Machine Learning, Image Processing, Network Security and Data Sciences : 5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers.

Format:

Book

Author/Creator:

Chauhan, Naveen.

Contributor:

Yadav, Divakar.

Verma, Gyanendra K.

Soni, Badal.

Lara, Jorge Morato.

Series:

Communications in Computer and Information Science Series

Communications in Computer and Information Science Series ; v.2128

Language:

English

Physical Description:

1 online resource (372 pages)

Edition:

1st ed.

Place of Publication:

Cham : Springer International Publishing AG, 2024.

Contents:

Intro

Preface

Organization

Contents

Machine Learning

SynText - Data Augmentation Algorithm in NLP to Improve Performance of Emotion Classifiers

1 Introduction

2 Literature Review

2.1 Emotion Taxonomy

2.2 Benchmark Datasets

2.3 Data Augmentation

3 Methodology

4 Experimentation

5 Conclusion

6 Future Scope

References

Internet of Medical Things: Empowering Mobility and Health Monitoring with a Smart Walking Stick

2 Related Works

3 Material and Design

4 Material and Methods

4.1 Fall Detection and Step Count

4.2 Heart Rate and SpO2 Measurement

4.3 Smart Home Control

4.4 Stopwatch

4.5 Weather and Helpline

4.6 Stress Monitoring and Wi-Fi Reset

5 Results and Discussion

6 Conclusion

MRI Based Spatio-Temporal Model for Alzheimer's Disease Prediction

2 Related Work

3 Proposed Methodology

3.1 Dataset

3.2 Spatio-Temporal Model

4 Results and Discussion

4.1 ConvLSTM

4.2 ConvLSTM with Other Spatio-Temporal Model

4.3 ConvLSTM with State of the Art

Comparative Analysis of Economy-Based Multivariate Oil Price Prediction Using LSTM

2 Literature Survey

3 Proposed Method

3.1 Data Collection

3.2 Data Pre-processing and Exploratory Data Analysis (EDA)

3.3 Model Training

3.4 Model Training

Deep Learning Based EV's Charging Network Management

2.1 EV Charging Stations

2.2 State of Charge (SoC)

3.1 Deploy EV Charging Station

3.2 Optimal Path to the EV Charging Station

3.3 SoC Estimation

4 Result

5 Conclusions

Crop Yield Prediction Using Machine Learning Approaches

1 Introduction.

2 Related Works

3 Proposed Work

3.2 Data Preprocessing

3.3 Model Selection

3.4 Evaluation

4 Results

6 Future Work

Detection and Classification of Waste Materials Using Deep Learning Techniques

3 Proposed Model

3.2 Pre Processing and Augmentation

3.3 Evaluation Metrics

3.4 Waste Garbage Detection Algorithms

4 Result and Simulation

4.1 SSD MobileNet

4.2 EfficientDet-D0

4.3 YOLOv7 and YOLOv8

4.4 Comparison of Model

A Comparative Analysis of ML Based Approaches for Identifying AQI Level

1.1 Arrangement of the Paper

3 Materials and Methods

3.1 Accumulation of Data and Dataset Description

3.2 Pre-processing of Data

3.3 Different ML Models

4.1 Experimental Result Analysis and Discussion

5 Conclusion and Future Scope

Marker-Based Augmented Reality Application in Education Domain

2 AR Approaches

3 Related Work

4 Proposed Solution

4.1 Development Architecture

5 Implementation

5.1 Building a Raw Mesh on the Marker Image and Marker Detection

5.2 Feature Extraction Using Vuforia Image Scanner

5.3 Implementing Virtual Buttons with C# Scripting

5.4 Creating 3D Models with Blender

6 Results

7 Conclusion

Hate Speech Detection Using Machine Learning and Deep Learning Techniques

2 Definitions and Taxonomy

3 Comprehensive Review of the Literature

3.1 Fact-Finding Process

3.2 Sources

3.3 Study Method Criteria

3.4 Research Focus

4 Challenges in Defining and Categorizing Hate Speech and Detection with ML/DL

4.1 Personalization and Explanation

4.2 Evolving Language.

4.3 Legal and Cultural Variations

4.4 Subtlety and Micro-aggression

4.5 Data Quality and Labeling

4.6 Data Imbalance

4.7 Multilingual and Multi Modal Content

4.8 Evolution of Hate Speech

4.9 Adversarial Attacks

4.10 Privacy Concerns

4.11 Bias and Fairness

4.12 Real-Time Detection

4.13 Scalability

4.14 User Behavior

4.15 Legal and Ethical Considerations

4.16 Intersectionality

4.17 Ambiguity

4.18 Freedom of Speech

4.19 Digital Evolution

4.20 Diverse Expressions

5 Hate Speech Detection Datasets

6 Machine Learning-Based Approaches

6.1 Data Preprocessing

6.2 Feature Extraction

6.3 Classification Algorithms

6.4 Ensemble Methods

6.5 Cross-Validation

7 Deep Learning-Based Approaches

7.1 Convolutional Neural Networks (CNNs)

7.2 Recurrent Neural Networks (RNNs)

7.3 Transformers

8 Evaluation Metrics

9 Result and Discussion

10 Conclusion

Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website

2.1 Whitelist-Based Techniques

2.2 Blacklist-Based Techniques

2.3 Content-Based Techniques

2.4 URL-Based Techniques

3.1 1D Convolutional Neural Network (1D-CNN)

