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Submit a ProposalInformation Visualization for Intelligent Systems
 | Edited by Premanand Singh Chauhan, Rajesh Arya, Rajesh Kumar Chakrawarti, Elammaran Jayamani, Neelam Sharma, Romil Rawat Copyright: 2025 | Expected Pub Date:2024// ISBN: 9781394311576 | Hardcover | 538 pages Price: $225 USD  |
One Line Description
Information Visualization for Intelligent Systems will help readers gain essential insights into the cutting-edge advancements in machine intelligence and discover how these transformative technologies are revolutionizing our ability to analyze data and make informed decisions in an increasingly complex world.
Description
Information Visualization for Intelligent Systems focuses on advanced computing, or machine intelligence, which refers to the ability of technology (a machine, device, or algorithm) to interact with its surroundings intelligently. This means that the technology can make decisions and take action that will increase the likelihood that its objectives will be met. In contrast to the natural intelligence exhibited by people, artificial intelligence (AI) 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 paradigm shifts as a result of technological breakthroughs. These developments brought in immense data creation, cloud data storage systems, near-instantaneous worldwide information exchange, and quick computing capabilities. 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. These latest developments have a lot of promise yet are still very fresh.
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Information Visualization for Intelligent Systems focuses on advanced computing, or machine intelligence, which refers to the ability of technology (a machine, device, or algorithm) to interact with its surroundings intelligently. This means that the technology can make decisions and take action that will increase the likelihood that its objectives will be met. In contrast to the natural intelligence exhibited by people, artificial intelligence (AI) 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 paradigm shifts as a result of technological breakthroughs. These developments brought in immense data creation, cloud data storage systems, near-instantaneous worldwide information exchange, and quick computing capabilities. 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. These latest developments have a lot of promise yet are still very fresh.
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Author / Editor Details
Premanand S. Chauhan, PhD is a director at the Sushila Devi Bansal College of Technology, Indore, India with seven years of industry experience and 20 years of teaching experience. He has edited one book, authored two books and 55 research articles, and has published three patents, one of which was granted. He is the editor of the proceedings of many reputed international conferences, technical adviser for many industries working in the field of manufacturing, and also a member of many professional bodies.
Premanand S. Chauhan, PhD is a director at the Sushila Devi Bansal College of Technology, Indore, India with seven years of industry experience and 20 years of teaching experience. He has edited one book, authored two books and 55 research articles, and has published three patents, one of which was granted. He is the editor of the proceedings of many reputed international conferences, technical adviser for many industries working in the field of manufacturing, and also a member of many professional bodies.
Rajesh Arya, PhD is a principal at the Sushila Devi Bansal College of Engineering, Indore, India. He has more than 15 years of experience teaching courses related to electrical and computer engineering. He has published more than 45 research papers in the journals and conferences of repute publishers and is an Associate Member of the Institution of Engineers.
Rajesh Kumar Chakrawarti, PhD is a professor and dean at the Department of Computer Science and Engineering/Information Technology, Sushila Devi Bansal College, Indore, India with over 21 years of experience in academia and industry. He is actively involved in teaching courses at both the undergraduate and postgraduate levels and is eagerly involved in teaching, training, research and development, and department, institution, and university development activities. He has organized and attended over 100 seminars, workshops, conferences, and certifications and has presented and published over 100 research papers, chapters in books, and abstracts in national and international conferences and journals.
Elammaran Jayamani, PhD is an associate professor in the Mechanical Engineering program in the Faculty of Engineering, Computing, and Science at the Swinburne University of Technology, Sarawak Campus. Dr. Elammaran has been a creative educator for over 23 years, promoting sustainable materials research and development and is well-versed in training and mentoring students, research scholars, and educators. He is a member of the Institution of Mechanical Engineers as a Chartered Engineer.
Neelam Sharma, PhD is an associate professor and the head of Electronics and Communication Engineering at Sushila Devi Bansal College of Technology, Indore, India with over 18 years of teaching experience. She has been published in various SCI and Scopus journals and IEEE conferences and is a life member of the International Society for Technology in Education.
