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

1Table 1.1 The core artifacts provided at the cognitive modeling of user interact...Table 1.2 Representational uses of cognitive modeling for decision support user ...

      2 Chapter 4Table 4.1 PSNR values for grayscale images (512×512) for different values of AWG...Table 4.2 SSIM values for grayscale images (512×512) for different values of AWG...

      3 Chapter 6Table 6.1 Study of existing methodology.Table 6.2 Sample of possible convex polyhedrons.Table 6.3 Comparative analysis of mean and standard deviation of point to point ...

      4 Chapter 7Table 7.1 LivDet 2015 dataset details.Table 7.2 LivDet 2015 dataset details.

      5 Chapter 8Table 8.1 Exhibition correlation of enhanced multilayer perception by different ...

      6 Chapter 9Table 9.1 The solid ability sets as controlled by area specialists.Table 9.2 The after-effects of the PCA examination. All highlights aside from Z-...Table 9.3 The coefficients and noteworthiness estimations of the summed up segme...Table 9.4 The models developed from highlights in the critical summed up parts. ...

      7 Chapter 10Table 10.1 Search measure synopsis.Table 10.2 Evolution of publications houses.Table 10.3 Outline of EU and USA publications by topics.

      8 Chapter 11Table 11.1 Cameras used in precision agriculture application.Table 11.2 Plant and fruit detection techniques.Table 11.3 Fruit grading and ripeness detection approaches.Table 11.4 Fruit counting and yield prediction.Table 11.5 Weed and disease detection.

      9 Chapter 12Table 12.1 Texture descriptor results for FVC2004DB1 107_2.tif.Table 12.2 Texture descriptor results for FVC2004DB2 101_2.tif.Table 12.3 Texture descriptor results for FVC2004DB3 107_7.tif.Table 12.4 Texture descriptor results for FVC2004DB4 110_8.tif.Table 12.5 Minutiae ratio results for the thinning technique.Table 12.6 Minutiae ratio results for mindset technique.Table 12.7 Minutiae ratios obtained for the proposed algorithm using the thinnin...Table 12.8 Minutiae ratios obtained for the proposed algorithm using the mindset...

      10 Chapter 13Table 13.1 Comparison of performance of applied classifiers using certain specif...Table 13.2 Analytical estimation of selected attributes.

      11 Chapter 14Table 14.1 Dataset statistics.Table 14.2 Performance comparison of different classifiers for the IMDB dataset ...Table 14.3 Performance comparison of different classifiers for Amazon product re...Table 14.4 Performance comparison of different classifiers for news headlines da...Table 14.5 Performance comparison of different classifiers for online blogs data...Table 14.6 Performance comparison of different classifiers for Wikipedia dataset...Table 14.7 Accuracy comparison of different classifiers for different datasets.

      12 Chapter 15Table 15.1 Features extracted for various grades.Table 15.2 Classification accuracies for various radii of subtractive clustering...Table 15.3 Accuracies for FCM with different clusters.Table 15.4 Sensitivity of the neural net with different number of hidden neurons...Table 15.5 Auto-grading accuracies (%).Table 15.6 Maximum and minimum classification accuracies (%).Table 15.7 Best classification accuracies.

      13 Chapter 16Table 16.1 Comparative analysis of submerged images.Table 16.2 Proposed method time and entropy measured value.

      Guide

      1  Cover

      2  Table of Contents

      3  Title page

      4  Copyright

      5  Preface

      6  Begin Reading

      7  Index

      8  End User License Agreement

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