Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Machine learning algorithms and applications / / edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta
| Machine learning algorithms and applications / / edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2021] |
| Descrizione fisica | 1 online resource (305 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Computer algorithms Deep learning (Machine learning) |
| ISBN |
1-119-76925-6
1-119-76926-4 1-119-76924-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- Title Page -- Copyright -- Acknowledgments -- Preface -- Part 1: Machine Learning for Industrial Applications -- 1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services -- 1.1 Introduction -- 1.2 Literature Survey -- 1.3 Implementation Details -- 1.4 Results and Discussions -- 1.5 Conclusion -- References -- 2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning -- 2.1 Introduction -- 2.2 Conventional Silkworm Egg Detection Approaches -- 2.3 Proposed Method -- 2.4 Dataset Generation -- 2.5 Results -- 2.6 Conclusion -- Acknowledgment -- References -- 3 A Wind Speed Prediction System Using Deep Neural Networks -- 3.1 Introduction -- 3.2 Methodology -- 3.3 Results and Discussions -- 3.4 Conclusion -- References -- 4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Preliminaries -- 4.4 Proposed Model -- 4.5 Experiments -- 4.6 Results -- 4.7 Conclusion -- References -- 5 Sakshi Aggarwal, Navjot Singh and K.K. Mishra -- 5.1 Genesis -- 5.2 The Big Picture: Artificial Neural Network -- 5.3 Delineating the Cornerstones -- 5.4 Deep Learning Architectures -- 5.5 Why is CNN Preferred for Computer Vision Applications? -- 5.6 Unravel Deep Learning in Medical Diagnostic Systems -- 5.7 Challenges and Future Expectations -- 5.8 Conclusion -- References -- 6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Proposed Model for Credit Scoring -- 6.4 Results and Discussion -- 6.5 Conclusion -- References -- 7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Feature Agglomeration Clustering.
7.4 Proposed Methodology -- 7.5 Results and Analysis -- 7.6 Conclusion -- References -- Part 2: Machine Learning for Healthcare Systems -- 8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier -- 8.1 Introduction -- 8.2 Materials and Methods -- 8.3 Results and Discussion -- 8.4 Conclusion -- References -- 9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data -- 9.1 Introduction -- 9.2 Related Works -- 9.3 An Overview of Gravitational Search Algorithm -- 9.4 Proposed Model -- 9.5 Simulation Results -- 9.6 Conclusion -- References -- Part 3: Machine Learning for Security Systems -- 10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching -- 10.1 Introduction -- 10.2 Preliminary Details -- 10.3 Experiments and Results -- 10.4 Conclusions -- References -- 11 Fake Social Media Profile Detection -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Methodology -- 11.4 Experimental Results -- 11.5 Conclusion and Future Work -- Acknowledgment -- References -- 12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Methods and Materials -- 12.4 Results -- 12.5 Conclusion -- Acknowledgements -- References -- 13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Proposed Method -- 13.4 Experimental Results -- 13.5 Conclusion -- Acknowledgement -- References -- Part 4: Machine Learning for Classification and Information Retrieval Systems -- 14 AnimNet: An Animal Classification Network using Deep Learning -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Proposed Methodology -- 14.4 Results -- 14.5 Conclusion -- References -- 15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis. 15.1 Introduction -- 15.2 Related Work -- 15.3 The Proposed System -- 15.4 Result Analysis -- 15.5 Conclusion -- References -- 16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding -- 16.1 Introduction -- 16.2 Related Work -- 16.3 Proposed Approach -- 16.4 Experimental Setup -- 16.5 Results -- 16.6 Conclusion -- References -- 17 Image Anonymization Using Deep Convolutional Generative Adversarial Network -- 17.1 Introduction -- 17.2 Background Information -- 17.3 Image Anonymization to Prevent Model Inversion Attack -- 17.4 Results and Analysis -- 17.5 Conclusion -- References -- Index -- End User License Agreement. |
| Record Nr. | UNINA-9910811112903321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning applications : from computer vision to robotics / / edited by Indranath Chatterjee, Sheetal Zalte
