Nayyar, Aboul Ella Hassanien, editors
| Development and future of internet of drones (IoD) : insights, trends and road ahead / / Rajalakshmi Krishnamurthi, Anand Nayyar, Aboul Ella Hassanien, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (275 pages) |
| Disciplina | 004.678 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Internet of things
Drone aircraft - Automatic control Aerial surveillance - Automatic control Drons Internet de les coses Aprenentatge automàtic Intel·ligència artificial |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-63339-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483663903321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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E-Learning Methodologies : Fundamentals, Technologies and Applications
| E-Learning Methodologies : Fundamentals, Technologies and Applications |
| Autore | Goyal Mukta |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2021 |
| Descrizione fisica | 1 online resource (352 pages) |
| Disciplina | 371.334 |
| Altri autori (Persone) |
KrishnamurthiRajalakshmi
YadavDivakar |
| Collana | Computing and Networks |
| Soggetto topico | Internet - Software |
| ISBN |
1-83724-559-2
1-5231-3656-1 1-83953-121-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Contents -- About the editors -- Preface -- Part I: Introduction and pedagogies of e-learning systems with intelligent techniques -- Part II: Technologies in e-learning -- Part III: Case studies -- Part I: Introduction and pedagogies of e-learning systems with intelligent techniques -- 1 Introduction -- 1.1 Asynchronous learning and synchronous learning -- 1.2 Blended learning, distance learning, and Classroom 2.0 -- 1.2.1 E-learning -- 1.2.2 Smart e-learning -- 1.3 Different frameworks of smart e-learning -- 1.3.1 AI in e-learning -- 1.3.2 Mobile learning -- 1.3.3 Cloud-based learning -- 1.3.4 Big data in e-learning -- 1.3.5 IoT framework of e-learning -- 1.3.6 Augmented reality in learning -- 1.4 Gaps in existing frameworks -- 1.5 Conclusion -- References -- 2 Goal-oriented adaptive e-learning -- 2.1 Introduction -- 2.2 Literature survey -- 2.2.1 State-of-the-art -- 2.3 Goal-oriented adaptive e-learning system -- 2.3.1 Goal-oriented course graph structure -- 2.3.1.1 CG components -- 2.3.1.2 Database -- 2.3.2 Registration module -- 2.3.3 Personalized assessment module -- 2.3.3.1 Dynamic learning ability -- 2.3.3.2 Dynamic learning success -- 2.3.4 ACO-based learning path generation -- 2.3.4.1 Objectives -- 2.3.4.2 Time constraint -- 2.3.4.3 Ant colony optimization -- 2.3.5 Persistence into database and self-learning -- 2.4 Experimental results -- 2.4.1 Data preparation -- 2.4.2 Evolution of learning path with regular improvement -- 2.4.2.1 Static learning path -- 2.4.2.2 Dynamic learning paths -- 2.4.3 Evolution of learning path with late improvement -- 2.4.3.1 Static learning path -- 2.4.3.2 Dynamic learning paths -- 2.5 Conclusion -- 2.6 Future scope -- References -- 3 Predicting students' behavioural engagement in microlearning using learning analytics model -- 3.1 Introduction -- 3.2 LA studies -- 3.3 Methods -- 3.4 Results.
