Читать книгу Machine Learning Paradigm for Internet of Things Applications - Группа авторов - Страница 3

1 Chapter 1Figure 1.1 Bhubaneswar smart city structure.Figure 1.2 Pune smart city overview.Figure 1.3 Funds raised by government of India.Figure 1.4 Physical infrastructure workflow.Figure 1.5 Water supply chain in city structure.Figure 1.6 Smart city control flow for command and control centers.

2 Chapter 2Figure 2.1 System diagram of “isRice”.Figure 2.2 Structure of RNN.Figure 2.3 Output feedback for RNN.Figure 2.4 Structure of a standard RNN cell.Figure 2.5 Structure of a standard LSTM cell.Figure 2.6 Chromosome structure.Figure 2.7 One-point crossover.Figure 2.8 Mutation: swap operator.Figure 2.9 Business model of “isRice”.Figure 2.10 “isRice” main interface.Figure 2.11 Harvest prediction interface.Figure 2.12 Demand prediction interface.Figure 2.13 MAE for train set and test set: Paddy harvest prediction.Figure 2.14 MAE for train set and test set: Rice demand prediction.Figure 2.15 Number of generations vs. fitness value.Figure 2.16 Time vs. number of consumer districts.

3 Chapter 3Figure 3.1 Data collection and publishing model.Figure 3.2 Classification accuracy.Figure 3.3 Comparison of privacy preservation rate.Figure 3.4 Data publishing security level.

4 Chapter 4Figure 4.1 Domain technologies aiding Industry 4.0.Figure 4.2 Industrial production monitoring system through IIoT.Figure 4.3 Production unit in industries.Figure 4.4 Programming chart of digital twin creation.Figure 4.5 Digital twin design of production monitoring unit.Figure 4.6 Network round trip delay time.

5 Chapter 5Figure 5.1 Example of text CAPTCHA.Figure 5.2 Image-based captcha.Figure 5.3 Confusing character in a Google CAPTCHA.Figure 5.4 Segmentation method based on individual character.Figure 5.5 Segmented CAPTCHA image.Figure 5.6 Graphical operation made CAPTCHA image.Figure 5.7 Graphical sesign CAPTCHA in online application.

6 Chapter 6Figure 6.1 CNN feature extraction structure diagram in deep learning.Figure 6.2 (a) Input image; (b) Output image. (c) Input image; (d) Output image....Figure 6.3 Smart IoT-enabled traffic signs recognizing with high accuracy using ...

7 Chapter 7Figure 7.1 Evaluation structure of recommender system.Figure 7.2 ROC-AUC curve.Figure 7.3 Types of user study.Figure 7.4 Basic structure of A/B test.Figure 7.5 Process of data mining in RS.Figure 7.6 Rating prediction through matrix factorization.Figure 7.7 Process flow of offline evaluation.Figure 7.8 Illustration of IBCF.Figure 7.9 Performance evaluation of Random vs. SVD.Figure 7.10 Performance evaluation of SVD vs. SVD++.Figure 7.11 Novelty calculation of Random, SVD, and SVD++.

8 Chapter 8Figure 8.1 Convolutional Neural Network (CNN).Figure 8.2 Dataflow diagram.Figure 8.3 Safety equipment detecting.Figure 8.4 Detecting mask using deep learning.Figure 8.5 Detecting body temperature using thermal sensor.Figure 8.6 Raspberry Pi 3 connected with smart Locking door.

9 Chapter 9Figure 9.1 Silhouette analysis on K-means clustering on sample data with n_clust...Figure 9.2 City market hub marked in red and the market locations to deliver goo...Figure 9.3 Assigned vehicle route for a key market hub.Figure 9.4 Sample output for the depot (0).

10 Chapter 10Figure 10.1 Fake news evaluation matrix.Figure 10.2 Feature extraction.Figure 10.3 Confusion matrix of logistic regression.Figure 10.4 Confusion matrix of Naïve Bayes.Figure 10.5 Confusion matrix for random forest classifier.Figure 10.6 Confusion matrix for XGBoost algorithm.Figure 10.7 Accuracy level of machine learning algorithms.Figure 10.8 ROC curve of random forest for all four classes.

11 Chapter 11Figure 11.1 The paradigm for ML on Big data (MLBiD).

12 Chapter 12Figure 12.1 Internet of Underwater Things basic model.Figure 12.2 Architecture of IoUT.Figure 12.3 Applications of IoUT.Figure 12.4 Average communication cost vs. node mobility.Figure 12.5 Energy consumption vs number of nodes.

13 Chapter 13Figure 13.1 Concept of IoUTs.Figure 13.2 Concept and devices used in IoUT.Figure 13.3 Different routing protocols in IoUT.Figure 13.4 Multipoint relays in OLSR.Figure 13.5 The relationship between S and L in GFGD.Figure 13.6 A 3D logical grid view of EMGGR protocol.Figure 13.7 The probability of ACK’s collision.Figure 13.8 Operations in DRP.Figure 13.9 Delivery ratio vs. number of nodes.Figure 13.10 Energy consumption vs. number of nodes.

14 Chapter 14Figure 14.1 (a) Pneumonia x-ray image, (b) Healthy x-ray image.Figure 14.2 Xception network architecture.Figure 14.3 (a) Model accuracy, (b) Model loss.Figure 14.4 Confusion matrix.