| Machine learning applications : from computer vision to robotics / / edited by Indranath Chatterjee, Sheetal Zalte |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] |
| Descrizione fisica | 1 online resource (240 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Machine learning
Deep learning (Machine learning) |
| ISBN |
1-394-17335-0
1-394-17333-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Statistical similarity in machine learning -- Development of machine learning-based methodologies for adaptive intelligent e-learning systems and time series analysis techniques -- Time-series forecasting for stock market using convolutional neural networks -- Comparative study for applicability of color histograms for CBIR used for crop leaf disease detection -- Stock Index Forecasting Using RNN-Long Short Term Memory -- Study and analysis of machine learning models for detection of phishing URLs -- Real-world applications of blockchain technology in internet of things -- Advanced persistent threat: Korean cyber security knack model impost and applicability -- Integration of Blockchain Technology and Internet of Things: Challenges and its Solutions -- Machine learning techniques for swot analysis of online education system -- Crop yield and soil moisture prediction using machine learning algorithms. -- Multi-rate signal processing in WSN for channel capacity and energy efficiency using machine learning. -- Introduction to mechanical design of AI-based robotic system. |
| Record Nr. | UNINA-9910829959703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings / / edited by Ghada Zamzmi [and five others]
| Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings / / edited by Ghada Zamzmi [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (243 pages) |
| Disciplina | 733 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico | Deep learning (Machine learning) |
| ISBN | 3-031-16760-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Efficient and Robust Annotation Strategies -- Heatmap Regression for Lesion Detection Using Pointwise Annotations -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Training via Heatmap Regression -- 3.2 Detection During Inference -- 3.3 Segmentation Transfer Learning -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Lesion Detection Results -- 4.3 Lesion Segmentation via Transfer Learning -- 5 Discussion and Conclusion -- References -- Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost -- 1 Introduction -- 2 Method -- 2.1 Selective Dice Loss -- 2.2 Optimization -- 3 Experimental Results -- 4 Conclusion -- References -- Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment -- 1 Introduction -- 2 Materials and Method -- 2.1 Data -- 2.2 Architecture -- 2.3 Multiple Instance Learning for Cancer Grade Assessment -- 2.4 Noisy Labels and Weak Supervision -- 3 Experiments -- 3.1 Implementation and Evaluation -- 3.2 Results -- 4 Conclusion -- References -- Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels -- 1 Introduction -- 2 Methodology -- 2.1 Meta Pixel Loss Correction -- 2.2 Optimization Algorithm -- 3 Experiment Results -- 3.1 Dataset -- 3.2 Experiment Setting -- 3.3 Experimental Results -- 3.4 Limitation -- 4 Conclusion -- References -- Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction -- 1 Introduction -- 2 Materials -- 3 Study Design -- 4 Methods -- 4.1 Label Induction Using Machine Annotator -- 4.2 Similar Nodule Retrieval Using Metric Learning -- 5 Experiments and Results -- 5.1 Implementation -- 5.2 Quantitative Evaluation -- 5.3 Discussion -- 6 Conclusion and Future Work -- References.
Weakly-Supervised, Self-supervised, and Contrastive Learning -- Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- BoxShrink: From Bounding Boxes to Segmentation Masks -- 1 Introduction -- 2 Related Work -- 3 Boxshrink Framework -- 3.1 Main Components -- 3.2 rapid-BoxShrink -- 3.3 robust-BoxShrink -- 4 Experiments -- 4.1 Qualitative and Quantitative Experiments -- 4.2 Reproducibility Details -- 5 Discussion -- 6 Conclusion -- References -- Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Conclusion -- References -- SB-SSL: Slice-Based Self-supervised Transformers for Knee Abnormality Classification from MRI -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Vision Transformer -- 3.2 Self-supervised Pretraining -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Optimizing Transformations for Contrastive Learning in a Differentiable Framework -- 1 Introduction -- 2 Transformation Network -- 2.1 Optimizing Transformations -- 2.2 Differentiable Formulation of the Transformations -- 