Artificial Intelligence for Sustainable Applications.
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...
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Full Text (via Skillsoft) |
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| Format: | eBook |
| Language: | English |
| Published: |
Newark :
John Wiley & Sons, Inc.,
2023.
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| Series: | Artificial Intelligence and Soft Computing for Industrial Transformation
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| Subjects: |
MARC
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| 100 | 1 | |a Umamaheswari, K. | |
| 245 | 1 | 0 | |a Artificial Intelligence for Sustainable Applications. |
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| 490 | 0 | |a Artificial Intelligence and Soft Computing for Industrial Transformation | |
| 505 | 0 | |a Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Medical Applications -- Chapter 1 Predictive Models of Alzheimer's Disease Using Machine Learning Algorithms -- An Analysis -- 1.1 Introduction -- 1.2 Prediction of Diseases Using Machine Learning -- 1.3 Materials and Methods -- 1.4 Methods -- 1.5 ML Algorithm and Their Results -- 1.6 Support Vector Machine (SVM) -- 1.7 Logistic Regression -- 1.8 K Nearest Neighbor Algorithm (KNN) -- 1.9 Naive Bayes -- 1.10 Finding the Best Algorithm Using Experimenter Application -- 1.11 Conclusion -- 1.12 Future Scope -- References | |
| 505 | 8 | |a Chapter 2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Dataset Used -- 2.4 Proposed Method -- 2.4.1 Histogram Equalization -- 2.4.2 Threshold-Based Segmentation -- 2.4.3 K-Means Clustering -- 2.4.4 Fuzzy-K-Means Clustering -- 2.5 Experimental Analysis -- 2.5.1 Results of Histogram Equalization -- 2.5.2 Findings of Bounding Box Segmentation -- 2.5.3 Evaluation Metrics -- 2.6 Conclusion -- References | |
| 505 | 8 | |a Chapter 3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Architecture -- 3.3.2 Data -- 3.3.3 Data Pre-Processing -- 3.3.4 Hyperparameter Optimization -- 3.3.5 Neural Network -- 3.3.6 Training -- 3.4 Experiment and Results -- 3.4.1 Benchmark -- 3.5 Conclusion -- References -- Chapter 4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer -- 4.1 Introduction -- 4.2 Literature Analysis | |
| 505 | 8 | |a 4.2.1 Review of Gene Selection Methods in SNP -- 4.2.2 Review of Classification Methods in SNP -- 4.2.3 Review of Deep Learning Classification Methods in SNP -- 4.3 Comparison Analysis -- 4.4 Issues of the Existing Works -- 4.5 Experimental Results -- 4.6 Conclusion and Future Work -- References -- Chapter 5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches -- 5.1 Introduction -- 5.2 Literature Survey -- 5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches -- 5.3.1 Trend Check Analysis Segregation 1 Algorithm | |
| 505 | 8 | |a 5.3.2 Trend Check Analysis Segregation 2 Algorithm -- 5.4 Results and Discussion -- 5.5 Conclusion -- References -- Chapter 6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Comprehensive Survey -- Applications in Healthcare Industry -- 6.2.2 Comparison of Various Models for Forecasting -- 6.2.3 Context of the Work -- 6.3 Proposed Work -- 6.3.1 Conceptual Architecture -- 6.3.2 Procedure -- 6.4 Experimental Results -- 6.5 Discussion and Conclusion -- 6.5.1 Future Scope -- References | |
| 500 | |a Chapter 7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks | ||
| 520 | |a 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Information technology. | |
| 700 | 1 | |a Kumar, B. Vinoth. | |
| 700 | 1 | |a Somasundaram, S. K. | |
| 776 | 0 | 8 | |i Print version: |a Umamaheswari, K. |t Artificial Intelligence for Sustainable Applications |d Newark : John Wiley & Sons, Incorporated,c2023 |z 9781394174584 |
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