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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  
Stevenage : , : Institution of Engineering & Technology, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine Learning, Image Processing, Network Security and Data Sciences : 5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers
Machine Learning, Image Processing, Network Security and Data Sciences : 5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers
Autore Chauhan Naveen
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2024
Descrizione fisica 1 online resource (372 pages)
Altri autori (Persone) YadavDivakar
VermaGyanendra K
SoniBadal
LaraJorge Morato
Collana Communications in Computer and Information Science Series
ISBN 9783031622175
9783031622168
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Machine Learning -- SynText - Data Augmentation Algorithm in NLP to Improve Performance of Emotion Classifiers -- 1 Introduction -- 2 Literature Review -- 2.1 Emotion Taxonomy -- 2.2 Benchmark Datasets -- 2.3 Data Augmentation -- 3 Methodology -- 4 Experimentation -- 5 Conclusion -- 6 Future Scope -- References -- Internet of Medical Things: Empowering Mobility and Health Monitoring with a Smart Walking Stick -- 1 Introduction -- 2 Related Works -- 3 Material and Design -- 4 Material and Methods -- 4.1 Fall Detection and Step Count -- 4.2 Heart Rate and SpO2 Measurement -- 4.3 Smart Home Control -- 4.4 Stopwatch -- 4.5 Weather and Helpline -- 4.6 Stress Monitoring and Wi-Fi Reset -- 5 Results and Discussion -- 6 Conclusion -- References -- MRI Based Spatio-Temporal Model for Alzheimer's Disease Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Spatio-Temporal Model -- 4 Results and Discussion -- 4.1 ConvLSTM -- 4.2 ConvLSTM with Other Spatio-Temporal Model -- 4.3 ConvLSTM with State of the Art -- 5 Conclusion -- References -- Comparative Analysis of Economy-Based Multivariate Oil Price Prediction Using LSTM -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Method -- 3.1 Data Collection -- 3.2 Data Pre-processing and Exploratory Data Analysis (EDA) -- 3.3 Model Training -- 3.4 Model Training -- 4 Results and Discussion -- 5 Conclusion -- References -- Deep Learning Based EV's Charging Network Management -- 1 Introduction -- 2 Literature Review -- 2.1 EV Charging Stations -- 2.2 State of Charge (SoC) -- 3 Methodology -- 3.1 Deploy EV Charging Station -- 3.2 Optimal Path to the EV Charging Station -- 3.3 SoC Estimation -- 4 Result -- 5 Conclusions -- References -- Crop Yield Prediction Using Machine Learning Approaches -- 1 Introduction.
2 Related Works -- 3 Proposed Work -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Model Selection -- 3.4 Evaluation -- 4 Results -- 5 Conclusion -- 6 Future Work -- References -- Detection and Classification of Waste Materials Using Deep Learning Techniques -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Data Collection -- 3.2 Pre Processing and Augmentation -- 3.3 Evaluation Metrics -- 3.4 Waste Garbage Detection Algorithms -- 4 Result and Simulation -- 4.1 SSD MobileNet -- 4.2 EfficientDet-D0 -- 4.3 YOLOv7 and YOLOv8 -- 4.4 Comparison of Model -- 5 Conclusion -- References -- A Comparative Analysis of ML Based Approaches for Identifying AQI Level -- 1 Introduction -- 1.1 Arrangement of the Paper -- 2 Related Work -- 3 Materials and Methods -- 3.1 Accumulation of Data and Dataset Description -- 3.2 Pre-processing of Data -- 3.3 Different ML Models -- 4 Results and Discussion -- 4.1 Experimental Result Analysis and Discussion -- 5 Conclusion and Future Scope -- References -- Marker-Based Augmented Reality Application in Education Domain -- 1 Introduction -- 2 AR Approaches -- 3 Related Work -- 4 Proposed Solution -- 4.1 Development Architecture -- 5 Implementation -- 5.1 Building a Raw Mesh on the Marker Image and Marker Detection -- 5.2 Feature Extraction Using Vuforia Image Scanner -- 5.3 Implementing Virtual Buttons with C# Scripting -- 5.4 Creating 3D Models with Blender -- 6 Results -- 7 Conclusion -- References -- Hate Speech Detection Using Machine Learning and Deep Learning Techniques -- 1 Introduction -- 2 Definitions and Taxonomy -- 3 Comprehensive Review of the Literature -- 3.1 Fact-Finding Process -- 3.2 Sources -- 3.3 Study Method Criteria -- 3.4 Research Focus -- 4 Challenges in Defining and Categorizing Hate Speech and Detection with ML/DL -- 4.1 Personalization and Explanation -- 4.2 Evolving Language.
