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1 Chapter 2Table 2.1 DLT vs Blockchain.

2 Chapter 3Table 3.1 Comparison of access control method for IoT.

3 Chapter 4Table 4.1 Different datasets on traffic signs.Table 4.2 Efficiency (in %) results for implemented classifiers.Table 4.3 Details of best performing architecture.Table 4.4 Architecture of FFNN.Table 4.5 Detailed parameters of FFNN.Table 4.6 Size details on FFNN.Table 4.7 Architecture of RNN.Table 4.8 Detailed parameters of RNN.Table 4.9 Size details on RNN.Table 4.10 Architecture of CNN.Table 4.11 Detailed parameters of CNN.Table 4.12 Size details on CNN.Table 4.13 Accuracy and loss of improved CNN.Table 4.14 Architecture of improved CNN.Table 4.15 Detailed parameters of improved CNN.Table 4.16 Size details on improved CNN.Table 4.17 Hyper-parameters of improved CNN.Table 4.18 Impact of pooling strategy on accuracy of improved CNN.Table 4.19 Accuracy of various pre trained models.Table 4.20 Comparative results with state-of-the-art schemes.

4 Chapter 6Table 6.1 Security assessment in IoT network frame.Table 6.2 Security assessment for IIoT.

5 Chapter 7Table 7.1 Individual robot performance using Jaya-DE, basic Jaya, and DE.Table 7.2 Individual robot performance using Jaya-DE and IGWO [26].

6 Chapter 8Table 8.1 Feature extraction for training dataset.Table 8.2 Severity score class rule.Table 8.3 Performance measures and comparative analysis.

7 Chapter 10Table 10.1 Conversion of attributes into numeric values.

8 Chapter 12Table 12.1 Link measurement data of the network having 200 number of data.Table 12.2 Average throughput (bp/S) with different network size.Table 12.3 Average hop-count with different network size.Table 12.4 Average link utilization (%) with different network size.

9 Chapter 13Table 13.1 Summary of COVID 19 symptoms.

10 Chapter 14Table 14.1 Table showing attributes of the dataset.Table 14.2 Table showing changing size of MaxPool2D(n,n) vs accuracy.Table 14.3 Table showing changing size of averagePooling2D(n,n) vs accuracy.Table 14.4 Table showing changing size of conv2D-32-64 layers vs accuracy.Table 14.5 Table showing accuracy of different activation function.Table 14.6 Table showing accuracy of different models.Table 14.7 Model vs accuracy.

11 Chapter 15Table 15.1 Path length (cm) deviation between simulation and experimental result...Table 15.2 Time spent (s) deviation between simulation and experimental result o...Table 15.3 Path length (cm) deviation between simulation and experimental result...Table 15.4 Time spent (s) deviation between simulation and experimental result o...Table 15.5 Path comparison (s) between proposed controller and Neuro-Fuzzy Contr...

12 Chapter 16Table 16.1 Score table indicating student’s prior knowledge in a subject.Table 16.2 Prediction accuracy.

13 Chapter 17Table 17.1 Parameter values for performance evaluation.Table 17.2 Prediction evaluation methods.Table 17.3 Evaluation metrics values for Google CPU workload.Table 17.4 Evaluation metrics values for Google memory workload.

14 Chapter 18Table 18.1 Findings from different chapters working on imbalance data through re...Table 18.2 Findings from different chapters working on outlier data through revi...Table 18.3 Findings from different chapters working on ensembling methods throug...

15 Chapter 19Table 19.1 Research contribution on use of AI & machine learning in agriculture.Table 19.2 Artificial intelligence on the basis of geographical segmentation.Table 19.3 Yield of major crops per hectare in India (kilogram per hectare).