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Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues

Table of contents :

Section I: Introduction about Data Science and Data Analytics
1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach
1.1 Introduction
1.2 Artificial Intelligence
1.3 Machine Learning (ML)
1.3.1 Regression
1.3.1.1 Linear Regression
1.3.1.2 Logistic Regression
Multi-class Logistic Regression
Polytomous Logistic Regression
1.3.2 Support Vector Machine (SVM)
1.4 Deep Learning (DL)
1.4.1 Methods for Deep Learning
1.4.1.1 Convolutional Neural Networks (CNNs)
General Model of Convolutional Neural Network
Convolution Layer
Nonlinear Activation Function
Pooling Layer
Fully Connected Layer
Last Layer Activation Function
1.4.1.2 Extreme Learning Machine
1.4.1.3 Transfer Learning (TL)
Important Considerations for Transfer Learning
Types of Transfer Learning
1.5 Bio-inspired Algorithms for Data Analytics
1.6 Conclusion
References
2. IoT Analytics/Data Science for IoT
2.1 Preface
2.1.1 Data Science Components
2.1.2 Method for Data Science
2.1.3 The Internet of Stuff
2.1.3.1 Difficulties in the Comprehension of Stuff on the Internet
2.1.3.2 Sub-domain of Data Science for IoT
2.1.3.3 IoT and Relationship with Data
2.1.3.4 IoT Applications in Data Science Challenges
2.1.3.5 Ways to Distribute Algorithms in Computer Science to IoT Data
2.2 Computational Methodology-IoT Science for Data Science
2.2.1 Regression
2.2.2 Set of Trainings
2.2.3 Pre-processing
2.2.4 Sensor Fusion Leverage for the Internet of Things
2.3 Methodology-IoT Mechanism of Privacy
2.3.1 Principles for IoT Security
2.3.2 IoT Architecture Offline
2.3.3 Offline IoT Architecture
2.3.4 Online IoT Architecture
2.3.5 IoT Security Issues
2.3.6 Applications
2.4 Consummation
References
3. A Model to Identify Agriculture Production Using Data Science Techniques
3.1 Agriculture System Application Based on GPS/GIS Gathered Information
3.1.1 Important Tools Required for Developing GIS/GPS-Based Agricultural System
3.1.1.1 Information (Gathered Data)
3.1.1.2 Map
3.1.1.3 System Apps
3.1.1.4 Data Analysis
3.1.2 GPS/GIS in Agricultural Conditions
3.1.2.1 GIS System in Agriculture
3.1.3 System Development Using GIS and GPS Data
3.2 Design of Interface to Extract Soil Moisture and Mineral Content in Agricultural Lands
3.2.1 Estimating Level of Soil Moisture and Mineral Content Using COSMIC-RAY (C-RAY) Sensing Technique
3.2.1.1 Cosmic
3.2.2 Soil Moisture and Mineral Content Measurement Using Long Duration Optical Fiber Grating (LDOPG)
3.2.3 Moisture Level and Mineral Content Detection System Using a Sensor Device
3.2.4 Soil Moisture Experiment
3.2.4.1 Dataset Description
3.2.5 Experimental Result
3.3 Analysis and Guidelines for Seed Spacing
3.3.1 Correct Spacing
3.3.2 System Components
3.3.2.1 Electronic Compass
3.3.2.2 Optical Flow Sensor
3.3.2.3 Motor Driver
3.3.2.4 Microcontroller
3.4 Analysis of Spread of Fertilizers
3.4.1 Relationship between Soil pH Value and Nutrient Availability
3.4.2 Methodology
3.4.2.1 Understand Define Phase
3.4.2.2 Analysis and Quick Design Phase
3.4.2.3 Prototype Development Phase
3.4.2.4 Testing Phase
3.4.3 System Architecture
3.4.4 Experimental Setup
3.4.5 Implementation Phase
3.4.6 Experimental Results
3.5 Conclusion and Future Work
References