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Table of Contents
Preface
1. Analysis of Restaurant Reviews Using Novel Hybrid Approach Algorithm Over Convolutional Neural Network Algorithm with Improved Accuracy
K. Abhilash Reddy and Uma Priyadarsini P.S.
Introduction
Related Work
Existing Methodology
Convolutional Neural Network Algorithm
Proposed Methodology
Novel Hybrid Approach Algorithm
Statistical Analysis
Results
Discussion
Conclusion
References
2. Forecasting of Product Demand Using Hybrid Regression Model in Comparison with Autoregressive Integrated Moving Average Model
Adibhatla Ajay Bharadwaj and M. Gunasekaran
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
C. Srinath and S. Parthiban
Abbreviations Used
3.1 Introduction
3.2 Materials and Methods
3.3 Statistical Analysis
3.4 Results
3.5 Discussions
3.6 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
4. Observing the Accuracy of Breast Cancer Using Support Vector Machine with Digital Mammogram Data in Comparison with Naive Bayes
M.A. Aasiya Banu and K. Thinakaran
Introduction
Materials and Methods
Support Vector Machine
Naive Bayes Algorithm
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
Keerthana P. and G. Mary Valantina
Introduction
Materials and Methods
AlexNet
Support Vector Machine
Statistical Analysis
Results
Discussion
Conclusion
References
6. Accurate Prediction of Vehicle Number Plate Segmentation and Classification with Inception Compared over Alexnet
E. K. Subramanian and V. Sudharshan Reddy
6.1 Introduction
6.2 Relevant Works
6.3 Proposed Methodology
6.4 Resources and Techniques
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
K. Mahesh Reddy and F. Mary Harin Fernandez
Abbreviations Used
7.1 Introduction
7.2 Materials and Methods
7.3 Statistical Analysis
7.4 Results
7.5 Discussion
Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
8. A Comparative Analysis of Twin Segmentation and Classification Over MultiClass SVM and Innovative CNN: An Innovative Approach
Prudhvi Venkata Narasimha Varma R. and Senthil Kumar R.
8.1 Introduction
Statistical Analysis
Results
Discussion
Conclusion
References
9. Prediction of Yields in Semiconductor Using XGBoost Classifier in Comparison with Random Forest Classifier
Soorya K. and Michael G.
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
P. Hemanth and P. Shyamala Bharathi
10.1 Introduction
10.1.1 Materials and Methods
10.1.2 Statistical Analysis
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
C. Chandu Ganesh and B. Anitha Vijayalakshmi
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
Mekalathuru Yuvaraj and K.V. Kanimozhi
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
A. Sai Abhishek and T. J. Nagalakshmi
13.1 Introduction
13.2 Materials and Methods
13.2.1 Statistical Analysis
13.3 Results
13.4 Discussion
13.5 Conclusion
References
14. Novel Design of Meta Ring Array Antenna Using FR4 for Biomedical Applications
Thota Lakshmi Deekshitha and R. Saravanakumar
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
Abhishek Tiwari and Pratosh Bansal
15.1 Introduction
15.1.1 Recommendation for Tourism Spots
15.2 Literature Review
15.2.1 Collaborative Filtering-Based Recommendation Systems for Tourism
15.2.2 Content Filtering-Based Recommendation Systems for Tourism
15.2.3 Recommendation System from Neural Network
15.2.4 CNN in Tourism Recommendation
15.2.5 Use of Semantic Analysis in Tourism Recommendation
15.2.6 Tourism Recommendation with Artificial Intelligence
15.2.7 Genetic Algorithms for Tourism Recommendations
15.2.8 Some Other Algorithms that are Used for Tourism Recommendation
15.3 Research Gaps
15.3.1 Effect of COVID-19 on Tourism
15.4 Conclusion
15.5 Future Work
Abbreviations Used
References
16. Secure and Reliable Routing for Hybrid Network to Support Disaster Recovery and Management
Sanat Jain, Amit Dangi, Garima Jain and Ajay Kumar Phulre
Abbreviations
16.1 Introduction
16.2 Related Work
16.3 Proposed Methodology
16.4 Experimental Results
16.4.1 Simulation Parameter
16.4.2 Simulation Result
16.5 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgments
References
