Advancement of Data Processing Methods for Artificial and Computing Intelligence

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
List of Contributors
List of Figures
List of Tables
List of Abbreviations
Introduction to Advancement of Data Processing Methods for Artifcial and Computing Intelligence
Section 1: Trends in Data Processing and Analytics
	Chapter 1: Novel Nonparametric Method of Multivariate Data Analysis and Interpretation
		1.1: Introduction
		1.2: Statistical Depth
		1.3: Prediction Sets
		1.4: Petunin Ellipsoids and Their Statistical Properties
			1.4.1: Case d = 2
			1.4.2: Case d > 2
			1.4.3: Statistical properties of Petunin`s ellipsoids
		1.5: Petunin Ellipsoids and Their Statistical Properties
			1.5.1: Uniform distribution
			1.5.2: Normal Distribution
			1.5.3: Laplace distribution
			1.5.4: Gamma distribution
			1.5.5: Exponential distribution
		1.6: Conclusion
		References
	Chapter 2: Data Analysis on Automation of Purging with IoT in FRAMO Cargo Pump
		2.1: Introduction
		2.2: FRAMO Cargo Pump
			2.2.1: Cofferdam
			2.2.2: Cargo pump purging operation
		2.3: ARDUINO Board
			2.3.1: Fork-type density meter
			2.3.2: pH Meter
			2.3.3: Color sensor
			2.3.4: Level sensor
			2.3.5: Internet of Things (IoT)
		2.4: Proposed Method
			2.4.1: Purging automation
			2.4.2: Data acquisition and analysis
			2.4.3: Communication
		2.5: Experimental Data Analysis
		2.6: Limitations
		2.7: Conclusion
		References
	Chapter 3: Big Data Analytics in Healthcare Sector: Potential Strength and Challenges
		3.1: Introduction
		3.2: Literature Review
		3.3: Research Methodology
		3.4: Existing Challenges and Benefts that are Prevalent in Big Data Healthcare
		3.5: Process Flow of Big Data Analytics in Healthcare
		3.6: Tools and Techniques in Healthcare Data Analytics
		3.7: Classifcation and Clustering Techniques Neural networks algorithms
		3.8: Limitations and Strength
		3.9: Conclusion
		References
	Chapter 4: Role of Big Data Analytics in the Cloud Applications
		4.1: Introduction
			4.1.1: About cloud computing
			4.1.2: About big data analytics
		4.2: Cloud Computing
		4.3: Conclusion
		References
	Chapter 5: Big Data Analytics with Artifcial Intelligence: A Comprehensive Investigation
		5.1: Introduction
		5.2: Context and Related Work
			5.2.1: Big data
			5.2.2: Applications of big data
			5.2.3: Big data challenges
			5.2.4: Big data platforms
		5.3: Artifcial intelligence
			5.3.1: Examples of AI applications
		5.4: Conclusion
		References
	Chapter 6: Cloud Computing Environment for Big Data Analytics
		6.1: Introduction to Cloud Computing
		6.2: Advantages of Cloud Computing
			6.2.1
			6.2.2: Type of clouds – the ways to deploy
			6.2.3: Service models of cloud
			6.2.4: Disadvantages of cloud computing
			6.2.5: Cloud computing use cases
		6.3: Defnition of Big Data
			6.3.1: Characteristics of big data
			6.3.2: Cloud-based big data management tools
			6.3.3: Big data lends help in various forms in a business
			6.3.4: Challenges
		6.4: Cloud Computing Versus Big Data
			6.4.1: The usefulness of integration of big data and cloud computing
			6.4.2: The architecture of cloud in big data
			6.4.3: Advantages of using cloud for big data analytics
		6.5: Conclusion
		References
Section 2: Advance Implementation of Artifcial Intelligence and Data Analytics
	Chapter 7: Artifcial Intelligence-based Data Wrangling Issues and Data Analytics Process for Various Domains
		7.1: Introduction
			7.1.1: Need for information inside the organization
		7.2: Categories of Data
			7.2.1: Qualitative data
			7.2.2: Quantitative information
		7.3: Why AI in Data Analysis
		7.4: Why AI Is Required for Data Manipulation?
			7.4.1: Data wrangling
			7.4.2: Why is data wrangling important?
			7.4.3: Data cleaning procedure
			7.4.4: Enrichment of data fne-tuning
			7.4.5: Data wrangling methods and instruments
		7.5: Data Science Lifecycle
			7.5.1: The evolution of data science
			7.5.2: Enhancements to data analytics
		7.6: IBM Watson’s Role in Data Analytics
			7.6.1: How IBM analytics operates
		7.7: Role of NLP in Data Analytics
			7.7.1: Tools for data analytics and visualization
			7.7.2: Applications of AI in data analytics
		7.8: Conclusion
		References
	Chapter 8: Artifcial Intelligence and Data Science in Various Domains
		8.1: Artifcial Intelligence
		8.2: Germination of Artifcial Intelligence
			8.2.1: Types of artifcial intelligence
			8.2.2: Fields that form artifcial intelligence
			8.2.3: Is artifcial intelligence a threat to human existence?