4. Identification and Classification of Paddy Crop Diseases Using Big Data Machine Learning Techniques
4.1 Introduction
4.1.1 Overview of Paddy Crop Diseases
4.1.2 Overview of Big Data
4.1.2.1 Features of Big Data
4.1.3 Overview of Machine Learning Techniques
4.1.3.1 K-Nearest Neighbor
4.1.3.2 Support Vector Machine
4.1.3.3 K-Means
4.1.3.4 Fuzzy C-Means
4.1.3.5 Decision Tree
4.1.4 Overview of Big Data Machine Learning Tools
4.1.4.1 Hadoop
4.1.4.2 Hadoop Distributed File System (HDFS)
4.1.4.3 YARN (“Yet Another Resource Negotiator”)
4.2 Related Work
4.2.1 Image Recognition/Processing
4.2.2 Classification and Feature Extraction
4.2.3 Problems and Diseases
4.3 Proposed Architecture
4.3.1 Image Acquisition
4.3.2 Image Enhancement
4.3.3 Image Segmentation
4.3.4 Feature Extraction
4.3.5 Classification
4.4 Proposed Algorithms and Implementation Details
4.4.1 Image Preprocessing
4.4.2 Image Segmentation and the Fuzzy C-Means Model Using Spark
4.4.3 Feature Extraction
4.4.4 Classification
4.4.4.1 Support Vector Machine (SVM)
4.4.4.2 Naïve Bayes
4.4.4.3 Decision Tree and Random Forest
4.5 Result Analysis
4.5.1 Comparison of Speed-up Performance between the Spark-Based and Hadoop-Based FCM Approach
4.5.2 Comparison of Scale-up Performance between the Spark-Based and Hadoop-Based FCM Approach
4.5.3 Result Analysis of Various Segmentation Techniques
4.5.4 Results of Disease Identification
4.6 Conclusion and Future Work
References
Section II: Algorithms, Methods, and Tools for Data Science and Data Analytics
5. Crop Models and Decision Support Systems Using Machine Learning
5.1 Introduction
5.1.1 Decision Support System
5.1.2 Decision Support System for Crop Yield
5.1.3 What Is Crop Modeling?
5.1.4 Necessity of Crop Modeling
5.1.5 Recent Trends in Crop Modeling
5.2 Methodologies
5.2.1 Machine-Learning-Based Techniques
5.2.2 Deep-Learning-Based Techniques
5.2.3 Hyper-Spectral Imaging
5.2.4 Popular Band Selection Techniques
5.2.5 Leveraging Conventional Neural Network
5.3 Role of Hyper-Spectral Data
5.3.1 Farm Based
5.3.2 Crop Based
5.3.3 Advanced HSI Processing
5.4 Potential Challenges and Strategies to Overcome the Challenges
5.5 Current and Future Scope
5.6 Conclusion
References
6. An Ameliorated Methodology to Predict Diabetes Mellitus Using Random Forest
6.1 Motivation to Use the “R” Language to Predict Diabetes Mellitus?
6.2 Related Work
6.3 Collection of Datasets
6.3.1 Implementation Methods
6.3.1.1 Decision Tree
6.3.1.2 Random Forest
6.3.1.3 Naïve Bayesian Algorithm
6.3.1.4 Support Vector Machine (SVM)
6.4 Visualization
6.5 Correlation Matrix
6.6 Training and Testing the Data
6.7 Model Fitting
6.8 Experimental Analysis
6.9 Results and Analysis
6.10 Conclusion
References
7. High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning
7.1 Problems Faced with High Dimensionality Data: An Introduction
7.2 Dimensionality Reduction Algorithms with Visualizations
7.2.1 Feature Selection Using Covariance Matrix
7.2.1.1 Importing the Modules
7.2.1.2 The Boston Housing Dataset
7.2.1.3 Perform Basic Data Visualization
7.2.1.4 Pearson Coefficient Correlation Matrix
7.2.1.5 Detailed Correlation Matrix Analysis
7.2.1.6 3-Dimensional Data Visualization
7.2.1.7 Extracting the Features and Target
7.2.1.8 Feature Scaling
7.2.1.9 Create Training and Testing Datasets
7.2.1.10 Training and Evaluating Regression Model with Reduced Dataset
