carcinoma.Figure 2.3 More sample images of correctly classified and misclassified carcinom...3 Chapter 3Figure 3.1 Relative frequency distribution of proteins that express the four SAR...Figure 3.2 Zoom over the Figure 3.1. The X-axis represents the five polar intera...Figure 3.3 Histograms SARS-CoV-2 structural proteins.
4 Chapter 4Figure 4.1 Cyclic nature of human gait (Vaughan et al. [9]).Figure 4.2 LSTM unit (Bouktif et al. [2]).Figure 4.3 Bidirectional LSTM (Yulita et al. [10]).
5 Chapter 5Figure 5.1 Network modeling approach that simplifies multi-omics data from the g...Figure 5.2 Classification of network embedding and algorithms used [17–47].Figure 5.3 Various network embedding tools and their applications [16].Figure 5.4 Taxonomy of biological network alignment.Figure 5.5 Illustration of data and methods used in analysis of Adverse Drug Rea...Figure 5.6 Illustration of data and methods used in multi-omics data analysis.
6 Chapter 6Figure 6.1 System architecture.Figure 6.2 Localization of heart.Figure 6.3 Outcome of segmentation.Figure 6.4 Feature extraction.Figure 6.5 Ensemble classification—Confusion matrix (using MLP).Figure 6.6 KNN classification—Confusion matrix.Figure 6.7 SVM classifier—Confusion matrix.Figure 6.8 XG Boost classifier—Confusion matrix.Figure 6.9 Logistic regression classifier—Confusion matrix.Figure 6.10 MLP classifier—Confusion matrix.Figure 6.11 Random forest classifier—Confusion matrix.Figure 6.12 Naïve Bayes classifier—Confusion matrix.
7 Chapter 7Figure 7.1 DL architecture [2].Figure 7.2 Autoencoders [36].Figure 7.3 Feedforward RNN [36].Figure 7.4 Long short-term memory (LSTM) [36].Figure 7.5 CNN architecture [36].Figure 7.6 Framework of CNN [36].Figure 7.7 Structure of BM [36].Figure 7.8 DBN architecture [36].
8 Chapter 8Figure 8.1 Different poses are illustrated in the figure with the detected key p...Figure 8.2 Image illustrating different body models [2].Figure 8.3 Challenges to human pose estimation [7].Figure 8.4 The figure illustrates 2D pose estimation extracted from an image and...Figure 8.5 The image illustrates multi-person pose estimation for different numb...Figure 8.6 Object annotated images from Pascal VOC Dataset.Figure 8.7 These images are from KTH Multi-view Football Dataset [18].Figure 8.8 MPII Human Pose Dataset with annotated body joints.Figure 8.9 BBC Pose Dataset with overlaid sign language interpreter.Figure 8.10 COCO Dataset with the object classes.Figure 8.11 Human3.6M Dataset.Figure 8.12 Image correspondence with DensePose.Figure 8.13 AMASS Dataset.Figure 8.14 Left: pose regression; Right: pose refinement.Figure 8.15 Overview of the cascaded architecture.Figure 8.16 Results from a convolutional pose machine.Figure 8.17 Iterative Error Feedback (IEF) mechanism for 2D human pose estimatio...Figure 8.18 Stacked hourglass module.Figure 8.19 Architecture for HRNet.Figure 8.20 Approaching 3D human pose estimation.Figure 8.21 Left: Results from DensePose-RCNN; Middle: DensePose COCO Dataset an...Figure 8.22 DensePose-R CNN model.Figure 8.23 Left: pose estimation without blanket; Right: pose estimation with o...Figure 8.24 Combined CNN-RNN model.
9 Chapter 9Figure 9.1 Estimated healthcare IoT device installation [14].Figure 9.2 System design flow chart.Figure 9.3 CNN architecture.Figure 9.4 Block diagram.Figure 9.5 Various classes of tumors.Figure 9.6 Codes used to train our CNN model.Figure 9.7 Loading page.Figure 9.8 Result displayed.Figure 9.9 Result displayed.Figure 9.10 About section.Figure 9.11 Accuracy score.Figure 9.12 Comparison chart.
