Information Visualization for Intelligent Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 788

Veröffentlichungsjahr: 2025

Beschreibung

Information Visualization for Intelligent Systems provides readers with essential insights into cutting-edge advancements in machine intelligence and explores how these transformative technologies are revolutionizing data analysis and decision-making in an increasingly complex world.

The book explores advanced computing, or machine intelligence, which enables technology—machines, devices, or algorithms—to interact intelligently with their surroundings, make decisions, and take actions to achieve objectives. Unlike natural human intelligence, artificial intelligence (AI) is demonstrated by machines.

Modern advancements in high-speed computing drive paradigm shifts, enabling complex machine intelligence systems and novel cyber systems that utilize data to perform specific tasks. While standalone cyber systems are common, integrating multiple systems into cohesive, intelligent structures interacting deeply with physical systems remains underexplored and primarily philosophical in existing literature.

These technological breakthroughs have revolutionized data generation, cloud storage, global information exchange, and rapid computing. For example, machine intelligence models analyze video surveillance to identify threats, support early infection detection in healthcare, and enhance chemical industry processes. While promising, these advancements remain in their infancy, offering significant potential for further development.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Analysis of Restaurant Reviews Using Novel Hybrid Approach Algorithm Over Convolutional Neural Network Algorithm with Improved Accuracy

Introduction

Related Work

Existing Methodology

Proposed Methodology

Results

Discussion

Conclusion

References

2 Forecasting of Product Demand Using Hybrid Regression Model in Comparison with Autoregressive Integrated Moving Average Model

2.1 Introduction

2.2 Materials and Methods

2.3 Tables and Figures

2.4 Results

2.5 Discussion

Conclusion

References

3 Identification of Stress in IT Employees by Image Processing Using Novel KNN Algorithm in Comparison of Accuracy with SVM

Abbreviations Used

3.1 Introduction

3.2 Materials and Methods

3.3 Statistical Analysis

3.4 Results

3.5 Discussions

3.6 Conclusion

Acknowledgements

References

4 Observing the Accuracy of Breast Cancer Using Support Vector Machine with Digital Mammogram Data in Comparison with Naive Bayes

Introduction

Materials and Methods

Statistical Analysis

Results

Discussion

Conclusion

References

5 Analyzing and Improving the Efficiency of Winning Prediction in Chess Game Using AlexNet Classifier in Comparison with Support Vector Machine for Improved Accuracy

Introduction

Materials and Methods

Results

Discussion

Conclusion

References

6 Accurate Prediction of Vehicle Number Plate Segmentation and Classification with Inception Compared over Alexnet

6.1 Introduction

Organization of Chapter

6.2 Relevant Works

6.3 Proposed Methodology

6.4 Resources and Techniques

Tables and Figures

6.5 Results and Discussion

6.6 Conclusion

References

7 A Novel Method to Analyze a Server Instance’s Performance During a Crypto-Jacking Attack Using Novel Random Forest Algorithm Compared with Logistic Regression

Abbreviations Used

7.1 Introduction

7.2 Materials and Methods

7.3 Statistical Analysis

7.4 Results

7.5 Discussion

Conclusion

Acknowledgements

References

8 A Comparative Analysis of Twin Segmentation and Classification Over MultiClass SVM and Innovative CNN: An Innovative Approach

8.1 Introduction

Statistical Analysis

Discussion

Conclusion

References

9 Prediction of Yields in Semiconductor Using XGBoost Classifier in Comparison with Random Forest Classifier

9.1 Introduction

Results

Discussion

Conclusion

References

10 A Robust Medical Image Watermarking Scheme with a Better Peak Signal-to-Noise Ratio Based on a Novel Modified Embedding Algorithm and Spatial Domain Algorithm

