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[null,"1.","Record Nr.",null,"UNINA9910855381503321"]
[null,null,"Autore",null,"Nayak Richi"]
[null,null,"Titolo",null,"Recent Advancements in Artificial Intelligence : Proceedings of ICRAAI 2023"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore Pte. Limited, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"981-9711-11-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (409 pages)"]
[null,null,"Collana",null,"Innovations in Sustainable Technologies and Computing Series"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[{"$nulls":3},"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Results and Evaluation -- 6 Conclusion -- References."]
[{"$nulls":4},"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."]
[{"$nulls":4},"2.2 Data-Driven Approaches and Integrative Reviews -- 2.3 Machine Learning-Assisted Approaches -- 2.4 Wearable Sensors and Real-Time Energy Management -- 2.5 Deep Learning in Smart Buildings -- 2.6 Sustainable Transportation in Smart Cities -- 2.7 Real-Time Energy Management in Smart Homes -- 2.8 Smart Grid and Machine Learning -- 2.9 Urban Energy Management in Smart Cities -- 3 Smart City Infrastructure and Energy Management -- 3.1 Smart City Ecosystem Overview -- 3.2 Urban Energy Consumption Dynamics -- 3.3 Deep Learning for Smart City Energy Management -- 3.4 Challenges in Smart City Energy Management -- 3.5 Wearable Sensors and Data Collection -- 3.6 Data Fusion and Feature Extraction -- 3.7 Real-Time Energy Management -- 3.8 Case Studies and Real-World Experiments -- 3.9 Performance Evaluation Metrics -- 4 Deep Learning Frameworks for Smart City Energy Efficiency -- 4.1 Introduction to Deep Learning Frameworks -- 4.2 Applications of Deep Learning in Smart City Energy Management -- 4.3 Strengths and Limitations of Deep Learning Frameworks -- 4.4 Challenges and Strategies in Real-World Deployment -- 4.5 Performance Evaluation Metrics for Energy Efficiency -- 4.6 Research Directions and Future Prospects -- 5 Robustness and Resilience of Deep Learning Models -- 5.1 Introduction to Robustness and Resilience -- 5.2 Challenges in Model Robustness -- 5.3 Adaptability to Disruptions -- 5.4 Strategies for Model Robustness -- 5.5 Resilience Testing and Benchmarking -- 5.6 Ethical Considerations -- 6 Result and Analysis -- 6.1 Overview of Deep Learning Model Performance -- 6.2 Methodologies and Benchmarking Criteria -- 6.3 Comparison Table: Key Metrics Across Research Papers -- 6.4 Interpretation of Key Findings -- 7 Discussion -- 7.1 Model Interpretability -- 7.2 Ethical Considerations -- 7.3 Data Privacy -- 8 "]
[{"$nulls":4},"Future Research Direction."]
[{"$nulls":4},"8.1 Enhancing Model Interpretability -- 8.2 Addressing Ethical Considerations -- 8.3 Advancements in Data Privacy Techniques -- 8.4 Improving Model Robustness and Resilience -- 8.5 Interdisciplinary Collaboration -- 8.6 Validation and Benchmarking Frameworks -- 8.7 Long-Term Sustainability and Scalability -- 9 Conclusion -- References -- 11 Transformative Potential of AI and Remote Sensing in Sustainable Groundwater Management -- 1 Introduction -- 2 Groundwater Research Landscape -- 2.1 Evolution of Groundwater Research -- 2.2 Challenges in Groundwater Research -- 2.3 Traditional Research Methods -- 2.4 Emerging Technologies -- 3 The Role of Digital Technologies in Groundwater Research -- 3.1 The Role of AI in Groundwater Research -- 3.2 Remote Sensing Techniques -- 4 AI with Remote Sensing (AI-RS) Technique -- 5 Digital Twin of Groundwater Using AI-Remote Sensing Technique -- 5.1 Reduced Environmental Impact -- 5.2 Efficient Resource Management -- 5.3 Cost Savings -- 5.4 Long-Term Sustainability -- 5.5 Community and Stakeholder Engagement -- 6 Case Studies -- 7 Challenges and Future Directions -- 7.1 Data Quality and Availability -- 7.2 Data Privacy and Ethical Concerns -- 7.3 Interdisciplinary and Global Collaboration -- 7.4 Algorithm Validation and Interpretability -- 7.5 Scaling up Sustainable Technologies -- 7.6 Climate Change Adaptation -- 8 Conclusion -- References -- 12 Impact on Ocean Acidification Along the Hawaii Coastline Using Learning Algorithm -- 1 Introduction -- 2 Proposed Algorithm -- 2.1 Tuning Process -- 2.2 Correlation Map of the Proposed Work -- 3 Results and Discussions -- 4 Conclusion -- References -- 13 Smart Crop Security System Using IoT -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Analysis and Discussion -- 4 Conclusion -- 4.1 Future Scope -- References."]
[{"$nulls":4},"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."]
[{"$nulls":4},"References."]