7.2.1.11 Limitations of the Correlation Matrix Analysis
7.2.2 t-Distributed Stochastic Neighbor Embedding (t-SNE)
7.2.2.1 The MNIST Handwritten Digits Dataset
7.2.2.2 Perform Exploratory Data Visualization
7.2.2.3 Random Sampling of the Large Dataset
7.2.2.4 T-Distributed Stochastic Neighboring Entities (t-SNE) – An Introduction
7.2.2.5 Probability and Mathematics behind t-SNE
7.2.2.6 Implementing and Visualizing t-SNE in 2-D
7.2.2.7 Implementing adn Visualizing t-SNE in 3-D
7.2.2.8 Applying k-Nearest Neighbors (k-NN) on the t-SNE MNIST Dataset
7.2.2.9 Data Preparation – Extracting the Features and Target
7.2.2.10 Create Training and Testing Dataset
7.2.2.11 Choosing the k-NN hyperparameter – k
7.2.2.12 Model Evaluation – Jaccard Index, F1 Score, Model Accuracy, and Confusion Matrix
7.2.2.13 Limitations of the t-SNE Algorithm
7.2.3 Principle Component Analysis (PCA)
7.2.3.1 The UCI Breast Cancer Dataset
7.2.3.2 Perform Basic Data Visualization
7.2.3.3 Create Training and Testing Dataset
7.2.3.4 Principal Component Analysis (PCA): An Introduction
7.2.3.5 Transposing the Data for Usage into Python
7.2.3.6 Standardization – Finding the Mean Vector
7.2.3.7 Computing the n-Dimensional Covariance Matrix
7.2.3.8 Calculating the Eigenvalues and Eigenvectors of the Covariance Matrix
7.2.3.9 Sorting the Eigenvalues and Corresponding Eigenvectors Obtained
7.2.3.10 Construct Feature Matrix – Choosing the k Eigenvectors with the Largest Eigenvalues
7.2.3.11 Data Transformation – Derivation of New Dataset by PCA – Reduced Number of Dimensions
7.2.3.12 PCA Using Scikit-Learn
7.2.3.13 Verification of Library and Stepwise PCA
7.2.3.14 PCA – Captured Variance and Data Lost
7.2.3.15 PCA Visualizations
7.2.3.16 Splitting the Data into Test and Train Sets
7.2.3.17 An Introduction to Classification Modeling with Support Vector Machines (SVM)
7.2.3.18 Types of SVM
7.2.3.19 Limitations of PCA
7.2.3.20 PCA vs. t-SNE
Conclusion
8. Hybrid Cellular Automata Models for Discrete Dynamical Systems
8.1 Introduction
8.2 Basic Concepts
8.2.1 Cellular Automaton
8.3 Discussions on CA Evolutions
8.3.1 Relation between Local and Global Transition Function of a Spatially Hybrid CA
8.4 CA Modeling of Dynamical Systems
8.4.1 Spatially Hybrid CA Models
8.4.2 Temporally Hybrid CA Models
8.4.3 Spatially and Temporally Hybrid CA Models
8.5 Conclusion
References
9. An Efficient Imputation Strategy Based on Adaptive Filter for Large Missing Value Datasets
9.1 Introduction
9.1.1 Motivation
9.2 Literature Survey
9.3 Proposed Algorithm
9.4 Experiment Procedure
9.4.1 Data Collection
9.4.2 Data Preprocessing
9.4.3 Classification
9.4.4 Evaluation
9.5 Experiment Results and Discussion
9.6 Conclusions and Future Work
References
10. An Analysis of Derivative-Based Optimizers on Deep Neural Network Models
10.1 Introduction
10.2 Methodology
10.2.1 SGD
10.2.2 SGD with Momentum
10.2.3 RMSprop
10.2.4 Adagrad
10.2.5 Adadelta
10.2.6 Adam
10.2.7 AdaMax
10.2.8 NADAM
10.3 Result and Analysis
10.4 Conclusion
References
Section III: Applications of Data Science and Data Analytics
11. Wheat Rust Disease Detection Using Deep Learning
11.1 Introduction
11.2 Literature Review
11.3 Proposed Model
11.4 Experiment and Results
11.4.1 Dataset Preparation
11.4.2 Image Pre-processing
11.4.3 Image Segmentation
11.4.4 Discussion for the Model on Grayscale Images
11.4.5 Evaluating the Model on RGB Images
