Artificial Intelligence: Towards Sustainable Intelligence

Name: Artificial Intelligence: Towards Sustainable Intelligence | First International Conference, AI4S 2023, Pune, India, September 4-5, 2023, Proceedings
Brand: Springer
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First International Conference, AI4S 2023, Pune, India, September 4-5, 2023, Proceedings

Sanju Tiwari Fernando Ortiz-Rodríguez Sashikala Mishra Edlira Vakaj Ketan Kotecha(Herausgeber*in)

Springer (Verlag)

Erschienen am 29. November 2023

XVIII, 218 Seiten

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978-3-031-47997-7 (ISBN)

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Beschreibung

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Inhalt

Intro
Preface
Organization
Keynote Abstracts
Data Analytics for Sustainable Global Supply Chains
Building Trustworthy Neuro-Symbolic AI Systems with Explainability and Safety: Knowledge is the Key
Contents
An Approach Towards Mitigation of Renewable Energy Curtailment
1 Introduction
2 Test Case Modifications and Assumptions
2.1 Test Case and Modifications
2.2 Assumptions
3 Solution Methodologies and Cases
3.1 Solution Methodology
3.2 Configuration of Cases
4 Results and Discussion
4.1 Results
4.2 Discussion
5 Conclusion and Future Scope
References
ESG and IoT: Ensuring Sustainability and Social Responsibility in the Digital Age
1 Introduction
2 Overview of ESG and Sustainability
2.1 Environmental, Social and Governance Impacts of IoT
2.2 The Contribution of Artificial Intelligence to ESG
2.3 Industry 4.0 and Its Potential Impact on ESG
3 Proposed Approach
3.1 Proposed Architecture
3.2 SAS® Intelligent Monitoring: Product Overview
4 Possible Applications
5 Future Work
6 Conclusion
References
AI and Assistive Technologies for Persons with Disabilities - Worldwide Trends in the Scientific Production Using Bibliometrix R Tool
1 Introduction
1.1 Background
1.2 Problem Statement
2 Methodology
2.1 Data Collection
2.2 Analysis
3 Results and Discussion
3.1 Production
3.2 Sources
3.3 Authors
3.4 Documents
4 Conclusion
4.1 Limitations and Future Research Directions
Appendix A:
References
Leaf Disease Detection Using Transfer Learning
1 Introduction
2 Related Work
3 Model Architecture and Design
3.1 ResNet
3.2 MobileNet
3.3 VGG16
3.4 Design Consideration
4 Dataset Preparation and Training
4.1 Kaggle Dataset: Potato, Tomato, and Pepper Black Diseases
4.2 Training the Models
5 Results
5.1 VGG16 Model
5.2 ResNet Model
5.3 MobileNet Model
5.4 Performance Comparison
6 Conclusion
References
Automated Scene Recognition for Environmental Monitoring: A Cluster Analysis Approach using Intel Image Classification Dataset
1 Introduction
2 Literature Review
3 Data Preprocessing and EDA
3.1 About the Dataset
3.2 Data Transformation
3.3 Dimensionality Reduction with PCA for Improved Clustering Efficiency
4 Clustering Methods for Scene Recognition
4.1 K Means Clustering Technique
4.2 Agglomerative Clustering
4.3 BIRCH (Balanced Iterative Reducing and Clustering Using Hierarchies)
4.4 DBSCAN (Density Based Spatial Clustering of Applications with Noise)
4.5 Spectral
5 Evaluation of Clustering Algorithms
5.1 Silhouette Score
5.2 Davis-Bouldin Score
5.3 Calinski-Harabasz Score
6 Conclusion and Future Scope
References
Unveiling the Potentials of Deep Learning Techniques for Accurate Alzheimer's Disease Neuro Image Classification
1 Introduction
2 Literature Survey
3 Methodology
4 The Proposed Bi-LSTM-AJSO Model Development
5 Experimentation
5.1 Datasets Used and Model Training and Testing
