Multi-Strategy Learning Environment

Name: Multi-Strategy Learning Environment | Proceedings of ICMSLE 2024
Brand: Springer
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Proceedings of ICMSLE 2024

Vrince Vimal Isidoros Perikos Amrit Mukherjee Vincenzo Piuri(Editor)

Springer (Publisher)

1st Edition

Published on 28. May 2024

XVI, 717 pages

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978-981-97-1488-9 (ISBN)

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Dr. Vrince Vimal is qualified with a Ph.D. (Communication Systems) IIT Roorkee, backed by M.Tech. (Electronics & Communications) and B.E. (Electronics & Telecommunications with the distinction of clearing GATE and innovative experience of 16+ years across Education/Research. He has published 19 Indian national patents, 7 S.C.I. articles, and SCOPUS-indexed articles with a total of 17 publications. He has an experimental attitude toward teaching methodologies, including curriculum design and development of student-centered, congenial learning techniques to instill enthusiasm toward learning in students. He is an innovative conceptualist with a sharp eye for fresh approaches and details. Overall, he is an academics-oriented, competent, and highly organized individual committed to professional development and continual knowledge acquisition. His critical competencies in structuring and implementing innovative administrative policies/procedures generate undivided commitment and dedication among the team members. He is Member IEEE, Life Member IAENG.

Dr. Isidoros Perikos completed his Ph.D. in Computer Engineering and Informatics, Computer Engineering and Informatics Department at University of Patras, Greece (2016), and M.Sc. in Computer Science and Technology, Computer Engineering and Informatics Department at University of Patras (2010). He has completed an Engineering Diploma (5-year program, M.Eng.) in Computer Engineering and Informatics, Computer Engineering and Informatics Department at University of Patras (2008). His research interests include Semantic Web and Ontology Engineering, Web Intelligence, Natural Language Processing and Understanding, Human Computer Interaction and Affective Computing Robotics. He has published in national and international journals and conferences.

Dr. Amrit Mukherjee obtained a Ph.D. from KIIT University, India. He is currently working in the Department of Computer Science, Faculty of Science, the University of SouthBohemia in Ceske Budejovice, Branisovska 1760, Ceske Budejovice, Czech Republic. He was Post-doc Research Fellow in the School of Computer Science and Communication Engineering, Jiangsu University, PR China. Also, he has been Reviewer in many IEEE journals and other prestigious conferences. He also served as Special Issue Guest Editor for IEEE Internet of Things Journal, Computer Communications; Multimedia Tool and Applications; Journal of System Architecture; Computers, Material and Continua; Frontiers in Physics, Sustainability. He is also Associate Editor in International Journal of Interactive Multimedia and Artificial Intelligence and Academic Editor in Wireless Communications and Mobile Computing and several other reputed journals. His interests include Artificial Intelligence, Next-Generation IoT Systems, Wireless Sensor Networks, Cognitive Radio, and Signal Processing.

Prof. Vincenzo Piuri has received his Ph.D. in computer engineering at Polytechnic of Milan, Italy (1989). He is Full Professor in computer engineering at the University of Milan, Italy (since 2000). He has been Associate Professor at Polytechnic of Milan, Italy, Visiting Professor at the University of Texas at Austin, USA, and Visiting Researcher at George Mason University, USA. His main research interests are artificial intelligence, computational intelligence, intelligent systems, machine learning, pattern analysis and recognition, signal and image processing, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, fault tolerance, cloud computing infrastructures, and Internet of things. Original results have been published in 400+ papers in international journals, proceedings of international conferences, books, and book chapters. He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS.

