دانلود کتاب Data Science: Techniques and Intelligent Applications

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Cover Half Title Title Page Copyright Page Table of Contents Preface Editors Contributors 1. Instigation and Development of Data Science 1.1 Data Science 1.1.1 Existence of Data Science 1.1.2 Data Science Process 1.1.2.1 Setting the Research Goal 1.1.2.2 Retrieving Data 1.1.2.3 Data Preparation 1.1.2.4 Data Exploration 1.1.2.5 Data Modeling 1.1.2.6 Presentation and Automation 1.1.3 Life Cycle – Data Science 1.2 Relation between Data Science and Machine Learning 1.2.1 Where Do We See Machine Learning in Data Science? 1.2.2 Which Machine Algorithms are used in Data Science? 1.2.2.1 Linear Regression Algorithm 1.2.2.2 Decision Tree 1.2.2.3 K-Means Clustering 1.2.3 Application of Machine Learning in Data Science 1.3 Tools for Data science 1.3.1 R Programming 1.3.2 Python 1.4 Benefits and Applications 1.5 Conclusion References 2. Role of Statistical Methods in Data Science 2.1 Introduction 2.2 Data Science and Statistics Terminologies 2.3 Types of Statistics 2.3.1 Descriptive 2.3.2 Inferential 2.4 How to Describe a Single Set of Data 2.5 Statistical Analysis 2.5.1 Quantitative Analysis 2.5.2 Qualitative Analysis 2.5.3 Measures of the Central Tendency 2.5.4 Measures of Dispersion 2.6 Tools to Measure Relationships 2.6.1 Covariance 2.6.2 Correlation 2.7 Probability Distribution Function 2.7.1 Cumulative Density Function 2.7.2 Continuous Data Distributions 2.7.3 Conditional Probability 2.7.4 Bayes’ Theorem 2.8 Hypothesis Testing 2.9 Conclusion References 3. Real-World Applications of Data Science 3.1 Banking and Finance 3.1.1 Customer Data Management 3.1.2 Real-Time Analytics 3.1.3 Algorithmic Trading 3.1.4 Providing Personalized Services 3.1.5 Fraud Detection 3.2 E-commerce and Retail Industry 3.2.1 Potential Customer Analysis 3.2.2 Customer Sentiment Analysis 3.2.3 Optimizing Prices 3.2.4 Inventory Management 3.2.5 Lifetime Value Prediction 3.3 Digital Marketing 3.3.1 Smarter Planning for Online Marketing 3.3.2 Business Intelligence with Smarter Decision-Making 3.3.3 Managing Business Efficiently 3.3.4 Automating Recruitment Process 3.4 Healthcare and Medical Diagnosis 3.4.1 Managing and Monitoring Patient Health and Data 3.4.2 Medical Image Analysis 3.4.3 Drug Research and Creation 3.4.4 Patient Diagnosis and Preventing Diseases 3.4.5 Providing Medical Virtual Assistance 3.5 Manufacturing Industry 3.5.1 Automating Product Design and Development 3.5.2 Inventory Management and Demand Forecasting 3.5.3 Monitoring of Manufacturing Units 3.5.4 Real-Time Data of Performance and Quality 3.6 Education System 3.6.1 Monitoring Students’ and Teachers’ Requirements 3.6.2 Measuring Students’ and Teachers’ Performance 3.6.3 Innovating the Curriculum 3.6.4 Automating Outcome-Based Teaching and Learning Process 3.7 Entertainment Industry 3.7.1 Predictive Analytics in the Film Industry 3.7.2 Tracking Progress of Movies 3.7.3 Generate Movie Revenue 3.7.4 Improve Post-production of Movies 3.8 Logistic Delivery and Transportation Units 3.8.1 Reducing Shipping Costs through Delivery Path Optimization 3.8.2 Monitoring Traffic and Weather Data from Sensors 3.9 Shipping Sensitive Goods with Higher Quality 3.9.1 Automation of Warehouses and the Supply Chain 3.10 Digital Advertising Systems 3.10.1 Price Comparison Websites 3.10.2 Website Recommendation 3.11 Internet Search Engines 3.11.1 