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Artificial Intelligence : First International Conference, AI4S 2023, Pune, India, September 4-5, 2023, Proceedings
| Artificial Intelligence : First International Conference, AI4S 2023, Pune, India, September 4-5, 2023, Proceedings |
| Autore | Tiwari Sanju |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2023 |
| Descrizione fisica | 1 online resource (234 pages) |
| Altri autori (Persone) |
Ortiz-RodríguezFernando
MishraSashikala VakajEdlira KotechaKetan |
| Collana | Communications in Computer and Information Science Series |
| ISBN | 3-031-47997-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNISA-996565862603316 |
Tiwari Sanju
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| Cham : , : Springer International Publishing AG, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Data Science and Intelligent Applications [[electronic resource] ] : Proceedings of ICDSIA 2020 / / edited by Ketan Kotecha, Vincenzo Piuri, Hetalkumar N. Shah, Rajan Patel
| Data Science and Intelligent Applications [[electronic resource] ] : Proceedings of ICDSIA 2020 / / edited by Ketan Kotecha, Vincenzo Piuri, Hetalkumar N. Shah, Rajan Patel |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (556 pages) |
| Disciplina | 005.7 |
| Collana | Lecture Notes on Data Engineering and Communications Technologies |
| Soggetto topico |
Computational intelligence
Artificial intelligence Engineering—Data processing Data mining Computer security Computational Intelligence Artificial Intelligence Data Engineering Data Mining and Knowledge Discovery Systems and Data Security |
| ISBN | 981-15-4474-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Hate Speech Detection: A Bird’s Eye View -- Intrusion Detection System Using Semi-supervised Machine Learning -- A Survey on Machine Learning and Deep Learning based Approaches for Sarcasm Identification in Social Media -- A Machine Learning Algorithm to Predict Financial Investment -- Genetic Algorithm based Task Scheduling for Load Balancing in Cloud -- Experimental Analysis of Measuring Neighbourhood Change in the Presence of Wormhole in Mobile Wireless Sensor Networks -- Privy Cloud/Web Server -- Automating Container Deployments Using CI/CD -- Security and Performance Evaluations of QUIC Protocol -- Comparative Survey of Digital Image Steganography Spatial Domain Techniques -- Automatic Evaluation of Analog Circuit Designs. |
| Record Nr. | UNINA-9910483850203321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Hybrid Intelligent Systems [[electronic resource] ] : 22nd International Conference on Hybrid Intelligent Systems (HIS 2022), December 13–15, 2022 / / edited by Ajith Abraham, Tzung-Pei Hong, Ketan Kotecha, Kun Ma, Pooja Manghirmalani Mishra, Niketa Gandhi
| Hybrid Intelligent Systems [[electronic resource] ] : 22nd International Conference on Hybrid Intelligent Systems (HIS 2022), December 13–15, 2022 / / edited by Ajith Abraham, Tzung-Pei Hong, Ketan Kotecha, Kun Ma, Pooja Manghirmalani Mishra, Niketa Gandhi |
| Autore | Abraham Ajith |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (1380 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
HongTzung-Pei
KotechaKetan MaKun Manghirmalani MishraPooja GandhiNiketa |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| Soggetto non controllato |
Artificial Intelligence
Engineering Computers Technology & Engineering |
| ISBN | 3-031-27409-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910728399503321 |
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Abraham Ajith
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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