10 Chapter 10Figure 10.1 Standard level of criteria air pollutants and their sources with hea...Figure 10.2 World regional capital city ranking, 2018 (Website: IQAir).Figure 10.3 Diagrammatic representation of component of Hawan Samagri along with...Figure 10.4 Society residents chanting Vedic Mantras and performing community Ya...Figure 10.5 People in Indian and South Asian continent celebrating Holi Festival...Figure 10.6 CCD image sensor for capturing images (Website: Wikipedia).Figure 10.7 IR proximity sensor for distance measurement (Website: Flipkart.com)...Figure 10.8 In Yajna, besides burning material objects, chanting and praying are...Figure 10.9 The IoT-based sensors capturing the humidity and temperature data fr...Figure 10.10 Measurement of different parameters of AQI on November 16, 2019 (Ya...Figure 10.11 Measurement of different parameters of AQI on November 17, 2019 (Ya...Figure 10.12 Measurement of different parameters of AQI on November 18, 2019 (Ya...Figure 10.13 Measurement of different parameters of AQI on November 19, 2019 (Ya...Figure 10.14 Comparative analysis of emission of different gaseous elements and ...Figure 10.15 Comparative analysis of averaged environmental parameters in fume e...Figure 10.16 Study of the comparative analysis of stability of the environment b...Figure 10.17 Collective recital of mantra helps in depression treatment and slee...
11 Chapter 11Figure 11.1 Analysis w.r.t. attribute-transformation clusters.Figure 11.2 Analysis w.r.t. rate-transformation clusters.
12 Chapter 12Figure 12.1 Benefits of Yagya or Yagyopathy [56].Figure 12.2 Graphical analysis for fasting blood sugar with the respect to the a...Figure 12.3 Graphical presentation for FBST PRANDAL blood sugar with the respect...Figure 12.4 Graphical representation to compare the sugar level results in 4-mon...Figure 12.5 Graphical representation to compare the FVC and FEV1 on parameters.Figure 12.6 Graphical representation to compare subject’s data on different date...Figure 12.7 Radiation variation of different electronic gadgets of subjects (lap...Figure 12.8 Left-hand analysis of happiness index of different subjects.Figure 12.9 Right-hand analysis of happiness index of different subjects.Figure 12.10 Age vs. happiness index (right hand).Figure 12.11 Age vs. happiness index (left hand).
13 Chapter 13Figure 13.1 Taxonomy.Figure 13.2 Three-layer architecture for remote health monitoring.
14 Chapter 14Figure 14.1 The Myo armbands usage for Sign Language Recognition.Figure 14.2 Smart ring + watch setup to detect movements of the finger during th...Figure 14.3 Figure shows the start, middle, and end frames along with the motion...Figure 14.4 Images showing annotations of various images extracted from ASLLVD a...Figure 14.5 Image showing variation in the training and test set images. The var...Figure 14.6 Image showing the capturing system used in the SMILE Swiss German Si...Figure 14.7 Image showing the contents of SMILE Swiss German Sign Language Datas...Figure 14.8 Figure representing the recording procedure for the SIGNUM corpus [1...Figure 14.9 The figure represents the 32 alphabets and the number of images obta...Figure 14.10 This table summarizes some glove sensor systems among which systems...Figure 14.11 Image showing the 3D reconstruction through Kinect-based images. Im...Figure 14.12 Figure representing usage of multi-feature extraction from an image...Figure 14.13 Capsule network for the Sign Language Recognition task [29].Figure 14.14 Real-Time Sign Language Recognition system using Pose Estimation an...Figure 14.15 Network showing the feature extraction and the usage of LSTM to per...Figure 14.16 Difference between a Sign Language Recognition and a Sign Language ...Figure 14.17 Image showing the Neural Sign Language Translation by employing seq...Figure 14.18 Image showing the different human body parts take into account and ...
List of Tables
1 Chapter 1Table 1.1 Comparative analysis of prediction techniques.Table 1.2 Dataset attributes.
2 Chapter 4Table 4.1 Confusion matrix for binary classification.Table 4.2 Performance analysis of different classifiers.
3 Chapter 5Table 5.1 List of major biological network data resources.
4 Chapter 6Table 6.1 Details of the extracted features.Table 6.2 Accuracy score of classification models.
5 Chapter 8Table 8.1 The table contains a list of datasets in 2D and 3D human pose estimati...
6 Chapter