11.4.5 Result Comparison of the Model on RGB Images Based on Learning Rate
11.5 Conclusion
References
12. A Novel Data Analytics and Machine Learning Model Towards Prediction and Classification of Chronic Obstructive Pulmonary Disease
12.1 Introduction
12.2 Literature Review
12.3 Research Methodology
12.3.1 Logistical Regression Model for Disease Classification
12.3.2 Random Forest (RF) for Disease Classification
12.3.3 SVM for Disease Classification
12.3.4 Decision Tree Analyses for Disease Classification
12.3.5 KNN Algorithm for Disease Classification
12.4 Experiment Results
12.5 Concluding Remarks and Future Scope
12.6 Declarations
References
13. A Novel Multimodal Risk Disease Prediction of Coronavirus by Using Hierarchical LSTM Methods
13.1 Introduction
13.2 Related Works
13.3 About Multimodality
13.3.1 Risk Factors
13.4 Methodology
13.4.1 Naïve Bayes (NB)
13.4.2 RNN-Multimodal
13.4.3 LSTM Model
13.4.4 Support Vector Machine (SVM)
13.4.5 Performation Evaluation
13.4.5.1 Accuracy
13.4.5.2 Specificity
13.4.5.3 Sensitivity
13.4.5.4 Precision
13.4.5.5 F1-Score
13.5 Experimental Analysis
13.6 Discussion
13.7 Conclusion
13.8 Future Enhancement
References
14. A Tier-based Educational Analytics Framework
14.1 Introduction
14.2 Related Works
14.3 The Three-Tiered Education Analysis Framework
14.3.1 Structured Data Analysis
14.3.1.1 Techniques for Structured Data Analysis
14.3.1.1.1 Correlation Analysis
14.3.1.1.2 Association Mining
14.3.1.1.3 Predictive Modeling
14.3.1.2 Challenges in Structured Data Analysis
14.3.2 Analysis of Semi-Structured Data and Text Analysis
14.3.2.1 Use Cases for Analysis of Semi-Structured and Text Content
14.3.2.2 Challenges of Semi-Structured/Textual Data Analysis
14.3.3 Analysis of Unstructured Data
14.3.3.1 Analysis of Unstructured Data: Study and Use Cases
14.3.3.2 Challenges in Unstructured and Multimodal Educational Data Analysis
14.4 Implementation of the Three-Tiered Framework
14.5 Scope and Boundaries of the Framework
14.6 Conclusion and Scope of Future Research
Note
References
15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology Images
15.1 Introduction
15.2 Background Study
15.2.1 Breast Cancer Detection Based on Machine Learning Approach
15.2.2 Breast Cancer Detection Based on Deep Convolutional Neural Network Approach
15.2.3 Breast Cancer Detection Based on Deep Transfer Learning Approach
15.3 Methodology
15.3.1 Data Acquisition
15.3.2 Data Preprocessing Stage
15.3.3 Transfer Learning Model
15.3.3.1 Visual Geometry Group Network (VGGNet)
15.3.3.2 Residual Neural Network (ResNet)
15.3.3.3 Dense Convolutional Networks (DenseNet)
15.4 Experimental Setup and Results
15.4.1 Performance Evaluation Metrics
15.4.2 Training Phase
15.4.3 Result Analysis
15.4.4 Comparison with Other State of Art Models
15.5 Discussion with Advantages and Future Work
15.5.1 Discussion
15.5.2 Advantages
15.5.3 Future Works
15.6 Conclusion
References
16. Prediction of Acoustic Performance Using Machine Learning Techniques
16.1 Introduction
16.2 Materials and Methods
16.3 Proposed Methodology
16.3.1 Step 1: Data Preprocessing
16.3.2 Step 2: Fitting Regression Model
16.3.3 Building a Backward Elimination Model
16.3.4 Building the Model Using Forward Selection Model
16.3.5 Step 3: Optimizing the Regressor Model—Mean Squared Error
16.3.6 Step 4: Understanding the Results and Cross Validation