Content

Intro
Preface
Contents
About the Editors
1 AI Powered Chat Assistant for Trauma Detection from Text and Voice Conversations with a Direct Doctor Connection
1 Introduction
2 Related Works
2.1 Emotional Intelligence in Communication: Research Challenges, Literature and Recent Achievements
2.2 Chatbot Mobile Isolation App for Depression
2.3 Mental Health Chatbot that Uses NLP and AI to Deliver Behavioral Insights and Remote Healthcare
2.4 Mental Health Support Chatbot Using NLP
2.5 Dost-Chatbot as Mental Health Assistant
2.6 Application of Cognitive Behavioral Therapy in Psychiatry: A Review
2.7 Revivify: Depression Research and Management Using Automated Tweets and Chatbots
2.8 Proposed Chatbot: Thinking and Problem-Solving Experience
2.9 Psykh, the Chatbot Using the Rasa Open Source Framework, to be Your Therapist and Stress Reliever
2.10 Identify Depression in a Person Using Speech Signals by Extracting Energy and Situations
3 Methodology
3.1 Module Description
4 Implementation
4.1 Implementation of Machine Learning Models
4.2 NLP Model Development
5 Result and Discussion
6 Conclusion
References
2 An Intelligent Car Locating System Based on Arduino for a Massive Parking Place
1 Introduction
2 Motivation
3 Problem Statement
4 Project Scope
5 Objectives
6 Related Study
7 Comparison with Other System
8 Background Tools and Technology
8.1 Software Tools
8.2 Arduino Simulator
8.3 Hardware Tools
9 PCB Design of Intelligent Car Locating System for a Massive Parking Place
10 Proposed Model of Smart Car Parking System
11 Work Flow Diagram of Smart Car Locating System
12 Final Circuit of Intelligent Car Locating System for a Massive Parking Place
13 Implementation
13.1 Final Output of Intelligent Car Locating System for a Massive Parking Place
14 Result and Discussion
15 Testing and Evaluation
15.1 Performance Analysis of IoT-Based Intelligent Car Parking System for Large Parking Lot Using Arduino
16 Contribution
17 Conclusion
18 Future Recommendation
References
3 Electricity Load Forecasting Using LSTM for Household Usage
1 Introduction
2 Literature Review
3 LSTM for Electricity Load Forecasting Methodology
3.1 Building Machine-Learning Model
3.2 Training ML Model
4 Results and Discussions
4.1 Electricity Load Prediction Over Different Periods
4.2 Error Analysis for Electricity Load Prediction
4.3 Comparison with Conventional Methods
5 Conclusion
References
4 A Cybersecurity Classification Model for Detecting Cyberattacks
1 Introduction
2 Literature Survey
2.1 Training with Support Vector Machine (SVM) for Cyberattack Detection
2.2 Normalization for Removing Noise from Given Datasets
2.3 Autoencoders with Cybersecurity
3 Dataset Description
4 Experimental Results
5 Conclusion
References
5 Implementation of Baumann Skin Type Indicator Using Machine Learning
1 Introduction
2 Related Work
3 Brief Overview of the Fundamental Dichotomies of BSTI
4 Proposed Architecture
4.1 Machine Learning Model Architecture
4.2 Dataset
5 Experiments and Results
5.1 Results of the InceptionV3 Model
5.2 Comparison with Other Deep Learning Techniques
6 Conclusion and Future Work
References
6 An On-demand Data Delivery and Secured Platform in Cloud Computing
1 Introduction
1.1 Methodology
2 Literature Survey
3 On-demand Data Delivery and Secured Platform (ODDSP)
3.1 Quality of Service (QoS)
4 Advanced Encryption Standard (AES) for Cloud Data
4.1 Performance Metrics
4.2 Evaluation Results
5 Conclusion
References
7 Real-Time Sign Language Interpreter Using MediaPipe, Dynamic Time Warping, and NLP
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Problem Domain
3.2 Problem Definition
3.3 Problem Statement
3.4 Dataset
4 Implementation
4.1 MediaPipe Detection/Holistic Model
4.2 Extract Landmarks
4.3 Draw Landmarks
4.4 Models
4.5 Dynamic Time Warping (DTW)
4.6 Sign Prediction
4.7 Phrase Generation
5 Result
6 Discussion
7 Conclusion
References
