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target="_blank" rel="nofollow" href="#ulink_a51a4962-020d-5d1a-96d4-1017e3681ddc">Figure 3.5 Identification of garment of interest in the presence of a single cus...Figure 3.6 Identification of garments of interest in the presence of a single cu...Figure 3.7 Identification of garments of interest in the presence of multiple cu...Figure 3.8 Variation of average confidence score with respect to changes in conf...Figure 3.9 Average confidence score obtained by garments of interest of particul...Figure 3.10 Total duration of time for which customers were interested in a garm...

      4 Chapter 4Figure 4.1 Complex plane Z(n).Figure 4.2 Adding points (P = M + N).Figure 4.3 Doubling a point (P = M + M).Figure 4.4 Experiment on encryption.Figure 4.5 Experiment on decryption.Figure 4.6 Visualization of a qubit state.

      5 Chapter 5Figure 5.1 ML application for communications (re-generated from [24]).Figure 5.2 Block diagram for the proposed blind identification method.Figure 5.3 Comparison of overall classification accuracy with benchmark network.Figure 5.4 Confusion matrix CNN with synthetic data set.Figure 5.5 Autoencoder model for CSI feedback.Figure 5.6 Inception block.Figure 5.7 Encoder and decoder blocks of InceptNet.Figure 5.8 Pseudo gray plots of (a) original image (b) image recovered by CsiNet...

      6 Chapter 6Figure 6.1 Transmission process.Figure 6.2 MANETs routing protocol.Figure 6.3 FANETs.Figure 6.4 Mobility model.Figure 6.5 Random way model.Figure 6.6 GMM model.Figure 6.7 Semi-random model.Figure 6.8 Mission model.Figure 6.9 Structure of CrANs [27].Figure 6.10 Proposed structure of CrANS.Figure 6.11 Single hop.Figure 6.12 Multihop.Figure 6.13 Protocol operation.Figure 6.14 Positioning of sensor nodes.Figure 6.15 Mesh formation.Figure 6.16 Minimum spanning tree.Figure 6.17 Throughput vs. delivery ratio.Figure 6.18 Ideal placement of nodes.Figure 6.19 Sensor nodes with faulty points.Figure 6.20 Delay.Figure 6.21 Packet delivery ratio.Figure 6.22 Throughput.Figure 6.23 Placement of nodes.Figure 6.24 Good and bad nodes.Figure 6.25 Fitness function.

      7 Chapter 7Figure 7.1 Schematic illustration of the setup used for experiments.Figure 7.2 Comparative plot of actual and predicted value of permeate flux (%) o...Figure 7.3 Comparative residual plot of training and prediction values.Figure 7.4 Plot between residual value and number of occurrence.Figure 7.5 Plot between initial values and predicted values.Figure 7.6 Sensitivity analysis.

      8 Chapter 8Figure 8.1 Distribution from NFR catalogs.Figure 8.2 The dataset definition process.Figure 8.3 SIG catalog of performance.Figure 8.4 SIG catalog of performance.Figure 8.5 The final SIG security catalog.Figure 8.6 Process of creating a classification.

      9 Chapter 9Figure 9.1 Flowchart of the proposed RL algorithm.Figure 9.2 Proposed technique.Figure 9.3 NNs used after the CNNs, viz., ResNet50 (left) and InceptionV3 (right...

      10 Chapter 10Figure 10.1 Block diagram of programmable logic controller.Figure 10.2 Flow chart of programmable logic controller.Figure 10.3 Fuzzy controller architecture.Figure 10.4 Basic blocks of fuzzy logic GUI toolbox.Figure 10.5 Basic neuron structure [25].Figure 10.6 Artificial neural network’s (ANN) layers [26].Figure 10.7 ANFIS editor.Figure 10.8 P & I diagram of the case study (intermediate tank).Figure 10.9 Electromagnetic flow meter.Figure 10.10 Resistance temperature device (RTD).Figure 10.11 Current-to-pressure (I/P) converter.Figure 10.12 Process control valve.Figure 10.13 FIS editor.Figure 10.14 The membership function editor for temperature.Figure 10.15 The membership function editor for fluid flow.Figure 10.16 Membership function editor for steam valve.Figure 10.17 Rule editor.Figure 10.18 Pictorial representation of rules.Figure 10.19 Surface viewer.Figure 10.20 Loaded training data.Figure 10.21 Loaded checking data.Figure 10.22 Generation of new FIS from loaded data.Figure 10.23 ANFIS editor showing results.Figure 10.24 Modified rules.Figure 10.25 Modified rules with new tags.Figure 10.26 Rule viewer with testing data 1.Figure 10.27 Rule viewer with testing data 2.Figure 10.28 Rule viewer with testing data 3.Figure 10.29 Rule viewer with testing data 4.Figure 10.30 Rule viewer with testing data 5.Figure 10.31 Modified surface viewer.

      11 Chapter 11Figure 11.1 Computers usage in modern concept of Manufacturing Industry 4.0.Figure 11.2 Computer usage in car designing and manufacturing.Figure 11.3 Optimization methods.Figure 11.4 Traditional methods and non-traditional methods of optimization.Figure 11.5 Injection molding process.Figure 11.6 Three-dimensional printing process.Figure 11.7 Arc welding process.Figure 11.8 Casting process.Figure 11.9 Machining process classification.Figure 11.10 ANN vs. human brain functioning similarities.Figure 11.11 Modular Neural Network.Figure 11.12 Structure of typical neural network 5-k-l-m-1 [1].Figure 11.13 Convoluted neural networks.Figure 11.14 Recurrent neural network.Figure 11.15 Closed-loop control systems.Figure 11.16 Radial neural structure (self-drawn).Figure 11.17 Multilayer perceptron.Figure 11.18 Kohonen self-organizing neural network.Figure 11.19 LSTM ANN structure.Figure 11.20 ANN training

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