Progresses in Artificial Intelligence & Robotics: Algorithms & Applications

Name: Progresses in Artificial Intelligence & Robotics: Algorithms & Applications | Proceedings of 3rd International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2021
Brand: Springer
Price: 160.49 EUR
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Proceedings of 3rd International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2021

Luigi Troiano Alfredo Vaccaro Nishtha Kesswani Irene Díaz Rodriguez Imene Brigui(Editor)

Springer (Publisher)

Published on 9. April 2022

VI, 184 pages

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978-3-030-98531-8 (ISBN)

€160.49incl. 7% vat

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Content

Intro
Contents
An Opinion Mining of Text in COVID-19 Issues Along with Comparative Study in ML, BERT & RNN
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Pre-processing
3.3 Training Parameter
3.4 RNN Model
3.5 BERT Model
3.6 Statistical Analysis
4 Result and Discussion
5 Conclusion and Future Work
References
AI-ML Based Smart Online Examination Framework
1 Introduction
1.1 Facial Recognition and ML
2 Literature Survey
3 Objectives
4 Existing System Architecture
5 Proposed System Architecture
5.1 Results
6 Conclusion
References
Spaced Repetition Based Adaptive E-Learning Framework
1 Introduction
2 Objectives
3 Literature Review
4 Existing System Architecture
5 Proposed Architecture
6 Conclusion
References
Automation of Supply Chain Management for Healthcare
1 Introduction
2 Literature Review
3 Objectives
4 Existing System Architecture
5 Proposed System Architecture
6 Conclusion
References
The Detection, Extraction, and Classification of Human Pose in Alzheimer's Patients
1 Introduction
2 Proposed Methodology
3 Development of the Model
4 Results
5 Conclusions
References
Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Long-Short Term Memory
3.3 LSTM Model Estimation with the Parameters
3.4 LSTM Model Analysis
4 Results and Discussion
5 Conclusion and Future Work
References
Leveraging Free-Form Text in Maintenance Logs Through BERT Transfer Learning
1 Introduction
2 Methodology
2.1 Dataset Preprocessing
2.2 Dataset Distribution, Augmentation and Splitting
2.3 Machine Learning Models
3 Results and Discussion
3.1 Performance with and Without Data Augmentation
4 Conclusion
5 Future Work
References
Context-Aware Explanations in Recommender Systems
1 Introduction
2 Related Works
2.1 Context-Aware Recommender Systems (CARSs)
2.2 Context-Aware Explanations in Recommender Systems (RSs)
3 Our Proposition
3.1 Recommendation Method
3.2 Experiment Setup
3.3 Baselines
4 Results of Experiments
4.1 Statistics About the Participants and Data Obtained
4.2 Observations of Users' Responses
5 Conclusions and Perspectives
References
Improved Local Binary Pattern for Face Recognition
1 Introduction
2 Illustration of LBP, HELBP, MBP and HOG Descriptors
2.1 Local Binary Pattern (LBP)
2.2 Horizontal Elliptical Local Binary Pattern (HELBP)
2.3 Median Binary Pattern (MBP)
2.4 Histogram of Oriented Gradients (HOG)
3 Description of the Proposed Descriptor and Full FR Framework
3.1 The Proposed Descriptor Improved Local Binary Pattern (ILBP)
3.2 Full FR Framework
4 Experiments
4.1 Dataset Details
4.2 Feature Size Particulars of the Descriptors
4.3 Estimation of Recognition Rate
4.4 Comparison of RR with Literature Methods
5 Conclusions with Future Scope
References
Incorporating Dynamic Information into Content-Based Recommender System in Online Learning Environment
1 Introduction
2 Related Work
2.1 Online Learning Environment (OLE)
2.2 Dynamic Information
2.3 Recommender System (RS)
3 Methodology
3.1 DCRS Framework
3.2 Activity Records
3.3 Dynamic Learner Model
3.4 Resource Profile
3.5 Recommendation
4 Discussion:Dynamic Factors Integration in Content-Based Recommender System (CB RS)
5 Conclusion
References
Towards Personalized Educational Resources Recommendations for Teachers
1 Introduction
2 Problem Statement
3 Related Work
3.1 Educational Data Integration
3.2 Educational Ontologies and Ontology-Based Recommender Systems
4 Approach Architecture
4.1 Ontology-Based Data
4.2 Hybrid Recommender Engine
5 Discussion
6 Conclusion and Perspectives
References
DEEC and EDEEC Routing Protocols for Heterogeneous Wireless Sensor Networks: A Brief Comparative Study
1 Introduction
2 Related Work
3 The Network Model
3.1 The Energy Model
3.2 The Heterogeneous Network Model
4 Simulation
5 Conclusion
References
Towards an Ontology-Based Recommender System for the Vehicle Sales Area
1 Introduction
2 Related Work
3 Ontology-Based Vehicle Recommender System
3.1 Needs for Building an Explainable RS in the Vehicle Sales Area
3.2 Development of an Ontology-Based Vehicle Recommender System
3.3 Data Gathering
3.4 Ontology Construction
3.5 Semantic Recommendations from Filtering and Reasoning
3.6 Recommendation Computation
4 Use Case Example
5 Conclusion and Perspective
References
Dominance Relation Based Ranking Procedure for Automated Reverse Auctions
1 Introduction
2 Principles of Auction Approach
2.1 Negotiation Algorithm
2.2 Bidding Process
3 Ranking Procedure
3.1 Dominance Relation
3.2 Partial Scores
3.3 Scoring Function
3.4 Ranking Function
4 Illustrative Example
5 Conclusion
References
Predicting Business Failure Using Neural Networks: An Empirical Comparison with Statistical Methods and Data Mining Method
1 Introduction
2 Related Works
3 Modelling Methods
4 Data Collection and Pre-processing
5 Criteria for Comparing the Models
6 Results
7 Conclusion and Future Directions
References
Pandemic Effect on Education System Among University Students
1 Introduction
2 Review Works
3 Research Methodology
3.1 Data Collection
3.2 Data Pre-processing
3.3 Data Cleaning
3.4 Data Modelling
4 Result and Discussion
4.1 Confusion Matrix
4.2 Classification Report
4.3 Accuracy
5 Comparative Analysis
6 Conclusion and Future Work
References
Prediction of Migration Outcome Using Machine Learning
1 Introduction
2 Review Works
3 Research Methodology
3.1 Research Subject and Instrumentation
3.2 Data Collection Procedure
3.3 Data Pre-processing
3.4 Attributes and Feature Selection
3.5 Algorithm for Predicting Migration Satisfaction
3.6 Decision Tree
3.7 Random Forest Classifier
4 Results
5 Experimental Discussion
5.1 Descriptive Analysis
5.2 Experimental Results
6 Conclusion
References
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Progresses in Artificial Intelligence & Robotics: Algorithms & Applications

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