Proper Filtering 3.11.2 Autocomplete 3.11.3 Recommendation Engines 3.12 Airline Routing Planning 3.12.1 Predicting Flight Delays 3.12.2 Decide Route of Flight In Case of Emergency 3.12.3 Running Customer Loyalty Programs Effectively 3.13 Image and Speech Recognition Systems 3.13.1 Image Recognition Systems 3.13.2 Speech Recognition Systems 3.14 Gaming/Sports 3.14.1 Use Previous Gaming Experience to the Next Level 3.14.2 Improve Player Moves Up to Higher Level 3.15 Social Life and Social Networking 3.15.1 Building and Maintaining Social Relationship 3.15.2 Maintaining Friend Circles through Social Media 3.15.3 Building Human Network for Social Causes 3.16 Augmented Reality 3.16.1 Operation Room Augmented with Remote Presence 3.16.2 Social Media with Augmented Reality 3.17 Self-Driving Cars and Robots 3.17.1 Intelligent Systems for Self-Driving Cars 3.17.2 Robotics and Automation 3.18 Email Filtering and Character Recognitions 3.18.1 Email Spam Filtering 3.18.2 Optical Character Recognitions 3.19 Genetics and Genomics Research 3.19.1 Analyzing Impact of the DNA on the Health 3.19.2 Analyzing Reaction of Genes to Various Medications 3.19.3 Analyzing Set of Chromosomes in Humans, Animals References 4. HDWR_SmartNet: A Smart Handwritten Devanagari Word Recognition System Using Deep ResNet-Based on Scan Profile Method 4.1 Introduction and Related Work 4.2 Features of Devanagari Script 4.3 Dataset Creation 4.4 Proposed System Architecture 4.4.1 Data Preprocessing and Data Augmentation 4.4.2 Proposed Handwritten Devanagari Word Recognition System with Novel No-Segmentation Approach 4.4.2.1 Cropper Method 4.4.2.2 First Approach: Sliding Window Method without Segmentation 4.4.2.3 Second Approach: Scan Profile Method 4.4.3 ResNet114 Model: Devanagari Character Recognition Model 4.5 Experiments, Results, and Discussion 4.5.1 Network Training Parameters 4.5.2 Experiment Results 4.6 Conclusion and Future Work Dataset Accessibility Link References 5. Safe Social Distance Monitoring and Face Mask Detection for Controlling COVID-19 Spread 5.1 Introduction 5.2 Literature Survey 5.3 Proposed Methodology 5.3.1 Social Distance Monitoring Model 5.3.2 Face Mask Detection Model 5.4 Results 5.4.1 For Social Distancing Monitoring Model 5.4.2 For Face Mask Detection Model 5.5 Conclusion References 6. Real-Time Virtual Fitness Tracker and Exercise Posture Correction 6.1 Introduction 6.2 Literature Review 6.2.1 Motivation for the Research 6.3 Methodology 6.3.1 Brief Overview of Need for the System 6.3.2 Enhancing 2D Body Tracking Performance 6.3.2.1 Initial Body Pose Detection Using PoseNet 6.3.2.2 Feature Tracking Using the Lucas– Kanade Algorithm 6.3.3 Statistical Model of Proposed Model 6.4 Results and Discussion 6.4.1 Real-Time 2D Pose Estimation 6.4.2 Repetition Counter Mechanism 6.4.3 User Feedback and Posture Correction Mechanism 6.5 Conclusion References 7. Role of Data Science in Revolutionizing Healthcare 7.1 Introduction 7.2 Applications of Data Science 7.3 Data Science Technique Used for Diabetes Detection 7.4 Methodology and Proposed Framework for Diabetes Detection 7.5 Results 7.6 Conclusion 7.7 Future Scope References 8. Application of Artificial Intelligence Techniques in the Early-Stage Detection of Chronic Kidney Disease 8.1 Introduction 8.2 Literature Review 8.2.1 Based on Supervised Machine Learning Algorithms 8.2.2 Based on Deep Learning Techniques 8.3 Methodology Used 8.3.1 Machine