Emerging Technologies for Healthcare

8 Chapter 8Figure 8.1 Polygonization [11].Figure 8.2 Thresholding [6].Figure 8.3 Binarization [24].Figure 8.4 Gray scaling [10].Figure 8.5 Noise reduction [10].Figure 8.6 Image inversion [10].Figure 8.7 Image dilation [10].Figure 8.8 Division of the image into zones [11].Figure 8.9 Zones detected in handwritten text [5].Figure 8.10 Word detection using contours [5].Figure 8.11 Detected enclosed space using Hough circle transform [5].Figure 8.12 The perceptron network [8].Figure 8.13 Artificial neural network [12].Figure 8.14 System architecture [13].Figure 8.15 System flow [14].Figure 8.16 Flowchart for personality prediction [15].Figure 8.17 Graph of the algorithm used for personality analysis versus its accu...Figure 8.18 Writer identification framework [18].Figure 8.19 Writer identification with a machine learning algorithm [25].

9 Chapter 9Figure 9.1 Steps for data acquisition and processing.Figure 9.2 Proposed system for risk mitigation in ASD individuals.Figure 9.3 Activations in parietal, temporal, and frontal regions showing decrea...Figure 9.4 Activations in frontal and temporal lobes showing reduced activity fo...Figure 9.5 sLORETA images of young individuals showing increase in activity in f...Figure 9.6 sLORETA images of young patients showing increase in activity in fron...

10 Chapter 10Figure 10.1 Machine learning ecosystem.Figure 10.2 Supervised and unsupervised learning models.Figure 10.3 Classification process.Figure 10.4 Decision tree example.

11 Chapter 11Figure 11.1 Factors modeled in risk-perception model.Figure 11.2 Schematic representation of the modeled parameters.Figure 11.3 Flowchart representing the weight up-gradation in risk assessment.

12 Chapter 12Figure 12.1 Importing of Pandas and Matplotlib and displaying the dataset.Figure 12.2 Bar graph of the dataset using the Seaborn Library.Figure 12.3 Replacing usernames and punctuations.Figure 12.4 Using common words and tokenizing.Figure 12.5 Using stemming and lemmatization.Figure 12.6 Using Bag of Words and TF-IDF count vectorizer and splitting the tra...Figure 12.7 Calculating result using accuracy score and f1 score.Figure 12.8 Architecture of random forest.

13 Chapter 13Figure 13.1 Correlation between COVID-19 new cases and stringency index.Figure 13.2 Proposed modules to build analyzer.Figure 13.3 Working flow of OpenCV.Figure 13.4 Basic architecture of TensorFlow.Figure 13.5 Sub-modules of person detection module.Figure 13.6 Bounding box with centroid point.Figure 13.7 System implementation.Figure 13.8 Work flow of proposed model.Figure 13.9 Annotated file JSON representation.Figure 13.10 Code snippet for frame creation.Figure 13.11 Code snippet for contour detection.Figure 13.12 TensorFlow workflow for model training.Figure 13.13 Code snippet for COCO model comparison.Figure 13.14 Code snippet for printing predictions.Figure 13.15 Final model.

14 Chapter 14Figure 14.1 Steps of IoT solution [15].Figure 14.2 IoT-enabled VAS system.Figure 14.3 Block diagram of the smart healthcare system.Figure 14.4 Architecture of the VAS ecosystem.

15 Chapter 15Figure 15.1 Bone repairing process [15].Figure 15.2 Bone substitute.Figure 15.3 Methodology of work.Figure 15.4 MRI to 3D model [6, 11].Figure 15.5 Process of additive manufacturing [11].Figure 15.6 Stereo-lithography [20].Figure 15.7 Fused deposition modeling [17].Figure 15.8 Schematic diagram of SLS [5, 24].Figure 15.9 Direct 3D printing [8].

1 Chapter 2 Table 2.1 List of mobile apps and online sites for smart healthcare system. Table 2.2 List of wearables commercially available for health monitoring. Table 2.3 Deep learning models for bioinformatics. Table 2.4 Deep learning models for bioimaging. Table 2.5 Deep learning models for biomedical imaging area. Table 2.6 Deep learning models for human-machine interface area. Table 2.7 Deep learning models for HMS and public care.

2 Chapter 4Table 4.1 Differences between security and privacy [1].Table 4.2 Common biometric traits [17].

3 Chapter 8Table 8.1 Personality characteristics for corresponding baseline level [5].Table 8.2 Personality characteristics for writing slant [5].Table 8.3 Position of t bar and traits.Table 8.4 Personality traits for corresponding space between words [6].Table 8.5 Personality traits for corresponding space between words [6].Table 8.6 Comparison table for personality analysis using various machine learni...Table 8.7 Comparison table for writer identification using various machine learn...

4 Chapter 9Table 9.1 Risk factors involved in ASD.Table 9.2 Technological interventions for autism.Table 9.3 Sensitivity of various assistive technologies.Table 9.4 Softwares available for diagnosis.Table 9.5 Categorization of activation in different regions of brain.Table 9.6 Type of activity and color coding.

5 Chapter 10Table 10.1 Data attribute information.

6 Chapter 11Table 11.1 Summary of automated ASD investigation techniques based on EEG signal...Table 11.2 Comparison of performance of the pre-trained deep learning–based netw...

7 Chapter 12Table 12.1 Summary of results by previous authors.

8 Chapter 15Table 15.1 Comparison in between bone grafting and bone scaffold [2, 15].

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