5.2 Comparison with Other Machine and Deep Learning Approaches
5.3 Execution Time Comparisons
5.4 Interpretability Analysis and Significance
6 Conclusion and Future Directions
References
Food Composition Knowledge Extraction from Scientific Literature
1 Introduction
2 Food Composition Knowledge
3 Food Composition Knowledge Extraction from Scientific Papers
3.1 Knowledge Sources
3.2 Knowledge Extraction
4 Knowledge Validation
4.1 Matching to Existing Vocabularies
5 Conclusion
References
Design and Analysis of an Algorithm Based on Biometric Block Chain for Efficient data sharing in VANET
1 Introduction
2 Literature Review
3 Proposed Work
4 Experimental Result
5 Conclusion
References
An Improved Deep Learning Model Implementation for Pest Species Detection
1 Introduction
2 Related Work
3 Methodology
3.1 Dataset
3.2 Deep Learning
3.3 Data Augmentation
3.4 Model Architecture
3.5 Classification
4 Results
5 Conclusion
References
Identification of Diseases Affecting Mango Leaves Using Deep Learning Models
1 Introduction
2 Literature Survey
2.1 Disease Affecting Mango Leaves
2.2 Factor Influencing Fungal Diseases
3 Dataset Description
4 Methodology
4.1 Feature Extraction
4.2 Classification
4.3 Model Evaluation
4.4 Prediction
5 Results and Discussion
6 Conclusion
References
RWNR: Radial Basis Feed Forward Neural Network Driven Semantically Inclined Strategy for Web 3.0 Compliant News Recommendation
1 Introduction
2 Related Works
3 Proposed Architecture
4 Implementation
5 Results and Performance Evaluation
6 Conclusion
References
WDNRegClass - A Hybrid ANN + Bayesian Learning Model to Reduce Temporal Predictive In-Variance Towards Mitigation of WDN Revenue Losses
1 Introduction
2 Literature Study
2.1 Leak Identification Using Hydraulic Model Parameters:
2.2 Data-Driven Approaches with Sequential or Temporal Data:
2.3 The Integration of Bayesian Belief Propagation:
3 Proposed Work
4 Result and Discussion
5 Conclusion
References
Real-Time Birds Shadow Detection for Autonomous UAVs
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Acquiring a Sample Dataset
3.2 Shadow Generation
3.3 Data Cleaning
3.4 Post-processing
3.5 Detection Model Training
4 Results and Discussion
5 Conclusion
References
Knowledge Graph for Fraud Detection: Case of Fraudulent Transactions Detection in Kenyan SACCOs
1 Introduction
2 Related Work
3 Proposed Approach
4 Results
4.1 Sample Fraudulent Funds Movement Detection
5 Conclusion and Future Work
References
Conceptual Framework for Representing Knowledge in the Energy Sector
1 Introduction
2 State of the Art
3 Methodology for Semantic Data Model Design and Construction
3.1 Step 1: Ontology Requirements Specification
3.2 Step 2: Ontology Analysis
3.3 Step 3: Overview of Ontological Modules
3.4 Step 4: Interaction with Stakeholders and Ontology Formalization
4 Overview of Main Pilots' Topics
5 Methodology Application
5.1 Application of Step 1 - Ontology Requirements Specification
5.2 Application of Step 2 - Ontology Analysis
5.3 Application of Step 3 - Overview of Ontological Model
5.4 Application of Step 4 - Formalization of Semantic Data Models
5.5 Use Case Instantiation with an Illustrative Example
6 Discussion
7 Conclusion
References
Semantic Carbon Footprint of Food Supply Chain Management
1 Introduction
2 Ontology Methodology
2.1 Ontology Requirements Specification
2.2 Competency Questions
2.3 Users
2.4 Intended Use
3 Ontology Design
3.1 Data Sources
3.2 Evaluation
4 Conclusion
References
Author Index

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