Learning (ML) Methods 8.3.1.1 Support Vector Machine (SVM) 8.3.1.2 K-Nearest Neighbors (KNN) 8.3.1.3 Decision Tree Classifier 8.3.1.4 Random Forest (RF) 8.3.1.5 XGBoost 8.3.2 Deep Learning (DL) Methods 8.3.2.1 Artificial Neural Networks (ANN) 8.3.2.2 Multilayer Perceptron (MLP) 8.3.2.3 Recurrent Neural Network (RNN) 8.4 Results and Discussion 8.5 Conclusion and Future Work References 9. Multi-Optimal Deep Learning Technique for Detection and Classification of Breast Cancer 9.1 Introduction 9.2 Literature Review 9.3 Material and Methodology 9.3.1 Convolution Neural Network 9.3.2 Image Acquisition 9.3.3 Image Pre-Processing 9.3.4 Image Segmentation 9.3.5 Feature Extraction 9.3.6 Classification 9.3.7 Detection 9.3.8 Performance Evaluation 9.4 Results and Discussion 9.5 Conclusion References 10. Realizing Mother’s Features Influential on Childbirth Experience, towards Creation of a Dataset 10.1 Introduction 10.1.1 Significance of Woman’s Reproductive Health 10.1.1.1 Maternal Health as a Global Issue 10.1.1.2 Significance of Maternal Health in India 10.1.2 Lifestyle 10.1.3 Data in Research 10.2 Study of Features Influencing Pregnancy and Childbirth Experience 10.2.1 Phases of a Woman’s Reproductive Age 10.2.2 Features Selected for Study 10.2.3 Designing Survey Form 10.3 Data Collection 10.3.1 Selection of Subjects 10.3.2 Reaching Out to Subjects 10.3.3 Challenges while Collecting Data 10.3.4 Collection of Data 10.3.5 Limitations 10.4 MSF Dataset 10.4.1 Dataset Description 10.4.2 MSF Dataset Analysis 10.5 Conclusion References 11. BERT- and FastText-Based Research Paper Recommender System 11.1 Introduction 11.2 Literature Review 11.3 Dataset Description 11.4 Proposed Methodology 11.4.1 Keyword Extraction 11.4.1.1 Add Norm 11.4.1.2 Feedforward Neural Network 11.4.1.3 Residual Connections 11.4.1.4 Masked Language Model 11.4.2 FastText 11.4.2.1 Word Embeddings 11.4.2.2 CBOW 11.4.2.3 Skip-Gram 11.4.2.4 Hierarchical Softmax 11.4.2.5 Word n-Grams 11.4.3 FastText Representation 11.4.4 Limitations 11.4.5 Future Scope 11.4.6 Conclusion 11.4.7 Applications References 12. Analysis and Prediction of Crime Rate against Women Using Classification and Regression Trees 12.1 Introduction 12.1.1 Machine Learning Approach 12.2 Literature Survey 12.3 Proposed Methodologies 12.3.1 Data Preprocessing 12.3.2 Splitting Train and Test Data 12.3.3 Classification and Regression Trees (CART) 12.3.4 Model Evaluation 12.3.5 Data Visualization 12.4 Result and Discussions 12.5 Conclusion References 13. Data Analysis for Technical Business Incubation Performance Improvement 13.1 Introduction 13.2 Evolution of Business Incubators and Their Current State 13.3 Success Factors 13.3.1 Affiliation to Education Hubs 13.3.2 Feasibility Study 13.3.3 Availability of Funding 13.3.4 Caliber of Entrepreneur 13.4 Successful Incubates and Graduates 13.4.1 Supportive Government Policies 13.4.2 Stakeholder Consensus 13.4.3 Competent and Properly Encouraged Management Team 13.4.4 An Able Advisory Board 13.4.4.1 Financial Sustainability 13.4.5 Entry and Exit Criteria 13.4.6 Networking 13.5 Services Provided by Incubator 13.5.1 Community Support 13.5.2 Modus Operandi of Successful Business Incubations 13.5.2.1 Principles 13.5.2.2 Best Practices 13.6 Result and Factor Analysis 13.6.1 Table KMO and Bartlett’s Test 13.6.2 Scree Plot of Individual Variances of Dimensions 13.6.3 Scree Plot of Eigenvalues of Dimensions 13.6.4 Correlation Plot 