2.3 Experimental Settings -- 2.4 Linear Evaluation -- 3 Results and Discussion -- 4 Conclusions and Perspectives -- References -- Stain Based Contrastive Co-training for Histopathological Image Analysis -- 1 Introduction -- 2 Stain Based Contrastive Co-training -- 2.1 Stain Separation -- 2.2 Contrastive Co-training -- 3 Experiments -- 3.1 Datasets -- 3.2 Model Selection, Training and Hyperparameters -- 3.3 Results -- 3.4 Co-training View Analysis -- 3.5 Ablation Studies -- 4 Conclusion -- References. Active and Continual Learning -- .26em plus .1em minus .1emCLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Preliminaries -- 2.1 Examples of Smi Functions -- 3 Clinical: Our Targeted Active Learning Framework for Binary and Long-Tail Imbalance -- 4 Experiments -- 4.1 Binary Imbalance -- 4.2 Long-Tail Imbalance -- 5 Conclusion -- References -- Real Time Data Augmentation Using Fractional Linear Transformations in Continual Learning -- 1 Introduction -- 2 Methodology -- 3 Experiments, Results and Discussion -- 4 Conclusion -- References -- DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures -- 1 Introduction -- 1.1 Problem Statement: OOD Scenarios in Medical Data -- 1.2 Related Work -- 1.3 Our Contributions -- 2 Preliminaries -- 3 Leveraging Submodular Information Measures for Multiple Out-of-Distribution Scenarios -- 4 Experimental Results -- 4.1 Scenario A - Unrelated Images -- 4.2 Scenario B - Incorrectly Acquired Images -- 4.3 Scenario C - Mixed View Images -- 5 Conclusion -- References -- Transfer Representation Learning -- Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning -- 1 Introduction -- 2 Method -- 3 Data and Experiments -- 4 Results -- 4.1 Numerical Validation and Ablation Study -- 5 Conclusion -- References -- Asymmetry and Architectural Distortion Detection with Limited Mammography Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiment Design -- 5 Experimental Results -- 5.1 Comparison with Other Methods -- 5.2 Ablation Study -- 6 Conclusions -- References -- Imbalanced Data and Out-of-Distribution Generalization -- Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT -- 1 Introduction -- 2 Methods -- 3 Experiments. 4 Results and Discussion -- 5 Conclusion -- References -- CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE -- 1 Introduction -- 2 Method -- 2.1 CVAD Architecture -- 2.2 Combined Loss Function -- 2.3 Network Details -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- Approaches for Noisy, Missing, and Low Quality Data -- Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss -- 1 Introduction -- 2 Method -- 2.1 Distance-Based Loss -- 3 Experiments -- 3.1 Dataset and Implementation -- 3.2 Results -- 4 Conclusion -- References -- Image Quality Classification for Automated Visual Evaluation of Cervical Precancer -- 1 Introduction -- 2 Image Quality Labeling Criteria and Data -- 2.1 The Labeling Criteria -- 2.2 Datasets -- 3 Methods -- 3.1 Cervix Detection -- 3.2 Quality Classification -- 3.3 Mislabel Identification -- 4 Experimental Results and Discussion -- 5 Conclusions -- Appendix -- References -- A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments -- 4.1 Models' Scalp Attention Pattern -- 4.2 Models' Sensitivity of Prediction on Inputs' Frequency -- 4.3 Model Sensitivity on Morphisms Between Samples -- 5 Conclusion -- References -- Automated Skin Biopsy Analysis with Limited Data -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Nerve Labeling -- 2.3 Dermis-Epidermis Boundary Detection -- 2.4 Nerve Crossing Identification -- 3 Experimental Setup -- 3.1 Evaluating the Nerve Tracing Model -- 3.2 Evaluating Dermis Model -- 4 Results -- 4.1 Nerve Labeling Results -- 4.2 Dermis Labeling Results -- 4.3 Crossing Count Results -- 5 Discussion -- References -- Author Index. |
| Record Nr. | UNISA-996490353303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modern deep learning design and application development : versatile tools to solve deep learning problems / / Andre Ye
| Modern deep learning design and application development : versatile tools to solve deep learning problems / / Andre Ye |
| Autore | Ye Andre |
| Pubbl/distr/stampa | New York, New York : , : Apress, , [2022] |
| Descrizione fisica | 1 online resource (463 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Deep learning (Machine learning) |