10.1 Introduction

10.2 Result

10.3 Discussion

10.4 Conclusion

References

11 BER Comparison of BPSK-DCO-OFDM and OOK-DCO-OFDM in Visible Light Communication

Abbreviations Used

11.1 Introduction

11.2 Materials and Methods

11.3 Statistical Analysis

11.4 Results

11.5 Discussions

11.6 Conclusion

References

12 Improved Accuracy in Blockchain-Based Smart Vehicle Transportation System Using KNN in Comparison with SVM

Abbreviations Used

12.1 Introduction

12.2 Materials and Methods

12.3 Tables and Figures

12.4 Results

12.5 Discussion

12.6 Conclusion

References

13 Improvement in Accuracy of Red Blood Cells (RBC), White Blood Cells (WBC), and Platelets Detection Using Artificial Neural Network and Comparison with Hybrid Convolution Neural Network

13.1 Introduction

13.2 Materials and Methods

13.3 Results

13.4 Discussion

13.5 Conclusion

References

14 Novel Design of Meta Ring Array Antenna Using FR4 for Biomedical Applications

14.1 Introduction

14.2 Related Work

14.3 Materials and Methods

14.4 Results

14.5 Discussions

14.6 Conclusion

Abbreviations Used

References

15 Review: Recommendation System in Tourism and Hospitality Based on Comparison of Different Algorithms

15.1 Introduction

15.2 Literature Review

15.3 Research Gaps

15.4 Conclusion

15.5 Future Work

Abbreviations Used

References

16 Secure and Reliable Routing for Hybrid Network to Support Disaster Recovery and Management

Abbreviations

16.1 Introduction

16.2 Related Work

16.3 Proposed Methodology

16.4 Experimental Results

16.5 Conclusion

Acknowledgments

References

17 Machine Learning Techniques for Sentimental Analysis

Abbreviations Used

17.1 Introduction

17.2 Applications of Sentimental Analysis

17.3 Related Work

17.4 Existing Methodology

17.5 Comparison and Discussion

17.6 Conclusion

References

18 Design of 40-mm Period, 0.8-Tesla Variable-Gap Pure Permanent Magnet Undulator Magnet in RADIA

18.1 Introduction

18.2 Undulator Modeling in RADIA

18.3 Results and Discussion

Acknowledgment

References

19 Predicting Academic Performance of Students: An ANN Approach

Abbreviations Used

19.1 Introduction

19.2 Literature Survey

19.3 Proposed ANN Model

19.4 Experimental Setup

19.5 Result Analysis

19.6 Conclusion and Future Scope

Acknowledgements

References

20 A Deep Study on Discriminative Supervised Learning Approach

20.1 Introduction

20.2 Literature Survey

20.3 Introductory Information About Deep Learning and Its Features

20.4 Methodology of DL Approaches

20.5 Deep Learning Network Structures

20.6 Conclusion

References

21 AI Medical Assistant Machine Learning Techniques

21.1 Introduction

21.2 Literature Review

21.3 Data and Methodology

21.4 Result and Discussion

21.5 Conclusion

References

22 Early Schizophrenia Prediction Using Wearable Devices and Machine Learning

22.1 Introduction

22.2 Related Works

22.3 Proposed Methodology

Methodology

22.4 Results and Discussion

22.5 Comparison with Existing Methods

22.6 Conclusion

References

23 Forecasting the Trends in Stock Market Employing Optimally Tuned Higher Order SVM and Swarm Intelligence

Abbreviations Used

23.1 Introduction

23.2 Related Work

23.3 Proposed Methodology

23.4 Result

23.5 Conclusion

Acknowledgements

References

24 Social Media Text Classification Analysis and Influence of Feature Selection Methods on Classification Performance

24.1 Introduction

24.2 Literature Review

24.3 Proposed Work

24.4 Results Analysis

24.5 Conclusions

References

25 4G Versus 5G Communication Using Machine Learning Techniques

25.1 Introduction

25.2 Literature Review

25.3 Data and Methodology

25.4 4G and 5G Methodology

25.5 4G and 5G Algorithm

25.6 Conclusion

References

26 Design and Development of Programmable and UV-Based Automated Disinfection for Sanitization of Package Surfaces

26.1 Introduction

26.2 Materials and Methodology

26.3 Result and Discussion

26.4 Conclusion

Funding

Acknowledgements

References

27 Fuzzy-Based Segmentations Performance Analysis for Breast Tumor Detection Using Spatial Fuzzy C-Means Filtering with Preconditions (SFCM-P) Over Bilateral Fuzzy K-Mean Clustering Algorithm (BiFKC)