8 A Support Vector Machine Classifier Approach for Predicting Preeclampsia and Gestational Hypertension
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Description of the Dataset
3.2 Data Preprocessing
3.3 Importance of Using SVM in ML Experiments
3.4 Training and Testing Data
3.5 Methodology
3.6 Selection of Algorithm
3.7 Evaluation of the Schemes' Performance
4 Results and Discussion
5 Conclusion
References
9 Multilingual Communication: NMT-Based On-Call Speech Translation for Indian Languages
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Dataset
3.2 Algorithms
3.3 Architecture
3.4 Model Training
3.5 Evaluation
4 Result
5 Conclusion
6 Future Work
References
10 Brain Tumor Segmentation and Classification Using Deep Learning
1 Introduction
2 Literature Survey
2.1 Problem Formulation
2.2 Research Gap
3 Data and Variables
3.1 About Dataset
3.2 Variables for Segmentation Model:
3.3 Variables for Classification Model:
4 Methodology and Model Specifications
4.1 Segmentation Model
4.2 Classification Model
5 Empirical Results
5.1 Segmentation Model
5.2 Classification Model
6 Conclusion
7 Future Scope
References
11 ACO-Optimized DRL Model for Energy-Efficient Resource Allocation in High-Performance Computing
1 Introduction
2 Literature Review
3 Problem Definition
4 Methods
4.1 Ant Colony Optimization
4.2 DRL for Resource Allocation in HPC
5 Proposed Model
5.1 ACO-Optimized DRL for Resource Allocation in HPC
6 Experimental Analysis
6.1 Response Time Analysis
6.2 Makespan Analysis
6.3 Energy Consumption Analysis
7 Conclusion and Future Work
References
12 Business Decision-Making Using Hybrid LSTM for Enhanced Operational Efficiency
1 Introduction
2 Literature Review
3 Problem Definition
4 Data Collection
4.1 Data Preprocessing
5 Hybrid Optimized LSTM Model for Sales Prediction
5.1 Loss Function
6 Experimental Analysis
6.1 Model Training
6.2 Forecast Analysis
6.3 Performance Analysis
7 Conclusion and Future Work
References
13 Unveiling New Horizons in Machine Learning, NLP-Driven Framework for Student Learning Behavior
1 Introduction
2 Literature Review
3 Modeling and Analysis of Student Learning Behavior
3.1 Role of Machine Learning in Analyzing the Student Behavior
4 Techniques for Modeling Student Learning Behavior
4.1 Employing Natural Language Processing
4.2 Employing Natural Language Processing
5 Conclusion and Scope for Future Work
References
14 Mirror Text Classification from Image Using Machine Learning Techniques
1 Introduction
2 Literature Review
3 Methodology
3.1 Input and Preprocessing
3.2 Clustering and Sub-clustering
3.3 Feature Extraction
3.4 Training
3.5 Mirror Text Identification
4 Results and Discussion
5 Conclusion
References
15 Using Deep Learning to Identify Types of Lung Diseases from X-Ray Images
1 Introduction
2 Objective
3 Literature Survey
4 Proposed System
5 Conclusion
References
16 A Light-Weight Data Storage and Delivery Platform in Cloud Computing
1 Introduction
1.1 Methodology
2 Literature Survey
3 Lempel-Ziv-Markov (LZMA) Compression
3.1 RSA-KEM (Key Encapsulation Mechanism)
3.2 Symmetric-Key Decryption
3.3 Dataset Description
3.4 Performance Metrics
4 Experimental Results
5 Conclusion
References
17 Big Data Analytics Security Issues and Solutions in Healthcare
1 Introduction
2 The Need for Medical Care Analytics for Big Data
3 Different Big Data Analytics Phases
3.1 Extracting, Data Cleaning, and Data Collection
3.2 Integration and Aggregation of Data
3.3 Data Model
3.4 Data Delivery, Interpretation, and Feedback
4 Role-Based Application Security Principles
5 Big Data Lifecycle
6 Technologies in Use
7 Big Data Security Challenge and Solution
8 Conclusion
References
18 Framework for Early-Stage Diabetes Mellitus Risk Prediction Using Hybrid Supervised Learning
1 Introduction
2 Literature Survey
3 Proposed Framework
4 Experimental Setup
4.1 Evaluation Metrics
5 Results and Discussion