| ISBN | 1-4842-7413-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: A Deep Dive Into Keras -- Chapter 2: Pre-training Strategies and Transfer Learning -- Chapter 3: The Versatility of Autoencoders -- Chapter 4: Model Compression for Practical Deployment -- Chapter 5: Automating Model Design with Meta-Optimization -- Chapter 6:Successful Neural Network Architecture Design -- Chapter 7:Reframing Difficult Deep Learning Problems. |
| Record Nr. | UNINA-9910523769603321 |
Ye Andre
|
||
| New York, New York : , : Apress, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Novel financial applications of machine learning and deep learning : algorithms, product modeling, and applications / / edited by Mohammad Zoynul Abedin and Petr Hajek
| Novel financial applications of machine learning and deep learning : algorithms, product modeling, and applications / / edited by Mohammad Zoynul Abedin and Petr Hajek |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (235 pages) |
| Disciplina | 006.31 |
| Collana | International Series in Operations Research & Management Science |
| Soggetto topico |
Deep learning (Machine learning)
Finance - Data processing |
| ISBN | 3-031-18552-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Recent Developments in FinTech -- 1. FinTech Risk Management and Monitoring -- 2. Digital Transformation of Supply Chain with Supportive Culture in Blockchain Environment -- 3. Integration of Artificial Intelligence Technology in Management Accounting Information System - An Empirical Study -- 4. The Impact of Big Data on Accounting Practices: Empirical Evidence from Africa -- Part 2: Financial Risk Prediction using Machine Learning -- 5. Using Outlier Modification Rule for Improvement of the Performance of Classification Algorithms in the Case of Financial Data -- 6. Default Risk Prediction Based on Support Vector Machine and Logit Support Vector Machine -- 7. Predicting Corporate Failure using Ensemble Extreme Learning Machine -- 8. Assessing and Predicting Small Enterprises’ Credit Ratings: A Multicriteria Approach -- Part 3: Financial Time-Series Forecasting -- 9. An Ensemble LGBM (Light Gradient Boosting Machine) Approach for Crude Oil Price Prediction -- 10. Model Development for Predicting the Crude Oil Price: Comparative Evaluation of Ensemble and Machine Learning Methods -- part 4: Emerging Technologies in Financial Education and Healthcare -- 11. Discovering the Role of M-learning among Finance Students: The Future of Online Education -- 12. Exploring the Role of Mobile Technologies in Higher Education: The Impact of Online Teaching on Traditional Learning.-13. Knowledge Mining from Health Data: Application of Feature Selection Approaches. |
| Record Nr. | UNINA-9910678261503321 |
| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of international conference on deep learning, computing and intelligence : ICDCI 2021 / / Gunasekaran Manogaran, A. Shanthini, G. Vadivu, editors
| Proceedings of international conference on deep learning, computing and intelligence : ICDCI 2021 / / Gunasekaran Manogaran, A. Shanthini, G. Vadivu, editors |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] |
| Descrizione fisica | 1 online resource (698 pages) |
| Disciplina | 006.31 |
| Collana | Advances in intelligent systems and computing |
| Soggetto topico |
Artificial intelligence
Computer science Deep learning (Machine learning) |
| ISBN |
981-16-5651-7
981-16-5652-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743227403321 |
| Singapore : , : Springer Nature Singapore Pte Ltd., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Search for exotic Higgs boson decays to merged diphotons : a novel CMS analysis using end-to-end deep learning / / Michael Andrews
| Search for exotic Higgs boson decays to merged diphotons : a novel CMS analysis using end-to-end deep learning / / Michael Andrews |
| Autore | Andrews Michael <1835-1917, > |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (193 pages) |
| Disciplina | 006.31 |
| Collana | Springer Theses, Recognizing Outstanding Ph.D. Research |
| Soggetto topico |
Deep learning (Machine learning)
Higgs bosons Particles (Nuclear physics) - Diffraction |
| ISBN |
9783031250910
9783031250903 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- The LHC and the CMS detector -- Theory & phenomenology -- Analysis strategy -- Data sets -- Signal selection -- a mass regression -- Analysis -- Results -- Conclusions -- Supplementary studies. |
| Record Nr. | UNINA-9910678250403321 |
|
Andrews Michael <1835-1917, >
|
||
| Berlin, Germany : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Semantic knowledge modelling via open linked ontologies : ontologies in e-governance / / Stamatios Theocharis and George A. Tsihrintzis
| Semantic knowledge modelling via open linked ontologies : ontologies in e-governance / / Stamatios Theocharis and George A. Tsihrintzis |
| Autore | Theocharis Stamatios |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (381 pages) |
| Disciplina | 006.31 |
| Collana | Artificial Intelligence-Enhanced Software and Systems Engineering |
| Soggetto topico |
Deep learning (Machine learning)
Internet in public administration |
| ISBN | 3-031-20585-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- e-Government: The concept, the environment & critical issues for the Back-Office systems -- Semantic Web: The evolution of the Web & the opportunities for the e-Government -- Representation and knowledge management for the benefit of e-Government - Opportunities through the tools of the Semantic Web -- Towards Open Data and Open Governance – Representation of knowledge and Triplification of data in the field of the Greek Open Government Data -- Production and publication of linked open data: The case of open ontologies -- Education and E-Government - The case of a Moodle based platform for the education and evaluation of civil servants -- Conclusions – Future Work. |
| Record Nr. | UNINA-9910640385403321 |
|
Theocharis Stamatios
|
||
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Sentiment Analysis and Deep Learning : Proceedings of ICSADL 2022 / / Subarna Shakya, Ke-Lin Du, and Klimis Ntalianis, editors
| Sentiment Analysis and Deep Learning : Proceedings of ICSADL 2022 / / Subarna Shakya, Ke-Lin Du, and Klimis Ntalianis, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (987 pages) |
| Disciplina | 006.31 |
| Collana | Advances in Intelligent Systems and Computing Series |
| Soggetto topico |
Artificial intelligence
Deep learning (Machine learning) Computational intelligence Computer vision Image processing - Digital techniques |
| ISBN | 981-19-5443-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- Ranking Roughly Tourist Destinations Using BERT-Based Semantic Search -- 1 Introduction -- 2 Semantic Search with SBERT -- 2.1 Semantic Search with SBERT -- 2.2 Extracting a Boundary by Applying Different Thresholds -- 3 System Architecture -- 3.1 Data -- 3.2 Text Cleaning and Pre-processing -- 3.3 Embeddings -- 3.4 Similarity Measure -- 3.5 Ranking Algorithm -- 4 Result of Ranking System -- 4.1 Result of Ranking System -- 4.2 Evaluation of Result -- 5 Discussion and Conclusion -- References -- A New Image Encryption Technique Built on a TPM-Based Secret Key Generation -- 1 Introduction -- 1.1 Neural Network and Tree Parity Machine (TPM) -- 2 Methodology -- 2.1 Traditional Method of Generating Secret Key Using Tree Parity Machine -- 2.2 Proposed Rule with Algorithm -- 3 Results and Discussion -- 3.1 Performance Parameters -- 3.2 Security Parameters -- 4 Conclusion -- References -- Application Prototypes for Human to Computer Interactions -- 1 Introduction -- 2 Related Work -- 2.1 Methodology -- 2.2 Literature Review -- 3 Conclusion -- References -- Feature Selection-Based Spam Detection System in SMS and Email Domain -- 1 Introduction -- 2 Literature Review -- 2.1 Phase 1 -- 2.2 Phase 2 -- 2.3 Phase 3 -- 3 Classification Methods Used for Spam Detection in SMS and Email Domains -- 3.1 Phase 1 -- 3.2 Phase 2 -- 3.3 Phase 3 -- 4 Experiment and Result -- 4.1 Data-sets Used -- 5 Conclusions and Future Work -- References -- Discerning the Application of Virtual Laboratory in Curriculum Transaction of Software Engineering Lab Course from the Lens of Critical Pedagogy -- 1 Introduction -- 2 Motivation for This Work -- 3 Objectives and Scope of This Research -- 3.1 Specific Objectives of the Research -- 3.2 Scope of the Research -- 4 Organization of This Research Paper -- 5 Virtual or Online Learning.
6 Traditional Versus Virtual Laboratory -- 7 Critical Pedagogy Theory -- 8 Instructional Design Delivery (IDD) -- 9 Students' Learning Outcomes (LO) -- 10 Assessments (ASS) -- 11 Students' Empowerment (EM) -- 12 Critical Thinking (CT) -- 13 Social Presence (SP) -- 14 Alignment (AL) -- 15 Approaches to Virtual Laboratories and Software Engineering Virtual Laboratory -- 16 Synthesis of Related Work and Concluding Remarks -- 17 Educational Implications -- 18 Limitations -- 19 Conclusion and Way Forward -- 19.1 Design Software Engineering Virtual Lab -- 19.2 CP-VLLM: Development of a Virtual Learning Measurement Tool -- 19.3 Evaluation of Students' Learning Performance in a Virtual Laboratory Learning Environment -- 19.4 Directions for Future Research -- References -- Chrome Extension for Text Sentiment Analysis -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Sentiment Analysis -- 3.2 Chrome Extension -- 4 Experimental Analysis and Results -- 4.1 Dataset Description -- 4.2 Performance Evaluation -- 5 Conclusion -- References -- Performance of RSA Algorithm Using Game Theory for Aadhaar Card -- 1 Introduction -- 2 Related Work -- 3 RSA Algorithm -- 4 Proposed GT-RSA Algorithm and Analysis -- 4.1 Game Theory -- 4.2 Games Are Classified in Several Ways -- 4.3 Pure Strategy Characteristics of a Two-Person, Zero-Sum Game -- 4.4 Pure Strategy Problems -- 4.5 GT-RSA -- 5 Experimental Results -- 5.1 Encryption Time -- 5.2 Decryption Time -- 5.3 Execution Time -- 5.4 Encryption Throughput -- 5.5 Decryption Throughput -- 5.6 Execution Throughput -- 5.7 Power Consumption -- 5.8 Avalanche Effect -- 6 Conclusion -- References -- Drought Prediction Using Recurrent Neural Networks and Long Short-Term Memory Model -- 1 Introduction -- 1.1 Literature Review -- 2 Methodology -- 2.1 Read the Data -- 2.2 Data Cleaning. 2.3 Data Preparation and Visualization -- 2.4 Applying RNN and LSTM -- 3 Results -- 3.1 Experimental Methodology -- 3.2 Results -- 4 Conclusion -- References -- Recogn-Eye: A Smart Medical Assistant for Elderly -- 1 Introduction -- 2 Literature Survey -- 3 Problem Statement -- 4 Proposed Method -- 5 Project Demonstration -- 6 Results and Discussions -- 7 Conclusion -- References -- Lossless Image Compression Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Image Representation -- 4 Problem Statement -- 5 Clustering Techniques -- 6 Image Compression Techniques -- 6.1 Generative Adversarial Networks -- 6.2 Gaussian Mixture Models -- 7 Methodology -- 8 Lossless and Lossy -- 9 Implementation -- 10 Experimental Results -- 11 Conclusion -- References -- An Energy-Efficient Approach to Transfer Data from WSN to Mobile Devices -- 1 Introduction -- 2 Related Work -- 3 Proposed Architecture -- 4 Wireless XML Stream -- 4.1 G-Node (G-N) -- 4.2 Lineage Code -- 5 Query Processing -- 5.1 Simple Query Processing -- 5.2 Twig Pattern Query Processing -- 6 Simulation Result -- 6.1 Query Processing Time with Lineage Encoding -- 6.2 Maximum Residual Energy -- 6.3 Maximum Number of Sensor Nodes with Optimum Energy -- 7 Conclusion and Future Work -- References -- Worker Safety Helmet -- 1 Introduction -- 2 Literature Review -- 3 Methodologies -- 4 Architecture Diagram -- 5 Prototype Implementation -- 6 Testing and Evaluation -- 7 Conclusion -- References -- Using IT in Descriptive Statistics and Way ANOVA Analysis to Assessment Development in Some Anthropometric Indicators of Thai Ethnic Students Born Between 2003 and 2006 in Thuan Chau District, Son La Province, Vietnam -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 3.1 Standing Height of Thai Ethnic Students -- 3.2 The Weight of Thai Students by Age and Sex. 3.3 BMI of Thai Students by Age and Sex -- 4 Conclusion -- References -- LSTM-Based Deep Learning Architecture for Recognition of Human Activities -- 1 Introduction -- 2 Literature Review -- 3 Proposed System -- 4 LSTM Architecture -- 5 Methodology -- 6 Results -- 7 Conclusion and Future Work -- References -- A Deep Learning Framework for Classification of Hyperspectral Images -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 4 Experimental Results -- 5 Conclusion -- References -- Improved Security on Mobile Payments Using IMEI Verification -- 1 Introduction -- 2 Literature Survey -- 3 Methodologies -- 4 Implementation and Result Analysis -- 5 Conclusions -- References -- Evaluating the Effectiveness of Classification Algorithms for EEG Sentiment Analysis -- 1 Introduction -- 1.1 Background -- 1.2 Motivation -- 1.3 Objective -- 2 Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Feature Extraction -- 2.4 Machine Learning Algorithms -- 2.5 Deep Learning Algorithms -- 2.6 Data Preparation -- 2.7 Evaluation Metrics -- 3 Results and Discussions -- 4 Conclusion and Future Research Direction -- 5 Data and Code Availability -- References -- Analytics and Data Computing for the Development of the Concept Digitalization in Business and Economic Structures -- 1 Introduction, Background, Motivation and Objective -- 2 Methods the Implementing Digital HR Capabilities -- 2.1 Implementation of Statistical Assessment of Functional Interactions of Users of Social and Economic Phenomena and Processes -- 2.2 Influence of Enterprise Corporate Culture Components on Individual Components of Enterprise Personnel Motivation -- 3 The Results of the Analysis the Components of Corporate Culture Motivation for Personnel -- 4 Discussions -- 5 Conclusions -- References -- The Social Hashtag Recommendation for Image and Video Using Deep Learning Approach. 