{"product_id":"natural-language-processing-for-software-engineering-hardback-9781394272433","title":"Natural Language Processing for Software Engineering (Hardback) 9781394272433","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eNatural Language Processing for Software Engineering\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eRajesh 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)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394272433, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 17 January 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e544 pages\u003cbr\u003e22.9 x 15.2 x 3.2 cm, 0.907 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eDiscover how \u003ci\u003eNatural Language Processing for Software Engineering\u003c\/i\u003e 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eAgile 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRavindra Bhardwaj, Sreenivasulu Gogula, Bidisha Bhabani, K. Kanagalakshmi, Aparajita Mukherjee and D. Vetrithangam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Need of Vulnerability Identification 4\u003c\/p\u003e \u003cp\u003e1.3 Vulnerabilities in IoT Web Applications 5\u003c\/p\u003e \u003cp\u003e1.4 Intrusion Detection System 7\u003c\/p\u003e \u003cp\u003e1.5 Machine Learning in Intrusion Detection System 10\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 12\u003c\/p\u003e \u003cp\u003eReferences 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Frequent Pattern Mining Using Artificial Intelligence and Machine Learning 15\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Deepika, Sreenivasulu Gogula, K. Kanagalakshmi, Anshu Mehta, S. J. Vivekanandan and D. Vetrithangam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 16\u003c\/p\u003e \u003cp\u003e2.2 Data Mining Functions 17\u003c\/p\u003e \u003cp\u003e2.3 Related Work 19\u003c\/p\u003e \u003cp\u003e2.4 Machine Learning for Frequent Pattern Mining 24\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 26\u003c\/p\u003e \u003cp\u003eReferences 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Classification and Detection of Prostate Cancer Using Machine Learning Techniques 29\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eD. Vetrithangam, Pramod Kumar, Shaik Munawar, Rituparna Biswas, Deependra Pandey and Amar Choudhary\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 30\u003c\/p\u003e \u003cp\u003e3.2 Literature Survey 32\u003c\/p\u003e \u003cp\u003e3.3 Machine Learning for Prostate Cancer Classification and Detection 35\u003c\/p\u003e \u003cp\u003e3.4 Conclusion 37\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 NLP-Based Spellchecker and Grammar Checker for Indic Languages 43\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBrijesh Kumar Y. Panchal and Apurva Shah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 44\u003c\/p\u003e \u003cp\u003e4.2 NLP-Based Techniques of Spellcheckers and Grammar Checkers 44\u003c\/p\u003e \u003cp\u003e4.2.1 Syntax-Based 44\u003c\/p\u003e \u003cp\u003e4.2.2 Statistics-Based 45\u003c\/p\u003e \u003cp\u003e4.2.3 Rule-Based 45\u003c\/p\u003e \u003cp\u003e4.2.4 Deep Learning-Based 45\u003c\/p\u003e \u003cp\u003e4.2.5 Machine Learning-Based 46\u003c\/p\u003e \u003cp\u003e4.2.6 Reinforcement Learning-Based 46\u003c\/p\u003e \u003cp\u003e4.3 Grammar Checker Related Work 47\u003c\/p\u003e \u003cp\u003e4.4 Spellchecker Related Work 58\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 66\u003c\/p\u003e \u003cp\u003eReferences 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Identification of Gujarati Ghazal Chanda with Cross-Platform Application 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBrijeshkumar Y. Panchal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 72\u003c\/p\u003e \u003cp\u003e5.1 Introduction 72\u003c\/p\u003e \u003cp\u003e5.1.1 The Gujarati Language 72\u003c\/p\u003e \u003cp\u003e5.2 Ghazal 75\u003c\/p\u003e \u003cp\u003e5.3 History and Grammar of Ghazal 77\u003c\/p\u003e \u003cp\u003e5.4 Literature Review 78\u003c\/p\u003e \u003cp\u003e5.5 Proposed System 85\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 92\u003c\/p\u003e \u003cp\u003eReferences 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Cancer Classification and Detection Using Machine Learning Techniques 95\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSyed Jahangir Badashah, Afaque Alam, Malik Jawarneh, Tejashree Tejpal Moharekar, Venkatesan Hariram, Galiveeti Poornima and Ashish Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 96\u003c\/p\u003e \u003cp\u003e6.2 Machine Learning Techniques 97\u003c\/p\u003e \u003cp\u003e6.3 Review of Machine Learning for Cancer Detection 101\u003c\/p\u003e \u003cp\u003e6.4 Methods 103\u003c\/p\u003e \u003cp\u003e6.5 Result Analysis 106\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 107\u003c\/p\u003e \u003cp\u003eReferences 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Text Mining Techniques and Natural Language Processing 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTzu-Chia Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 113\u003c\/p\u003e \u003cp\u003e7.2 Text Classification and Text Clustering 115\u003c\/p\u003e \u003cp\u003e7.3 Related Work 116\u003c\/p\u003e \u003cp\u003e7.4 Methodology 121\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 123\u003c\/p\u003e \u003cp\u003eReferences 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 An Investigation of Techniques to Encounter Security Issues Related to Mobile Applications 127\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDevabalan Pounraj, Pankaj Goel, Meenakshi, Domenic T. Sanchez, Parashuram Shankar Vadar, Rafael D. Sanchez and Malik Jawarneh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 128\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 130\u003c\/p\u003e \u003cp\u003e8.3 Results and Discussions 137\u003c\/p\u003e \u003cp\u003e8.4 Conclusion 138\u003c\/p\u003e \u003cp\u003eReferences 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Machine Learning for Sentiment Analysis Using Social Media Scrapped Data 143\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGaliveeti Poornima, Meenakshi, Malik Jawarneh, A. Shobana, K.P. Yuvaraj, Urmila R. Pol and Tejashree Tejpal Moharekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 144\u003c\/p\u003e \u003cp\u003e9.2 Twitter Sentiment Analysis 146\u003c\/p\u003e \u003cp\u003e9.3 Sentiment Analysis Using Machine Learning Techniques 149\u003c\/p\u003e \u003cp\u003e9.4 Conclusion 152\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Opinion Mining Using Classification Techniques on Electronic Media Data 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMeenakshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 156\u003c\/p\u003e \u003cp\u003e10.2 Opinion Mining 158\u003c\/p\u003e \u003cp\u003e10.3 Related Work 159\u003c\/p\u003e \u003cp\u003e10.4 Opinion Mining Techniques 161\u003c\/p\u003e \u003cp\u003e10.4.1 Naïve Bayes 162\u003c\/p\u003e \u003cp\u003e10.4.2 Support Vector Machine 162\u003c\/p\u003e \u003cp\u003e10.4.3 Decision Tree 163\u003c\/p\u003e \u003cp\u003e10.4.4 Multiple Linear Regression 163\u003c\/p\u003e \u003cp\u003e10.4.5 Multilayer Perceptron 164\u003c\/p\u003e \u003cp\u003e10.4.6 Convolutional Neural Network 164\u003c\/p\u003e \u003cp\u003e10.4.7 Long Short-Term Memory 165\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 166\u003c\/p\u003e \u003cp\u003eReferences 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Spam Content Filtering in Online Social Networks 169\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMeenakshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 169\u003c\/p\u003e \u003cp\u003e11.1.1 E-Mail Spam 170\u003c\/p\u003e \u003cp\u003e11.2 E-Mail Spam Identification Methods 171\u003c\/p\u003e \u003cp\u003e11.2.1 Content-Based Spam Identification Method 171\u003c\/p\u003e \u003cp\u003e11.2.2 Identity-Based Spam Identification Method 172\u003c\/p\u003e \u003cp\u003e11.3 Online Social Network Spam 172\u003c\/p\u003e \u003cp\u003e11.4 Related Work 173\u003c\/p\u003e \u003cp\u003e11.5 Challenges in the Spam Message Identification 177\u003c\/p\u003e \u003cp\u003e11.6 Spam Classification with SVM Filter 178\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 179\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 An Investigation of Various Techniques to Improve Cyber Security 183\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShoaib Mohammad, Ramendra Pratap Singh, Rajiv Kumar, Kshitij Kumar Rai, Arti Sharma and Saloni Rathore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 184\u003c\/p\u003e \u003cp\u003e12.2 Various Attacks 185\u003c\/p\u003e \u003cp\u003e12.3 Methods 189\u003c\/p\u003e \u003cp\u003e12.4 Conclusion 190\u003c\/p\u003e \u003cp\u003eReferences 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Brain Tumor Classification and Detection Using Machine Learning by Analyzing MRI Images 193\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChandrima Sinha Roy, K. Parvathavarthini, M. Gomathi, Mrunal Pravinkumar Fatangare, D. Kishore and Anilkumar Suthar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 194\u003c\/p\u003e \u003cp\u003e13.2 Literature Survey 197\u003c\/p\u003e \u003cp\u003e13.3 Methods 200\u003c\/p\u003e \u003cp\u003e13.4 Result Analysis 202\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 203\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Optimized Machine Learning Techniques for Software Fault Prediction 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChetan Shelke, Ashwini Mandale (Jadhav), Shaik Anjimoon, Asha V., Ginni Nijhawan and Joshuva Arockia Dhanraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 208\u003c\/p\u003e \u003cp\u003e14.2 Literature Survey 211\u003c\/p\u003e \u003cp\u003e14.3 Methods 214\u003c\/p\u003e \u003cp\u003e14.4 Result Analysis 216\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 216\u003c\/p\u003e \u003cp\u003eReferences 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Pancreatic Cancer Detection Using Machine Learning and Image Processing 221\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShashidhar Sonnad, Rejwan Bin Sulaiman, Amer Kareem, S. Shalini, D. Kishore and Jayasankar Narayanan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 222\u003c\/p\u003e \u003cp\u003e15.2 Literature Survey 225\u003c\/p\u003e \u003cp\u003e15.3 Methodology 227\u003c\/p\u003e \u003cp\u003e15.4 Result Analysis 228\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 228\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 An Investigation of Various Text Mining Techniques 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajashree Gadhave, Anita Chaudhari, B. Ramesh, Vijilius Helena Raj, H. Pal Thethi and A. Ravitheja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 234\u003c\/p\u003e \u003cp\u003e16.2 Related Work 236\u003c\/p\u003e \u003cp\u003e16.3 Classification Techniques for Text Mining 240\u003c\/p\u003e \u003cp\u003e16.3.1 Machine Learning Based Text Classification 240\u003c\/p\u003e \u003cp\u003e16.3.2 Ontology-Based Text Classification 241\u003c\/p\u003e \u003cp\u003e16.3.3 Hybrid Approaches 241\u003c\/p\u003e \u003cp\u003e16.4 Conclusion 241\u003c\/p\u003e \u003cp\u003eReferences 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Automated Query Processing Using Natural Language Processing 245\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDivyanshu Sinha, G. Ravivarman, B. Rajalakshmi, V. Alekhya, Rajeev Sobti and R. Udhayakumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 246\u003c\/p\u003e \u003cp\u003e17.1.1 Natural Language Processing 246\u003c\/p\u003e \u003cp\u003e17.2 The Challenges of NLP 248\u003c\/p\u003e \u003cp\u003e17.3 Related Work 249\u003c\/p\u003e \u003cp\u003e17.4 Natural Language Interfaces Systems 253\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 255\u003c\/p\u003e \u003cp\u003eReferences 