Demystifying Emerging Trends in Machine Learning

Name: Demystifying Emerging Trends in Machine Learning
Brand: Bentham Science Publishers Singapore Pte. Ltd.
Availability: OnlineOnly

Pankaj Kumar Mishra Satya Prakash Yadav(Editor)

Bentham Science Publishers Singapore Pte. Ltd.

Published on 29. August 2025

600 pages

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978-981-5305-39-5 (ISBN)

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Content

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,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Problem Formulation
Dataset Description
System Model
Procedure
Intake
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
AI Model for Text Classification Using FastText
Sorabh Sharma1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
FastText Model
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
An Algorithm for Textual Classification of News Utilizing Artificial Intelligence Technology
Rahul Mishra1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Level 1
Level 2
Level 3
Preprocessing
Level 1
Level 2
Level 3
News Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Analysis of the Sentiment of Tweets Regarding COVID-19 Vaccines Using Natural Language Processing and Machine Learning Sectionification Algorithms
Sukhman Ghumman1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
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)
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Classification of Medical Text using ML and DL Techniques
Sulabh Mahajan1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Problem Formulation
BERT Model
ML and DL Models
ML Methods
DL Methods
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Evaluation of ML and Advanced Deep Learning Text Classification Systems
Tarun Kapoor1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Text Classification Methods
Supervised Text Classification
Unsupervised Text Classification
Preprocessing
Data Cleaning and Preprocessing
Lowercasing
Stop Word Removal
Lemmatization
TF-IDF
DCNN with GA for Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Machine Learning Method Employed for the Objective of Identifying Text on Tweet Dataset
Sakshi Pandey1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Data Collection
Data Preprocessing
Word Embedding
Feature Extraction
Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Textual Classification Utilizing the Integration of Semantics and Statistical Methodology
Ayush Gandhi1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
GRU
Proposed GRU
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
The Use of Machine Learning Techniques to Classify Content on the Web
Dikshit Sharma1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
SVM
Proposed Classifier
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Lexical Methods for Identifying Emotions in Text Based on Machine Learning
Mridula Gupta1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Research Gaps
System Model
Word Embedding
Speech Emotion Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Identification of Websites Using an Efficient Method Employing Text Mining Methods
Madhur Taneja1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Research Gaps
System Model
Gathering Website Information & Preprocessing
Feature Extraction using CNN with LSTM
RESULTS AND DISCUSSION
Dataset Description
Hyper parameters Description
Description of results
CONCLUSION
REFERENCES
Machine Learning-based High-Dimensional Text Document Classification and Clustering
Ansh Kataria1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Background
Machine Learning-Based Text Classification
Preprocessing
Stop Words
Feature Engineering
Feature Clustering
Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions
Pratibha Sharma1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Problem Statement
Proposed Methodology
Skip-Gram
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Method for Adaptive Combination of Multiple Features for Text Classification in Agriculture
Jaskirat Singh1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Background
Text Classification using Bi-GRU & CNN
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Deep Learning-based Text-Retrieval System with Relevance Feedback
Simran Kalra1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Research Gaps
System Model
ConvNets
Example Scenario:
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Domain Knowledge-based BERT Model with Deep Learning for Text Classification
Akhilesh Kalia1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Problem Formulation
System Model