17. Machine Learning Techniques for Sentimental Analysis
Ghanshyam Prasad Dubey, Sahil Upadhyay and Ayush Giri
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
G. Mishra, Geetanjali Sharma and Vikesh Gupta
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
Priyanka Asthana and Manish Maheshwari
Abbreviations Used
19.1 Introduction
19.2 Literature Survey
19.3 Proposed ANN Model
19.3.1 Data Gathering
19.3.2 Data Preprocessing
19.3.3 Splitter
19.3.4 Build Model
19.3.5 Performance Analysis
19.4 Experimental Setup
19.4.1 Environmental Setting
19.4.2 Configuration Settings
19.5 Result Analysis
19.6 Conclusion and Future Scope
Acknowledgements
References
20. A Deep Study on Discriminative Supervised Learning Approach
Garima Jain, Sanat Jain, Harshlata Vishwakarma and Shilpa Suman
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
S. Padmakala
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
R. Deepa and A. Packialatha
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
Rahul Maheshwari and Vivek Kapoor
Abbreviations Used
23.1 Introduction
23.2 Related Work
23.3 Proposed Methodology
23.4 Result
23.4.1 Performance Analysis
23.5 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
24. Social Media Text Classification Analysis and Influence of Feature Selection Methods on Classification Performance
Vedpriya Dongre and Pragya Shukla
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
S. Padmakala
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
Padmakar Pachorkar, P. S. Chauhan, Akash Pawar, Anil Singh Yadav and Neeraj Agrawal
26.1 Introduction
26.2 Materials and Methodology
26.3 Result and Discussion
26.4 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
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)
K. Surya Prakash and D. Sungeetha
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
Prakash Dilli, Nelson Kennedy Babu C. and A. Akilandeswari
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
Kishore Kumar R., Nelson Kennedy Babu C. and A. Akilandeswari
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
P. Srinivasan, R. Thandaiah Prabu and A. Ezhil Grace
Abbreviations Used
30.1 Introduction
30.2 Materials and Methods
30.3 Statistical Analysis
30.4 Results
30.5 Discussion
30.6 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
31. Conversational AI – Security Aspects for Modern Business Applications
Hitesh Rawat, Anjali Rawat, Jean-François Mascari, Ludovica Mascari and Romil Rawat
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
Hitesh Rawat, Anjali Rawat, Anand Rajavat and Romil Rawat
Abbreviations Used
32.1 Introduction
32.1.1 Available Research and Findings
32.1.2 Research Objectives
32.1.3 Contributions
32.2 Literature Survey
32.3 Research Techniques
32.3.1 Analysis of Observations
32.3.2 Parameters for Manuscript (Inclusion and Exclusion) IE
32.3.3 Outcome Identification
32.3.4 IE-Qualitative
32.3.5 Statistics and Facts Extraction
32.3.6 Statistics and Facts IE
32.3.6.1 Publications
32.4 Observational Values
32.5 Analysis
32.5.1 What are the OSCMN Applications Focused ICSS- RQ1?
32.5.2 Analysis of Disparate CCA-CCIE Techniques and Methods-RQ2?
32.5.3 Availability of Datasets with CTLI-Related Statistics- RQ3
32.6 CICS -CCSC Future Scope
32.7 Future Work
References
33. Fractal Natural Language Semantics and Fractal Machine Learning Engineering: Cultural Heritage Generative Management Systems
Jean-François Mascari, Ludovica Mascari, Hitesh Rawat, Anjali Rawat and Romil Rawat
33.1 Introduction
33.2 Frameworks, Directions, and Domains
33.3 CH-GeMS Architecture
33.3.1 Material: “Landscapes, Heritage, and Culture” Interaction System
33.3.1.1 Components, Tools, and Contexts
33.3.1.2 Interaction Networks
33.3.1.3 Networks of Networks
33.3.1.4 Networks of Networks of Networks N3
33.3.2 Services Dualities and Dynamic Data–Driven Simulations
33.3.2.1 Services Dualities
33.3.3 Dynamic Data–Driven Applications Systems
33.4 Conclusions
References
About the Editors
Index
Back to Top
Preface
1. Analysis of Restaurant Reviews Using Novel Hybrid Approach Algorithm Over Convolutional Neural Network Algorithm with Improved Accuracy
