Demystifying Emerging Trends in Machine Learning

Pankaj Kumar Mishra, Satya Prakash Yadav

• Publisher: Bentham Science Publishers
• Year: 2025
• eBook ISBN: 9789815305395
• Edition: 0
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Demystifying Emerging Trends in Machine Learning (Volume 2) offers a deep dive into emerging and trending topics in the field of machine learning (ML). This edited volume showcases several machine learning methods for a variety of tasks. A key focus of this volume is the application of text classification for cybersecurity, E-commerce, sentiment analysis, public health and web content analysis. The 49 chapters highlight a wide variety of machine learning methods including SVNs, K-Means Clustering, CNNs, DCNNs, among others. Each chapter includes accessible information through summaries, discussions and reference lists. This comprehensive volume is essential for students, researchers, and professionals eager to understand the emerging trends reshaping machine learning today. Readership Scholars and professionals interested in machine learning trends and research.

Table of contents

• Welcome
• Table of Content
• Title
• BENTHAM SCIENCE PUBLISHERS LTD.
• PREFACE
• List of Contributors
• A Method Based on Machine Learning to Classify Text for the Field of Cybersecurity
• A Practicable E-commerce-Based Text-Classification System
• AI Model for Text Classification Using FastText
• An Algorithm for Textual Classification of News Utilizing Artificial Intelligence Technology
• Analysis of the Sentiment of Tweets Regarding COVID-19 Vaccines Using Natural Language Processing and Machine Learning Sectionification Algorithms
• Classification of Medical Text using ML and DL Techniques
• Evaluation of ML and Advanced Deep Learning Text Classification Systems
• Machine Learning Method Employed for the Objective of Identifying Text on Tweet Dataset
• Textual Classification Utilizing the Integration of Semantics and Statistical Methodology
• The Use of Machine Learning Techniques to Classify Content on the Web
• Lexical Methods for Identifying Emotions in Text Based on Machine Learning
• Identification of Websites Using an Efficient Method Employing Text Mining Methods
• Machine Learning-based High-Dimensional Text Document Classification and Clustering
• The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions
• Method for Adaptive Combination of Multiple Features for Text Classification in Agriculture
• Deep Learning-based Text-Retrieval System with Relevance Feedback
• Domain Knowledge-based BERT Model with Deep Learning for Text Classification
• Applying Deep Learning to Classify Massive Amounts of Text Using Convolutional Neural Systems
• An Algorithm for Categorizing Opinions in Text from Various Social Media Platforms
• Text Classification Method for Tracking Rare Events on Twitter
• Text Document Preprocessing and Classification Using SVM and Improved CNN
• Identification of Text Emotions Through the Use of Convolutional Neural Network Models
• Classification & Clustering of Text Based on Doc2Vec & K-means Clustering based Similarity Measurements
• Categorization of COVID-19 Twitter Data Based on an Aspect-Oriented Sentiment Analysis and Fuzzy Logic
• Feature-Level Sentiment Analysis of Data Collected through Electronic Commerce
• Classification Algorithms for Evaluating Customer Opinions using AI
• Analysis of Sentiment Employing the Word2vec with CNN-LSTM Classification System
• Hadoop-based Twitter Sentiment Analysis Using Deep Learning
• A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis
• Text Emotion Categorization Using a Convolutional Recurrent Neural Network Enhanced by an Attention Mechanism-based Skip-Gram Method
• Multimodal Sentiment Analysis in Text, Images, and GIFs Using Deep Learning
• Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques
• CNN-based Deep Learning Techniques for Movie Review Analysis of Sentiments
• Machine Learning and Deep Learning Models for Sentiment Analysis of Product Reviews
• Sentiment Analysis of Hotel Reviews Based on Deep Learning
• Utilizing Machine Learning for Natural Language Processing to Conduct Sentiment Analysis on Twitter Data in Multiple Languages
• The Use of Machine Learning to Analyze the Sentiment for Social Media Networks
• Sentiment Classification of Textual Content using Hybrid DNN and SVM Models
• Big Data Analysis and Information Quality: Challenges, Solutions, and Open Problems
• Using Deep Learning Techniques to Detect Traffic Information in Social Media Texts
• Deep Sentiment Classification in COVID-19 Using LSTM Recurrent Neural Network
• Machine Learning-Based Data Preprocessing as well as Visualization Techniques for Predicting Students' Tasks
• The Prediction of Faults Using Large Amounts of Industrial Data
• Comparison Analysis of Logical Regression and Random Forest with Word Embedding Techniques for Twitter Sentiment Analysis
• The Classification of News Articles Through the Use of Deep Learning and the Doc2Vec Modeling
• Investigating the Utility of Data Mining for Automated Credit Scoring
• Investigating the Use of Data Mining for Knowledge Discovery
• Exploring the Role of Big Data in Predictive Analytics
• Implementing Automated Reasoning in Natural Language Processing