Natural Language Processing for Software Engineering

Chapter 1 Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment
	1.1 Introduction
	1.2 Need of Vulnerability Identification
	1.3 Vulnerabilities in IoT Web Applications
	1.4 Intrusion Detection System
	1.5 Machine Learning in Intrusion Detection System
	1.6 Conclusion
	References
Chapter 2 Frequent Pattern Mining Using Artificial Intelligence and Machine Learning
	2.1 Introduction
	2.2 Data Mining Functions
	2.3 Related Work
	2.4 Machine Learning for Frequent Pattern Mining
	2.5 Conclusion
	References
Chapter 3 Classification and Detection of Prostate Cancer Using Machine Learning Techniques
	3.1 Introduction
	3.2 Literature Survey
	3.3 Machine Learning for Prostate Cancer Classification and Detection
	3.4 Conclusion
	References
Chapter 4 NLP-Based Spellchecker and Grammar Checker for Indic Languages
	4.1 Introduction
	4.2 NLP-Based Techniques of Spellcheckers and Grammar Checkers
		4.2.1 Syntax-Based
		4.2.2 Statistics-Based
		4.2.3 Rule-Based
		4.2.4 Deep Learning-Based
		4.2.5 Machine Learning-Based
		4.2.6 Reinforcement Learning-Based
	4.3 Grammar Checker Related Work
	4.4 Spellchecker Related Work
	4.5 Conclusion
	References
Chapter 5 Identification of Gujarati Ghazal Chanda with Cross-Platform Application
	Abbreviations
	5.1 Introduction
		5.1.1 The Gujarati Language
	5.2 Ghazal
	5.3 History and Grammar of Ghazal
	5.4 Literature Review
	5.5 Proposed System
	5.6 Conclusion
	References
Chapter 6 Cancer Classification and Detection Using Machine Learning Techniques
	6.1 Introduction
	6.2 Machine Learning Techniques
	6.3 Review of Machine Learning for Cancer Detection
	6.4 Methods
	6.5 Result Analysis
	6.6 Conclusion
	References
Chapter 7 Text Mining Techniques and Natural Language Processing
	7.1 Introduction
	7.2 Text Classification and Text Clustering
	7.3 Related Work
	7.4 Methodology
	7.5 Conclusion
	References
Chapter 8 An Investigation of Techniques to Encounter Security Issues Related to Mobile Applications
	8.1 Introduction
	8.2 Literature Review
	8.3 Results and Discussions
	8.4 Conclusion
	References
Chapter 9 Machine Learning for Sentiment Analysis Using Social Media Scrapped Data
	9.1 Introduction
	9.2 Twitter Sentiment Analysis
	9.3 Sentiment Analysis Using Machine Learning Techniques
	9.4 Conclusion
	References
Chapter 10 Opinion Mining Using Classification Techniques on Electronic Media Data
	10.1 Introduction
	10.2 Opinion Mining
	10.3 Related Work
	10.4 Opinion Mining Techniques
		10.4.1 Naïve Bayes
		10.4.2 Support Vector Machine
		10.4.3 Decision Tree
		10.4.4 Multiple Linear Regression
		10.4.5 Multilayer Perceptron
		10.4.6 Convolutional Neural Network
		10.4.7 Long Short-Term Memory
	10.5 Conclusion
	References
Chapter 11 Spam Content Filtering in Online Social Networks
	11.1 Introduction
		11.1.1 E-Mail Spam
	11.2 E-Mail Spam Identification Methods
		11.2.1 Content-Based Spam Identification Method
		11.2.2 Identity-Based Spam Identification Method
	11.3 Online Social Network Spam
	11.4 Related Work
	11.5 Challenges in the Spam Message Identification
	11.6 Spam Classification with SVM Filter
	11.7 Conclusion
	References
Chapter 12 An Investigation of Various Techniques to Improve Cyber Security
	12.1 Introduction
	12.2 Various Attacks
	12.3 Methods
	12.4 Conclusion
	References
Chapter 13 Brain Tumor Classification and Detection Using Machine Learning by Analyzing MRI Images
	13.1 Introduction
	13.2 Literature Survey
	13.3 Methods
	13.4 Result Analysis
	13.5 Conclusion
	References
Chapter 14 Optimized Machine Learning Techniques for Software Fault Prediction
	14.1 Introduction
	14.2 Literature Survey
	14.3 Methods
	14.4 Result Analysis
	14.5 Conclusion
	References
Chapter 15 Pancreatic Cancer Detection Using Machine Learning and Image Processing
	15.1 Introduction
	15.2 Literature Survey
	15.3 Methodology
	15.4 Result Analysis
	15.5 Conclusion
	References
Chapter 16 An Investigation of Various Text Mining Techniques
	16.1 Introduction
	16.2 Related Work
	16.3 Classification Techniques for Text Mining
		16.3.1 Machine Learning Based Text Classification
		16.3.2 Ontology-Based Text Classification
		16.3.3 Hybrid Approaches
	16.4 Conclusion
	References
Chapter 17 Automated Query Processing Using Natural Language Processing
	17.1 Introduction
	17.1.1 Natural Language Processing
	17.2 The Challenges of NLP
	17.3 Related Work
	17.4 Natural Language Interfaces Systems
	17.5 Conclusion
	References
Chapter 18 Data Mining Techniques for Web Usage Mining
	18.1 Introduction
		18.1.1 Web Usage Mining
	18.2 Web Mining
		18.2.1 Web Content Mining
		18.2.2 Web Structure Mining
		18.2.3 Web Usage Mining
			18.2.3.1 Preprocessing
			18.2.3.2 Pattern Discovery
			18.2.3.3 Pattern Analysis
	18.3 Web Usage Data Mining Techniques
	18.4 Conclusion
	References