3.2 FeedForward-Convolutional Neural Network (FF-CNN)

4 Dataset and Pre-processing

5 Experimentation and Results

5.1 Performance Measures

5.2 Experiment 1: Comparison of Performance of Proposed 1D-CNN-based Approach on Different Datasets

5.3 Experiment 2: Comparison of the Performance of Proposed 1D-CNN-based Approach with PCA and Without PCA

5.4 Experiment 3: Comparison of Performance of the proposed FF-CNN-based Approach on Different Datasets

5.5 Experiment 4: Comparison of Performance of the Proposed 1D-CNN-based Approach and FF-CNN-based Approach.

5.6 Comparison Proposed 1D-CNN-based Approach and FF-CNN-based Approach with Other ML Models

Diabetes Prediction Using Machine Learning Classifiers

3 Dataset

4 Results and Discussions

A Deep Learning Method for Obfuscated Android Malware Detection

3.1 Adversarial Sample Generation

3.2 Autoencoder

3.3 LSTM Autoencoder

3.4 Image Based Autoencoder

3.5 Web Application

4.1 Dataset Description

4.2 LSTM Based Autoencoder

4.3 Image-Based Autoencoder

5 Result Comparisons

5.1 Non-adversarial Training

5.2 Adversarial Training

Code-Mixed Language Understanding Using BiLSTM-BERT Multi-attention Fusion Mechanism

1.1 Contributions

3.1 Problem Definition

3.2 BiLSTM Attention Mechanism for Code-Mixed Intent Classification and Slot Filling

3.3 mBERT Code-Mixed Domain Knowledge Adaption

3.4 Multi-head Query Attention Mechanism

4 Result Analysis

4.1 Baseline Methods

The Potential of 1D-CNN for EEG Mental Attention State Detection

3.1 Dataset Selection and Description

3.2 Pre-processing

3.3 The Application of Machine Learning Models

4 Results Discussion

Potato Leaf Disease Classification Using Deep Learning Model

2 Motivation

3 Literature Review

4 Problem Statement

5 Methodology

5.1 Dataset

5.2 Data Splitting

6 Model Architecture

6.1 Convolutional Neural Network

7 Results and Discussion

7.1 Model Evaluation.

7.2 Model Predictions

8 Conclusion

Breast Cancer Detection: An Evaluation of Machine Learning, Ensemble Learning, and Deep Learning Algorithms

3.2 Data Pre-processing

3.3 Apply Learning Algorithms

3.4 Evaluation Criteria

3.5 Training Data and Testing Data

4 Result and Discussion

4.1 Results of Machine Learning Models

4.2 Results of Ensemble Learning Models

4.3 Results of Deep Learning Model

Advancements in Facial Expression Recognition: A Comprehensive Analysis of Techniques

2 Background

3 Methods for Facial Expression Recognition

3.1 Traditional Methods

3.2 Deep Learning Methods

3.3 Hybrid Methods

4 Models Used for Facial Expression Recognition

4.1 Model 1: ResNet-50

4.2 Model 2: FERNet

4.3 Model 3: Attentional Convolutional Network

5 Performance Metrics and Evaluation

5.1 ResNet-50

5.2 FERNet

5.3 Attentional Convolutional Network

6 Comparative Analysis

6.1 Model Architectures

6.2 Training Approaches

6.3 Computational Efficiency

6.4 Robustness and Generalization

7 Current Implementations

9 Future Scope

Image Processing

Sparse Representation with Residual Learning Model for Medical Image Classification

2.1 Dictionary Learning

2.2 ResNet

3 The Proposed Method

3.1 Dictionary Learning and Sparse Representation

3.2 Residual-CNN Network Features

3.3 Dimensionality Reduction with PCA

3.4 Deep Neural Network (DNN) for Classification

4 Experimental Results

4.1 Description of Datasets

4.2 System Implementation

4.3 Results and Analysis

5 Ablation Study

References.

COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Chauhan, Naveen Machine Learning, Image Processing, Network Security and Data Sciences

ISBN:

9783031622175