14.21 Prediction of closing price—Mahindra & Mahindra Limited (regression...Figure 14.22 Prediction of closing price—Mahindra & Mahindra Limited (LSTM).Figure 14.23 Prediction of closing price—Maruti Suzuki India Limited (regression...Figure 14.24 Prediction of closing price—Maruti Suzuki India Limited (LSTM).15 Chapter 15Figure 15.1 Apache Hadoop architecture.Figure 15.2 MapReduce framework.Figure 15.3 Hadoop master-slave architecture.Figure 15.4 Input dataset snap.Figure 15.5 MapReduce job.Figure 15.6 Use case diagram.Figure 15.7 Class diagram.Figure 15.8 Entity relationship diagram.Figure 15.9 Histogram of age.Figure 15.10 Histogram of logarithm of age.Figure 15.11 Aadhar applicants by gender.Figure 15.12 Percentage of overall applications per state.Figure 15.13 Percentage of aadhar cards generated per state.Figure 15.14 Percentage of aadhar card rejected per state.Figure 15.15 Percentage of emails registered with aadhar card.Figure 15.16 Percentage of mobiles registered with aadhar card.
16 Chapter 16Figure 16.1 Deep learning techniques.Figure 16.2 Summarized architectural view of the integration of deep learning an...Figure 16.3 Open research challenges and future directions.
17 Chapter 17Figure 17.1 Working for machine learning algorithm [167].Figure 17.2 Data Processing by machine learning.Figure 17.3 Different AI-based machine learning used systems [168].
18 Chapter 18Figure 18.1 Framework of predictive modeling of anthropomorphic predictive model...Figure 18.2 Accounts used in making transaction.Figure 18.3 List of transactions on the network.Figure 18.4 Principal component analysis on diabetes patient data and heat map o...Figure 18.5 Different ML algorithms’ output for computing best predictive model....
List of Tables
1 Chapter 5Table 5.1 Comparison of algorithms.
2 Chapter 7Table 7.1 Comparative literature survey.
3 Chapter 8Table 8.1 Technologies in CNN.Table 8.2 Case study of IoT platforms smart products and systems.
4 Chapter 9Table 9.1 ECT [14, 26, 27] matrix for DAG1 model.Table 9.2 Computation of upward rank of the tasks of DAG1.Table 9.3 Computation of downward rank of the tasks of DAG1.Table 9.4 Computation of task priority TPriority [20] of the tasks of DAG1.Table 9.5 Computation of the task priority TPriority.Table 9.6 Sorting of task level.Table 9.7 AET computation.Table 9.8 DCT computation.Table 9.9 Computation of PTR, RANK, and Priority.Table 9.10 VM rate for DAG1.Table 9.11 ECT [15, 27] matrix for DAG2 model.Table 9.12 VM cost for DAG2.Table 9.13 Comparison results.
5 Chapter 10Table 10.1 Major constituents in M2M and its challenges.Table 10.2 Technological challenges and architecture and heterogeneity.Table 10.3 Description of data models based on key features and framework.Table 10.4 Comparison of various algorithms used for various data models.Table 10.5 Characteristic of smart data in smart cities.Table 10.6 Overview of ML algorithms for smart environment.Table 10.7 Strengthens and weakness of ML techniques in smart farming.
6 Chapter 11Table 11.1 WBAN areas of application.
7 Chapter 13Table 13.1 Test cases comparison.
8 Chapter 14Table 14.1 Comparison of RMSE value for linear regression model and long short-t...
9 Chapter 15Table 15.1 Literature review.
10 Chapter 16Table 16.1 Comparison of existing surveys in blockchain and machine learning.Table 16.2 Blockchain services in 5G future generation communication networks.Table 16.3 Summarized taxonomy for resource management using deep reinforcement ...
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