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Natural Language Processing for Software Engineering
Rajesh Kumar Chakrawarti (Edited by), RK Chakrawarti (Author), Ranjana Sikarwar (Edited by), Sanjaya Kumar Sarangi (Edited by), Samson Arun Raj Albert Raj (Edited by), Shweta Gupta (Edited by), K. Sakthidasan Sankaran (Edited by), Romil Rawat (Edited by)
9781394272433, Wiley
Hardback, published 17 January 2025
544 pages
22.9 x 15.2 x 3.2 cm, 0.907 kg
Discover how Natural Language Processing for Software Engineering can transform your understanding of agile development, equipping you with essential tools and insights to enhance software quality and responsiveness in today’s rapidly changing technological landscape. Agile development enhances business responsiveness through continuous software delivery, emphasizing iterative methodologies that produce incremental, usable software. Working software is the main measure of progress, and ongoing customer collaboration is essential. Approaches like Scrum, eXtreme Programming (XP), and Crystal share these principles but differ in focus: Scrum reduces documentation, XP improves software quality and adaptability to changing requirements, and Crystal emphasizes people and interactions while retaining key artifacts. Modifying software systems designed with Object-Oriented Analysis and Design can be costly and time-consuming in rapidly changing environments requiring frequent updates. This book explores how natural language processing can enhance agile methodologies, particularly in requirements engineering. It introduces tools that help developers create, organize, and update documentation throughout the agile project process.
Preface xvii 1 Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment 1 1.1 Introduction 2 1.2 Need of Vulnerability Identification 4 1.3 Vulnerabilities in IoT Web Applications 5 1.4 Intrusion Detection System 7 1.5 Machine Learning in Intrusion Detection System 10 1.6 Conclusion 12 References 12 2 Frequent Pattern Mining Using Artificial Intelligence and Machine Learning 15 2.1 Introduction 16 2.2 Data Mining Functions 17 2.3 Related Work 19 2.4 Machine Learning for Frequent Pattern Mining 24 2.5 Conclusion 26 References 26 3 Classification and Detection of Prostate Cancer Using Machine Learning Techniques 29 3.1 Introduction 30 3.2 Literature Survey 32 3.3 Machine Learning for Prostate Cancer Classification and Detection 35 3.4 Conclusion 37 References 38 4 NLP-Based Spellchecker and Grammar Checker for Indic Languages 43 4.1 Introduction 44 4.2 NLP-Based Techniques of Spellcheckers and Grammar Checkers 44 4.2.1 Syntax-Based 44 4.2.2 Statistics-Based 45 4.2.3 Rule-Based 45 4.2.4 Deep Learning-Based 45 4.2.5 Machine Learning-Based 46 4.2.6 Reinforcement Learning-Based 46 4.3 Grammar Checker Related Work 47 4.4 Spellchecker Related Work 58 4.5 Conclusion 66 References 67 5 Identification of Gujarati Ghazal Chanda with Cross-Platform Application 71 Abbreviations 72 5.1 Introduction 72 5.1.1 The Gujarati Language 72 5.2 Ghazal 75 5.3 History and Grammar of Ghazal 77 5.4 Literature Review 78 5.5 Proposed System 85 5.6 Conclusion 92 References 92 6 Cancer Classification and Detection Using Machine Learning Techniques 95 6.1 Introduction 96 6.2 Machine Learning Techniques 97 6.3 Review of Machine Learning for Cancer Detection 101 6.4 Methods 103 6.5 Result Analysis 106 6.6 Conclusion 107 References 108 7 Text Mining Techniques and Natural Language Processing 113 7.1 Introduction 113 7.2 Text Classification and Text Clustering 115 7.3 Related Work 116 7.4 Methodology 121 7.5 Conclusion 123 References 123 8 An Investigation of Techniques to Encounter Security Issues Related to Mobile Applications 127 8.1 Introduction 128 8.2 Literature Review 130 8.3 Results and Discussions 137 8.4 Conclusion 138 References 139 9 Machine Learning for Sentiment Analysis Using Social Media Scrapped Data 143 9.1 Introduction 144 9.2 Twitter Sentiment