Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms. Группа авторов

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      1 Chapter 1Figure 1.1 Cognitive behavioral elements of broad view of human-computer interfa...Figure 1.2 Decision processing system user interface device management as extern...Figure 1.3 Cognitive modeling process in the visualization decision processing u...Figure 1.4 Supporting cognitive model for the interaction of decision supportive...Figure 1.5 Basic elements of management information user interactive device syst...Figure 1.6 Model of memory, information passes through distinct stages in order ...

      2 Chapter 2Figure 2.1 The utility of HCI.Figure 2.2 The basic of HCI and related spaces.Figure 2.3 The making of intelligent ease of use.Figure 2.4 The connected fields of HCI and ease of use designing.Figure 2.5 Pictured models of smart devices, (a) Model-1 (b) Model-2 (c) Model-3...

      3 Chapter 3Figure 3.1 Human brain bisected in the sagittal plane.Figure 3.2 Functional areas of the human brain.Figure 3.3 Parts of the human ear.Figure 3.4 Regenerative feedback system of the teaching-learning process.Figure 3.5 Conceptual diagram of the teaching-learning process.Figure 3.6 Structure of a neuron.Figure 3.7 Block diagram of a typical neurofeedback system.Figure 3.8 BCI architecture.

      4 Chapter 4Figure 4.1 PDF of Gaussian noise.Figure 4.2 Single-level decomposition of 2D image.Figure 4.3 Single-level DWT decomposition.Figure 4.4 Three-level DWT decomposition.Figure 4.5 Single-level composition step of four sub-images.Figure 4.6 Filter arrangement for the dual-tree complex wavelet transform.Figure 4.7 Hard thresholding scheme: (a) original signal and (b) after hard thre...Figure 4.8 Soft thresholding scheme: (a) original signal and (b) after soft thre...Figure 4.9 Neighborhood window centered at thresholded wavelet coefficient.Figure 4.10 2 x 2 block partition for a wavelet sub-band.Figure 4.11 Image denoising using DTCWT-based thresholding technique.Figure 4.12 Flow chart for the wavelet-based thresholding technique.Figure 4.13 Standard gray images (512 × 512): (a) lena image; (b) barbara image;...Figure 4.14 (a) Noisy image (noise level = 10); (b) Denoise image (SURE shrink);...Figure 4.15 PSNR values obtained various thresholding techniques.Figure 4.16 SSIM values obtained various thresholding techniques.

      5 Chapter 6Figure 6.1 Block diagram of the proposed methodology.Figure 6.2 (a) Original image. (b) Face detection from the right angle using Vio...Figure 6.3 3D wireframe concerning central cell 14.Figure 6.4 (a) 3 x 3 x 3 size of voxels array. (b) Smallest possible three-dimen...Figure 6.5 Comparative analysis of proposed algorithm with existing technique on...Figure 6.6 Comparative analysis of error maps for 3D detailed reconstruction. Th...Figure 6.7 Reconstruction result of USF dataset. The numbers under error image r...

      6 Chapter 7Figure 7.1 Hierarchy of biometric traits [2].Figure 7.2 Block diagram of the proposed methodology.Figure 7.3 Proposed framework for expert one.Figure 7.4 Proposed framework for expert two.

      7 Chapter 8Figure 8.1 Machine learning framework.Figure 8.2 Comparison of machine learning structure with classifiers using accur...

      8 Chapter 9Figure 9.1 Predictive analytics process.Figure 9.2 Decision tree.Figure 9.3 Regression model.Figure 9.4 Artificial Neural Network.Figure 9.5 Bayesian statistics.Figure 9.6 Ensemble classifier.Figure 9.7 Gradient boosting.Figure 9.8 Support Vector Machine.Figure 9.9 Time series analysis.Figure 9.10 Regression utilizing k-NN.Figure 9.11 Principle component analysis.

      9 Chapter 10Figure 10.1 Virtual continuum.Figure 10.2 Research strategies followed.Figure 10.3 Advancement of publications.Figure 10.4 Development of AR and VR advancements in the cycle of hype.Figure 10.5 Advancement of games published in STEAM.Figure 10.6 On the top is the level of nations which made an exploration on AR o...Figure 10.7 Examination of papers picked step by step: Ihe blue line is the rela...Figure 10.8 Conveyance of developed fields.Figure 10.9 Publications development, everything being equal.Figure 10.10 Publications in R&D by nations.Figure 10.11 Data about distributions on medical care: The upper left picture is...Figure 10.12 Data about educational publications: in the upper left picture is t...Figure 10.13 Data about distributions on the industry: The upper left picture is...

      10 Chapter 11Figure 11.1 Basic ANN architecture.Figure 11.2 Vision-based vehicle navigation system.Figure 11.3 The contadino autonomous implement carrier can be used for seeding, ...Figure 11.4 Swarm size agriculture robots [60].

      11 Chapter 12Figure 12.1 Flowchart of proposed algorithm. Workflow diagram.Figure 12.2 (a) Original image (DB1 107_2.tif). (b) Coherence filter. (c) Gabor ...Figure 12.3 (a) Original image. (b) Proposed algorithm using a thinning techniqu...

      12 Chapter 13Figure 13.1 Diagrammatical view.Figure 13.2 (a-d) is Performance evaluation on state of art parameters.

      13 Chapter 14Figure 14.1 Five tuples used to evaluate sentiment.Figure 14.2 ttree values of sentiment.Figure 14.3 Types of sentiments.Figure 14.4 Architecture of SA system.Figure 14.5 Challenges of sentiment classifier.Figure 14.6 Real-life applications of sentiment analysis.Figure 14.7 Framework for the proposed model.Figure 14.8 Comparison chart of different classifiers for different datasets.

      14 Chapter 15Figure 15.1 Shapes of EBN of grade AA, grade A, and grade B. Adapted from [4].Figure 15.2 Block diagram for feature extraction.Figure 15.3 Histogram of intensities of saturation layer for various grades.Figure 15.4 Original images (top row) and the impurities detected (bottom row) f...Figure 15.5 Original image (top row) and HSV colour model (bottom row) of EBNs (...Figure 15.6 Original images (top row) and the area detected for each image (bott...

      15 Chapter СКАЧАТЬ