Demystifying Emerging Trends in Machine Learning.

Format:

Book

Author/Creator:

Mishra, Pankaj Kumar.

Contributor:

Yadav, Satya Prakash.

Series:

Emerging Trends in Computation Intelligence and Disruptive Technologies Series

Language:

English

Physical Description:

1 online resource (590 pages)

Edition:

1st ed.

Place of Publication:

Sharjah : Bentham Science Publishers, 2025.

Summary:

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.

Contents:

Title

Copyright

End User License Agreement

Preface

List of Contributors

A Method Based on Machine Learning to Classify Text for the Field of Cybersecurity

Siddharth Sriram1,*

INTRODUCTION

RELATED WORK

PROPOSED WORK

Preliminary Knowledge

Dataset Description

Machine Learning Algorithms for Text Classification

Naive Bayes

Support Vector Machines

RESULTS AND DISCUSSION

CONCLUSION

REFERENCES

A Practicable E-commerce-Based Text-Classification System

Sidhant Das1,*

Problem Formulation

System Model

Procedure

Intake

AI Model for Text Classification Using FastText

Sorabh Sharma1,*

FastText Model

An Algorithm for Textual Classification of News Utilizing Artificial Intelligence Technology

Rahul Mishra1,*

Level 1

Level 2

Level 3

Preprocessing

News Text Classification

Analysis of the Sentiment of Tweets Regarding COVID-19 Vaccines Using Natural Language Processing and Machine Learning Sectionification Algorithms

Sukhman Ghumman1,*

Pre-processing

Noise Removal

Corrections

Tokenization

Normalization

Stemming

PoS Tagging

ML Techniques

Supervised Machine Learning

Unsupervised Machine Learning

Semi-Supervised Machine Learning

Logistic Regression (LR)

Decision Tree (DT)

Random Forest (RF)

REFERENCES.

Classification of Medical Text using ML and DL Techniques

Sulabh Mahajan1,*

BERT Model

ML and DL Models

ML Methods

DL Methods

Evaluation of ML and Advanced Deep Learning Text Classification Systems

Tarun Kapoor1,*

Text Classification Methods

Supervised Text Classification

Unsupervised Text Classification

Data Cleaning and Preprocessing

Lowercasing

Stop Word Removal

Lemmatization

TF-IDF

DCNN with GA for Text Classification

Machine Learning Method Employed for the Objective of Identifying Text on Tweet Dataset

Sakshi Pandey1,*

Data Collection

Data Preprocessing

Word Embedding

Feature Extraction

Text Classification

Textual Classification Utilizing the Integration of Semantics and Statistical Methodology

Ayush Gandhi1,*

GRU

Proposed GRU

The Use of Machine Learning Techniques to Classify Content on the Web

Dikshit Sharma1,*

SVM

Proposed Classifier

Lexical Methods for Identifying Emotions in Text Based on Machine Learning

Mridula Gupta1,*

Research Gaps

Speech Emotion Classification

Identification of Websites Using an Efficient Method Employing Text Mining Methods

Madhur Taneja1,*

Gathering Website Information &amp

Feature Extraction using CNN with LSTM

Hyper parameters Description

Description of results

Machine Learning-based High-Dimensional Text Document Classification and Clustering

Ansh Kataria1,*

Background

Machine Learning-Based Text Classification

Stop Words

Feature Engineering

Feature Clustering

The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions

Pratibha Sharma1,*

Problem Statement

Proposed Methodology

Skip-Gram

Method for Adaptive Combination of Multiple Features for Text Classification in Agriculture

Jaskirat Singh1,*

Text Classification using Bi-GRU &amp

CNN

Deep Learning-based Text-Retrieval System with Relevance Feedback

Simran Kalra1,*

ConvNets

Example Scenario:

Domain Knowledge-based BERT Model with Deep Learning for Text Classification

Akhilesh Kalia1,*

Bi-GRU for text classification

CONCLUSION.

REFERENCES

Applying Deep Learning to Classify Massive Amounts of Text Using Convolutional Neural Systems

Shubhansh Bansal1,*

An Algorithm for Categorizing Opinions in Text from Various Social Media Platforms

Pavas Saini1,*

Overview

Multimodal Sentiment Classification

Text Classification Method for Tracking Rare Events on Twitter

Prabhjot Kaur1,*

Dataset

Feature Extraction and Classification

Datasets

Text Document Preprocessing and Classification Using SVM and Improved CNN

Jaspreet Sidhu1,*

CNN with SVM for Text Classification

Identification of Text Emotions Through the Use of Convolutional Neural Network Models

Vaibhav Kaushik1,*

Convolution Layer

Max Combining Layer

Classification &amp

Clustering of Text Based on Doc2Vec &amp

K-means Clustering based Similarity Measurements

Prakriti Kapoor1,*

Data Preparing

Document Demonstration

Document Clustering

Categorization of COVID-19 Twitter Data Based on an Aspect-Oriented Sentiment Analysis and Fuzzy Logic

Tarang Bhatnagar1,*

Data Mining of Tweets.

Preprocessing and Labeling

Outcomes and Discussion

Feature-Level Sentiment Analysis of Data Collected through Electronic Commerce

Preetjot Singh1,*

Customer Reviews

Parts-of-Speech tagging

Feature Pruning

Classification

Classification Algorithms for Evaluating Customer Opinions using AI

Saniya Khurana1,*

Collection and Preprocessing of Data 3.1

Feature Extraction Methods

Artificial Neural Networks

Decision Trees

C4.5. Decision Tree Classifier

KNN

Analysis of Sentiment Employing the Word2vec with CNN-LSTM Classification System

Rajat Saini1,*

In-Depth Information Gathering 3.1.1

Text Classification using CNN-LSTM

Hadoop-based Twitter Sentiment Analysis Using Deep Learning

Manpreet Singh1,*

System Overview

Sentiment Analysis using Hadoop

Testing environment

Performance metrics

A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis

Manish Nagpal1,*

Text Preprocessing

Removal and corrections

Replacement

PoS tagging

Text Emotion Categorization Using a Convolutional Recurrent Neural Network Enhanced by an Attention Mechanism-based Skip-Gram Method.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789815305395

9815305395

OCLC:

1500736462