16.3.7 Step 5: Deployment and Optimization
16.3.7.1 Structural Parameters of Each Layer Material Is Shown in
16.4 Results and Discussions
16.4.1 Error Analysis and Validating Model Performance for All Test Samples
16.5 Conclusion
References
Section IV: Issue and Challenges in Data Science and Data Analytics
17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony
17.1 Introduction
17.2 Nature-Inspired Metaheuristics
17.3 Genetic Algorithm Overview
17.4 Proposed Hybridized GA Metaheuristic
17.5 MLP Training by GGEABC
17.6 Simulation Setup and Results
17.7 Conclusion
Acknowledgment
References
18. Algorithmic Trading Using Trend Following Strategy: Evidence from Indian Information Technology Stocks
18.1 Introduction
18.2 Literature Survey
18.2.1 Data and Period of Study
18.3 Methodology
18.4 Results and Discussions
18.5 Conclusions
18.5.1 Future Scope
References
19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement Using Twitter Data and News Sentiments
19.1 Introduction
19.2 Review of Literature
19.3 Proposed Methodology
19.3.1 Sentiment Score
19.3.2 Labeling
19.3.3 Feature Matrix
19.3.4 Probabilistic Neural Network
19.4 Numerical Results and Discussion
19.4.1 Data Description
19.4.2 Statistical Measure
19.5 Simulation Results and Validation
19.5.1 Comparative Analysis over Existing and Proposed Decision-Making Methods
19.6 Conclusion and Future Enhancement
References
20. Churn Prediction in the Banking Sector
20.1 Introduction
20.1.1 Problem Statement
20.1.2 Current Scenario
20.1.3 Motivation
20.1.4 Objective
20.2 Related Work
20.3 Methodology
20.3.1 Dataset
20.3.2 Proposed System for Customer Churn Prediction
20.4 Results
20.4.1 Analysis of Clustering of Churned Customers
20.5 Conclusion
20.6 Future Work
References
21. Machine and Deep Learning Techniques for Internet of Things Based Cloud Systems
21.1 Introduction
21.1.1 Power of Remote Computing
21.1.2 Security and Privacy Policies
21.1.3 Integration of Data
21.1.4 For Hosting, Providers Remove Entry Barrier
21.1.5 Improves Business Continuity
21.1.6 Facilitates Inter-device Communication
21.1.7 Pairing with Edge Computing
21.1.8 How IoT and Cloud Complement Each Other?
21.1.9 Cloud and IoT: Which Is Better?
21.1.10 The Challenges Posed by the Cloud and IoT Together?
21.1.10.1 Handling an Outsized Amount of Knowledge
21.1.10.2 Networking and Communication Protocols
21.1.10.3 Sensor Networks
21.1.10.4 Security Challenges
21.2 Security Issues in IoT-Based Cloud Systems
21.2.1 Attacks in IoT
21.2.1.1 Active Attack
21.2.1.2 Passive Attack
21.3 Machine Learning and Deep Learning: A Solution to Cyber Security Challenges in IoT-Based Cloud Systems
21.3.1 Machine Learning and Deep Learning Techniques Introduction
21.3.1.1 A Tour of Machine Learning Algorithms
21.3.1.1.1 Regression Algorithms
21.3.1.1.2 Instance-Based Algorithms
21.3.1.1.3 Regularization Algorithms
21.3.1.1.4 Decision Tree Algorithms
21.3.1.1.5 Bayesian Algorithms
21.3.1.1.6 Clustering Algorithms
21.3.1.1.7 Association Rule Learning Algorithms
21.3.1.1.8 Artificial Neural Network Algorithms
21.3.1.1.9 Deep Learning Algorithms
21.3.1.1.10 Dimensionality Reduction Algorithms
21.3.1.1.11 Ensemble Algorithms
21.3.2 Machine Learning and Deep Learning Techniques Used in IoT Security
21.3.2.1 Supervised Machine Learning
21.3.2.1.1 Decision Trees
