Читать книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов - Страница 4

1 Chapter 1Figure 1.1 Flow chart of the study.Figure 1.2 Basic neural network.Figure 1.3 Sigmoid function.Figure 1.4 Tanh function.Figure 1.5 ReLU function.Figure 1.6 Basic Bernoulli’s restricted Boltzmann machine.Figure 1.7 Accuracy plot for one hidden layer–based ANN.Figure 1.8 Accuracy plot for two hidden layer–based ANN.Figure 1.9 Accuracy plot for three hidden layer–based ANN.Figure 1.10 Accuracy plot for four hidden layer–based ANN.Figure 1.11 Accuracy vs. hidden layer.

2 Chapter 2Figure 2.1 Schematic diagram of biological neuron.Figure 2.2 Propagation of signal through neurons.Figure 2.3 Linear threshold function.Figure 2.4 Schematic diagram of linear threshold gate.Figure 2.5 ANN model with continuous characteristics.Figure 2.6 Graphical representation of logistic sigmoid function.Figure 2.7 Single layer neural network.Figure 2.8 Multilayer neural network.Figure 2.9 Formation of axon. (a) Input and output neuron oligonucleotides and t...Figure 2.10 Formation of output molecule using DNA neural network. (a) Axon mole...Figure 2.11 Structure of perceptron [5].Figure 2.12 Catalytic activity of deoxyribozyme.Figure 2.13 Mechanism to switch on deoxyribozyme logic gate.Figure 2.14 Mechanism of YES gate.Figure 2.15 Mechanism of NOT gate.Figure 2.16 Mechanism of AND gate.Figure 2.17 Structure of ANDANDNOT gate.Figure 2.18 Toehold-mediated DNA branch migration and strand displacement [7].Figure 2.19 Mechanism of two-input AND gate.Figure 2.20 AND reaction.Figure 2.21 OR reaction.Figure 2.22 PROP reaction.

3 Chapter 3Figure 3.1 Architecture of proposed framework.Figure 3.2 GMG background subtraction model [18].Figure 3.3 Keypoints for pose output [10].Figure 3.4 Samples from the dataset.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 [55, 56].Figure 11.21 R or the correlation coefficient = 1.Figure 11.22 CNC machine.

12 Chapter 12Figure 12.1 Block diagram for training phase.Figure 12.2 Block diagram for testing phase.Figure 12.3 ASR block diagram.Figure 12.4 Data collection modes.Figure 12.5 Block diagram of system.

13 Chapter 13Figure 13.1 Extractive summarization model using graph-based approach.Figure 13.2 Architecture of GRAPHSUM model.

14 Chapter 14Figure 14.1 Twitter users’ worldwide data January 2021 [32].Figure 14.2 The Google Cloud console [6].Figure 14.3 Sentiment analysis techniques [35].Figure 14.4 Fetching of data using BigQuery [37].Figure 14.5 The architecture of Google BigQuery [38].Figure 14.6 Architectural view of the proposed system [42].Figure 14.7 Data refining using BigQuery [39].Figure 14.8 Google App Engine launcher [40].Figure 14.9 (a) and (b) Twitter for BigQuery result window [41].

15 Chapter 15Figure 15.1 Graphical model representation of LDA.Figure 15.2 Process pipeline.Figure 15.3 Work flow of the proposed system.Figure 15.4 Creation of dataset and training of data.

16 Chapter 16Figure 16.1 Four-fold plot of predicted class vs. actual class and a ROC curve f...Figure 16.2 Four-fold plot of predicted class vs. actual class and a ROC curve f...Figure 16.3 Four-fold plot of predicted class vs. actual class and an ROC curve ...Figure 16.4 Four-fold plot of predicted class vs. actual class and a ROC curve f...

17 Chapter 17Figure 17.1 Inventory management system.Figure 17.2 Behavior of cost function I with respect to inventory level.Figure 17.3 Behavior of cost function II with respect to inventory level.

18 Chapter 18Figure 18.1 Supply chain network.Figure 18.2 Mixed integer programming.Figure 18.3 Inventory in supply chain performance.Figure 18.4 Closed-loop supply chain management.Figure 18.5 Supply chain network design under uncertainty.

19 Chapter 19Figure 19.1 Architecture of the proposed ensemble model.

20 Chapter 20Figure 20.1 Type-A SPS designed using microstrip TL.Figure 20.2 Simulated result showing PD of Type-A SPS shown in Figure 20.1.Figure 20.3 Input impedance at port 1 (or 2) of the (a) Type-A SPS has shown in ...Figure 20.4 Modified Type-A SPS with single stub tuner.Figure 20.5 Fabricated PS: (a) signal plane and (b) ground plane.Figure 20.6 Simulated and measured (a) magnitude response, (b) phase response, a...Figure 20.7 Equivalent spice model of varactor diode (SMV2019).Figure 20.8 (a) SPS with single varactor diode. (b) Equivalent lumped RLGC model...Figure 20.9 SPS with (a) two varactor diodes and (b) three varactor diodes.Figure 20.10 For single varactor diode: (a) RL plot; (b) IL plot; (c) PD plot.Figure 20.11 For two varactor diodes: (a) RL plot; (b) IL plot; (c) PD plot.Figure 20.12 For three varactor diodes: (a) RL plot; (b) IL plot; (c) PD plot.

21 Chapter 21Figure 21.1 Tools used in Fuzzy logic toolbox.Figure 21.2 FIS procedure for present study.Figure 21.3 Empirical transfer function.Figure 21.4 Transfer function in fuzzy format of PC.Figure 21.5 Transfer function in fuzzy format of PPC.Figure 21.6 Transfer function in fuzzy format of QC.Figure 21.7 Transfer function in fuzzy format of MGT.Figure 21.8 Transfer function in fuzzy format of result.Figure 21.9 Fuzzy rules.Figure 21.10 Fuzzy rules.Figure 21.11 Rule viewer.Figure 21.12 Rule viewer.Figure 21.13 Transfer function in fuzzy format of result.Figure 21.14 Fuzzy rules.Figure 21.15 Rule viewer.

22 Chapter 22Figure 22.1 Inventory optimization.Figure 22.2 Multi-product inventory network.Figure 22.3 Three stage processes.

23 Chapter 23Figure 23.1 3D Euclidean space.Figure 23.2 (a) 3D model of function. (b) Gradient of function.Figure 23.3 Traveling salesman problem.Figure 23.4 Wing design parameters.Figure 23.5 (a) Convex function. (b) Non-convex function.Figure 23.6 Classification of optimization algorithms.Figure 23.7 Genetic algorithm.Figure 23.8 Torquigener albomaculosus [39].Figure 23.9 Circular structure of pufferfish and zones: (a) sideview and (b) upp...Figure 23.10 Illustration of circular structures.Figure 23.11 The modeling of circular structure.Figure 23.12 Beale function.Figure 23.13 The change of objective function value versus number of iterations.Figure 23.14 Change of circular structures for Beale function.

24 Chapter 24Figure 24.1 Sperm Swam and the global best value (winner).

25 Chapter 25Figure 25.1 Diagram depicting the flow of optimization process.Figure 25.2 Complete usage of optimization type of problems.Figure 25.3 Complete framework of metaheuristics approach.

26 Chapter 27Figure 27.1 Representation of a three-diode model for a solar cell.Figure 27.2 Different phases of Harris hawks optimization (HHO) [18].Figure 27.3 Convergence curve for TDM (Kyocera KC200GT).Figure 27.4 Convergence curve for TDM (Canadian Solar CS6K-280M).Figure 27.5 Convergence curve for TDM (Schutten Solar STM6 40-36).Figure 27.6 Convergence curve for TDM (SolarWorld Pro. SW255).Figure 27.7 Kruskal-Wallis test performance for TDM (KC200GT).Figure 27.8 Kruskal-Wallis test results for TDM (CS6K-280M).Figure 27.9 Kruskal-Wallis test diagram for TDM (STM6 40-36).Figure 27.10 Kruskal-Wallis test results for TDM (Pro. SW255 model).

27 Chapter 28Figure 28.1 System model of the proposed CSS system with M number of secondary u...Figure 28.2 Illustration showing SVM classifier for two set of data points, mark...Figure 28.3 Illustration of decision thresholds and quantization points.Figure 28.4 (a) SU node consisting of an antenna, RTL-SDR, and RPI SBC. (b) Inte...Figure 28.5 Arrangement of SU nodes. LoS path between PU and SUs is obstructed b...Figure 28.6 ROC for different fusion techniques applied on soft energy values. V...Figure 28.7 ROC for different fusion techniques applied on soft energy values. S...Figure 28.8 ROC plots with EGC decision fusion applied on OQ, UQ, and soft data ...Figure 28.9 ROC plots with KMC decision fusion applied on OQ, UQ, and soft data ...Figure 28.10 ROC plots with SVM decision fusion applied on OQ, UQ, and soft data...Figure 28.11 PD variation with SNR for different with EGC decision fusion. Val...Figure 28.12 PD variation with SNR for different in case of KMC decision fusio...Figure 28.13 PD variation with SNR for different in case of SVM decision fusio...Figure 28.14 PD variation with SNR for different K in case of EGC decision fusio...Figure 28.15 PD variation with SNR for different K in case of KMC decision fusio...Figure 28.16 PD variation with SNR for different K in case of SVM decision fusio...Figure 28.17 PD variation with number of samples for EGC decision fusion.Figure 28.18 PD variation with number of samples for KMC decision fusion.Figure 28.19 PD variation with number of samples for SVM decision fusion.Figure 28.20 PD variation with SNR for different number of SU for EGC decision f...Figure 28.21 PD variation with SNR for different number of SU for KMC decision f...Figure 28.22 PD variation with SNR for different number of SU for SVM decision f...