27.1 Introduction

27.2 Materials and Methods

27.3 Results

27.4 Discussion

27.5 Conclusion

References

28 Analysis of Vehicle Accident Prediction Using GoogleNet Classifier Compared with AlexNet Algorithm to Enhance Accuracy

28.1 Introduction

28.2 Significance of GoogleNet and AlexNet for Vehicle Accidents

28.3 Related Work

28.4 Proposed Methodology

28.5 Results Analysis

28.6 Conclusion

References

29 Maximizing the Accuracy of Fake Indian Currency Prediction Using Particle Swarm Optimization Classifier in Comparison with Lasso Regression

29.1 Introduction

29.2 Significance of PCO and Lasso Regression

29.3 Related Work

29.4 Proposed Methodology

29.5 Result Analysis

29.6 Conclusion

References

30 Convolutional Neural Network Algorithm for Proliferative Diabetic Retinopathy Detection and Comparison with GoogleNet Algorithm to Improve Accuracy

Abbreviations Used

30.1 Introduction

30.2 Materials and Methods

30.3 Statistical Analysis

30.4 Results

30.5 Discussion

30.6 Conclusion

Acknowledgements

References

31 Conversational AI – Security Aspects for Modern Business Applications

Abbreviations Used

31.1 Introduction

31.2 CAI – Security Threats

31.3 Literature Review

31.4 Mitigation Strategies

31.5 CAI Models

31.6 Future Research Directions

31.7 Conclusion

References

32 Literature Review Analysis for Cyberattacks at Management Applications and Industrial Control Systems

Abbreviations Used

32.1 Introduction

32.2 Literature Survey

32.3 Research Techniques

32.4 Observational Values

32.5 Analysis

32.6 CICS -CCSC Future Scope

32.7 Future Work

Acknowledgements

References

33 Fractal Natural Language Semantics and Fractal Machine Learning Engineering: Cultural Heritage Generative Management Systems

33.1 Introduction

33.2 Frameworks, Directions, and Domains

33.3 CH-GeMS Architecture

33.4 Conclusions

References

About the Editors

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 An analysis of accuracy and loss was conducted on a novel hybrid app...

Table 1.2 A CNN with a sample size of 10 is used to analyze its accuracy and l...

Table 1.3 The group statistics for the novel hybrid model show a mean accuracy...

Table 1.4 Performing a T-test on an independent sample to establish significan...

Table 1.5 Comparison of Hybrid Algorithm and CNN with their accuracy. The accu...

Chapter 2

Table 2.1 The group statistics of the data were performed for 20 iterations be...

Table 2.2 The independent sample T-test between NHR and ARIMA.

Chapter 3

Table 3.1 Precision assessment of conventional and suggested method.

Table 3.2 The group’s mean, standard deviation, and precision of the KNN and S...

Table 3.3 The outcomes of the independent sample assessment indicated that the...

Chapter 4

Table 4.1 Comparison of prediction accuracy using SVM and naive Bayes.

Table 4.2 The mean of the SVM is 85.91 and naive Bayes is 92.64.

Chapter 5

Table 5.1 The AlexNet attained accuracy of 99.92% compared to SVM having 94.99...

Table 5.2 The group’s mean and standard deviation, along with the accuracy met...

Table 5.3 The independent sample test detected a significant improvement in ac...

Chapter 6

Table 6.1 Accuracy assessment.

Table 6.2 The mean, standard deviation, and standard error imply statistical c...

Table 6.3 Independent sample statistical calculations and the inception approa...