5.1 Prediction Performance of ML Techniques
5.2 Optimization of Prediction Accuracy
6 Conclusion
References
19 Brain Tumor Classification in MRI Images: A CNN and U-Net Approach
1 Introduction
2 Proposed Method
2.1 Data Collection
2.2 Image Preprocessing
2.3 Image Segmentation
2.4 Feature Extraction
2.5 Convolutional Neural Network (CNN)
2.6 U-Net Architecture
2.7 Callback Functions
3 Result and Discussion
3.1 Evaluation Metrics
3.2 Experimental Analysis and Result
3.3 Prediction of Tumor
4 Conclusion
References
20 Cross-Modal Text-to-Video Retrieval Using Deep Learning
1 Introduction
2 Related Work
3 System Architecture
4 Methodology
5 Model Implementation and Output
6 Results
7 Comparative Analysis of Cross-Model Text-to-Video Retrieval Approaches
8 Conclusion
9 Future Scope
References
21 Breast Cancer Prediction Using Hybrid Logistic Regression
1 Introduction
2 Literature Review
3 Proposed Work
4 Results and Discussion
5 Conclusion
References
22 Hybrid Bat Harris Hawks Optimized Approach for Data Retrieval Using Deep Convolution Neural Networks
1 Introduction
2 Literature Review
3 Proposed Model for Data Retrieval
4 Implementation of Hybrid Bat Harris Hawks Optimized Approach for Data Retrieval via Deep CNNs
5 Results and Discussion
6 Conclusion and Future Scope
References
23 VCard: An Optimal, Secured and Centralized Database Solution to Fulfill Digital Bangladesh Dream
1 Introduction
2 Literature Review
2.1 Centralized Solution in Bangladesh Perspective
2.2 System Security and Functionality
3 Proposed Centralized Solution
3.1 User Registration
3.2 Medical Team and Hospital
3.3 Criminology Databases
3.4 Consumer Transaction Data
3.5 Stakeholder Workflow
3.6 Synchronizing with the Old Database
4 Performance Evaluation
4.1 Environmental Setup
4.2 Load Balance Testing Results and Discussion
5 Conclusion
References
24 A Comprehensive Review on Advances in Detection of Knee Osteoarthritis
1 Introduction
2 Related Work
2.1 Prediction of Joint Space Narrowing Progression in KOA Patients
2.2 KOA Prediction Using Cartilage Damage Index
2.3 Grading Methods for KOA
2.4 Early Detection of KOA
3 Summary of Review
3.1 Comparative Analysis
3.2 Challenges
3.3 Future Directions
4 Conclusion
References
25 Systematic Approach for Detection of Fake News on Social Media Platform
1 Introduction
2 Literature Review
3 Problem Statement
4 Proposed Method for Detection of False Information
5 Expected Outcome and Conclusion
6 Future Work
References
26 Leveraging Metaheuristic Optimized Machine Learning Classifiers to Determine Employee Satisfaction
1 Introduction
2 Related Works
2.1 XGboost
2.2 Metaheuristic Optimization
3 Methods
3.1 Original SCHO
3.2 Modified Algorithm
4 Experimental Setup
5 Simulation Outcomes
6 Conclusion
References
27 Exploring the Impact of Blockchain, AI, and ML on Financial Accounting Efficiency and Transformation
1 Introduction
2 Background
2.1 Blockchain
2.2 Artificial Intelligence
3 Objectives
4 Methodology
5 Results
5.1 Blockchain Technology in Financial Accounting
5.2 Artificial Intelligence in Financial Accounting
5.3 Artificial Intelligence and Blockchain Integration in Financial Accounting
5.4 Machine Learning in Financial Accounting
6 Analysis
6.1 Security Risk
7 Conclusion and Future Directions
References
28 Data Stream Learning with Selective Base Learners of Ensemble Classifiers: Perspectives for Better Information Systems
1 Introduction
2 Data Stream Learning
3 Ensemble for Data Stream Learning
4 Research Methodology
4.1 Algorithmic Workflow of Research Approach
5 About Datasets
5.1 Forest-Cover Type Dataset
5.2 Electricity Dataset
6 Experimental Work
7 Results and Discussion
7.1 Evaluation Criteria
8 Experimental Results-Graphs
9 Conclusion and Further Research
Annexure
References
29 Text-to-Face Generation Using DCGAN with Bert-Embedding Vectors
1 Introduction
2 Literature Survey
3 Proposed System