21.3.2.1.2 Support Vector Machines (SVMs)
21.3.2.1.3 Bayesian Theorem-Based Algorithms
21.3.2.1.4 K-Nearest Neighbor (KNN)
21.3.2.1.5 Random Forest (RF)
21.3.2.1.6 Association Rule (AR) Algorithms
21.3.2.1.7 Ensemble Learning (EL)
21.3.2.2 Unsupervised ML
21.3.2.2.1 K-Means Clustering
21.3.2.2.2 Principal Component Analysis (PCA)
21.3.2.3 Deep Learning (DL) Methods for IoT Security
21.3.2.3.1 Convolution Neural Networks (CNNs)
21.3.2.3.2 Recurrent Neural Networks (RNNs)
21.3.2.4 Unsupervised DL (Generative Learning)
21.3.2.4.1 Deep Auto Encoders (AEs)
21.3.2.4.2 Restricted Boltzmann Machines (RBMs)
21.3.2.4.3 Deep Belief Networks (DBNs)
21.3.2.5 Semi-Supervised or Hybrid DL
21.3.2.5.1 Generative Adversarial Networks (GANs)
21.3.2.5.2 Ensemble of DL Networks (EDLNs)
21.3.2.5.3 Deep Reinforcement Learning (DRL)
21.4 Conclusion
References
Section V: Future Research Opportunities towards Data Science and Data Analytics
22. Dialect Identification of the Bengali Language
22.1 Introduction
22.2 Previous Works
22.3 Proposed Methodology
22.3.1 Computation of Features
22.3.1.1 Feature Selection
22.3.1.1.1 Zero Crossing Rate (ZCR) Based Feature Computation
22.3.1.1.2 Mel Frequency Cepstral Coefficients (MFCCs) Based Feature Computation
22.3.1.1.3 Skewness-Based Feature Computation
22.3.1.1.4 Spectral Flux Based Feature Computation
22.3.2 Formation of Feature Vector and Classification
22.4 Experimental Results
22.4.1 Relative Analysis
22.5 Conclusion
References
23. Real-Time Security Using Computer Vision
23.1 Introduction
23.1.1 Biometric
23.1.2 Computer Vision
23.1.3 Opencv Library
23.2 Data Security
23.3 Technology
23.3.1 Face Detection
23.3.2 Face Recognition
23.3.3 Haar Cascade Classifier
23.4 Algorithm
23.4.1 Algorithm to Capture the Image for Database
23.4.2 Algorithm to Recognize the Face
23.4.3 Algorithm to Train the Face Recognizer
23.4.4 Algorithm for Security
23.5 Result
23.6 Conclusion
23.7 Future Scope
Reference
24. Data Analytics for Detecting DDoS Attacks in Network Traffic
24.1 Introduction
24.2 Background
24.3 Related Work
24.4 Methodology
24.4.1 Oversampling and Synthetic Sampling of Data
24.4.2 Detection of Stealthy DDoS attacks
24.4.3 Performance Evaluation by Ranking Machine Learning Algorithms
24.5 Result and Discussion
24.5.1 Datasets Used for Evaluation
24.5.2 Evaluation Metrics Used
24.5.3 Observations
24.6 Conclusion
Notes
References
25. Detection of Patterns in Attributed Graph Using Graph Mining
25.1 Introduction
25.2 Research Background
25.3 Literature Survey
25.4 General Definitions
25.4.1 Multi-relational Edge-attributed Graph
25.4.2 Multi-layer Edge-attributed Graph
25.4.3 Attributed Graph
25.5 Problem Definition
25.6 Proposed Approach
25.6.1 Pattern Length of 4, 5, and 6
25.6.1.1 For Length = 4
25.6.1.2 For Length = 5
25.6.1.3 For Length = 6
25.6.2 Node-Pair Generations
25.6.2.1 Node-Pair Generation for Three Attributed Line and Loop Patterns
25.6.2.2 Node-Pair Generation for Four Attributed Line and Loop Patterns
25.6.2.3 Node-Pair Generation for Four Attributed Star Patterns
25.6.2.4 Node-Pair Generation for Five Attributed Elongated Star Patterns
25.6.3 Pattern Detections
25.6.3.1 Three-Attributed Line Pattern
25.6.3.2 Three-Attributed Loop Pattern
25.6.3.3 Four-Attributed Line Pattern
25.6.3.4 Four-Attributed Loop Pattern
25.6.3.5 Four-Attributed Star Pattern