Chapter 7

Table 7.1 Accuracy values of novel RF and LR algorithms.

Table 7.2 Calculating the mean, standard deviation, and standard error of the ...

Table 7.3 The independent sample T-test findings show that there is no statist...

Table 7.4 When comparing the accuracy of RF to LR, performance differences app...

Chapter 8

Table 8.1 Enhanced precision in twin identification is observed with a convolu...

Table 8.2 It summarizes the outcomes of the independent samples T-Test conduct...

Table 8.3 Statistical evaluation employing convolutional neural network and mu...

Chapter 9

Table 9.1 Accuracy and loss analysis of novel XGB and RF.

Table 9.2 Group statistical analysis of novel XGB and RF. Mean, standard devia...

Table 9.3 The independent sample t-test was performed between XGB and RF for 1...

Chapter 10

Table 10.1 Comparative performance analysis of PSNR for Group 1 and Group 2 in...

Table 10.2 Statistical analysis of novel modified embedding and spatial domain...

Table 10.3 Independent sample test for significance and standard error determi...

Chapter 11

Table 11.1 Comparison of BPSK and OOK for BER values.

Table 11.2 Group statistics for BER.

Table 11.3 Independent sample test.

Chapter 12

Table 12.1 Accurateness and loss examination of K-nearest neighbor.

Table 12.2 Accurateness and loss examination of support vector machine.

Table 12.3 Descriptive analysis of KNN and SVM. KNN improved accuracy and less...

Table 12.4 K-nearest neighbor is better than SVM.

Chapter 13

Table 13.1 Group statistics of artificial neural network and hybrid CNN-based ...

Table 13.2 Group of independent sample tests of Levene’s test for equality of ...

Chapter 14

Table 14.1 Comparison of different studies carried out for antenna design.

Table 14.2 Design parameters of S patch antenna designed using HFSS.

Table 14.3 A comparison is made between the gain of the multi-band meta ring a...

Table 14.4 Design parameters of meta ring array antenna designed using HFSS.

Chapter 15

Table 15.1 Systematic literature review.

Chapter 16

Table 16.1 Parameter setting.

Table 16.2 Parameter setting.

Chapter 19

Table 19.1 Configuration parameters.

Table 19.2 CFFNN model (Predict G3 on demo).

Table 19.3 CFFNN model (predict G3 on demo and G1).

Table 19.4 CFFNN model (predict G3 on demo, G1 and G2).

Chapter 20

Table 20.1 Description of CNN architecture.

Chapter 21

Table 21.1 Amalgamated correlation coefficients (R2) between LBNP and CRM and ...

Chapter 22

Table 22.1 Performance evaluation for schizophrenia prediction using machine l...

Table 22.2 Comparison of F1-score for schizophrenia prediction.

Chapter 23

Table 23.1 Various features of utilized data.

Table 23.2 Training data (before normalization).

Table 23.3 Testing results.

Table 23.4 Performance evaluations results (linear SVM).

Table 23.5 Linear SVR comparative values.

Table 23.6 Performance evaluations results (quadratic SVM).

Table 23.7 Quadratic SVR comparative values.

Table 23.8 Performance evaluations results (cubic SVM).

Table 23.9 Cubic SVR comparative values.

Table 23.10 Performance evaluations results (fine Gaussian SVM).

Table 23.11 Fine Gaussian SVR comparative values.

Chapter 24

Table 24.1 Dataset used.

Chapter 25

Table 25.1 The comparison: 4G vs. 5G.

Table 25.2 Theoretical 4G vs. 5G, speed monitor various parameters result anal...

Table 25.3 Theoretical 4G vs. 5G, speed monitor various parameters result anal...

Chapter 27

Table 27.1 Sfcm-p is the spatial fuzzy c-means filtering the accuracy values a...

Table 27.2 The accuracy for the BiFKC model is tabulated.

Table 27.3 Group statistical analysis of mean, standard deviation (STD) and st...

Table 27.4 SPSS statistics depicts the accuracy of the SFCM-P and the BiFKC-ba...