3.1 Dataset
4 System Architecture
5 Methodology
5.1 Data Collection and Preprocessing
5.2 Bert Embedding Model
5.3 DCGANs Model
5.4 Training
6 Deployment
7 Result and Analysis
8 Conclusion
References
30 Intelligent Transportation System for Sustainable and Efficient Urban Mobility: Machine Learning Approach for Traffic Flow Prediction
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Exploratory Data Analysis (EDA)
3.2 Feature Engineering
3.3 Prediction Models
4 Results and Discussion
4.1 Hardware and Software Setup
4.2 Performance Evaluation
4.3 Summary of Findings
5 Conclusion
References
31 MediCrypt: Survey on Automated Recognition of Handwritten Medical Prescriptions for Enhanced Healthcare Efficiency
1 Introduction
2 Related Work
3 Observation Table
4 Proposed Work
5 Architecture
6 Conclusion
References
32 Segmentation and Multi-Label Classification of Visual Cervical Pathology by Deep Neural Networks
1 Introduction
2 Background and Other Related Works
3 Methodology
3.1 Datasets
3.2 Models
3.3 Workflow: Data Augmentation, Labeling, Metrics
4 Results
5 Discussion
6 Conclusions
References
33 Comparative Analysis of Advanced Deep Learning Algorithms for Object Detection
1 Introduction
2 Background
2.1 RetinaNet
2.2 DETR
2.3 YOLOv5
2.4 FCOS
3 Methodology
3.1 Dataset Preparation
3.2 Comparison Criteria
3.3 Experimental Setup
4 Results and Discussion
4.1 Comparative Analysis
5 Conclusion
References
34 Enhanced Skin Disease Image Analysis Using Hybrid CLAHE-Median Filter and Salient K-Means Cluster
1 Introduction
2 Literature Review
3 Methodology
4 Result and Discussion
4.1 Dataset Description
4.2 Noise Filtering
4.3 Segmentation
4.4 Feature Extraction
4.5 Classification
5 Conclusion
References
35 Entropy Based Defect Detection for Patterned Fabrics
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset Selection
3.2 Preprocessing (Denoising Fabric)
3.3 Extraction and Selection of Features
4 Result and Discussion
4.1 The Structural Feature Extraction and Entropy-Based Feature Selection
4.2 Performance Measure for Structural-Based Feature Extraction and Entropy-Based Feature Selection
5 Conclusion
References
36 Importance of Drug Features in Drug-Drug Interaction: A Comparative Study
1 Introduction
2 Literature Review
3 Dataset
3.1 Drug-Drug Interaction Dataset
3.2 Drug Features Dataset
3.3 Drug Names Dataset
4 Methodology
4.1 Dataset Preprocessing
4.2 Comparative Analysis of Models
4.3 Selection of Model
4.4 Feature Importance Estimation
5 Results and Discussion
6 Conclusion and Future Work
References
37 Classification of Fine-Grained Emotions
1 Introduction
2 Annotated Punjabi Emotional Text Classification
2.1 Support Vector Machine
2.2 Decision Tree
2.3 Logistic Regression
2.4 Random Forest
2.5 Multi-layer Perceptron
2.6 Gaussian Naïve Bayes
2.7 Hybrid Classifier
2.8 Average Recognition Rates
3 Emotion-Wise Analysis of Recognition Rate
4 Experimentations
5 Conclusion and Future Scope
References
38 Implementation of Pothole Detection System and Automated Complaint Redressal with Road Transport Licensing Committee
1 Introduction
2 Proposed Research Problem
3 Novel Approach to Pothole Detection
4 Proposed Methodology
4.1 Existing System
4.2 Model Architecture
4.3 Exploring the Dataset
5 Design and Implementation
5.1 System Architecture
5.2 YOLOv5
5.3 SMTP
6 Performance Evaluation and Result Analysis
6.1 Model Summary
7 Result Analysis
7.1 Confusion Matrix
7.2 Precision-Recall Curve
8 Conclusion and Future Scope
References
39 Resource Management in Hadoop Clusters at the Storing Level of Hadoop Distributed File System
1 Introduction
2 Related Work
3 Proposed Work
4 Results
5 Conclusion and Future Scope
References
40 Machine Learning-Based Rice Seed Quality Assessment: A Comprehensive Study
1 Introduction
2 Literature Survey
3 Model
3.1 Naïve Bayes (NB)
3.2 K-Nearest Neighbor (KNN)
3.3 Multi-layer Perceptron (MLP)