Analysis 146 9.3 Sentiment Analysis Using Machine Learning Techniques 149 9.4 Conclusion 152 References 152 10 Opinion Mining Using Classification Techniques on Electronic Media Data 155 10.1 Introduction 156 10.2 Opinion Mining 158 10.3 Related Work 159 10.4 Opinion Mining Techniques 161 10.4.1 Naïve Bayes 162 10.4.2 Support Vector Machine 162 10.4.3 Decision Tree 163 10.4.4 Multiple Linear Regression 163 10.4.5 Multilayer Perceptron 164 10.4.6 Convolutional Neural Network 164 10.4.7 Long Short-Term Memory 165 10.5 Conclusion 166 References 166 11 Spam Content Filtering in Online Social Networks 169 11.1 Introduction 169 11.1.1 E-Mail Spam 170 11.2 E-Mail Spam Identification Methods 171 11.2.1 Content-Based Spam Identification Method 171 11.2.2 Identity-Based Spam Identification Method 172 11.3 Online Social Network Spam 172 11.4 Related Work 173 11.5 Challenges in the Spam Message Identification 177 11.6 Spam Classification with SVM Filter 178 11.7 Conclusion 179 References 180 12 An Investigation of Various Techniques to Improve Cyber Security 183 12.1 Introduction 184 12.2 Various Attacks 185 12.3 Methods 189 12.4 Conclusion 190 References 191 13 Brain Tumor Classification and Detection Using Machine Learning by Analyzing MRI Images 193 13.1 Introduction 194 13.2 Literature Survey 197 13.3 Methods 200 13.4 Result Analysis 202 13.5 Conclusion 203 References 203 14 Optimized Machine Learning Techniques for Software Fault Prediction 207 14.1 Introduction 208 14.2 Literature Survey 211 14.3 Methods 214 14.4 Result Analysis 216 14.5 Conclusion 216 References 217 15 Pancreatic Cancer Detection Using Machine Learning and Image Processing 221 15.1 Introduction 222 15.2 Literature Survey 225 15.3 Methodology 227 15.4 Result Analysis 228 15.5 Conclusion 228 References 229 16 An Investigation of Various Text Mining Techniques 233 16.1 Introduction 234 16.2 Related Work 236 16.3 Classification Techniques for Text Mining 240 16.3.1 Machine Learning Based Text Classification 240 16.3.2 Ontology-Based Text Classification 241 16.3.3 Hybrid Approaches 241 16.4 Conclusion 241 References 241 17 Automated Query Processing Using Natural Language Processing 245 17.1 Introduction 246 17.1.1 Natural Language Processing 246 17.2 The Challenges of NLP 248 17.3 Related Work 249 17.4 Natural Language Interfaces Systems 253 17.5 Conclusion 255 References 256 18 Data Mining Techniques for Web Usage Mining 259 18.1 Introduction 260 18.1.1 Web Usage Mining 260 18.2 Web Mining 263 18.2.1 Web Content Mining 264 18.2.2 Web Structure Mining 264 18.2.3 Web Usage Mining 265 18.2.3.1 Preprocessing 265 18.2.3.2 Pattern Discovery 265 18.2.3.3 Pattern Analysis 266 18.3 Web Usage Data Mining Techniques 266 18.4 Conclusion 268 References 269 19 Natural Language Processing Using Soft Computing 271 19.1 Introduction 272 19.2 Related Work 273 19.3 NLP Soft Computing Approaches 276 19.4 Conclusion 279 References 279 20 Sentiment Analysis Using Natural Language Processing 283 20.1 Introduction 284 20.2 Sentiment Analysis Levels 285 20.2.1 Document Level 285 20.2.2 Sentence Level 285 20.2.3 Aspect Level 286 20.3 Challenges in Sentiment Analysis 286 20.4 Related Work 288 20.5 Machine Learning Techniques for Sentiment Analysis 290 20.6 Conclusion 292 References 292 21 Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data 295 21.1 Introduction 296 21.2 Web Mining 298 21.3 Taxonomy of Web Data Mining 299 21.3.1 Web Usage Mining 300 21.3.2 Web Structure Mining 301 21.3.3 Web Content Mining 301 21.4 Web Content Mining Methods 302 21.4.1 Unstructured Text Data Mining 302 21.4.2 Structured Data Mining 303 21.4.3 Semi-Structured Data Mining 303 21.5 Efficient Algorithms for Web Data Extraction 304 21.6 Machine Learning Based Web Content Extraction Methods 305 21.7 Conclusion 307 References 307 22 Intelligent Pattern Discovery Using Web Data Mining 311 22.1 Introduction 312 22.2 Pattern Discovery from Web Server Logs 313 22.2.1 Subsequently