Table 27.5 Comparative analysis of different modules proposed in this research...

Chapter 28

Table 28.1 Accuracy comparison of GoogleNet and AlexNet algorithm.

Table 28.2 The average accuracy and standard deviation for the Novel GoogleNet...

Table 28.3 The independent sample test indicated a significant difference in a...

Chapter 29

Table 29.1 Accuracy comparison of PSO classifier and LR.

Table 29.2 The average accuracy and standard deviation for the novel PSO and L...

Table 29.3 The independent sample test highlighted a notable difference in the...

Chapter 30

Table 30.1 Statistical analysis of GoogleNet and CNN.

Table 30.2 Independent sample T-test: CNN is significantly better than GoogleN...

Chapter 31

Table 31.1 CAI aspects [16–20].

Table 31.2 CAI for business applications [21–24].

Table 31.3 CAI tools [25, 26].

Table 31.4 Comparative study of techniques.

Table 31.5 CAI techniques.

Table 31.6 CAI issues.

Chapter 32

Table 32.1 Comparative study of literature reviews.

Table 32.2 E-Publication database’s IE.

Table 32.3 Research publications.

Table 32.4 Keywords in IE.

Chapter 33

Table 33.1 Processes of the knowledge and the valorization frameworks.

Table 33.2 Networks dynamic interactions.

Table 33.3 Demand-supply mechanism.

List of Illustrations

Chapter 1

Figure 1.1 A flowchart illustrating a cutting-edge hybrid method that combines...

Figure 1.2 Comparative analysis of the Hybrid Approach Algorithm and CNN. The ...

Chapter 2

Figure 2.1 Comparison of NHR and ARIMA model. The NHR (84.61%) regression outp...

Chapter 3

Figure 3.1 Flow diagram of stress detection.

Figure 3.2 In terms of predicting stress among IT workers due to work pressure...

Chapter 4

Figure 4.1 Detecting the accuracy for two algorithms, the accuracy of the naiv...

Chapter 5

Figure 5.1 The proposed method accuracy is greater than the conventional one. ...

Chapter 6

Figure 6.1 The accuracy rate of the inception classifier and the Alexnet algor...

Chapter 7

Figure 7.1 Flow diagram for the proposed system.

Figure 7.2 The graphical depiction compares novel RF against LR classifiers on...

Chapter 8

Figure 8.1 A comparative analysis is performed on the accuracies of the multic...

Chapter 9

Figure 9.1 Comparison of RF and novel XGB Classifier in terms of mean accuracy...

Chapter 10

Figure 10.1 Block diagram of robust medical image watermarking based on embedd...

Figure 10.2 (a) Original medical image, (b) a watermarked image with correspon...

Figure 10.3 Outputs of CT image watermarking, (a) DCT recovered image, and (b)...

Figure 10.4 Bar chart representing the comparison of mean PSNR of the watermar...

Chapter 11

Figure 11.1 The block diagram for BPSK and OOK with DCO-OFDM.

Figure 11.2 Simulation result.

Figure 11.3 Comparison of BPSK and OOK performance.

Chapter 12

Figure 12.1 K-nearest neighbor is better than SVM.

Chapter 13

Figure 13.1 Flowchart for finding the accuracy for hybrid CNN, artificial neur...

Figure 13.2 The artificial neural network and hybrid CNN-based cells and plate...

Figure 13.3 Independent sample tests were used to design and examine the convo...

Chapter 14

Figure 14.1 The design of a multi-band meta ring array is shown.

Figure 14.2 Radiated power of meta ring array antenna 5 dB at 1 GHz to 6 GHz (...

Figure 14.3 Bar chart comparing the mean (+/−2 SD) of radiated power of double...

Chapter 15

Figure 15.1 Tourism in India.

Figure 15.2 Recommendation system for tourism using Facebook check-in data [11...

Figure 15.3 Collaborative filtering model.

Figure 15.4 Content-based filtering model.