4 Results and Discussion
4.1 Dataset
4.2 Performance Metrics
4.3 Results
5 Conclusion
References
41 Diagnosis of Ovarian Cancer Using Convolutional Neural Network and Attention Mechanism
1 Introduction
2 Related Works
2.1 Methods Work with Machine Learning
2.2 Methods Work with Deep Learning
3 Proposed Methodology
3.1 CNN Framework and Descriptors
4 Design
5 Experimental Results
6 Conclusion
References
42 Advances in Deep Learning-Based Object Detection and Tracking for Autonomous Driving: A Review and Future Directions
1 Introduction
2 Related Work
3 Comparison of Object Detection Methods
4 Challenges and Future Directions
4.1 Challenges
4.2 Future Directions
5 Conclusion
References
43 Resume Screening Using Hybrid Deep Learning Model
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Dataset
3.2 Data Preprocessing
3.3 Model Training
4 Evaluation of the Models
5 Results and Analysis of the Models
6 Conclusion
References
44 Sentiment Forecasting in Women's Fashion E-Commerce: A Machine Learning Perspective
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Dataset Description
3.2 Reading the Dataset and Importing Modules
3.3 Including Word Counts in the Data Frame and Determining the Frequency of Specific Words
3.4 Using a Word Cloud to Show the Densities of Selected Words, Class Names, and All Words in the Reviews
3.5 Examining the Relationship Among Age, Class Name, and Rating
3.6 Creating a Classifier for Sentiment
4 Evaluating Models
4.1 Confusion Matrices
4.2 Experimental Results and Analysis
5 Conclusion
References
45 Physio at Home: Survey on AI Motion Tracking for Medical Recovery Exercises and Suggestions Based on Accuracy
1 Introduction
2 Related Work
2.1 Future Works Suggested in the Paper
3 Proposed Work
4 Conclusion
References
46 Secondary Testosterone Deficiency Identification Using Hybrid Machine Learning Classifiers
1 Introduction
2 Relationship Between Heart Disease and Testosterone Deficiency
2.1 Insufficiency of Testosterone
2.2 Elevated Levels of Testosterone
2.3 Age-Related Factor
3 Literature Survey on Various Methods Involved in Identifying Disease Using Ml Technique
4 Inference from Literature Survey
5 Conclusion
References
47 Machine Learning for Company Review Sentiment Analysis Interpretation
1 Introduction
2 Related Works
3 Methods
3.1 Text Encoding Via Term Frequency-Inverse Document Frequency
3.2 Decision Trees
3.3 Random Forest
3.4 XGBoost
3.5 Support Vector Machines
3.6 Multilayer Perceptrons
3.7 K-Nearest Neighbor
3.8 Shapley Additive Explanations
4 Experimental Setup
5 Simulation Outcomes
5.1 Best Model Interpretation
6 Conclusion
References
48 Compressive Embedding Method for Reversible Steganography Using XOR Approach
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Result and Analysis
5 Conclusion and Future Scope
References
49 Elevating Large-scale Forest Surveillance: A Deep Learning Analysis of Inception V3 and EfficientNet for IoT-Driven Fire Detection
1 Introduction
2 Related Work
3 Problem Statement
4 Methodology
4.1 Data Collection and Preprocessing
4.2 Dataset
4.3 Base Model: Inception V3 Fine-Tuning
5 Training
5.1 EfficientNet Model Comparison
5.2 IoT Sensor Integration
5.3 Real-Time Aerial Surveillance
5.4 System Scalability and Real-Time Processing
5.5 Data Analysis
6 Proposed Solution
7 Results
8 Proposed Solution
9 Conclusion
References
50 Algorithm for Simplifying Procedures for Digital Measurement of Signal Parameters and Reducing Signal Errors
1 Introduction
2 Signal Representation Models
3 Simulation-Based Error Study for Known Component Isolation
4 Error Analysıs in Digıtal Transmıssıon Systems
5 Increasing Measurement Accuracy
6 Materials and Methods
7 Results and Discussion
8 Conclusions
References
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Multi-Strategy Learning Environment

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