1 Introduction -- 2 Literature Survey -- 2.1 Tag Recommendation for Micro-video -- 2.2 Social Tag Recommendation for Images -- 2.3 Social Tag Recommendation for Micro-video -- 2.4 Social Tag Recommendation for Image -- 3 Filtering Methods for Hashtag Recommendation -- 3.1 Collaborative Filtering -- 3.2 Content-Based Filtering -- 3.3 Filtering Based on Hybrids Approach -- 3.4 Social Popularity Prediction -- 3.5 Technique for Tag Recommendation for Images/Videos -- 4 Evaluation Parameter -- 5 Conclusion and Future Scope -- References -- Smart Door Locking System Using IoT-A Security for Railway Engine Pilots -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results and Implementation -- 5 Conclusion -- References -- AdaSmooth: An Adaptive Learning Rate Method Based on Effective Ratio -- 1 Introduction -- 1.1 Gradient Descent -- 2 Related Work -- 2.1 Momentum -- 2.2 AdaGrad -- 2.3 AdaDelta -- 3 AdaSmooth Method -- 3.1 Effective Ratio (ER) -- 3.2 AdaSmooth -- 3.3 AdaSmoothDelta -- 4 Experiments -- 4.1 Experiment: Multi-layer Perceptron -- 4.2 Experiment: Convolutional Neural Networks -- 4.3 Experiment: Logistic Regression -- 5 Conclusion -- References -- Designing and Implementing a Distributed Database for Microservices Cloud-Based Online Travel Portal -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition -- 3.1 Why Distributed Database? -- 3.2 DDB Systems Types -- 3.3 Advantages of Distributed Database System -- 3.4 Limitations of Relational Database -- 3.5 Major Characteristics of RDBMS -- 3.6 Why Microservice Architecture? -- 4 Methodology -- 4.1 System Design -- 4.2 Data Collection Methods -- 4.3 Database Design -- 4.4 Data Analysis Tools -- 5 Results and Discussions -- 6 Conclusion -- References -- A Comparative Study of a New Customized Bert for Sentiment Analysis -- 1 Introduction -- 2 Proposed Sentiment Analysis Framework. 2.1 Proposed Model. |
| Record Nr. | UNINA-9910637707103321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Session-Based Recommender Systems Using Deep Learning / / Reza Ravanmehr and Rezvan Mohamadrezaei
| Session-Based Recommender Systems Using Deep Learning / / Reza Ravanmehr and Rezvan Mohamadrezaei |
| Autore | Ravanmehr Reza |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2024] |
| Descrizione fisica | 1 online resource (314 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Deep learning (Machine learning)
Recommender systems (Information filtering) |
| ISBN | 3-031-42559-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Aims and Scope -- Main Emphasis -- Target Audience -- Prerequisites -- Short Summary -- Acknowledgements -- Contents -- About the Authors -- Chapter 1: Introduction to Session-Based Recommender Systems -- 1.1 Introduction -- 1.2 Recommender Systems -- 1.3 Fundamentals of Session-Based Recommender Systems -- 1.3.1 Basic Concepts of SBRS -- 1.3.2 Challenges of SBRS -- 1.3.3 Session-Based vs. Sequential vs. Session-Aware Recommender Systems -- 1.4 Session-Based Recommender System Approaches -- 1.4.1 Traditional SBRS -- 1.4.1.1 Pattern/Rule Mining -- 1.4.1.2 K-Nearest Neighbors -- 1.4.1.3 Markov Chain -- 1.4.1.4 Generative Probabilistic Model -- 1.4.1.5 Latent Representation -- 1.4.2 Deep Learning SBRS -- 1.5 Conclusion -- References -- Chapter 2: Deep Learning Overview -- 2.1 Introduction -- 2.2 Fundamentals of Deep Learning -- 2.2.1 History of Deep Learning -- 2.2.2 AI, ML, and DL -- 2.2.3 Advantages of Deep Learning -- 2.2.4 General Process of Deep Learning-Based Solutions -- 2.2.5 Taxonomy of Deep Learning Models -- 2.3 Deep Discriminative Models -- 2.3.1 Multilayer Perceptron -- 2.3.2 Convolutional Neural Network -- 2.3.3 Recurrent Neural Network -- 2.3.3.1 LSTM -- 2.3.3.2 GRU -- 2.4 Deep Generative Models -- 2.4.1 Autoencoders -- 2.4.1.1 Sparse Autoencoder -- 2.4.1.2 Denoising Autoencoder -- 2.4.1.3 Contractive Autoencoder -- 2.4.1.4 Convolutional Autoencoder -- 2.4.1.5 Variational Autoencoder -- 2.4.2 Generative Adversarial Networks -- 2.4.3 Boltzmann Machines -- 2.4.3.1 Restricted Boltzmann Machine -- 2.4.3.2 Deep Belief Network -- 2.4.3.3 Deep Boltzmann Machine -- 2.5 Graph-Based Models -- 2.5.1 Graph Neural Network -- 2.5.2 Graph Convolutional Network -- 2.6 Conclusion -- References -- Chapter 3: Deep Discriminative Session-Based Recommender System -- 3.1 Introduction -- 3.2 Fundamentals -- 3.2.1 Datasets.