Figure 15.5 Neural network architecture.

Figure 15.6 CNN for recommendation.

Figure 15.7 Semantic analysis for recommender system.

Figure 15.8 Genetic algorithm approach.

Chapter 16

Figure 16.1 Example of ad hoc networks.

Figure 16.2 Proposed hybrid network architecture for the disaster recovery sys...

Figure 16.3 Network topology of hybrid network.

Figure 16.4 Network topology of secure hybrid model.

Figure 16.5 Network throughput between hybrid and secure hybrid models.

Figure 16.6 Packet-delivery ratio between hybrid and secure hybrid model.

Figure 16.7 Load on the network.

Figure 16.8 Packet drop rate.

Chapter 17

Figure 17.1 Classification algorithms in detail [8].

Figure 17.2 Diagram of social media sentiment analysis [23].

Chapter 18

Figure 18.1 Magnet design with a cut at the corners for RADIA.

Figure 18.2 Magnet design without cutting at the corners from RADIA.

Figure 18.3 Symmetric end field configuration with a half magnet at each undul...

Figure 18.4 Symmetric end field configuration with a half magnet at each undul...

Figure 18.5 Magnetic flux density versus longitudinal position of the undulato...

Figure 18.6 First field integral for the symmetric end design.

Figure 18.7 Second field integral for the symmetric end design.

Figure 18.8 Magnetic flux density versus longitudinal position of the undulato...

Figure 18.9 First field integral for symmetric end design.

Figure 18.10 Second field integral for the symmetric end design.

Figure 18.11 Magnetic flux density versus gap.

Figure 18.12 Difference in magnetic flux density versus gap from both designs.

Figure 18.13 Comparison of first field integral versus gap for both designs.

Figure 18.14 Comparison of second field integral versus gap for both designs.

Figure 18.15 Angular offset versus undulator gap for both the design.

Figure 18.16 Trajectory deviation versus undulator gap.

Chapter 19

Figure 19.1 Process diagram of the proposed approach.

Figure 19.2 CFFNN with traingdx function.

Figure 19.3 CFFNN with trainlm function.

Chapter 20

Figure 20.1 Deep learning roles in data science [22].

Figure 20.2 Deep learning approaches [19].

Figure 20.3 Classification of supervised DL.

Figure 20.4 Classification of semi-supervised DL.

Figure 20.5 Classification of unsupervised DL.

Figure 20.6 Schematic structure of MLP.

Figure 20.7 Schematic structure of RvNN.

Figure 20.8 Schematic structure of CNN layers.

Chapter 21

Figure 21.1 The architecture of AI medical assistant. AR enhancing perception.

Figure 21.2 Correlation coefficients R2 between LBNP and CRM and CRI.

Chapter 22

Figure 22.1 Dataset class distribution on schizophrenia.

Figure 22.2 Proposed methodology for schizophrenia prediction.

Figure 22.3 ROC curve for the proposed methodology.

Figure 22.4 Confusion matrix for the algorithms used for schizophrenia predict...

Figure 22.5 Precision-recall curve for the proposed methodology.

Figure 22.6 F1-scores comparison between various methods.

Chapter 23

Figure 23.1 Architecture of the model designed for predicting stock opening pr...

Figure 23.2 Selected opening stock prices and their corresponding trading date...

Figure 23.3 Graph indicating the opening stock prices.

Figure 23.4 Actual opening stock market (blue) price vs quadratic and cubic pr...

Figure 23.5 Comparison of actual stock price (blue) vs predicted on quadratic ...

Figure 23.6 Actual opening (blue) vs. linear predicted (red).

Figure 23.7 Comparisons of actual (blue) vs. predicted on fine Gaussian (red).

Chapter 24

Figure 24.1 Comparison of different methods of classification.

Figure 24.2 The common architecture of text classification.

Figure 24.3 Fake news classification model.

Figure 24.4 Proposed text classification model.

Figure 24.5 Performance of different feature selection technique with CNN clas...

