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Shaveta Malik, Shaveta (Terna Engineering College Malik, Mire, a Mire, Archana Mire, Archana (Terna Engineering College Mire...
Advanced Analytics and Deep Learning Models
English · Hardback
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Description
Advanced Analytics and Deep Learning Models
The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.
Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools.
However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc.
This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence.
Audience
Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
List of contents
Preface xix
Part 1: Introduction to Computer Vision 1
1 Artificial Intelligence in Language Learning: Practices and Prospects 3
Khushboo Kuddus
1.1 Introduction 4
1.2 Evolution of CALL 5
1.3 Defining Artificial Intelligence 7
1.4 Historical Overview of AI in Education and Language Learning 7
1.5 Implication of Artificial Intelligence in Education 8
1.5.1 Machine Translation 9
1.5.2 Chatbots 9
1.5.3 Automatic Speech Recognition Tools 9
1.5.4 Autocorrect/Automatic Text Evaluator 11
1.5.5 Vocabulary Training Applications 12
1.5.6 Google Docs Speech Recognition 12
1.5.7 Language MuseTM Activity Palette 13
1.6 Artificial Intelligence Tools Enhance the Teaching and Learning Processes 13
1.6.1 Autonomous Learning 13
1.6.2 Produce Smart Content 13
1.6.3 Task Automation 13
1.6.4 Access to Education for Students with Physical Disabilities 14
1.7 Conclusion 14
References 15
2 Real Estate Price Prediction Using Machine Learning Algorithms 19
Palak Furia and Anand Khandare
2.1 Introduction 20
2.2 Literature Review 20
2.3 Proposed Work 21
2.3.1 Methodology 21
2.3.2 Work Flow 22
2.3.3 The Dataset 22
2.3.4 Data Handling 23
2.3.4.1 Missing Values and Data Cleaning 23
2.3.4.2 Feature Engineering 24
2.3.4.3 Removing Outliers 25
2.4 Algorithms 27
2.4.1 Linear Regression 27
2.4.2 LASSO Regression 27
2.4.3 Decision Tree 28
2.4.4 Support Vector Machine 28
2.4.5 Random Forest Regressor 28
2.4.6 XGBoost 29
2.5 Evaluation Metrics 29
2.6 Result of Prediction 30
References 31
3 Multi-Criteria-Based Entertainment Recommender System Using Clustering Approach 33
Chandramouli Das, Abhaya Kumar Sahoo and Chittaranjan Pradhan
3.1 Introduction 34
3.2 Work Related Multi-Criteria Recommender System 35
3.3 Working Principle 38
3.3.1 Modeling Phase 39
3.3.2 Prediction Phase 39
3.3.3 Recommendation Phase 40
3.3.4 Content-Based Approach 40
3.3.5 Collaborative Filtering Approach 41
3.3.6 Knowledge-Based Filtering Approach 41
3.4 Comparison Among Different Methods 42
3.4.1 MCRS Exploiting Aspect-Based Sentiment Analysis 42
3.4.1.1 Discussion and Result 43
3.4.2 User Preference Learning in Multi-Criteria Recommendation Using Stacked Autoencoders by Tallapally et al. 46
3.4.2.1 Dataset and Evaluation Matrix 46
3.4.2.2 Training Setting 49
3.4.2.3 Result 49
3.4.3 Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts by Zheng 49
3.4.3.1 Evaluation Setting 50
3.4.3.2 Experimental Result 50
3.4.4 Utility-Based Multi-Criteria Recommender Systems by Zheng 51
3.4.4.1 Experimental Dataset 51
3.4.4.2 Experimental Result 52
3.4.5 Multi-Criteria Clustering Approach by Wasid and Ali 53
3.4.5.1 Experimental Evaluation 53
3.4.5.2 Result and Analysis 53
3.5 Advantages of Multi-Criteria Recommender System 54
3.5.1 Revenue 57
3.5.2 Customer Satisfaction 57
3.5.3 Personalization 57
3.5.4 Discovery 58
3.5.5 Provide Reports 58
3.6 Challenges of Multi-Criteria Recommender System 58
3.6.1 Cold Start Problem 58
3.6.2 Sparsity Problem 59
3.6.3 Scalability 5
About the author
Archana Mire, PhD, is an assistant professor in the Computer Engineering Department, Terna Engineering College, Navi Mumbai, India. She has published many research articles in peer-reviewed journals. Shaveta Malik, PhD, is an associate professor in the Computer Engineering Department (NBA accredited), Terna Engineering College, Nerul, India. She has published many research articles in peer-reviewed journals. Amit Kumar Tyagi, PhD, is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber-physical systems, and computer vision.
Summary
Advanced Analytics and Deep Learning Models
The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.
Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools.
However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc.
This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence.
Audience
Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
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