4.3 Legal and Cultural Variations -- 4.4 Subtlety and Micro-aggression -- 4.5 Data Quality and Labeling -- 4.6 Data Imbalance -- 4.7 Multilingual and Multi Modal Content -- 4.8 Evolution of Hate Speech -- 4.9 Adversarial Attacks -- 4.10 Privacy Concerns -- 4.11 Bias and Fairness -- 4.12 Real-Time Detection -- 4.13 Scalability -- 4.14 User Behavior -- 4.15 Legal and Ethical Considerations -- 4.16 Intersectionality -- 4.17 Ambiguity -- 4.18 Freedom of Speech -- 4.19 Digital Evolution -- 4.20 Diverse Expressions -- 5 Hate Speech Detection Datasets -- 6 Machine Learning-Based Approaches -- 6.1 Data Preprocessing -- 6.2 Feature Extraction -- 6.3 Classification Algorithms -- 6.4 Ensemble Methods -- 6.5 Cross-Validation -- 7 Deep Learning-Based Approaches -- 7.1 Convolutional Neural Networks (CNNs) -- 7.2 Recurrent Neural Networks (RNNs) -- 7.3 Transformers -- 8 Evaluation Metrics -- 9 Result and Discussion -- 10 Conclusion -- References -- Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website -- 1 Introduction -- 2 Literature Survey -- 2.1 Whitelist-Based Techniques -- 2.2 Blacklist-Based Techniques -- 2.3 Content-Based Techniques -- 2.4 URL-Based Techniques -- 3 Proposed Work -- 3.1 1D Convolutional Neural Network (1D-CNN) -- 3.2 FeedForward-Convolutional Neural Network (FF-CNN) -- 4 Dataset and Pre-processing -- 5 Experimentation and Results -- 5.1 Performance Measures -- 5.2 Experiment 1: Comparison of Performance of Proposed 1D-CNN-based Approach on Different Datasets -- 5.3 Experiment 2: Comparison of the Performance of Proposed 1D-CNN-based Approach with PCA and Without PCA -- 5.4 Experiment 3: Comparison of Performance of the proposed FF-CNN-based Approach on Different Datasets -- 5.5 Experiment 4: Comparison of Performance of the Proposed 1D-CNN-based Approach and FF-CNN-based Approach.
5.6 Comparison Proposed 1D-CNN-based Approach and FF-CNN-based Approach with Other ML Models -- 6 Conclusion -- References -- Diabetes Prediction Using Machine Learning Classifiers -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 Results and Discussions -- 5 Conclusion -- 6 Future Work -- References -- A Deep Learning Method for Obfuscated Android Malware Detection -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Work -- 3.1 Adversarial Sample Generation -- 3.2 Autoencoder -- 3.3 LSTM Autoencoder -- 3.4 Image Based Autoencoder -- 3.5 Web Application -- 4 Results and Discussions -- 4.1 Dataset Description -- 4.2 LSTM Based Autoencoder -- 4.3 Image-Based Autoencoder -- 5 Result Comparisons -- 5.1 Non-adversarial Training -- 5.2 Adversarial Training -- 6 Conclusion -- References -- Code-Mixed Language Understanding Using BiLSTM-BERT Multi-attention Fusion Mechanism -- 1 Introduction -- 1.1 Contributions -- 2 Related Work -- 3 Proposed Model -- 3.1 Problem Definition -- 3.2 BiLSTM Attention Mechanism for Code-Mixed Intent Classification and Slot Filling -- 3.3 mBERT Code-Mixed Domain Knowledge Adaption -- 3.4 Multi-head Query Attention Mechanism -- 4 Result Analysis -- 4.1 Baseline Methods -- 5 Conclusion -- References -- The Potential of 1D-CNN for EEG Mental Attention State Detection -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset Selection and Description -- 3.2 Pre-processing -- 3.3 The Application of Machine Learning Models -- 4 Results Discussion -- 5 Conclusions -- References -- Potato Leaf Disease Classification Using Deep Learning Model -- 1 Introduction -- 2 Motivation -- 3 Literature Review -- 4 Problem Statement -- 5 Methodology -- 5.1 Dataset -- 5.2 Data Splitting -- 6 Model Architecture -- 6.1 Convolutional Neural Network -- 7 Results and Discussion -- 7.1 Model Evaluation.
7.2 Model Predictions -- 8 Conclusion -- References -- Breast Cancer Detection: An Evaluation of Machine Learning, Ensemble Learning, and Deep Learning Algorithms -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Pre-processing -- 3.3 Apply Learning Algorithms -- 3.4 Evaluation Criteria -- 3.5 Training Data and Testing Data -- 4 Result and Discussion -- 4.1 Results of Machine Learning Models -- 4.2 Results of Ensemble Learning Models -- 4.3 Results of Deep Learning Model -- 5 Conclusion -- References -- Advancements in Facial Expression Recognition: A Comprehensive Analysis of Techniques -- 1 Introduction -- 2 Background -- 3 Methods for Facial Expression Recognition -- 3.1 Traditional Methods -- 3.2 Deep Learning Methods -- 3.3 Hybrid Methods -- 4 Models Used for Facial Expression Recognition -- 4.1 Model 1: ResNet-50 -- 4.2 Model 2: FERNet -- 4.3 Model 3: Attentional Convolutional Network -- 5 Performance Metrics and Evaluation -- 5.1 ResNet-50 -- 5.2 FERNet -- 5.3 Attentional Convolutional Network -- 6 Comparative Analysis -- 6.1 Model Architectures -- 6.2 Training Approaches -- 6.3 Computational Efficiency -- 6.4 Robustness and Generalization -- 7 Current Implementations -- 8 Conclusion -- 9 Future Scope -- References -- Image Processing -- Sparse Representation with Residual Learning Model for Medical Image Classification -- 1 Introduction -- 2 Related Work -- 2.1 Dictionary Learning -- 2.2 ResNet -- 3 The Proposed Method -- 3.1 Dictionary Learning and Sparse Representation -- 3.2 Residual-CNN Network Features -- 3.3 Dimensionality Reduction with PCA -- 3.4 Deep Neural Network (DNN) for Classification -- 4 Experimental Results -- 4.1 Description of Datasets -- 4.2 System Implementation -- 4.3 Results and Analysis -- 5 Ablation Study -- 6 Conclusion -- References.
COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis.
Record Nr. UNINA-9910865234703321
Chauhan Naveen  
Cham : , : Springer International Publishing AG, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui