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.
Deep Learning-Based Approaches for Sentiment Analysis [[electronic resource] /] / edited by Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik
| Deep Learning-Based Approaches for Sentiment Analysis [[electronic resource] /] / edited by Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XII, 319 p.) |
| Disciplina | 006.31 |
| Collana | Algorithms for Intelligent Systems |
| Soggetto topico |
Signal processing
Image processing Speech processing systems Data mining Optical data processing Natural language processing (Computer science) Computational intelligence Neural networks (Computer science) Signal, Image and Speech Processing Data Mining and Knowledge Discovery Computer Imaging, Vision, Pattern Recognition and Graphics Natural Language Processing (NLP) Computational Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 981-15-1216-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Application of Deep Learning Approaches for Sentiment Analysis: A Survey -- Chapter 2. Recent Trends and Advances in Deep Learning based Sentiment Analysis -- Chapter 3. - Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews -- Chapter 4. Toxic Comment Detection in Online Discussions -- Chapter 5. Aspect Based Sentiment Analysis of Financial Headlines and Microblogs -- Chapter 6. Deep Learning based frameworks for Aspect Based Sentiment Analysis -- Chapter 7. Transfer Learning for Detecting Hateful Sentiments in Code Switched Language -- Chapter 8. Multilingual Sentiment Analysis -- Chapter 9. Sarcasm Detection using deep learning -- Chapter 10. Deep Learning Approaches for Speech Emotion Recognition -- Chapter 11. Bidirectional Long Short Term Memory Based Spatio-Temporal In Community Question Answering -- Chapter 12. Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. |
| Record Nr. | UNINA-9910373905203321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Prominent Feature Extraction for Sentiment Analysis [[electronic resource] /] / by Basant Agarwal, Namita Mittal
| Prominent Feature Extraction for Sentiment Analysis [[electronic resource] /] / by Basant Agarwal, Namita Mittal |
| Autore | Agarwal Basant |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (118 p.) |
| Disciplina | 610 |
| Collana | Socio-Affective Computing |
| Soggetto topico |
Neurosciences
Natural language processing (Computer science) Computational linguistics Data mining Application software Natural Language Processing (NLP) Computational Linguistics Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-319-25343-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Literature Survey -- Machine Learning Approach for Sentiment Analysis -- Semantic Parsing using Dependency Rules -- Sentiment Analysis using ConceptNet Ontology and Context Information -- Semantic Orientation based Approach for Sentiment Analysis -- Conclusions and FutureWork -- References -- Glossary -- Index. |
| Record Nr. | UNINA-9910253872903321 |
Agarwal Basant
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Recent Advancements in Artificial Intelligence : Proceedings of ICRAAI 2023
| Recent Advancements in Artificial Intelligence : Proceedings of ICRAAI 2023 |
| Autore | Nayak Richi |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore Pte. Limited, , 2024 |
| Descrizione fisica | 1 online resource (409 pages) |
| Altri autori (Persone) |
MittalNamita
KumarManoj PolkowskiZdzislaw KhuntetaAjay |
| Collana | Innovations in Sustainable Technologies and Computing Series |
| ISBN | 981-9711-11-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- About This Book -- Contents -- Editors and Contributors -- 1 A Survey on Computer Vision Methods and Approaches for the Detection of Humans in Video Surveillance Systems -- 1 Introduction -- 2 Database -- 3 Pre-Processing Techniques -- 4 Feature Extraction -- 5 Classification -- 6 Real Time Detection Systems -- 7 Human Detection Challenges -- 7.1 Image Plane Variations -- 7.2 Variation in Pose -- 7.3 Texture and Lighting Variation -- 7.4 Variation in Background -- 7.5 Variation in Shape -- 8 Conclusion -- References -- 2 UNFAZEDROADS: Pothole Management System -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Research Design -- 3.2 Data Collection -- 3.3 Data Analysis -- 4 Proposed System -- 5 Table of Analysis -- 6 Results and Discussions -- 7 Scope of Research -- 8 Future Scope -- 9 Conclusion -- References -- 3 DeepMint: Non-fungible Token Generation Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 2.1 Review of Existing Systems -- 2.2 Limitations of Existing Systems -- 3 Analysis -- 4 Implementation -- 4.1 Design Details -- 4.2 Architecture and Methodology -- 5 Results -- 5.1 Performance Evaluation Parameters -- 5.2 Implementation Results -- 6 Conclusion -- References -- 4 Empowering Gestures: Composing Succinct Meaning Using Vision and Swin Transformers for Indian Sign Language -- 1 Introduction -- 2 Literature Survey -- 3 Overview of Techniques -- 3.1 Convolutional Neural Networks -- 3.2 Vision Transformers -- 3.3 Swin Transformer -- 3.4 ReLU Activation Function -- 3.5 SoftMax Activation Function -- 3.6 GELU Activation Function -- 4 Experimentation and Implementation -- 4.1 Data -- 4.2 Design Methodology -- 5 Results and Evaluation -- 6 Conclusion -- References.
