Machine Learning: Theoretical Foundations and Practical Applications

✍ Scribed by Manjusha Pandey; Siddharth Swarup Rautaray

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 178
• Category: Library

✦ Synopsis

This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.

✦ Table of Contents

Preface
Contents
Editors and Contributors
What Do RDMs Capture in Brain Responses and Computational Models?
1 Introduction
2 Methods
2.1 Representational Similarity Analysis (RSA)
2.2 RDM Construction for Human Brain Responses and Deep Neural Nets (DNNs)
2.3 Description of Algonauts Challenge Datasets
2.4 Experiments with DNNs
2.5 Selecting the Best Models
2.6 Qualitative Analysis of High and Low Values in RDMs
3 Results
3.1 Analysis of the Human Brain Responses
3.2 Deep Networks Mimicking Human Brain Responses
4 Discussion
4.1 Qualitative Analysis of Human Responses and DNN Responses
4.2 The Way Forward
References
Challenges and Solutions in Developing Convolutional Neural Networks and Long Short-Term Memory Networks for Industry Problems
1 Introduction
1.1 Inception
1.2 Regional CNN
1.3 Recurrent Neural Networks
2 Application One: Image Recognition in a Document by Using Convolutional Neural Networks
2.1 Analyzing the Problem
2.2 Handling the Challenge of Skewness
2.3 Architecture of CNN
2.4 Prediction/Testing
2.5 Results
3 Application 2: Predicting Equated Monthly Instalment Payments
3.1 Analyzing the Problem
3.2 Data Representation
3.3 Design of the LSTM
3.4 Results
4 Review of the Two Applications
References
Speed, Cloth and Pose Invariant Gait Recognition-Based Person Identification
1 Introduction
2 Literature Review of Related Work
2.1 GEI
2.2 HOG
2.3 Radon Transform and Zernike Moments
2.4 Classifier Model
3 Result and Discussion
3.1 About the Database
3.2 Speed Invariance
3.3 Cloth Invariance
3.4 Pose Invariance
4 Comparison with Existing Approaches
5 Conclusion and Future Scope
6 Declarations
6.1 Funding
References
Application of Machine Learning in Industry 4.0
1 Industry 4.0
2 Evolution of Industry 1.0–4.0
2.1 Industry 1.0
2.2 Industry 2.0
2.3 Industry 3.0
2.4 Industry 4.0
3 Nine Pillars of Industry 4.0
3.1 Big Data and Analytics
3.2 Autonomous Robots
3.3 Simulation
3.4 Internet of Things
3.5 Cloud
3.6 System Integration
3.7 Additive Manufacturing
3.8 Augmented Reality
3.9 Cyber Security
4 Machine Learning
4.1 Regressors
4.2 Classifiers
4.3 Clustering
4.4 Reinforcement Learning
4.5 Natural Language Processing
4.6 Deep Learning
5 Conclusion
References
Web Semantics and Knowledge Graph
1 Introduction
1.1 Advantages of Using the Semantic Technologies
1.2 Challenges in Note
2 Basics of Taxonomies
2.1 Advantages of Using a Taxonomy
2.2 Components of the Taxonomy
3 RDF Data Model
4 Basics of Ontologies
4.1 Classes and Relationships
5 Taxonomy and Ontologies, the Difference and Similarity
6 Introduction to Text Mining
7 Semantic Text Mining
8 Related Thing to Keep in Mind While Text Mining (State of the Art)
9 Resolving the Diverse Database Conflict
10 Conclusion
References
Machine Learning-Based Wireless Sensor Networks
1 Introduction
1.1 Challenges
2 State-of-the-Art Applications of Machine Learning in WSNs
3 ML in WSNs
3.1 Detection
3.2 Target Tracking
3.3 Localization
3.4 Security Enhancement
3.5 Routing
3.6 Clustering
4 Conclusion
References
AI to Machine Learning: Lifeless Automation and Issues
1 Introduction
2 Current State of Art
2.1 Computer Vision
2.2 Generative Adversarial Networks
2.3 Training and Deployment
3 From AI to ML
4 Applications
4.1 COVID
4.2 Disease Detection
4.3 Wind Power Detection in Power Systems
4.4 Agriculture
4.5 Politics
4.6 Genomics
4.7 Networking
4.8 Energy Forecasting
5 Conclusion
References
Analysis of FDIs in Different Sectors of the Indian Economy
1 Introduction
1.1 Technologies Used
2 State of the Art
3 ARIMA Model for Forcasting
3.1 Model 1: AR MODEL
3.2 Model 2: MA MODEL
3.3 Hybrid Model 1: ARMA MODEL
3.4 Hybrid Model 2: ARIMA MODEL
4 Implementation and Results
4.1 Box–Jenkins Method
4.2 Predicting Future Trends
5 Conclusion
References
Customer Profiling and Retention Using Recommendation System and Factor Identification to Predict Customer Churn in Telecom Industry
1 Introduction
2 Literature Review
3 Problem Definition
4 Proposed Model for Customer Churn Prediction
4.1 Data Set Description
4.2 Data Pre-processing
4.3 Performance Evaluation Matrix
5 Factors Identifying Customer Churn
6 Customer Profiling and Retention
7 Customer Retention Using Recommendation System
8 Implementation and Results
9 Conclusion and Future Work
References