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Advanced Analytics and Deep Learning Models
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Страница 1
Table of Contents
List of Table
List of Figures
Guide
Pages
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1
Artificial Intelligence in Language Learning: Practices and Prospects
1.1 Introduction
1.2 Evolution of CALL
1.3 Defining Artificial Intelligence
1.4 Historical Overview of AI in Education and Language Learning
1.5 Implication of Artificial Intelligence in Education
1.5.1 Machine Translation
1.5.2 Chatbots
1.5.3 Automatic Speech Recognition Tools
1.5.4 Autocorrect/Automatic Text Evaluator
1.5.5 Vocabulary Training Applications
1.5.6 Google Docs Speech Recognition
1.5.7 Language MuseTM Activity Palette
1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 1.6.1 Autonomous Learning
1.6.2 Produce Smart Content
1.6.3 Task Automation
1.6.4 Access to Education for Students with Physical Disabilities
1.7 Conclusion
References
Страница 32
2
Real Estate Price Prediction Using Machine Learning Algorithms
2.1 Introduction
2.2 Literature Review
2.3 Proposed Work 2.3.1 Methodology
2.3.2 Work Flow
2.3.3 The Dataset
2.3.4 Data Handling
2.3.4.1 Missing Values and Data Cleaning
2.3.4.2 Feature Engineering
2.3.4.3 Removing Outliers
2.4 Algorithms 2.4.1 Linear Regression
2.4.2 LASSO Regression
2.4.3 Decision Tree
2.4.4 Support Vector Machine
2.4.5 Random Forest Regressor
2.4.6 XGBoost
2.5 Evaluation Metrics
2.6 Result of Prediction
References
Страница 51
3
Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach
3.1 Introduction
3.2 Work Related Multi-Criteria Recommender System
3.3 Working Principle
3.3.1 Modeling Phase
3.3.2 Prediction Phase
3.3.3 Recommendation Phase
3.3.4 Content-Based Approach
3.3.5 Collaborative Filtering Approach
3.3.6 Knowledge-Based Filtering Approach
3.4 Comparison Among Different Methods
3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis
3.4.1.1 Discussion and Result
3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally
et al.
3.4.2.1 Dataset and Evaluation Matrix
3.4.2.2 Training Setting
3.4.2.3 Result
3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng
3.4.3.1 Evaluation Setting
3.4.3.2 Experimental Result
3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng
3.4.4.1 Experimental Dataset
3.4.4.2 Experimental Result
3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali
3.4.5.1 Experimental Evaluation
3.4.5.2 Result and Analysis
3.5 Advantages of Multi-Criteria Recommender System
3.5.1 Revenue
3.5.2 Customer Satisfaction
3.5.3 Personalization
3.5.4 Discovery
3.5.5 Provide Reports
3.6 Challenges of Multi-Criteria Recommender System
3.6.1 Cold Start Problem
3.6.2 Sparsity Problem
3.6.3 Scalability
3.6.4 Over Specialization Problem
3.6.5 Diversity
3.6.6 Serendipity
3.6.7 Privacy
3.6.8 Shilling Attacks
3.6.9 Gray Sheep
3.7 Conclusion
References
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