Applied computational technologies : proceedings of ICCET 2022 / / edited by Brijesh Iyer, Tom Crick, and Sheng-Lung Peng

• Pubblicazione: Gateway East, Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (776 pages)
• Disciplina: 621.382
• Soggetto topico: Electrical engineering; Information technology; Telecommunication
• Persona (resp. second.): CrickTom; PengSheng-Lung; IyerBrijesh
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Patron -- Program and Organizing Chairs -- International Advisory Board -- Technical Program Committee -- Organizing and Publicity Committee -- Editor's of the Conference Proceedings -- Steering Committee -- Contents -- About the Authors -- Applied Computations using Deep and Machine Learning -- Classification of Traffic Signs Using Deep Learning-Based Approach for Smart Cities -- 1 Introduction -- 2 Related Works -- 3 Material and Methods -- 3.1 Dataset Collection and Preprocessing -- 3.2 Model Architecture and Learning -- 4 Performance Evaluation -- 5 Conclusions -- References -- Sentimental Analysis of Twitter Data on Online Learning During Unlock Phase of COVID-19 -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Extracting Dataset -- 3.2 Data Pre-processing and Cleaning -- 3.3 Exploratory Data Analysis -- 4 Results and Discussions -- 5 Conclusion -- References -- Bonafide Satellite Landslide Image Detection Using Deep Learning -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 3.1 Data Pre-processing -- 3.2 Functioning of Error Level Analysis (ELA) -- 3.3 ANN Model Construction -- 3.4 Experimental Setup -- 4 Result and Analysis -- 4.1 Performance Analysis -- 5 Conclusions -- References -- Deep Learning Approach for Predicting the Price of Cryptocurrencies -- 1 Introduction -- 1.1 Related Work -- 2 Methodologies -- 2.1 Problem Statement -- 2.2 System Architecture -- 2.3 Long Short-Term Memory (LSTM) -- 2.4 Evaluation Matrices -- 3 The Experimental Analysis -- 3.1 Dataset Used -- 3.2 Data Pre-processing and Implementation -- 3.3 Results -- 3.4 Evaluation -- 4 Conclusions -- References -- Deep Learning and Super-Hybrid Textual Feature Based Multi-category Thematic Classifier for Punjabi Poetry -- 1 Introduction -- 2 Literature Survey -- 3 Methodology. ; 3.1 Building Punjabi Poetry Corpus -- 3.2 Poetry-Pre-processing -- 3.3 Feature Selection -- 3.4 Model Building -- 3.5 Performance Evaluation -- 4 Result and Analysis -- 4.1 Results Using LEX Feature -- 4.2 Results Using LEXSYN Feature -- 4.3 Results Using LEXSEM Feature -- 4.4 Results Using SUPER HYBRID Feature -- 4.5 Accuracy Based Comparative Analysis -- 4.6 F1-Measure Based Comparative Analysis -- 5 Conclusions -- References -- Attention Based Deep Learning Techniques for Question Classification in Question Answering Systems -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Conclusions -- References -- Optimized Fuzzy Hypersphere Neural Network with Online Adaptation Capability -- 1 Introduction -- 2 Literature Survey -- 3 The OFHSNNwOC Approach -- 3.1 Basic Definitions -- 3.2 The OFHSNNwOC Algorithm -- 3.3 The OFHSNNwOC Architecture -- 4 Performance Evaluation -- 4.1 2-D Example: Optimized Centre Points and Radii Calculation of FHSs -- 4.2 2-D Example: For Online Adaptation -- 4.3 Comparison of Recognition Rate and Number of HBs/Hss -- 4.4 Analysis of Online Adaptation Capability -- 5 Conclusions -- References -- Built-Up Area Extraction on Multispectral Satellite Data Using Simple CNN -- 1 Introduction -- 2 Related Works -- 3 Dataset -- 4 Methodology and Experimentation -- 5 Results and Discussions -- 6 Conclusion and Future scope -- References -- Framework of CNN Architecture for Fashion Image Classification -- 1 Introduction -- 2 Literature Review -- 3 Proposed Architecture -- 3.1 Convolutional Neural Network Architecture -- 4 Experimental Setup and Results -- 4.1 Dataset -- 4.2 Evaluation Measures -- 4.3 Comparative Evaluation with Existing Methods -- 5 Conclusions -- References -- A Novel CNN Framework for Early-Stage Detection of Blindness in Diabetic Patients -- 1 Introduction -- 2 Dataset -- 3 Methodology. ; 3.1 Image Preprocessing and Augmentation -- 3.2 Model Selection -- 3.3 Training the Model -- 3.4 Validation -- 4 Results -- 5 Conclusions -- References -- Design and Development of Loan Predictor Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 About Data Preprocessing -- 3.2 About Dataset -- 4 Results and Discussions -- 4.1 Hyper-parameter Tuning -- 4.2 Limitations -- 5 Conclusions -- References -- An Analysis of Document Summarization for Educational Data Classification Using NLP with Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Existing Methodologies -- 3.1 Principal Component Analysis: -- 3.2 Linear Discriminate Analysis -- 3.3 Random Forest -- 4 The Educational Document Abstraction Using Text Mining with Machine Learning Classifiers -- 4.1 Text Mining Algorithm -- 4.2 Fuzzy Latent Semantic