3.4.1 Analysis of using NN -- 3.4.2 Analysis using LR -- 3.5 Comparison analysis using NN and LR -- 3.6 Conclusion -- 3.7 Future scope -- References -- 4 Student performance prediction for adaptive e-learning systems -- 4.1 Introduction -- 4.2 Literature survey -- 4.2.1 Learner profile -- 4.2.2 Soft computing techniques -- 4.3 Methodology -- 4.3.1 Conversion of numeric to intuitionistic fuzzy value -- 4.3.2 Learning style model -- 4.3.3 Personality model -- 4.3.4 Assessment of knowledge level -- 4.3.5 Intuitionistic fuzzy optimization algorithm and KNN classifier -- 4.4 Experimental results -- 4.5 Future work -- 4.6 Conclusion -- References -- Part II: Technologies in e-learning -- 5 AI in e-learning -- 5.1 Artificial intelligence in India -- 5.2 Artificial intelligence in education -- 5.3 AI in e-learning -- 5.4 Analysis and data -- 5.5 Emphasis on the area that needs improvement in e-learning -- 5.6 Creating comprehensive curriculum -- 5.7 Immersive learning -- 5.8 Intelligent tutoring systems -- 5.9 Virtual facilitators and learning environment -- 5.10 Content analytics -- 5.11 Paving new pathways in the coming decade: AI and e-learning -- 5.12 Improving accessibility for e-learning by AI -- 5.13 Artificial intelligence in personalized learning -- 5.14 Cuts costs for students, eases burden on teachers -- 5.15 Artificial intelligence in academic connectivity -- 5.16 Artificial intelligence in crowd service learning -- 5.17 How to improve registration and completion of e-learning courses by using AI -- 5.18 Expectations of participant in artificial intelligence in e-learning -- 5.19 Future of AI in e-learning -- 5.20 Conclusion -- References -- 6 Mobile learning as the future of e-learning -- 6.1 Introduction -- 6.2 E-learning -- 6.3 Mobile learning -- 6.3.1 Smartphone penetration in India -- 6.4 Need for mobile learning. 6.5 Mobile learning in higher education -- 6.5.1 Intelligent technologies -- 6.6 Benefits of smartphone in academic learning -- 6.7 Different types of e-learning -- 6.7.1 Learning management system -- 6.7.2 Blended learning -- 6.7.3 Artificial intelligence -- 6.7.4 Internet of Things -- 6.7.5 Flipped classrooms -- 6.7.5.1 M-learning and government -- 6.8 M-learning challenges -- 6.8.1 Cons of mobile learning -- 6.9 Education 4.0 -- 6.10 Conclusion -- 6.11 Future scope -- References -- 7 Smart e-learning transition using big data: perspectives and opportunities -- 7.1 Introduction -- 7.2 Big data applications in e-learning -- 7.2.1 Performance prediction -- 7.2.2 Attrition risk detection -- 7.2.3 Data visualization -- 7.2.4 Intelligent feedback -- 7.2.5 Course recommendation -- 7.2.6 Student skill estimation -- 7.2.7 Behavior detection -- 7.2.8 Collaboration and social network analysis -- 7.2.9 Developing concept maps -- 7.2.10 Constructing courseware -- 7.2.11 Planning and scheduling -- 7.3 Big data techniques for e-learning -- 7.3.1 Classification in e-learning -- 7.3.1.1 Fuzzy logic -- 7.3.1.2 ANN and evolutionary computation -- 7.3.1.3 Association rule -- 7.4 Big data tools -- 7.4.1 Hadoop platform for e-learning -- 7.4.1.1 Apache Hadoop -- 7.4.1.2 Hadoop Distributed File System -- 7.4.1.3 MapReduce -- 7.4.1.4 YARN -- 7.4.2 Spark -- 7.4.3 Orange -- 7.5 Recent research perspectives and future direction -- 7.5.1 Future direction -- 7.6 Conclusion -- References -- 8 E-learning using big data and cloud computing -- 8.1 Introduction -- 8.2 Conventional e-learning system and its issues -- 8.3 E-learning on cloud computing -- 8.4 Characteristics of cloud in e-learning -- 8.5 Cloud-based e-learning architecture -- 8.6 Cloud computing service-oriented architecture for e-learning -- 8.7 Big data in e-learning -- 8.7.1 The need for big data in e-learning. 8.8 Review on big data-based e-learning systems -- 8.9 Association of big data and cloud computing -- 8.9.1 Infrastructure as a service (IaaS) in the public cloud -- 8.9.2 Platform as a service (PaaS) private cloud -- 8.9.3 Software as a service (SaaS) in a hybrid cloud -- 8.10 Use of big data and cloud technology for e-learning -- 8.11 Casestudies on e-learning -- 8.12 Case study of a cloud and big data-based Evaluation and Feedback Management System (EFMS) in e-learning -- 8.13 Open research challenges -- 8.13.1 Limited control over security and privacy -- 8.13.2 Limited control over compliance -- 8.13.3 Limited control over institutional data -- 8.13.4 Network dependency issues -- 8.13.5 Latency problem -- 8.14 Conclusion -- 8.15 Future work -- References -- 9 E-learning through virtual laboratory environment: developing of IoT workshop course based on Node-RED -- 9.1 Introduction -- 9.2 Virtual laboratory -- 9.3 Building blocks of IoT -- 9.3.1 Edge level -- 9.3.2 Connectivity level -- 9.3.3 Communications level -- 9.3.4 Service level -- 9.4 Node-RED tool -- 9.4.1 Why Node-RED? -- 9.4.2 Installation of Node-RED -- 9.5 IoT workshop -- 9.6 Teaching methodology -- 9.7 Course details -- 9.8 Experiment and result discussion -- 9.9 Conclusion -- References -- 10 Mnemonics in e-learning using augmented reality -- 10.1 Introduction -- 10.2 Literature survey -- 10.2.1 E-learning -- 10.2.2 Augmented reality (tools and techniques) -- 10.2.2.1 Display techniques -- 10.2.2.2 Tracking techniques -- 10.2.3 Method of loci -- 10.3 Related work -- 10.4 Theory and research approach -- 10.5 Implementation and results -- 10.5.1 Concept-1 -- 10.5.2 Concept-2 -- 10.5.3 Concept-3 -- 10.5.4 Concept-4 -- 10.5.5 Concept-5 -- 10.5.6 Concept-6 -- 10.5.7 Concept-7 -- 10.5.8 Concept-8 -- 10.5.9 Concept-9 -- 10.5.10 Concept-10 -- 10.6 Conclusion -- 10.7 Future work -- References. 11 E-learning tools and smart campus: boon or bane during COVID-19 -- 11.1 Introduction -- 11.2 E-learning -- 11.2.1 Synchronous e-learning -- 11.2.2 Asynchronous e-learning -- 11.3 Tools for synchronous e-learning -- 11.4 Side effects of using online learning tools or e-learning -- 11.4.1 Technical challenges -- 11.4.2 Health issues -- 11.4.3 Social and economic challenges -- 11.5 Future of education: e-learning + smart campus -- 11.5.1 Smart campus -- 11.5.2 Smart classroom -- 11.5.3 Importance of smart classrooms in e-learning application -- 11.5.4 What turns an ordinary classroom into a smart classroom that is required for e-learning? -- 11.6 Conclusion -- 11.7 Future work -- References -- Part III: Case studies -- 12 Bioinformatics algorithms: course, teaching pedagogy and assessment -- 12.1 Introduction -- 12.2 Course content: creation and access, course outcomes -- 12.2.1 Access of course content -- 12.2.2 Course outcomes -- 12.2.3 Course content -- 12.3 Strategies of lecture delivery -- 12.4 Details of the topics discussed -- 12.4.1 Topic 1: algorithms and complexity -- 12.4.2 Topic 2: molecular biology -- 12.4.3 Topic 3: exhaustive search-mapping, searching -- 12.4.4 Topic 4: greedy algorithms -- 12.4.5 Topic 5: dynamic programming algorithms -- 12.4.6 Topic 6: divide-and-conquer algorithms -- 12.4.7 Topic 7: graph algorithms -- 12.4.8 Topic 8: combinatorial pattern matching -- 12.4.9 Topic 9: clustering and trees -- 12.4.10 Topic 10: applications -- 12.5 In-class assessment approaches -- 12.5.1 Self-assessment by students -- 12.6 Discussion -- 12.7 Conclusions and future scope -- References -- 13 Active learning in E-learning: a case study to teach elliptic curve cryptosystem, its fast computational algorithms and authenti -- 13.1 Introduction -- 13.2 Related work -- 13.3 The methodology of active learning process. 13.4 Introduction to elliptic curve cryptography. |
| Altri titoli varianti | E-learning Methodologies |
| Record Nr. | UNINA-9911007131503321 |
Goyal Mukta
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| Stevenage : , : Institution of Engineering & Technology, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Serverless Computing: Principles and Paradigms / / edited by Rajalakshmi Krishnamurthi, Adarsh Kumar, Sukhpal Singh Gill, Rajkumar Buyya
| Serverless Computing: Principles and Paradigms / / edited by Rajalakshmi Krishnamurthi, Adarsh Kumar, Sukhpal Singh Gill, Rajkumar Buyya |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (320 pages) |
| Disciplina | 004.6782 |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Engineering - Data processing
Cooperating objects (Computer systems) Computational intelligence Big data Artificial intelligence Internet programming Data Engineering Cyber-Physical Systems Computational Intelligence Big Data Artificial Intelligence Web Development |
| ISBN |
9783031266331
3031266331 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Serverless Computing: New Trends and Future Directions -- Punching Holes in the Cloud: Direct Communication Between Serverless Functions -- Hybrid Serverless Computing: Opportunities and Challenges -- A Taxonomy of Performance Forecasting Systems in the Serverless Cloud Computing Environments -- Open-Source Serverless for Edge Computing: A Tutorial -- Accelerating and Scaling Data Products With Serverless -- QoS Analysis for Serverless Computing using Machine Learning -- A Blockchain-Enabled Serverless Approach for IoT Healthcare Applications -- Cost Control and Efficiency Optimization in Maintainability Implementation of Wireless Sensor Networks based on Serverless Computing -- Scheduling Mechanisms in Serverless Computing -- Serverless Cloud Computing: State of the Art and Challenges. |
| Record Nr. | UNINA-9910726291003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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