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Details
Table of Contents
Intro
DoSCI-2022 Steering Committee Members
Preface
Contents
Editors and Contributors
TransCRF-Hybrid Approach for Adverse Event Extraction
1 Introduction
1.1 Motivation
1.2 Contributions
2 Related Work
3 Proposed Method
3.1 Preprocessing and Feature Engineering
3.2 Phrase Extraction
4 Experimental Results
4.1 Datasets
4.2 Metrics
4.3 Evaluation
5 Conclusions
References
Process of Recognition of Plant Diseases by Using Hue Histogram, K-Means Clustering and Forward-Propagation Deep Neural Networks
1 Introduction
2 Related Works
3 Proposed Methodology
3.1 Preprocessing
3.2 Hue-Based Disease Spot Identification
3.3 Hue Histogram and K-means-Based Segmentation
3.4 Features Extraction
3.5 Disease Classification Using FPNN
4 Experimental Results
4.1 Dataset
4.2 Result of Segmentation Algorithms
4.3 Neural Network Performance
5 Conclusions
References
Analysis of Variants of BERT and Big Bird on Question Answering Datasets in the Context of Scientific Research Article Reviews
1 Introduction
2 Related Works
3 BERT and Big Bird a Quick View
3.1 BERT
3.2 Big Bird
3.3 Question Answering Datasets
4 Evaluation and Comparison of Various BERT and Big Bird Models
5 Conclusions and Observations
References
Twitter Opinion Mining on COVID-19 Vaccinations by Machine Learning Presence
1 Introduction
1.1 Valence Aware Dictionary and Sentiment Reasoner (VADER)
1.2 Word to Vector (W-V)
1.3 Term Frequency-Inverse Document Frequency (TF-IDF)
2 Background Study
3 Methodologies
3.1 Data Collection (DC)
3.2 Text Preparation (TP)
3.3 Feature Engineering (FE) and Exploratory Data Analysis (EDA)
3.4 Sentiment Analysis (SA)/Opinion Mining (OM)
3.5 Sentiment Analysis (SA)/Opinion Mining (OM) with VADER
3.6 Lexicon-based Models
3.7 Function to Clean and Remove Noise
3.8 Summary of VADER Lexicon Analysis (SVLA)
3.9 Analysis of Time Based
3.10 Inference Based on Probability
3.11 VADER Sentiment Analysis
3.12 Exploratory Data Analysis
3.13 Probabilistic Inference (PI)
3.14 Analysis of Correlation
3.15 Analyses of Users
3.16 Analysis of Word Decomposition
3.17 Long Short-term Memory (LSTM) with Confusion Matrix
3.18 Matrix of Confusion
4 Conclusion
DoSCI-2022 Steering Committee Members
Preface
Contents
Editors and Contributors
TransCRF-Hybrid Approach for Adverse Event Extraction
1 Introduction
1.1 Motivation
1.2 Contributions
2 Related Work
3 Proposed Method
3.1 Preprocessing and Feature Engineering
3.2 Phrase Extraction
4 Experimental Results
4.1 Datasets
4.2 Metrics
4.3 Evaluation
5 Conclusions
References
Process of Recognition of Plant Diseases by Using Hue Histogram, K-Means Clustering and Forward-Propagation Deep Neural Networks
1 Introduction
2 Related Works
3 Proposed Methodology
3.1 Preprocessing
3.2 Hue-Based Disease Spot Identification
3.3 Hue Histogram and K-means-Based Segmentation
3.4 Features Extraction
3.5 Disease Classification Using FPNN
4 Experimental Results
4.1 Dataset
4.2 Result of Segmentation Algorithms
4.3 Neural Network Performance
5 Conclusions
References
Analysis of Variants of BERT and Big Bird on Question Answering Datasets in the Context of Scientific Research Article Reviews
1 Introduction
2 Related Works
3 BERT and Big Bird a Quick View
3.1 BERT
3.2 Big Bird
3.3 Question Answering Datasets
4 Evaluation and Comparison of Various BERT and Big Bird Models
5 Conclusions and Observations
References
Twitter Opinion Mining on COVID-19 Vaccinations by Machine Learning Presence
1 Introduction
1.1 Valence Aware Dictionary and Sentiment Reasoner (VADER)
1.2 Word to Vector (W-V)
1.3 Term Frequency-Inverse Document Frequency (TF-IDF)
2 Background Study
3 Methodologies
3.1 Data Collection (DC)
3.2 Text Preparation (TP)
3.3 Feature Engineering (FE) and Exploratory Data Analysis (EDA)
3.4 Sentiment Analysis (SA)/Opinion Mining (OM)
3.5 Sentiment Analysis (SA)/Opinion Mining (OM) with VADER
3.6 Lexicon-based Models
3.7 Function to Clean and Remove Noise
3.8 Summary of VADER Lexicon Analysis (SVLA)
3.9 Analysis of Time Based
3.10 Inference Based on Probability
3.11 VADER Sentiment Analysis
3.12 Exploratory Data Analysis
3.13 Probabilistic Inference (PI)
3.14 Analysis of Correlation
3.15 Analyses of Users
3.16 Analysis of Word Decomposition
3.17 Long Short-term Memory (LSTM) with Confusion Matrix
3.18 Matrix of Confusion
4 Conclusion