K. Abhilash Reddy and Uma Priyadarsini P.S.
Introduction
Related Work
Existing Methodology
Convolutional Neural Network Algorithm
Proposed Methodology
Novel Hybrid Approach Algorithm
Statistical Analysis
Results
Discussion
Conclusion
References
2. Forecasting of Product Demand Using Hybrid Regression Model in Comparison with Autoregressive Integrated Moving Average Model
Adibhatla Ajay Bharadwaj and M. Gunasekaran
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
C. Srinath and S. Parthiban
Abbreviations Used
3.1 Introduction
3.2 Materials and Methods
3.3 Statistical Analysis
3.4 Results
3.5 Discussions
3.6 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
4. Observing the Accuracy of Breast Cancer Using Support Vector Machine with Digital Mammogram Data in Comparison with Naive Bayes
M.A. Aasiya Banu and K. Thinakaran
Introduction
Materials and Methods
Support Vector Machine
Naive Bayes Algorithm
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
Keerthana P. and G. Mary Valantina
Introduction
Materials and Methods
AlexNet
Support Vector Machine
Statistical Analysis
Results
Discussion
Conclusion
References
6. Accurate Prediction of Vehicle Number Plate Segmentation and Classification with Inception Compared over Alexnet
E. K. Subramanian and V. Sudharshan Reddy
6.1 Introduction
6.2 Relevant Works
6.3 Proposed Methodology
6.4 Resources and Techniques
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
K. Mahesh Reddy and F. Mary Harin Fernandez
Abbreviations Used
7.1 Introduction
7.2 Materials and Methods
7.3 Statistical Analysis
7.4 Results
7.5 Discussion
Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
8. A Comparative Analysis of Twin Segmentation and Classification Over MultiClass SVM and Innovative CNN: An Innovative Approach
Prudhvi Venkata Narasimha Varma R. and Senthil Kumar R.
8.1 Introduction
Statistical Analysis
Results
Discussion
Conclusion
References
9. Prediction of Yields in Semiconductor Using XGBoost Classifier in Comparison with Random Forest Classifier
Soorya K. and Michael G.
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
P. Hemanth and P. Shyamala Bharathi
10.1 Introduction
10.1.1 Materials and Methods
10.1.2 Statistical Analysis
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
C. Chandu Ganesh and B. Anitha Vijayalakshmi
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
Mekalathuru Yuvaraj and K.V. Kanimozhi
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
A. Sai Abhishek and T. J. Nagalakshmi
13.1 Introduction
13.2 Materials and Methods
13.2.1 Statistical Analysis
13.3 Results
13.4 Discussion
13.5 Conclusion
References
14. Novel Design of Meta Ring Array Antenna Using FR4 for Biomedical Applications
Thota Lakshmi Deekshitha and R. Saravanakumar
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
Abhishek Tiwari and Pratosh Bansal
15.1 Introduction
15.1.1 Recommendation for Tourism Spots
15.2 Literature Review
15.2.1 Collaborative Filtering-Based Recommendation Systems for Tourism
15.2.2 Content Filtering-Based Recommendation Systems for Tourism
15.2.3 Recommendation System from Neural Network
15.2.4 CNN in Tourism Recommendation
15.2.5 Use of Semantic Analysis in Tourism Recommendation
15.2.6 Tourism Recommendation with Artificial Intelligence
15.2.7 Genetic Algorithms for Tourism Recommendations
15.2.8 Some Other Algorithms that are Used for Tourism Recommendation
15.3 Research Gaps
15.3.1 Effect of COVID-19 on Tourism
15.4 Conclusion
15.5 Future Work
Abbreviations Used
References
16. Secure and Reliable Routing for Hybrid Network to Support Disaster Recovery and Management
Sanat Jain, Amit Dangi, Garima Jain and Ajay Kumar Phulre
Abbreviations
16.1 Introduction
16.2 Related Work
16.3 Proposed Methodology
16.4 Experimental Results
16.4.1 Simulation Parameter
16.4.2 Simulation Result
16.5 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgments
References
17. Machine Learning Techniques for Sentimental Analysis
Ghanshyam Prasad Dubey, Sahil Upadhyay and Ayush Giri