Algorithm -- 5 Comparison of Existing Algorithms -- 6 Conclusions -- References -- Detection of Disease in Plants with Android Integration Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Process Flow -- 4 Implementation of System -- 4.1 Dataset Collection -- 4.2 Image Acquisition -- 4.3 Pre-processing of the Images -- 4.4 Disease Detection and Classification -- 5 Results and Discussions -- 5.1 Android Implementation -- 6 Conclusions -- References s -- Analysis of Post-flood Impacts on Sentinel-2 Data Using Non-parametric Machine Learning Classifiers: A Case Study from Bihar Floods, Saharsa, India -- 1 Introduction -- 2 Flood Mapping Using Satellite Data -- 3 Study Area, Data Preparation, and Pre-processing -- 4 The Methodology and Classifiers Used -- 5 Results and Discussion -- 6 Conclusions -- References -- Performance Analysis of Quantitative Software Vulnerability Prioritization Techniques -- 1 Introduction. ; 2 Quantitative Vulnerability Prioritization Techniques -- 2.1 CVSS -- 2.2 VRSS -- 2.3 WIVSS -- 3 Other Techniques -- 3.1 Prioritization of Vulnerability Types Using MCDM Techniques -- 3.2 Prioritization Using Vulnerability Description -- 3.3 Estimation of Vulnerability Exploitation -- 4 Performance Analysis -- 5 Conclusion and Future Work -- References -- Accessibility and Performance Evaluation of Healthcare and E-Learning Sites in India: A Comparative Study Using TAW and GTMetrix -- 1 Introduction -- 2 Literature Review -- 2.1 Limitations of the Studies -- 2.2 Research Objectives -- 3 Methodology -- 3.1 Automated Tools -- 3.2 Accessibility and Performance Metrics -- 4 Results and Discussions -- 4.1 Accessibility Evaluation -- 4.2 Performance Evaluation -- 5 Conclusion and Future Recommendations -- References -- Performance Analysis of Cardiovascular Diseases Using Machine Learning -- 1 Introduction -- 2 Literature Survey -- 3 Issues and Challenges -- 4 Methodologies -- 4.1 Dataset -- 4.2 Pre-processing of ECG Signals -- 4.3 Clustering of Unsupervised Data -- 4.4 Applying Machine Learning Classification for Final Prediction -- 5 Conclusions -- References -- A Deep Learning Paradigm for Computer Aided Diagnosis of Emphysema from Lung HRCT Images -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Proposed Method -- 2.4 Simulated Database Description -- 3 Implementation of Deep Learning Models -- 3.1 Feature Extraction Using Basic CNN -- 3.2 Feature Extraction Using Pre-trained VGG16 for Transfer Learning -- 4 Results and Discussion -- 5 Conclusions -- References -- Deep Learning Convolution Neural Network for Tomato Leaves Disease Detection by Inception -- 1 Introduction -- 2 Literature Survey -- 3 Convolution Neural Network (CNN) -- 3.1 Convolution Layer -- 3.2 Pooling Layers -- 3.3 Fully Connected Layers. ; 3.4 Activation Function -- 4 Methodology -- 4.1 A Block Diagram -- 4.2 Data Collection/Image Acquisition -- 4.3 Image Preprocessing -- 4.4 Model Architecture -- 4.5 Lab Setup -- 5 Result and Discussion -- 5.1 Statistical Analysis -- 6 Conclusions -- References -- Sarcasm Detection in Hindi-English Code-Mixed Tweets Using Machine Learning Algorithms -- 1 Introduction -- 1.1 Techniques Used to Detect Sarcasm -- 2 Literature Survey -- 3 Dataset and Performance Measure -- 4 Methodology -- 4.1 Data Preprocessing -- 4.2 Feature Extraction -- 4.3 Feature Selection -- 4.4 Classification Models -- 5 Result and Analysis -- 6 Conclusion and Future Work -- References -- Efficient Automated Disease Diagnosis Using Machine Learning Models -- 1 Introduction -- 2 Literature Review -- 3 Research Objective -- 4 Methodology -- 5 Conclusion and Future Scope -- References -- Plant Disease Classification Using Transfer Learning -- 1 Introduction -- 2 Related Studies -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Transfer Learning-Based Architecture -- 4 Performance Assessment -- 5 Conclusions -- References -- Performance Assessment for Heart-Disease Prediction Using Machine Learning Algorithms -- 1 Introduction -- 2 Literature Review -- 3 Material and Methods -- 3.1 Dataset -- 3.2 Classification Schemes -- 4 Performance Analysis -- 5 Conclusions -- References -- Human Behavior Analysis: Applications and Machine Learning Algorithms -- 1 Introduction -- 2 Domains and Applications -- 2.1 Education -- 2.2 Transportation -- 2.3 Online Social Network -- 2.4 Health Care -- 2.5 Corporate World -- 2.6 Surveillance -- 2.7 Future Domains and Applications -- 3 Features and Input Capturing -- 3.1 Features -- 3.2 Input Devices -- 4 Algorithms -- 4.1 Machine Learning Algorithms -- 5 Conclusions -- References. ; Advancement of Deep Learning and Its Substantial Impact on the Diagnosis of COVID-19 Cases.
• Titolo autorizzato: Applied computational technologies
• ISBN: 981-19-2719-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910568290203321
• Lo trovi qui: Univ. Federico II
• Serie: Smart Innovation, Systems and Technologies