3.2.2 Evaluation -- 3.3 Session-Based Recommender System Using RNN -- 3.3.1 Why RNN? -- 3.3.2 GRU Approaches -- 3.3.3 LSTM Approaches -- 3.4 Session-Based Recommender System Using CNN -- 3.4.1 Why CNN? -- 3.4.2 CNN Approaches -- 3.5 Discussion -- 3.6 Conclusion -- References -- Chapter 4: Deep Generative Session-Based Recommender System -- 4.1 Introduction -- 4.2 Fundamentals -- 4.2.1 Datasets -- 4.2.2 Evaluation -- 4.3 Session-Based Recommender System Using Autoencoder -- 4.3.1 Why Autoencoder? -- 4.3.2 Autoencoder Approaches -- 4.4 Session-Based Recommender System Using GAN -- 4.4.1 Why GAN? -- 4.4.2 GAN Approaches -- 4.5 Session-Based Recommender System Using FBM -- 4.5.1 Why Flow-Based Models? -- 4.5.2 Flow-Based Approaches -- 4.6 Discussion -- 4.7 Conclusion -- References -- Chapter 5: Hybrid/Advanced Session-Based Recommender Systems -- 5.1 Introduction -- 5.2 Fundamentals -- 5.2.1 Datasets -- 5.2.2 Evaluation -- 5.3 SBRS Using Hybrid Deep Neural Networks -- 5.3.1 Why Hybrid Deep Neural Network? -- 5.3.2 Approaches Based on CNN and LSTM -- 5.3.3 Approaches Based on CNN and GRU -- 5.3.4 Approaches Based on RNN and Autoencoder -- 5.4 SBRS Using Deep Graph Neural Network -- 5.4.1 Why Graph Neural Network? -- 5.4.2 Approaches Based on GNN -- 5.4.3 Approaches Based on GNN and RNN -- 5.4.4 Approaches Based on GCN -- 5.5 SBRS Using Deep Reinforcement Learning -- 5.5.1 Why Deep Reinforcement Learning? -- 5.5.2 Approaches Based on Deep Q-Learning -- 5.5.3 Approaches Based on DRL and RNN -- 5.5.4 Approaches Based on DRL and CNN -- 5.5.5 Approaches Based on DRL and GAN -- 5.6 Discussion -- 5.7 Conclusion -- References -- Chapter 6: Learning to Rank in Session-Based Recommender Systems -- 6.1 Introduction -- 6.2 Fundamentals -- 6.2.1 Ranking Creation -- 6.2.2 Ranking Aggregation -- 6.2.3 Datasets -- 6.3 Ranking Creation -- 6.3.1 Pointwise Methods. 6.3.1.1 Pointwise Methods in Information Retrieval -- 6.3.1.2 Pointwise Methods in Recommender Systems -- 6.3.2 Pairwise Methods -- 6.3.2.1 Pairwise Methods in Information Retrieval -- 6.3.2.2 Pairwise Methods in Recommender Systems -- 6.3.3 Listwise Methods -- 6.3.3.1 Listwise Methods in Information Retrieval -- 6.3.3.2 Listwise Methods in Recommender Systems -- 6.3.4 Hybrid Methods -- 6.4 Ranking Aggregation -- 6.4.1 Ranking Aggregation Methods in Information Retrieval -- 6.4.2 Ranking Aggregation Methods in Recommender Systems -- 6.5 Discussion -- 6.6 Conclusion -- References -- Summary -- Index. |
| Record Nr. | UNINA-9910799492003321 |
|
Ravanmehr Reza
|
||
| Cham, Switzerland : , : Springer, , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