Bi-GRU for text classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Applying Deep Learning to Classify Massive Amounts of Text Using Convolutional Neural Systems
Shubhansh Bansal1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
CNN
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
An Algorithm for Categorizing Opinions in Text from Various Social Media Platforms
Pavas Saini1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Overview
Feature Extraction
Multimodal Sentiment Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Text Classification Method for Tracking Rare Events on Twitter
Prabhjot Kaur1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Research Gaps
Dataset
Data Preprocessing
Feature Extraction and Classification
RESULTS AND DISCUSSION
Datasets
CONCLUSION
REFERENCES
Text Document Preprocessing and Classification Using SVM and Improved CNN
Jaspreet Sidhu1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
CNN with SVM for Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Identification of Text Emotions Through the Use of Convolutional Neural Network Models
Vaibhav Kaushik1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Preprocessing
CNN
Convolution Layer
Max Combining Layer
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Classification & Clustering of Text Based on Doc2Vec & K-means Clustering based Similarity Measurements
Prakriti Kapoor1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Data Preparing
Document Demonstration
Document Clustering
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Categorization of COVID-19 Twitter Data Based on an Aspect-Oriented Sentiment Analysis and Fuzzy Logic
Tarang Bhatnagar1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Data Mining of Tweets
Preprocessing and Labeling
Text Classification
Outcomes and Discussion
CONCLUSION
REFERENCES
Feature-Level Sentiment Analysis of Data Collected through Electronic Commerce
Preetjot Singh1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Overview
Customer Reviews
Parts-of-Speech tagging
Feature Extraction
Feature Pruning
Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Classification Algorithms for Evaluating Customer Opinions using AI
Saniya Khurana1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Collection and Preprocessing of Data 3.1
Feature Extraction Methods
Text Classification Methods
SVM
Artificial Neural Networks
Naive Bayes
Decision Trees
C4.5. Decision Tree Classifier
KNN
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Analysis of Sentiment Employing the Word2vec with CNN-LSTM Classification System
Rajat Saini1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
In-Depth Information Gathering 3.1.1
Data Preprocessing
Text Classification using CNN-LSTM
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Hadoop-based Twitter Sentiment Analysis Using Deep Learning
Manpreet Singh1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Overview
Sentiment Analysis using Hadoop
RESULTS AND DISCUSSION
Testing environment
Performance metrics
CONCLUSION
REFERENCES
A Contrast Between Bert and Word2vec's Approaches to Text Sentiment Analysis
Manish Nagpal1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Text Preprocessing
Tokenization
Removal and corrections
Replacement
PoS tagging
Word Embedding
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Text Emotion Categorization Using a Convolutional Recurrent Neural Network Enhanced by an Attention Mechanism-based Skip-Gram Method
Madhur Grover1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Research Gaps
Skip Gram Model for Text Classification
Attention-based CNN
Attention Maps Estimation Issue
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Multimodal Sentiment Analysis in Text, Images, and GIFs Using Deep Learning
Deepak Minhas1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Dataset
Multimodal Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques
Abhinav Mishra1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Overview
Dataset Description
Data Preprocessing, Handling, and Tokenization
The VADER Emotion Analyzer
Feature Extraction and Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
CNN-based Deep Learning Techniques for Movie Review Analysis of Sentiments
Prateek Garg1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
CNN for Movies Review Classification
RESULTS AND DISCUSSION
Data Collection
Data Normalization
CONCLUSION
REFERENCES
Machine Learning and Deep Learning Models for Sentiment Analysis of Product Reviews
Saket Mishra1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Data Collection and Processing
Vocabulary Development
DL Models
Proposed DL Model
RESULTS AND DISCUSSION
Accessing the Amazon Customer Reviews Dataset
CONCLUSION
REFERENCES
Sentiment Analysis of Hotel Reviews Based on Deep Learning