Accessed Interesting Page Categories 314 22.2.2 Subsequent Probable Page of Visit 314 22.2.3 Strongly and Weakly Linked Web Pages 314 22.2.4 User Groups 315 22.2.5 Fraudulent and Genuine Sessions 315 22.2.6 Web Traffic Behavior 315 22.2.7 Purchase Preference of Customers 315 22.3 Data Mining Techniques for Web Server Log Analysis 316 22.4 Graph Theory Techniques for Analysis of Web Server Logs 318 22.5 Conclusion 319 References 320 23 A Review of Security Features in Prominent Cloud Service Providers 323 23.1 Introduction 324 23.2 Cloud Computing Overview 324 23.3 Cloud Computing Model 326 23.4 Challenges with Cloud Security and Potential Solutions 327 23.5 Comparative Analysis 332 23.6 Conclusion 332 References 332 24 Prioritization of Security Vulnerabilities under Cloud Infrastructure Using AHP 335 24.1 Introduction 336 24.2 Related Work 338 24.3 Proposed Method 341 24.4 Result and Discussion 346 24.5 Conclusion 352 References 352 25 Cloud Computing Security Through Detection & Mitigation of Zero-Day Attack Using Machine Learning Techniques 357 25.1 Introduction 358 25.2 Related Work 360 25.2.1 Analysis of Zero-Day Exploits and Traditional Methods 364 25.3 Proposed Methodology 367 25.4 Results and Discussion 376 25.4.1 Prevention & Mitigation of Zero Day Attacks (ZDAs) 381 25.5 Conclusion and Future Work 383 References 384 26 Predicting Rumors Spread Using Textual and Social Context in Propagation Graph with Graph Neural Network 389 26.1 Introduction 390 26.2 Literature Review 391 26.3 Proposed Methodology 393 26.3.1 Tweep Tendency Encoding 394 26.3.2 Network Dynamics Extraction 395 26.3.3 Extracted Information Integration 396 26.4 Results and Discussion 398 26.5 Conclusion 399 References 400 27 Implications, Opportunities, and Challenges of Blockchain in Natural Language Processing 403 27.1 Introduction 404 27.2 Related Work 406 27.3 Overview on Blockchain Technology and NLP 409 27.3.1 Blockchain Technology, Features, and Applications 409 27.3.2 Natural Language Processing 410 27.3.3 Challenges in NLP 411 27.3.4 Data Integration and Accuracy in NLP 411 27.4 Integration of Blockchain into NLP 412 27.5 Applications of Blockchain in NLP 414 27.6 Blockchain Solutions for NLP 417 27.7 Implications of Blockchain Development Solutions in NLP 418 27.8 Sectors That can be Benified from Blockchain and NLP Integration 419 27.9 Challenges 420 27.10 Conclusion 422 References 422 28 Emotion Detection Using Natural Language Processing by Text Classification 425 28.1 Introduction 426 28.2 Natural Language Processing 427 28.3 Emotion Recognition 429 28.4 Related Work 430 28.4.1 Emotion Detection Using Machine Learning 430 28.4.2 Emotion Detection Using Deep Learning 432 28.4.3 Emotion Detection Using Ensemble Learning 435 28.5 Machine Learning Techniques for Emotion Detection 437 28.6 Conclusion 439 References 439 29 Alzheimer Disease Detection Using Machine Learning Techniques 443 29.1 Introduction 444 29.2 Machine Learning Techniques to Detect Alzheimer’s Disease 445 29.3 Pre-Processing Techniques for Alzheimer’s Disease Detection 446 29.4 Feature Extraction Techniques for Alzheimer’s Disease Detection 448 29.5 Feature Selection Techniques for Diagnosis of Alzheimer’s Disease 449 29.6 Machine Learning Models Used for Alzheimer’s Disease Detection 451 29.7 Conclusion 453 References 454 30 Netnographic Literature Review and Research Methodology for Maritime Business and Potential Cyber Threats 457 30.1 Introduction 458 30.2 Criminal Flows Framework 460 30.3 Oceanic Crime Exchange and Categorization 462 30.4 Fisheries Crimes and Mobility Crimes 469 30.5 Conclusion 470 30.6 Discussion 470 References 470 31 Review of Research Methodology and IT for Business and Threat Management 475 Abbreviation Used 476 31.1 Introduction 477 31.2 Conclusion 484 References 485 About the Editors 487 Index 489
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Subject Areas: Electronics & communications engineering [TJ]