25.6.3.6 Five-Attributed Elongated Star Pattern
25.7 Proposed Algorithm for Detection of Patterns – Line, Loop, Star, and Elongated Star
25.7.1 Algorithm PDAGraph345( )
25.7.2 Procedure for Node-Pair Assignment
25.7.3 Procedure to Create Three-Attributed Line and Loop Patterns
25.7.4 Procedure to Display Three-Attributed Line and Loop Patterns
25.7.5 Procedure to Create Four-Attributed Line and Loop Patterns
25.7.6 Procedure to Display Four-Attributed Line and Loop Patterns
25.7.7 Procedure to Create Four-Attributed Star Patterns
25.7.8 Procedure to Display Four-Attributed Star Patterns
25.7.9 Procedure to Create Five-Attributed Elongated Star Patterns
25.7.10 Procedure to Assign Node IDs of Five-Attributed Elongated Star Patterns
25.7.11 Procedure to Display Five-Attributed Elongated Star Patterns
25.7.12 Procedure to Generate Node-Pairs
25.7.13 Explanation of PDAGraph345( )
25.8 Experimental Results
25.8.1 Using C++ Programming Language
25.8.1.1 Three-Attributed Line Pattern (1-2-3)
25.8.1.2 Three-Attributed Loop Pattern (2-3-4-2)
25.8.1.3 Four-Attributed Line Pattern (1-3-2-4)
25.8.1.4 Four-Attributed Loop Pattern (1-3-4-2-1)
25.8.1.5 Four Attributed Star Pattern (1-3-2-3-4)
25.8.1.6 Five-Attributed Elongated Star Pattern (1-2-3-4-3-2)
25.8.2 Using Python Programming Language
25.8.2.1 Three-Attributed Line Pattern (1-2-3)
25.8.2.2 Three-Attributed Loop Pattern (2-3-4-2)
25.8.2.3 Four-Attributed Line Pattern (1-3-2-4)
25.8.2.4 Four Attributed Loop Pattern (1-3-4-2-1)
25.8.2.5 Four-Attributed Star Pattern (1-3-2-3-4)
25.8.2.6 Five-Attributed Elongated Star Pattern (1-2-3-4-3-2)
25.9 Analysis of Experimental Results
25.10 Conclusion
References
26. Analysis and Prediction of the Update of Mobile Android Version
26.1 Introduction
26.1.1 Mobile Fragmentation
26.1.2 Treble – Google
26.1.3 Security Fix Support and Android Update
26.2 Systematic Literature Survey
26.2.1 API Compatibility Issues and Android Updates
26.2.2 Android Updates and Software Aging
26.2.3 Android Updates and Google Play Store
26.2.4 Security Standards Hardware Rooted in Mobile Phones
26.2.5 Security Fixes and Android Update
26.2.6 Machine Learning and Android Antivirus Updates
26.2.7 Smells Detection in Android Using Machine Learning
26.2.8 Android Malicious Classification Using Various ML Algorithms
26.3 Existing Techniques
26.4 Methodology and Tools Used in Existing Techniques
26.5 Proposed System
26.5.1 Schematic Overview of Mobile Android Update Prediction and Analysis
26.5.2 Flow Chart Depicting Mobile Android Update Prediction and Analysis
26.5.3 Algorithm for the Prediction and Analysis
26.5.3.1 Algorithm for Linear Regression Model and R Programming
26.5.3.2 Algorithm for Logistic Regression Model
26.5.3.3 Algorithm for Decision Tree Model
26.5.4 Methodology
26.5.5 Software Packages Used
26.5.6 Dataset Description
26.5.6.1 Attribute and Values Information
26.5.6.2 Missing Attribute Values: None
26.6 Experimental Results and Discussions
26.6.1 Graphical Representation
26.7 Conclusions and Future Work
References
Appendix: Datasets Sample Attachments
Index

Product information

Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (September 23, 2021)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 482 pages
ISBN-10 ‏ : ‎ 0367628821
ISBN-13 ‏ : ‎ 978-0367628826

 

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