Chapter 19 Natural Language Processing Using Soft Computing
	19.1 Introduction
	19.2 Related Work
	19.3 NLP Soft Computing Approaches
	19.4 Conclusion
	References
Chapter 20 Sentiment Analysis Using Natural Language Processing
	20.1 Introduction
	20.2 Sentiment Analysis Levels
		20.2.1 Document Level
		20.2.2 Sentence Level
		20.2.3 Aspect Level
	20.3 Challenges in Sentiment Analysis
	20.4 Related Work
	20.5 Machine Learning Techniques for Sentiment Analysis
	20.6 Conclusion
	References
Chapter 21 Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
	21.1 Introduction
	21.2 Web Mining
	21.3 Taxonomy of Web Data Mining
		21.3.1 Web Usage Mining
		21.3.2 Web Structure Mining
		21.3.3 Web Content Mining
	21.4 Web Content Mining Methods
		21.4.1 Unstructured Text Data Mining
		21.4.2 Structured Data Mining
		21.4.3 Semi-Structured Data Mining
	21.5 Efficient Algorithms for Web Data Extraction
	21.6 Machine Learning Based Web Content Extraction Methods
	21.7 Conclusion
	References
Chapter 22 Intelligent Pattern Discovery Using Web Data Mining
	22.1 Introduction
	22.2 Pattern Discovery from Web Server Logs
		22.2.1 Subsequently Accessed Interesting Page Categories
		22.2.2 Subsequent Probable Page of Visit
		22.2.3 Strongly and Weakly Linked Web Pages
		22.2.4 User Groups
		22.2.5 Fraudulent and Genuine Sessions
		22.2.6 Web Traffic Behavior
		22.2.7 Purchase Preference of Customers
	22.3 Data Mining Techniques for Web Server Log Analysis
	22.4 Graph Theory Techniques for Analysis of Web Server Logs
	22.5 Conclusion
	References
Chapter 23 A Review of Security Features in Prominent Cloud Service Providers
	23.1 Introduction
	23.2 Cloud Computing Overview
	23.3 Cloud Computing Model
	23.4 Challenges with Cloud Security and Potential Solutions
	23.5 Comparative Analysis
	23.6 Conclusion
	References
Chapter 24 Prioritization of Security Vulnerabilities under Cloud Infrastructure Using AHP
	24.1 Introduction
	24.2 Related Work
	24.3 Proposed Method
	24.4 Result and Discussion
	24.5 Conclusion
	References
Chapter 25 Cloud Computing Security Through Detection & Mitigation of Zero-Day Attack Using Machine Learning Techniques
	25.1 Introduction
	25.2 Related Work
		25.2.1 Analysis of Zero-Day Exploits and Traditional Methods
	25.3 Proposed Methodology
	25.4 Results and Discussion
		25.4.1 Prevention & Mitigation of Zero Day Attacks (ZDAs)
	25.5 Conclusion and Future Work
	References
Chapter 26 Predicting Rumors Spread Using Textual and Social Context in Propagation Graph with Graph Neural Network
	26.1 Introduction
	26.2 Literature Review
	26.3 Proposed Methodology
		26.3.1 Tweep Tendency Encoding
		26.3.2 Network Dynamics Extraction
		26.3.3 Extracted Information Integration
	26.4 Results and Discussion
	26.5 Conclusion
	References
Chapter 27 Implications, Opportunities, and Challenges of Blockchain in Natural Language Processing
	27.1 Introduction
	27.2 Related Work
	27.3 Overview on Blockchain Technology and NLP
		27.3.1 Blockchain Technology, Features, and Applications
		27.3.2 Natural Language Processing
		27.3.3 Challenges in NLP
		27.3.4 Data Integration and Accuracy in NLP
	27.4 Integration of Blockchain into NLP
	27.5 Applications of Blockchain in NLP
	27.6 Blockchain Solutions for NLP
	27.7 Implications of Blockchain Development Solutions in NLP
	27.8 Sectors That can be Benified from Blockchain and NLP Integration
	27.9 Challenges
	27.10 Conclusion
	References
Chapter 28 Emotion Detection Using Natural Language Processing by Text Classification
	28.1 Introduction
	28.2 Natural Language Processing
	28.3 Emotion Recognition
	28.4 Related Work
		28.4.1 Emotion Detection Using Machine Learning
		28.4.2 Emotion Detection Using Deep Learning
		28.4.3 Emotion Detection Using Ensemble Learning
	28.5 Machine Learning Techniques for Emotion Detection
	28.6 Conclusion
	References
Chapter 29 Alzheimer Disease Detection Using Machine Learning Techniques
	29.1 Introduction
	29.2 Machine Learning Techniques to Detect Alzheimer’s Disease
	29.3 Pre-Processing Techniques for Alzheimer’s Disease Detection
	29.4 Feature Extraction Techniques for Alzheimer’s Disease Detection
	29.5 Feature Selection Techniques for Diagnosis of Alzheimer’s Disease
	29.6 Machine Learning Models Used for Alzheimer’s Disease Detection
	29.7 Conclusion
	References
Chapter 30 Netnographic Literature Review and Research Methodology for Maritime Business and Potential Cyber Threats
	30.1 Introduction
	30.2 Criminal Flows Framework
	30.3 Oceanic Crime Exchange and Categorization
	30.4 Fisheries Crimes and Mobility Crimes
	30.5 Conclusion
	30.6 Discussion
	References
Chapter 31 Review of Research Methodology and IT for Business and Threat Management
	Abbreviation Used
	31.1 Introduction
	31.2 Conclusion
	References
About the Editors
Index
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