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
G. Mishra, Geetanjali Sharma and Vikesh Gupta
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
Priyanka Asthana and Manish Maheshwari
Abbreviations Used
19.1 Introduction
19.2 Literature Survey
19.3 Proposed ANN Model
19.3.1 Data Gathering
19.3.2 Data Preprocessing
19.3.3 Splitter
19.3.4 Build Model
19.3.5 Performance Analysis
19.4 Experimental Setup
19.4.1 Environmental Setting
19.4.2 Configuration Settings
19.5 Result Analysis
19.6 Conclusion and Future Scope
Acknowledgements
References
20. A Deep Study on Discriminative Supervised Learning Approach
Garima Jain, Sanat Jain, Harshlata Vishwakarma and Shilpa Suman
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
S. Padmakala
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
R. Deepa and A. Packialatha
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
Rahul Maheshwari and Vivek Kapoor
Abbreviations Used
23.1 Introduction
23.2 Related Work
23.3 Proposed Methodology
23.4 Result
23.4.1 Performance Analysis
23.5 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
24. Social Media Text Classification Analysis and Influence of Feature Selection Methods on Classification Performance
Vedpriya Dongre and Pragya Shukla
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
S. Padmakala
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
Padmakar Pachorkar, P. S. Chauhan, Akash Pawar, Anil Singh Yadav and Neeraj Agrawal
26.1 Introduction
26.2 Materials and Methodology
26.3 Result and Discussion
26.4 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
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)
K. Surya Prakash and D. Sungeetha
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
Prakash Dilli, Nelson Kennedy Babu C. and A. Akilandeswari
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
Kishore Kumar R., Nelson Kennedy Babu C. and A. Akilandeswari
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
P. Srinivasan, R. Thandaiah Prabu and A. Ezhil Grace
Abbreviations Used
30.1 Introduction
30.2 Materials and Methods
30.3 Statistical Analysis
30.4 Results
30.5 Discussion
30.6 Conclusion
Statement for Conflict of Interest
Disclosure of Potential Conflicts of Interest
Funding
Acknowledgements
References
31. Conversational AI – Security Aspects for Modern Business Applications
Hitesh Rawat, Anjali Rawat, Jean-François Mascari, Ludovica Mascari and Romil Rawat
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
Hitesh Rawat, Anjali Rawat, Anand Rajavat and Romil Rawat
Abbreviations Used
32.1 Introduction
32.1.1 Available Research and Findings
32.1.2 Research Objectives
32.1.3 Contributions
32.2 Literature Survey
32.3 Research Techniques
32.3.1 Analysis of Observations
32.3.2 Parameters for Manuscript (Inclusion and Exclusion) IE
32.3.3 Outcome Identification
32.3.4 IE-Qualitative
32.3.5 Statistics and Facts Extraction
32.3.6 Statistics and Facts IE
32.3.6.1 Publications
32.4 Observational Values
32.5 Analysis
32.5.1 What are the OSCMN Applications Focused ICSS- RQ1?
32.5.2 Analysis of Disparate CCA-CCIE Techniques and Methods-RQ2?
32.5.3 Availability of Datasets with CTLI-Related Statistics- RQ3
32.6 CICS -CCSC Future Scope
32.7 Future Work
References
33. Fractal Natural Language Semantics and Fractal Machine Learning Engineering: Cultural Heritage Generative Management Systems
Jean-François Mascari, Ludovica Mascari, Hitesh Rawat, Anjali Rawat and Romil Rawat
33.1 Introduction
33.2 Frameworks, Directions, and Domains
33.3 CH-GeMS Architecture
33.3.1 Material: “Landscapes, Heritage, and Culture” Interaction System
33.3.1.1 Components, Tools, and Contexts
33.3.1.2 Interaction Networks
33.3.1.3 Networks of Networks
33.3.1.4 Networks of Networks of Networks N3
33.3.2 Services Dualities and Dynamic Data–Driven Simulations
33.3.2.1 Services Dualities
33.3.3 Dynamic Data–Driven Applications Systems
33.4 Conclusions
References
About the Editors
Index
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