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Intelligent Technologies for Automated Electronic Systems

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Title: Intelligent Technologies for Automated Electronic Systems
Description: This volume explores a diverse range of applications for automated machine learning and predictive analytics. The content provides use cases for machine learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation. Chapter 1 introduces an innovative device for automatically notifying and analyzing the impact of automobile accidents. Chapter 2 focuses on the detection of malaria using systematized image processing techniques. In Chapter 3, an intelligent technique based on LMEPOP and fuzzy logic for the segmentation of defocus blur is discussed. Predictive analytics is introduced in Chapter 4, providing an overview of this emerging field. Chapter 5 delves into discrete event system simulation, offering insights into its applications. The performance analysis of different hypervisors in OS virtualization is explored in Chapter 6. Load balancing in cloud computing is the subject of investigation in Chapter 7. Chapter 8 presents a survey on a facial and fingerprint-based voting system utilizing deep learning techniques. Chapter 9 explores IoT-based automated decision-making with data analytics in agriculture. Biometric recognition through modality fusion is investigated in Chapter 10. Chapter 11 offers a new perspective on evaluating machine learning algorithms for predicting employee performance. Pre-process methods for cardiovascular diseases diagnosis using CT angiography images are discussed in Chapter 12. Chapter 13 presents the implementation of a smart wheelchair using ultrasonic sensors and LabVIEW. Cryptography using the Internet of Things is the focus of Chapter 14. Chapter 15 explores machine learning applications for traffic sign recognition, and the book concludes with Chapter 16, which analyzes machine learning algorithms in healthcare.The book is a resource for academics, researchers, educators and professionals in the technology sector who want to learn about current trends in intelligent technologies.
Authors: S., Kannadhasan, R., Nagarajan, N., Shanmugasundaram, Jyotir, Moy Chatterjee, P., Ashok
Resource Type: eBook.
Subjects: Cloud computing, Predictive analytics, Machine learning
Categories: TECHNOLOGY & ENGINEERING / Automation, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning
Database: eBook Collection (EBSCOhost)
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  Data: This volume explores a diverse range of applications for automated machine learning and predictive analytics. The content provides use cases for machine learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation. Chapter 1 introduces an innovative device for automatically notifying and analyzing the impact of automobile accidents. Chapter 2 focuses on the detection of malaria using systematized image processing techniques. In Chapter 3, an intelligent technique based on LMEPOP and fuzzy logic for the segmentation of defocus blur is discussed. Predictive analytics is introduced in Chapter 4, providing an overview of this emerging field. Chapter 5 delves into discrete event system simulation, offering insights into its applications. The performance analysis of different hypervisors in OS virtualization is explored in Chapter 6. Load balancing in cloud computing is the subject of investigation in Chapter 7. Chapter 8 presents a survey on a facial and fingerprint-based voting system utilizing deep learning techniques. Chapter 9 explores IoT-based automated decision-making with data analytics in agriculture. Biometric recognition through modality fusion is investigated in Chapter 10. Chapter 11 offers a new perspective on evaluating machine learning algorithms for predicting employee performance. Pre-process methods for cardiovascular diseases diagnosis using CT angiography images are discussed in Chapter 12. Chapter 13 presents the implementation of a smart wheelchair using ultrasonic sensors and LabVIEW. Cryptography using the Internet of Things is the focus of Chapter 14. Chapter 15 explores machine learning applications for traffic sign recognition, and the book concludes with Chapter 16, which analyzes machine learning algorithms in healthcare.The book is a resource for academics, researchers, educators and professionals in the technology sector who want to learn about current trends in intelligent technologies.
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