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 (μM − μF), (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].
List of Tables
1 Chapter 2Table 2.1 Truth table for AND gate.Table 2.2 Truth table for OR gate.Table 2.3 Truth table for NOT gate.Table 2.4 Truth table for YES gate.Table 2.5 Truth table for NOT gate.Table 2.6 Truth table for ANDANDNOT gate.
2 Chapter 3Table 3.1 Layers involved in ResNet-101 architecture.Table 3.2 Precision and recall of active garment detection.
3 Chapter 5Table 5.1 CNN architecture layout for TF images using RML synthetic data set.Table 5.2 Performance comparison analysis for 100 epochs between CsiNet and Ince...
4 Chapter 6Table 6.1 Comparison of proactive reactive and hybrid protocol.Table 6.2 Comparison of MANETs, VANETs.
5 Chapter 7Table 7.1 Characteristics of the feed.Table 7.2 Advantages and disadvantages of GMDH-NN method.Table 7.3 Pros and cons of ANN method.Table 7.4 Error analysis of GMDH-NN model.Table 7.5 Comparative study between GMDH-NN model and ANN model.
6 Chapter 8Table 8.1 Evolution of search string.Table 8.2 Results after filters.Table 8.3 Performance keywords.Table 8.4 Keywords of performance.Table 8.5 Merge of performance keywords.Table 8.6 Adaptability keywords.Table 8.7 Keywords extracted from SIG catalogs.Table 8.8 Overview of nonfunctional requirements.Table 8.9 The binary and multi-class metrics.Table 8.10 Common words.Table 8.11 The binary and multi-class classification metrics.
7 Chapter 9Table 9.1 Distribution of data experimentally.Table 9.2 Classification accuracy of various approaches on ImageNet (Second Clas...Table 9.3 Classification accuracy of various approaches on Cats and Dogs dataset...Table 9.4 Classification accuracy of various approaches on Caltech-101 dataset (...
8 Chapter 10Table 10.1 Main instruments used in this process and instrumentation (P & I) dia...Table 10.2 Comparison table.
9 Chapter 11Table 11.1 Percentage relative error in output responses by ANN modeling [53, 54...
10 Chapter 12Table 12.1 Corpus detail of automatic speech recognition system.Table 12.2 Experiment results of speech recognition based on background noise pa...
11 Chapter 13Table 13.1 Automatic text summarization techniques.Table 13.2 Summarization systems ROUGE score.
12 Chapter 14Table 14.1 The framework evaluation and administration ML algorithms.
13 Chapter 16Table 16.1 The different classifiers based on 0.5 threshold.Table 16.2 Different classifiers based on the best threshold generated by ROC th...
14 Chapter 17Table 17.1 Costs for steady-state equations.Table 17.2 Total cost and optimum value.
15 Chapter 19Table 19.1 Study inquiry matrix.