Figure 24.6 Efficiency of the feature selection techniques in terms of (a) tra...

Chapter 25

Figure 25.1 Theoretical 4G vs. 5G speed.

Figure 25.2 Live 4G vs. 5G speed.

Chapter 26

Figure 26.1 Flow chart of developed machine.

Figure 26.2 Frame and parts fitting.

Figure 26.3 3D frame and parts fitting.

Figure 26.4 Percentage of disinfection.

Chapter 27

Figure 27.1 Bar chart comparison of accuracy of the SFCM-P model and the BiFKC...

Figure 27.2 Proposed RFC-ConvNet works with the BiFKC segmentation and classif...

Figure 27.3 Similarity index between the detected BT lesion spot region and it...

Figure 27.4 Clinical observation for infectious depth spreads of BT lesion seg...

Chapter 28

Figure 28.1 A comparison of mean accuracy between the Novel GoogleNet and Alex...

Chapter 29

Figure 29.1 A comparison of the mean accuracy rates for detecting counterfeit ...

Chapter 30

Figure 30.1 Block diagram of novel convolutional neural network algorithm and ...

Figure 30.2 Preprocessed images with novel CNN and GoogleNet.

Figure 30.3 Simple bar mean of accuracy.

Chapter 32

Figure 32.1 RQ.

Figure 32.2 Observational studies filtering technique.

Figure 32.3 Review criteria.

Figure 32.4 Paper processing and stratification.

Figure 32.5 Observational studies theme chart.

Figure 32.6 Most recent IE ICSS-focused CICS applications.

Figure 32.7 Patterns of threats analysis.

Figure 32.8 CCSC datasets.

Chapter 33

Figure 33.1 The hexagon of LHC processes interactions.

Figure 33.2 The landscapes, heritage, and culture x-management system.

Figure 33.3 Informative query-answer learning interaction mechanism.

Figure 33.4 Operative demand-supply learning interaction mechanism.

Figure 33.5 Management of the dual roles of two entities i and j.

Figure 33.6 Dynamic data–driven application systems for LHC learning interacti...

Figure 33.7 Distributed augmented (human-artificial) intelligence systems.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

About the Editors

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Information Visualization for Intelligent Systems

Edited by

Premanand Singh Chauhan

Rajesh Arya

Rajesh Kumar Chakrawarti

Elammaran Jayamani

Neelam Sharma

and

Romil Rawat

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-30578-0

Front cover images supplied by Adobe FireflyCover design by Russell Richardson

Preface

The book focusses on advanced computing, or machine intelligence, the ability of a technology (a machine, device, or algorithm) to interact with its surroundings intelligently. This means that the technology can make decisions and take actions that will increase the likelihood that its objectives will be met. In contrast to the natural intelligence exhibited by people, artificial intelligence (AI), sometimes referred to as machine intelligence, is intelligence manifested by machines. The modern world is experiencing a period of paradigm shifts. New technologies have contributed to these shifts in part because they offer high-speed computing capabilities that make complicated machine intelligence systems possible. These advancements are paving the way for the creation of new cyber systems, which employ continually generated data to construct machine intelligence models that carry out specific functions inside the system. While the isolated use of cyber systems is becoming more common, the synchronic integration of these systems with other cyber systems to create a compact and intelligent structure that can interact deeply and independently with a physical system is still largely unanswered and has only been briefly discussed from a philosophical perspective in a few works.

Modern civilisation has undergone many paradigms shifts as a result of technological breakthroughs. These developments brought in immense data creation, cloud data storage systems, near-instantaneous worldwide information exchange, very quick computer capabilities, etc. Additionally, they paved the way for the development of cutting-edge cyber systems that employ systematically created data pipelines to carry out certain tasks. For instance, in certain nations, video surveillance imagery is used to detect criminals or possible criminals using machine intelligence (MI) models. Moreover, autonomous MI systems have applications in the medical field, where they enable prompt detection of infections like COVID-19. The chemical industry also uses a variety of applications.