
Demystifying Emerging Trends in Machine Learning
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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
- Subject Index
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