5 An Accuracy of Identifying Recyclable Objects and the Number of Objects Identified from Municipal Waste Without Occlusion Using Computer Vision Techniques -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Implementation -- 5 Result and Discussion -- 6 Conclusion -- References -- 6 A Hybrid Approach for Summarizing Text and Image Data Using ResNet and BART -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results and Dicussion -- 5 Conclusion -- References -- 7 Epileptic Seizure Recognition System Using Neural Networks and Support Vector Machine Models -- 1 Introduction -- 2 Dataset -- 2.1 UCI Dataset -- 2.2 CHB-MIT Dataset -- 3 Survey of Existing Systems -- 4 Proposed Methodology -- 4.1 Support Vector Machine Model -- 4.2 Neural Networks Model -- 5 Results and Discussions -- 5.1 UCI -- 5.2 CHB-MIT -- 6 Conclusion -- References -- 8 Intelligent Tutoring Systems for Multidisciplinary Education -- 1 Introduction -- 1.1 Digital Pedagogy -- 1.2 Intelligent Tutoring System -- 1.3 Methodology -- 2 Pedagogy for Multidisciplinary Digital Education -- 3 Comparison of Learning Algorithms -- 3.1 Computer Programming Learning Algorithm -- 3.2 Recommender Algorithm for Philosophy Education -- 4 Analysis and Discussion -- References -- 9 A Comprehensive Study of SOMs, iSOMs, and Hybrid SOMs for Complex Data -- 1 Introduction -- 2 Literature Review -- 2.1 Self-Organizing Maps (SOM) -- 3 Comparison of SOM, iSOM and Hybrid SOM -- 4 Conclusion and Future Work -- References -- 10 Enhancing Energy Efficiency in Smart Cities Through Robust Deep Learning Frameworks -- 1 Introduction -- 1.1 Significance of Deep Learning in Smart City Energy Management -- 1.2 Challenges in Smart City Energy Management -- 1.3 Objectives and Structure -- 2 Literature Review -- 2.1 Deep Learning for Smart City Energy Management. 2.2 Data-Driven Approaches and Integrative Reviews -- 2.3 Machine Learning-Assisted Approaches -- 2.4 Wearable Sensors and Real-Time Energy Management -- 2.5 Deep Learning in Smart Buildings -- 2.6 Sustainable Transportation in Smart Cities -- 2.7 Real-Time Energy Management in Smart Homes -- 2.8 Smart Grid and Machine Learning -- 2.9 Urban Energy Management in Smart Cities -- 3 Smart City Infrastructure and Energy Management -- 3.1 Smart City Ecosystem Overview -- 3.2 Urban Energy Consumption Dynamics -- 3.3 Deep Learning for Smart City Energy Management -- 3.4 Challenges in Smart City Energy Management -- 3.5 Wearable Sensors and Data Collection -- 3.6 Data Fusion and Feature Extraction -- 3.7 Real-Time Energy Management -- 3.8 Case Studies and Real-World Experiments -- 3.9 Performance Evaluation Metrics -- 4 Deep Learning Frameworks for Smart City Energy Efficiency -- 4.1 Introduction to Deep Learning Frameworks -- 4.2 Applications of Deep Learning in Smart City Energy Management -- 4.3 Strengths and Limitations of Deep Learning Frameworks -- 4.4 Challenges and Strategies in Real-World Deployment -- 4.5 Performance Evaluation Metrics for Energy Efficiency -- 4.6 Research Directions and Future Prospects -- 5 Robustness and Resilience of Deep Learning Models -- 5.1 Introduction to Robustness and Resilience -- 5.2 Challenges in Model Robustness -- 5.3 Adaptability to Disruptions -- 5.4 Strategies for Model Robustness -- 5.5 Resilience Testing and Benchmarking -- 5.6 Ethical Considerations -- 6 Result and Analysis -- 6.1 Overview of Deep Learning Model Performance -- 6.2 Methodologies and Benchmarking Criteria -- 6.3 Comparison Table: Key Metrics Across Research Papers -- 6.4 Interpretation of Key Findings -- 7 Discussion -- 7.1 Model Interpretability -- 7.2 Ethical Considerations -- 7.3 Data Privacy -- 8 Future Research Direction. 8.1 Enhancing Model Interpretability -- 8.2 Addressing Ethical Considerations -- 8.3 Advancements in Data Privacy Techniques -- 8.4 Improving Model Robustness and Resilience -- 8.5 Interdisciplinary Collaboration -- 8.6 Validation and Benchmarking Frameworks -- 8.7 Long-Term Sustainability and Scalability -- 9 Conclusion -- References -- 11 Transformative Potential of AI and Remote Sensing in Sustainable Groundwater Management -- 1 Introduction -- 2 Groundwater Research Landscape -- 2.1 Evolution of Groundwater Research -- 2.2 Challenges in Groundwater Research -- 2.3 Traditional Research Methods -- 2.4 Emerging Technologies -- 3 The Role of Digital Technologies in Groundwater Research -- 3.1 The Role of AI in Groundwater Research -- 3.2 Remote Sensing Techniques -- 4 AI with Remote Sensing (AI-RS) Technique -- 5 Digital Twin of Groundwater Using AI-Remote Sensing Technique -- 5.1 Reduced Environmental Impact -- 5.2 Efficient Resource Management -- 5.3 Cost Savings -- 5.4 Long-Term Sustainability -- 5.5 Community and Stakeholder Engagement -- 6 Case Studies -- 7 Challenges and Future Directions -- 7.1 Data Quality and Availability -- 7.2 Data Privacy and Ethical Concerns -- 7.3 Interdisciplinary and Global Collaboration -- 7.4 Algorithm Validation and Interpretability -- 7.5 Scaling up Sustainable Technologies -- 7.6 Climate Change Adaptation -- 8 Conclusion -- References -- 12 Impact on Ocean Acidification Along the Hawaii Coastline Using Learning Algorithm -- 1 Introduction -- 2 Proposed Algorithm -- 2.1 Tuning Process -- 2.2 Correlation Map of the Proposed Work -- 3 Results and Discussions -- 4 Conclusion -- References -- 13 Smart Crop Security System Using IoT -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Analysis and Discussion -- 4 Conclusion -- 4.1 Future Scope -- References. 14 Energy Efficient Fault Tolerance System for a Reliable IoT Environment -- 1 Introduction -- 2 Related Work -- 3 Software Defined FTM for IOT -- 4 System Design -- 5 Results and Discussion -- 6 Conclusions -- References -- 15 A Novel Authentication Approach Based on Level 2 Minutiae-based Feature Extraction Using Gabor Filter -- 1 Introduction -- 1.1 Features in Fingerprint -- 1.2 Fingerprint Databases and Its Classification -- 2 Related Work -- 3 Proposed Methodology for Ridge Detection and Level 2 Feature Extraction -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- 16 A Blockchain Based Electronic Health Record Management System with PoA Consensus -- 1 Introduction -- 2 Literature Review -- 3 Proposed Blockchain Architecture -- 4 Results and Discussion -- 4.1 Decentralized Data Management -- 4.2 Proof of Authority Consensus -- 4.3 Enhanced Scalability -- 4.4 Data Security -- 4.5 Lightweight Cryptography -- 5 Conclusion -- References -- 17 Voice-Based Classification of Parkinson's Disease Using Machine Learning: An Extensive Study -- 1 Introduction -- 2 Relevant Studies -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Methods -- 4 Experimental Results -- 4.1 Accuracy and Classification Reports -- 4.2 Confusion Matrix -- 4.3 Comparison with Relevant Studies -- 5 Conclusion -- References -- 18 A Brief Perusal of Image-based Diagnosis for COVID-19 Using Image Processing Perspective -- 1 First Section -- 2 Corona Virus Disease (COVID-19) -- 2.1 Symptoms of Coronavirus -- 3 Image-based Classification Method -- 3.1 Extraction of Features -- 3.2 Parallel Implementation -- 3.3 Manta Ray Foraging Optimization (MRFO) -- 3.4 Improved MRFO with Feature Selection Based on D.E -- 4 Appraisal of the Suggested Model -- 5 Motivation -- 6 Literature Review -- 6.1 Research Trend -- 6.2 Research Gap -- 7 Comparative Analysis -- 8 Conclusion. References. |
| Record Nr. | UNINA-9910855381503321 |
|
Nayak Richi
|
||
| Singapore : , : Springer Singapore Pte. Limited, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||