Jagmeet Sohal1,*
INTRODUCTION
Contributions
RELATED WORK
PROPOSED WORK
System Model
Brief Outline
Text Preprocessing
Stage 1 - Data Assortment
Stage 2 - Sentimentality Gloss
Stage 3 - Text Cleansing
LSTM-GRU for Text Classification
RESULTS AND DISCUSSION
Dataset Description
CONCLUSION
REFERENCES
Utilizing Machine Learning for Natural Language Processing to Conduct Sentiment Analysis on Twitter Data in Multiple Languages
Rahul Mishra1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Research Gaps
System Model
LSTM for Tweets Classification
Components of LSTMs
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
The Use of Machine Learning to Analyze the Sentiment for Social Media Networks
Darleen Grover1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Collecting Initial Data
Preprocessing Phase
Word Embedding
Tweets Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Sentiment Classification of Textual Content using Hybrid DNN and SVM Models
Abhishek Singla1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Feature Engineering Model
Sentiment Lexicon Layer
BERT Model
Hybrid DNN for Classification
RESULTS AND DISCUSSION
Dataset Description
Baseline Methods
CONCLUSION
REFERENCES
Big Data Analysis and Information Quality: Challenges, Solutions, and Open Problems
Sahil Suri1,*
INTRODUCTION
LITERATURE REVIEW
PROPOSED MODEL
Problem Formulation
Proposed Methodology
Big Data Processing Steps
Big data quality challenges and issues
Best practices for managing big data quality
EXPERIMENTAL RESULTS
CONCLUSION
REFERENCES
Using Deep Learning Techniques to Detect Traffic Information in Social Media Texts
Sourav Rampal1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
Data Collection and Text Pre-processing
Feature Extraction and Word Embedding
Text Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Deep Sentiment Classification in COVID-19 Using LSTM Recurrent Neural Network
Jatin Khurana1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Overview
Preparing the Input Data
Removing Noise and Stop-Words
Classification
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Machine Learning-Based Data Preprocessing as well as Visualization Techniques for Predicting Students' Tasks
Pratik Mahajan1,*
INTRODUCTION
LITERATURE REVIEW
PROPOSED MODEL
Problem Formulation
Proposed Methodology
Data Preprocessing
Quality Data
Data Processing Task
ML for Placement Prediction
EXPERIMENTAL RESULTS
CONCLUSION
REFERENCES
The Prediction of Faults Using Large Amounts of Industrial Data
Jagtej Singh1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
System Model
CNN Model
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Comparison Analysis of Logical Regression and Random Forest with Word Embedding Techniques for Twitter Sentiment Analysis
Dhiraj Singh1,*
INTRODUCTION
LITERATURE REVIEW
PROPOSED WORK
Data Collection, Preparation and Cleaning
Vectorization
Vectors of Words
Text Classification Models
RESULTS AND DISCUSSION
The Logistic Regression of TF-IDF
Word2Vec Logistic Regression
TF-IDF Random Forest
Word2Vec's Random Forest
CONCLUSION
REFERENCES
The Classification of News Articles Through the Use of Deep Learning and the Doc2Vec Modeling
Himanshu Makhija1,*
INTRODUCTION
RELATED WORK
TECHNIQUES AND MATERIALS
TECHNIQUES AND MATERIALS
Database Description
Doc2Vec
Naive Bayes
Gauss Naive Bayes
Random Forest
Support Vector Machine
Convolutional Neural Network (CNN)
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Investigating the Utility of Data Mining for Automated Credit Scoring
Amarpal Yadav1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Genetic Algorithm
General view of the proposed model
The proposed methodology
RESULTS AND DISCUSSION
Running time
DISCUSSION
CONCLUSION
REFERENCES
Investigating the Use of Data Mining for Knowledge Discovery
Sover Singh Bisht1,*
INTRODUCTION
RELATED WORK
PROPOSED WORK
Graph Construction
Data Retrieval from Constructed Graph
RESULTS AND DISCUSSION
Analysis of Retrieved Data 4.2
CONCLUSION
REFERENCES
Exploring the Role of Big Data in Predictive Analytics
T. R. Mahesh1,2,*
INTRODUCTION
RELATED WORKS
PROPOSED WORK
Data Sources and Populations
A Machine Learning Analysis of the Fundamental model
Healthy Habits
Long-term Care
EXPERIMENTAL ANALYSIS
CONCLUSION
REFERENCES
Implementing Automated Reasoning in Natural Language Processing
N. Sengottaiyan1,* and Rohaila Naaz2
INTRODUCTION
RELATED WORK
PROPOSED WORK
Convolutional Neural Networks
CNN-Based Text Classification
MapReduce-CNN
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
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Demystifying Emerging Trends in Machine Learning

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