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Data Mining Techniques for Web Usage Mining 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNavdeep Kumar Chopra, Chinnem Rama Mohan, Snehal Dipak Chaudhary, Manisha Kasar, Trupti Suryawanshi and Shikha Dubey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 260\u003c\/p\u003e \u003cp\u003e18.1.1 Web Usage Mining 260\u003c\/p\u003e \u003cp\u003e18.2 Web Mining 263\u003c\/p\u003e \u003cp\u003e18.2.1 Web Content Mining 264\u003c\/p\u003e \u003cp\u003e18.2.2 Web Structure Mining 264\u003c\/p\u003e \u003cp\u003e18.2.3 Web Usage Mining 265\u003c\/p\u003e \u003cp\u003e18.2.3.1 Preprocessing 265\u003c\/p\u003e \u003cp\u003e18.2.3.2 Pattern Discovery 265\u003c\/p\u003e \u003cp\u003e18.2.3.3 Pattern Analysis 266\u003c\/p\u003e \u003cp\u003e18.3 Web Usage Data Mining Techniques 266\u003c\/p\u003e \u003cp\u003e18.4 Conclusion 268\u003c\/p\u003e \u003cp\u003eReferences 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Natural Language Processing Using Soft Computing 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Rajkumar, Viswanathasarma Ch, Anandhi R. J., D. Anandhasilambarasan, Om Prakash Yadav and Joshuva Arockia Dhanraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 272\u003c\/p\u003e \u003cp\u003e19.2 Related Work 273\u003c\/p\u003e \u003cp\u003e19.3 NLP Soft Computing Approaches 276\u003c\/p\u003e \u003cp\u003e19.4 Conclusion 279\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Sentiment Analysis Using Natural Language Processing 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBrijesh Goswami, Nidhi Bhavsar, Soleman Awad Alzobidy, B. Lavanya, R. Udhayakumar and Rajapandian K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 284\u003c\/p\u003e \u003cp\u003e20.2 Sentiment Analysis Levels 285\u003c\/p\u003e \u003cp\u003e20.2.1 Document Level 285\u003c\/p\u003e \u003cp\u003e20.2.2 Sentence Level 285\u003c\/p\u003e \u003cp\u003e20.2.3 Aspect Level 286\u003c\/p\u003e \u003cp\u003e20.3 Challenges in Sentiment Analysis 286\u003c\/p\u003e \u003cp\u003e20.4 Related Work 288\u003c\/p\u003e \u003cp\u003e20.5 Machine Learning Techniques for Sentiment Analysis 290\u003c\/p\u003e \u003cp\u003e20.6 Conclusion 292\u003c\/p\u003e \u003cp\u003eReferences 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data 295\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eC. V. Guru Rao, Nagendra Prasad Krishnam, Akula Rajitha, Anandhi R. J., Atul Singla and Joshuva Arockia Dhanraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 296\u003c\/p\u003e \u003cp\u003e21.2 Web Mining 298\u003c\/p\u003e \u003cp\u003e21.3 Taxonomy of Web Data Mining 299\u003c\/p\u003e \u003cp\u003e21.3.1 Web Usage Mining 300\u003c\/p\u003e \u003cp\u003e21.3.2 Web Structure Mining 301\u003c\/p\u003e \u003cp\u003e21.3.3 Web Content Mining 301\u003c\/p\u003e \u003cp\u003e21.4 Web Content Mining Methods 302\u003c\/p\u003e \u003cp\u003e21.4.1 Unstructured Text Data Mining 302\u003c\/p\u003e \u003cp\u003e21.4.2 Structured Data Mining 303\u003c\/p\u003e \u003cp\u003e21.4.3 Semi-Structured Data Mining 303\u003c\/p\u003e \u003cp\u003e21.5 Efficient Algorithms for Web Data Extraction 304\u003c\/p\u003e \u003cp\u003e21.6 Machine Learning Based Web Content Extraction Methods 305\u003c\/p\u003e \u003cp\u003e21.7 Conclusion 307\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Intelligent Pattern Discovery Using Web Data Mining 311\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVidyapati Jha, Chinnem Rama Mohan, T. Sampath Kumar, Anandhi R.J., Bhimasen Moharana and P. Pavankumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 312\u003c\/p\u003e \u003cp\u003e22.2 Pattern Discovery from Web Server Logs 313\u003c\/p\u003e \u003cp\u003e22.2.1 Subsequently Accessed Interesting Page Categories 314\u003c\/p\u003e \u003cp\u003e22.2.2 Subsequent Probable Page of Visit 314\u003c\/p\u003e \u003cp\u003e22.2.3 Strongly and Weakly Linked Web Pages 314\u003c\/p\u003e \u003cp\u003e22.2.4 User Groups 315\u003c\/p\u003e \u003cp\u003e22.2.5 Fraudulent and Genuine Sessions 315\u003c\/p\u003e \u003cp\u003e22.2.6 Web Traffic Behavior 315\u003c\/p\u003e \u003cp\u003e22.2.7 Purchase Preference of Customers 315\u003c\/p\u003e \u003cp\u003e22.3 Data Mining Techniques for Web Server Log Analysis 316\u003c\/p\u003e \u003cp\u003e22.4 Graph Theory Techniques for Analysis of Web Server Logs 318\u003c\/p\u003e \u003cp\u003e22.5 Conclusion 319\u003c\/p\u003e \u003cp\u003eReferences 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 A Review of Security Features in Prominent Cloud Service Providers 323\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbhishek Mishra, Abhishek Sharma, Rajat Bhardwaj, Romil Rawat, T.M.Thiyagu and Hitesh Rawat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 324\u003c\/p\u003e \u003cp\u003e23.2 Cloud Computing Overview 324\u003c\/p\u003e \u003cp\u003e23.3 Cloud Computing Model 326\u003c\/p\u003e \u003cp\u003e23.4 Challenges with Cloud Security and Potential Solutions 327\u003c\/p\u003e \u003cp\u003e23.5 Comparative Analysis 332\u003c\/p\u003e \u003cp\u003e23.6 Conclusion 332\u003c\/p\u003e \u003cp\u003eReferences 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Prioritization of Security Vulnerabilities under Cloud Infrastructure Using AHP 335\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbhishek Sharma and Umesh Kumar Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 336\u003c\/p\u003e \u003cp\u003e24.2 Related Work 338\u003c\/p\u003e \u003cp\u003e24.3 Proposed Method 341\u003c\/p\u003e \u003cp\u003e24.4 Result and Discussion 346\u003c\/p\u003e \u003cp\u003e24.5 Conclusion 352\u003c\/p\u003e \u003cp\u003eReferences 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Cloud Computing Security Through Detection \u0026amp; Mitigation of Zero-Day Attack Using Machine Learning Techniques 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbhishek Sharma and Umesh Kumar Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 358\u003c\/p\u003e \u003cp\u003e25.2 Related Work 360\u003c\/p\u003e \u003cp\u003e25.2.1 Analysis of Zero-Day Exploits and Traditional Methods 364\u003c\/p\u003e \u003cp\u003e25.3 Proposed Methodology 367\u003c\/p\u003e \u003cp\u003e25.4 Results and Discussion 376\u003c\/p\u003e \u003cp\u003e25.4.1 Prevention \u0026amp; Mitigation of Zero Day Attacks (ZDAs) 381\u003c\/p\u003e \u003cp\u003e25.5 Conclusion and Future Work 383\u003c\/p\u003e \u003cp\u003eReferences 384\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Predicting Rumors Spread Using Textual and Social Context in Propagation Graph with Graph Neural Network 389\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSiddharath Kumar Arjaria, Hardik Sachan, Satyam Dubey, Ayush Pandey, Mansi Gautam, Nikita Gupta and Abhishek Singh Rathore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 390\u003c\/p\u003e \u003cp\u003e26.2 Literature Review 391\u003c\/p\u003e \u003cp\u003e26.3 Proposed Methodology 393\u003c\/p\u003e \u003cp\u003e26.3.1 Tweep Tendency Encoding 394\u003c\/p\u003e \u003cp\u003e26.3.2 Network Dynamics Extraction 395\u003c\/p\u003e \u003cp\u003e26.3.3 Extracted Information Integration 396\u003c\/p\u003e \u003cp\u003e26.4 Results and Discussion 398\u003c\/p\u003e \u003cp\u003e26.5 Conclusion 399\u003c\/p\u003e \u003cp\u003eReferences 400\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Implications, Opportunities, and Challenges of Blockchain in Natural Language Processing 403\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNeha Agrawal, Balwinder Kaur Dhaliwal, Shilpa Sharma, Neha Yadav and Ranjana Sikarwar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 404\u003c\/p\u003e \u003cp\u003e27.2 Related Work 406\u003c\/p\u003e \u003cp\u003e27.3 Overview on Blockchain Technology and NLP 409\u003c\/p\u003e \u003cp\u003e27.3.1 Blockchain Technology, Features, and Applications 409\u003c\/p\u003e \u003cp\u003e27.3.2 Natural Language Processing 410\u003c\/p\u003e \u003cp\u003e27.3.3 Challenges in NLP 411\u003c\/p\u003e \u003cp\u003e27.3.4 Data Integration and Accuracy in NLP 411\u003c\/p\u003e \u003cp\u003e27.4 Integration of Blockchain into NLP 412\u003c\/p\u003e \u003cp\u003e27.5 Applications of Blockchain in NLP 414\u003c\/p\u003e \u003cp\u003e27.6 Blockchain Solutions for NLP 417\u003c\/p\u003e \u003cp\u003e27.7 Implications of Blockchain Development Solutions in NLP 418\u003c\/p\u003e \u003cp\u003e27.8 Sectors That can be Benified from Blockchain and NLP Integration 419\u003c\/p\u003e \u003cp\u003e27.9 Challenges 420\u003c\/p\u003e \u003cp\u003e27.10 Conclusion 422\u003c\/p\u003e \u003cp\u003eReferences 422\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Emotion Detection Using Natural Language Processing by Text Classification 425\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyoti Jayal, Vijay Kumar, Paramita Sarkar and Sudipta Kumar Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 426\u003c\/p\u003e \u003cp\u003e28.2 Natural Language Processing 427\u003c\/p\u003e \u003cp\u003e28.3 Emotion Recognition 429\u003c\/p\u003e \u003cp\u003e28.4 Related Work 430\u003c\/p\u003e \u003cp\u003e28.4.1 Emotion Detection Using Machine Learning 430\u003c\/p\u003e \u003cp\u003e28.4.2 Emotion Detection Using Deep Learning 432\u003c\/p\u003e \u003cp\u003e28.4.3 Emotion Detection Using Ensemble Learning 435\u003c\/p\u003e \u003cp\u003e28.5 Machine Learning Techniques for Emotion Detection 437\u003c\/p\u003e \u003cp\u003e28.6 Conclusion 439\u003c\/p\u003e \u003cp\u003eReferences 439\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Alzheimer Disease Detection Using Machine Learning Techniques 443\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Prabavathy, Paramita Sarkar, Abhrendu Bhattacharya and Anil Kumar Behera\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 444\u003c\/p\u003e \u003cp\u003e29.2 Machine Learning Techniques to Detect Alzheimer’s Disease 445\u003c\/p\u003e \u003cp\u003e29.3 Pre-Processing Techniques for Alzheimer’s Disease Detection 446\u003c\/p\u003e \u003cp\u003e29.4 Feature Extraction Techniques for Alzheimer’s Disease Detection 448\u003c\/p\u003e \u003cp\u003e29.5 Feature Selection Techniques for Diagnosis of Alzheimer’s Disease 449\u003c\/p\u003e \u003cp\u003e29.6 Machine Learning Models Used for Alzheimer’s Disease Detection 451\u003c\/p\u003e \u003cp\u003e29.7 Conclusion 453\u003c\/p\u003e \u003cp\u003eReferences 454\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Netnographic Literature Review and Research Methodology for Maritime Business and Potential Cyber Threats 457\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHitesh Rawat, Anjali Rawat and Romil Rawat\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 458\u003c\/p\u003e \u003cp\u003e30.2 Criminal Flows Framework 460\u003c\/p\u003e \u003cp\u003e30.3 Oceanic Crime Exchange and Categorization 462\u003c\/p\u003e \u003cp\u003e30.4 Fisheries Crimes and Mobility Crimes 469\u003c\/p\u003e \u003cp\u003e30.5 Conclusion 470\u003c\/p\u003e \u003cp\u003e30.6 Discussion 470\u003c\/p\u003e \u003cp\u003eReferences 470\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Review of Research Methodology and IT for Business and Threat Management 475\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHitesh Rawat, Anjali Rawat, Sunday Adeola Ajagbe and Yagyanath Rimal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviation Used 476\u003c\/p\u003e \u003cp\u003e31.1 Introduction 477\u003c\/p\u003e \u003cp\u003e31.2 Conclusion 484\u003c\/p\u003e \u003cp\u003eReferences 485\u003c\/p\u003e \u003cp\u003eAbout the Editors 487\u003c\/p\u003e \u003cp\u003eIndex 489\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433243504920,"sku":"9781394272433","price":153.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394272433.jpg?v=1784852909","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/natural-language-processing-for-software-engineering-hardback-9781394272433","provider":"Freshly Printed Books","version":"1.0","type":"link"}