ARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas. With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In…mehr
ARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS
The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas.
With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore.
This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results.
Audience
AI researchers as well as engineers in information technology and computer science.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Artificial Intelligence and Soft Computing for Industrial Transformation
K. Umamaheswari, PhD, is a professor and head with 27 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India. B. Vinoth Kumar, PhD, is an associate professor with 19 years of experience in the Department of Information Technology at PSG College of Technology, Coimbatore, India. S. K. Somasundaram, PhD, is an assistant professor in the Department of Information Technology, PSG College of Technology, Coimbatore, India.
Inhaltsangabe
Preface xv
Part I: Medical Applications 1
1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis 3 Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan
1.1 Introduction 3
1.2 Prediction of Diseases Using Machine Learning 4
1.3 Materials and Methods 5
1.4 Methods 6
1.5 ML Algorithm and Their Results 7
1.6 Support Vector Machine (SVM) 11
1.7 Logistic Regression 11
1.8 K Nearest Neighbor Algorithm (KNN) 12
1.9 Naive Bayes 15
1.10 Finding the Best Algorithm Using Experimenter Application 17
1.11 Conclusion 18
1.12 Future Scope 19
2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering 23 Kavitha S. and Hannah Inbarani
2.1 Introduction 23
2.2 Literature Review 24
2.3 Dataset Used 26
2.4 Proposed Method 26
2.5 Experimental Analysis 29
2.6 Conclusion 33
3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters 37 Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen
3.1 Introduction 38
3.2 Literature Review 39
3.3 Methodology 41
3.4 Experiment and Results 46
3.5 Conclusion 51
4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer 55 L.R. Sujithra and A. Kuntha
4.1 Introduction 56
4.2 Literature Analysis 58
4.3 Comparison Analysis 66
4.4 Issues of the Existing Works 70
4.5 Experimental Results 70
4.6 Conclusion and Future Work 73
5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches 79 Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth
5.1 Introduction 79
5.2 Literature Survey 80
5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches 81
5.4 Results and Discussion 83
5.5 Conclusion 86
6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression 89 Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen
6.1 Introduction 90
6.2 Background 91
6.3 Proposed Work 98
6.4 Experimental Results 104
6.5 Discussion and Conclusion 110
7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks 117 John Nisha Anita and Sujatha Kumaran
7.1 Introduction 118
7.2 Literature Survey Based on Brain Tumor Detection Methods 118
7.3 Literature Survey Based on WMSN 122
7.4 Literature Survey Based on Data Fusion 123
7.5 Conclusions 125
Part II: Data Analytics Applications 127
8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System 129 P. Vasantha Kumari and G. Sujatha
8.1 Introduction 130
8.2 Related Work 133
8.3 Proposed Architecture for Air Quality Prediction System 134
8.4 Results and Discussion 140
8.5 Conclusion 145
9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks 147 R. Tamilselvan, A. Prabhu and R. Rajagopal
9.1 Introduction 148
9.2 Related Work 149
9.3 K-Means Algorithm 151
9.4 Data Partitioning 152
9.5 Experimental Results 154
9.6 Conclusion 159
10 An Analysis on Detection and Visualization of Code Smells 163 Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gup
1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis 3 Karpagam G. R., Swathipriya M., Charanya A. G. and Murali Murugan
1.1 Introduction 3
1.2 Prediction of Diseases Using Machine Learning 4
1.3 Materials and Methods 5
1.4 Methods 6
1.5 ML Algorithm and Their Results 7
1.6 Support Vector Machine (SVM) 11
1.7 Logistic Regression 11
1.8 K Nearest Neighbor Algorithm (KNN) 12
1.9 Naive Bayes 15
1.10 Finding the Best Algorithm Using Experimenter Application 17
1.11 Conclusion 18
1.12 Future Scope 19
2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering 23 Kavitha S. and Hannah Inbarani
2.1 Introduction 23
2.2 Literature Review 24
2.3 Dataset Used 26
2.4 Proposed Method 26
2.5 Experimental Analysis 29
2.6 Conclusion 33
3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters 37 Bineeshia J., Vinoth Kumar B., Karthikeyan T. and Syed Khaja Mohideen
3.1 Introduction 38
3.2 Literature Review 39
3.3 Methodology 41
3.4 Experiment and Results 46
3.5 Conclusion 51
4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer 55 L.R. Sujithra and A. Kuntha
4.1 Introduction 56
4.2 Literature Analysis 58
4.3 Comparison Analysis 66
4.4 Issues of the Existing Works 70
4.5 Experimental Results 70
4.6 Conclusion and Future Work 73
5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches 79 Alamelu M., M. Naveena, Rakshitha M. and M. Hari Prasanth
5.1 Introduction 79
5.2 Literature Survey 80
5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches 81
5.4 Results and Discussion 83
5.5 Conclusion 86
6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression 89 Karpagam G. R., Keerthna M., Naresh K., Sairam Vaidya M., Karthikeyan T. and Syed Khaja Mohideen
6.1 Introduction 90
6.2 Background 91
6.3 Proposed Work 98
6.4 Experimental Results 104
6.5 Discussion and Conclusion 110
7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks 117 John Nisha Anita and Sujatha Kumaran
7.1 Introduction 118
7.2 Literature Survey Based on Brain Tumor Detection Methods 118
7.3 Literature Survey Based on WMSN 122
7.4 Literature Survey Based on Data Fusion 123
7.5 Conclusions 125
Part II: Data Analytics Applications 127
8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System 129 P. Vasantha Kumari and G. Sujatha
8.1 Introduction 130
8.2 Related Work 133
8.3 Proposed Architecture for Air Quality Prediction System 134
8.4 Results and Discussion 140
8.5 Conclusion 145
9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks 147 R. Tamilselvan, A. Prabhu and R. Rajagopal
9.1 Introduction 148
9.2 Related Work 149
9.3 K-Means Algorithm 151
9.4 Data Partitioning 152
9.5 Experimental Results 154
9.6 Conclusion 159
10 An Analysis on Detection and Visualization of Code Smells 163 Prabhu J., Thejineaswar Guhan, M. A. Rahul, Pritish Gup
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