28 Chapter 29Figure 29.1 GIoT–based layered architecture for smart irrigation monitoring.Figure 29.2 Monitored parameters in machine learning–based precision agriculture...Figure 29.3 Monitored parameters in edge Computing–based precision agriculture.Figure 29.4 Monitored parameters in GIoT–based precision agriculture.Figure 29.5 Power, costs, and security optimization in GIoT–based precision agri...Figure 29.6 LPWAN technologies’ paper distribution used in GIoT–based agricultur...

29 Chapter 30Figure 30.1 Block diagram of the proposed model for sentiment analysis.Figure 30.2 (a) Comparison with time taken to classify data. (b) Result comparis...

30 Chapter 31Figure 31.1 Proposed approach based fuzzy and A* star techniques with phase spli...Figure 31.2 The problem of overlapping sensors.Figure 31.3 Theoretical description to the overlapping problem present in most w...Figure 31.4 A hypothetical deployment scenario for the deployment phase.Figure 31.5 Deployment phase constraint relation.Figure 31.6 Comparison of coverage through iterations in the deployment phase.Figure 31.7 Travel distance through number of nodes.Figure 31.8 Comparison of iterations through energy consumption.Figure 31.9 Shortest path approach 1.Figure 31.10 Shortest path approach 2.

31 Chapter 33Figure 33.1 Three-dimensional indicators of sustainability performance.Figure 33.2 MICMAC analysis.Figure 33.3 ISM-based model.

32 Chapter 34Figure 34.1 Procedure of sampling from the NHNES 2015–2016.Figure 34.2 The posterior distribution of (1) differences of means (μ_M − μ_F), (2...

33 Chapter 35Figure 35.1 Convolutional Neural Network. Source: Convolutional Neural Network T...Figure 35.2 Cascaded Convolutional LSTM architecture without dilation.Figure 35.3 Cascaded Convolutional LSTM architecture with dilation.Figure 35.4 Cartesian masks. (a) 4× undersampling. (b) 6× undersampling.Figure 35.5 Reconstruction results of CNN with a Cartesian undersampling rate of...Figure 35.6 Reconstruction results of ConvLSTM with a Cartesian undersampling ra...Figure 35.7 Reconstruction results of dilated ConvLSTM with a Cartesian undersam...

34 Chapter 36Figure 36.1 Multispectral face images collected in six fixed illumination condit...Figure 36.2 Gender classification based on Affine hull method.Figure 36.3 Gender classification based on wavelet average fusion method.

35 Chapter 37Figure 37.1 Total number of new cancer cases of males in 2018 (all ages) [3].Figure 37.2 Total number of new cancer cases of females in 2018 (all ages) [3].Figure 37.3 Colonoscopy equipment [4].Figure 37.4 Polyp present in colon [5].Figure 37.5 Proposed methodology.Figure 37.6 Image quality improvement filters.Figure 37.7 Architecture of VGG16 and VGG19 deep neural networks [24].Figure 37.8 VGG16 model summary.Figure 37.9 VGG19 model summary.Figure 37.10 Polyp detection using VGG19 model.Figure 37.11 Graphical representation of test accuracy and test loss.Figure 37.12 Accuracy comparison of CVC-ClinicDB and new test dataset.

36 Chapter 38Figure 38.1 Proposed exon prediction model.Figure 38.2 Approach used for collecting homologous sequences.Figure 38.3 Steps used for exon prediction.Figure 38.4 Example of multiple sequence alignment consisting of exons and intro...Figure 38.5 Steps used in complete gene prediction.Figure 38.6 Interface for boundary exon predictor.

37 Chapter 39Figure 39.1 Normalized spectra of glucose recorded with Jasco V770.Figure 39.2 System block diagram.Figure 39.3 A three-layer BP-ANN architecture.Figure 39.4 Activation function.Figure 39.5 Flowchart of BP-ANN.Figure 39.6 MSSL vs. iterations.Figure 39.7 Bland-Altman plots. (a) PLSR. (b) BP-ANN.Figure 39.8 CEGA plot. (a) PLSR. (b) BP-ANN.Figure 39.9 Regression analysis. (a) PLSR model. (b) BP-ANN.

38 Chapter 40Figure 40.1 Location map of study area.Figure 40.2 Flowchart of the methodology.Figure 40.3 Mapping of novel COVID-19 of Gujarat state, India. (a) Cases tested ...

39 Chapter 41Figure 41.1 Roadmap of health monitoring system [2].Figure 41.2 Basic contigrade temparature sensor (2°C–150°C) [12].Figure 41.3 A Photoplethysmogram (PPG) waveform (amplitude vs. time) [7].Figure 41.4 CC2530 Development Kit Contents (courtesy: Texas Instruments) [6].Figure 41.5 USB UART [13].Figure 41.6 Overview of ZigBee ports connections [16].Figure 41.7 Roadmap of Z-Stack [4].Figure 41.8 Some more basic devices.Figure 41.9 Screenshot showing the interface of developed app: (a) upside and (b...Figure 41.10 Flow chart of health monitoring system’s mobile application [15].Figure 41.11 The health report of patient [1].