13.7 Conclusion References 14. Satellite Imagery-Based Wildfire Detection Using Deep Learning 14.1 Introduction to the Proposed Chapter 14.2 Literature Review 14.3 Gaps in the Present Study 14.4 Proposed System and Algorithm 14.4.1 Algorithm 14.4.1.1 Adam Optimizer In-Depth 14.4.1.2 Adam Configuration/Hyper Parameters 14.4.1.3 Window/Block-Based Analysis 14.4.1.4 Binary Cross-Entropy Loss 14.5 Detailed Design 14.5.1 System Architecture 14.5.2 Design Diagrams 14.6 Conclusion References 15. Low-Resource Language Document Summarization: A Challenge 15.1 Introduction 15.2 Literature Survey 15.3 Approaches for Automatic Summarization 15.3.1 Lexical Chaining Approach 15.4 BERT Approach 15.5 Conclusion References 16. Eclectic Analysis of Classifiers for Fake News Detection 16.1 Introduction 16.2 Related Work 16.3 Dataset Description 16.3.1 Preprocessing 16.4 Modeling and Evaluation 16.4.1 Performance Metrics 16.4.1.1 Accuracy 16.4.1.2 F1-Score 16.4.1.3 Recall 16.4.1.4 Precision Score 16.4.1.5 Confusion Matrix 16.4.2 Hyperparameter Tuning 16.4.2.1 RandomizedSearchCV 16.4.2.2 GridSearchCV 16.4.3 Evaluation and Analysis 16.4.3.1 Model Implementation Using Logistic Regression 16.4.3.2 Model Implementation Using Naïve Bayes 16.4.3.3 Model Implementation Using KNN 16.4.3.4 Model Implementation Using Decision Trees 16.4.3.5 Model Implementation Using Random Forest 16.4.3.6 Model Implementation Using Boosting Ensemble Classifiers 16.4.3.7 Model Implementation Using LSTM 16.5 Conclusion, Limitations and Future Scope References 17. Data Science and Machine Learning Applications for Mental Health 17.1 Introduction 17.2 Review of Literature 17.3 Detection of Mental Health Disorders through Social Media 17.4 Study of Data Mining and Machine Learning Techniques for Diagnosing Depression 17.4.1 Data Mining Approach to Discover Association Rules to Diagnose Depression 17.4.2 Machine Learning Approach to Detect Depression 17.5 Conclusion and Future Scope References 18. Analysis of Ancient and Modern Meditation Techniques on Human Mind and Body and Their Effectiveness in COVID-19 Pandemic 18.1 Introduction 18.2 Meditation and Mindfulness 18.3 Literature Survey 18.4 Foundation of Study 18.5 Data Collection 18.5.1 SOS-S 18.5.2 BITe 18.5.3 SPANE 18.6 Data Analysis 18.6.1 Data Description 18.6.2 Chi-Square Test for Independence of Groups 18.6.2.1 Chi-Square Test for Determining a Relation between the Groups and Their Gender 18.6.2.2 Chi-Square Test for Determining a Relation between the Groups and Their Age 18.6.3 Data Visualization for the Ancient and Modern Meditation 18.6.4 Statistical Analysis 18.6.4.1 Jarque–Bera Test for Goodness of Fit 18.6.4.2 Comparison of SOS-S Scores of Ancient and Modern Meditation Groups 18.6.4.3 Comparison of BITe Scores of Ancient and Modern Meditation Groups 18.6.4.4 Comparison of SPANE-P Scores of Ancient and Modern Meditation Groups 18.6.4.5 Comparison of SPANE-N Scores of Ancient and Modern Meditation Groups 18.6.4.6 Comparison of SPANE-B Scores of Ancient and Modern Meditation Groups 18.7 Time Series Modeling 18.7.1 Modeling SOS-S Scores for Ancient and Modern Meditation Groups 18.7.2 Modeling BITe Scores for Ancient and Modern Meditation Groups 18.7.3 Modeling SPANE-B Scores for Ancient and Modern Meditation Groups 18.8 MEDit Architecture 18.8.1 Meditation Component 18.8.2 Evaluation Component 18.8.3 Discovery Component 18.9 Conclusion and Future Work References Index