			8.2.4: Branches of artifcial intelligence
			8.2.5: Applications of artifcial intelligence
			8.2.6: Data collection
			8.2.7: Data science
			8.2.8: Data with artifcial intelligence
		8.3: Workfows of Artifcial Intelligence and Its Tools
			8.3.1: Workfow driven by AI
			8.3.2: Artifcial Intelligence in data science
			8.3.3: Data science
		8.4: Artifcial Neural Networks
			8.4.1: Algorithm of neural network (ANN)
			8.4.2: TensorFlow 2.0
			8.4.3: TensorFlow: features and applications
			8.4.4: Tensors
			8.4.5: Generative adversarial network
			8.4.6: Applications of GANN
		8.5: Real Life-based Examples
			8.5.1: Case study 1
			8.5.2: Result and discussion
		8.6: Conclusion
		References
	Chapter 9: Method for Implementing Time-Control Functions in Real-time Operating Systems
		9.1: Introduction
		9.2: Materials and Methods
			9.2.1: Problems related to control
			9.2.2: Technological proposal based on real-time programming
			9.2.3: Solution confguration
			9.2.4: Implementation of functions
		9.3: Results
		9.4: Conclusion
		Acknowledgments
		References
	Chapter 10: Effcient Blurred and Deblurred Image Classifcation using Machine Learning Approach
		10.1: Introduction
		10.2: Related Works
		10.3: System Design
			10.3.1: DWT and NN classifer
			10.3.2: Image denoizing
			10.3.3: The image deblurring problem
			10.3.4: Artifcial neural network classifer
		10.4: Results and Discussion
		10.5: Conclusion
		10.6: Future Work
		References
	Chapter 11: Method for Characterization of Brain Activity using Brain–Computer Interface Devices
		11.1: Introduction
		11.2: Materials and Methods
			11.2.1: Description of the requirements
			11.2.2: Method description
			11.2.3: Analysis of data interpretation
		11.3: Results
		11.4: Conclusion
		Acknowledgments
		References
	Chapter 12: Streaming Highway Traffc Alerts using Twitter API
		12.1: Introduction
			12.1.1: Twitter and Twitter API
			12.1.2: Preprocessing
			12.1.3: Model building
			12.1.4: Web application
			12.1.5: Model building, training, and classifcation
		12.2: Related Works
		12.3: Background
		12.4: Objectives
		12.5: Methodology
		12.6: Technologies Used
		12.7: TWITTER: Natural Language Processing
			12.7.1: Vectorization
			12.7.2: Terminologies
			12.7.3: Count Vectorizer
		12.8: Results and Discussion
			12.8.1: Exploratory analysis on datasets
			12.8.2: ROC curve and AUROC
			12.8.3: Preprocessing outputs
			12.8.4: Test inputs and outputs
			12.8.5: Deployment
		12.9: Conclusion
		References
	Chapter 13: Harnessing the Power of Artifcial Intelligence and Data Science
		13.1: Introduction
		13.2: Different Domains in AI and Data Science
			13.2.1: Healthcare
			13.2.2: Finance
			13.2.3: E-commerce
			13.2.4: Education
			13.2.5: Transportation
			13.2.6: Agriculture
		13.3: Conclusion
		Acknowlegdements
		References
	Chapter 14: Determining the Severity of Diabetic Retinopathy through Neural Network Models
		14.1: Introduction
		14.2: Background
		14.3: Algorithms under Consideration
			14.3.1: VGG16: (without Gaussian blur)
			14.3.2: MobileNetV2
			14.3.3: ResNet50
			14.3.4: InceptionV3
		14.4: Data Collection
		14.5: Implementation Steps of the Algorithms
			14.5.1: Implementation steps of VGG16
			14.5.2: Implementation of the MobileNetV2: model
			14.5.3: Implementation steps of ResNet50
			14.5.4: Implementation of InceptionV3
		14.6: Results
			14.6.1: VGG16
			14.6.2: MobileNetV2
			14.6.3: ResNet50
			14.6.4: InceptionV3
		14.7: Conclusion
		References
	Chapter 15: Method for Muscle Activity Characterization using Wearable Devices
		15.1: Introduction
		15.2: Materials and Methods
			15.2.1: Description of the problem
			15.2.2: Description of the method
			15.2.3: Protocol for use of the method
			15.2.4: Results of the method
			15.2.5: Analysis and interpretation of results
		15.3: Results
		15.4: Conclusion
		Acknowlegdements
		References
	Index
	About the Editors