Artificial Intelligence: Theory and Applications : Proceedings of AITA 2023, Volume 1 / edited by Harish Sharma, Antorweep Chakravorty, Shahid Hussain, Rajani Kumari.

Format:

Book

Contributor:

Sharma, Harish, editor.

Series:

Lecture Notes in Networks and Systems, 2367-3389 ; 843

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Big data.

Computational Intelligence.

Artificial Intelligence.

Big Data.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Big Data.

Physical Description:

1 online resource (495 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This book features a collection of high-quality research papers presented at International Conference on Artificial Intelligence: Theory and Applications (AITA 2023), held during 11–12 August 2023 in Bengaluru, India. The book is divided into two volumes and presents original research and review papers related to artificial intelligence and its applications in various domains including health care, finance, transportation, education, and many more.

Contents:

Intro

Preface

Contents

Editors and Contributors

Control Techniques for Vision-Based Autonomous Vehicles for Agricultural Applications: A Meta-analytic Review

1 Introduction

2 State-of-The Art Studies

2.1 Target Detection in Autonomous Vehicle System

2.2 Vision-Based System

3 Mathematical Modeling of Autonomous System

4 Conclusion

References

Co-GA: A Bio-inspired Semi-supervised Framework for Fake News Detection on Scarcely Labeled Data

2 Related Work

2.1 Supervised Fake News Detection Using Linguistic Content

2.2 Semi-supervised Fake News Detection Using Linguistic Content

2.3 Metaheuristics-Based Approaches for Feature Selection

2.4 Metaheuristics-Based Fake News Detection

3 Data

4 Proposed Methodology

4.1 Pre-processing

4.2 Feature Extraction

4.3 Bio-inspired Feature Selection

4.4 Multi-view Co-training Model

5 Results and Analysis

6 Future Research Directions

7 Conclusion

Kernel Methods for Conformal Prediction to Detect Botnets

2 Related Works

2.1 Signature-Based and Heuristic-Based Botnet Detection

2.2 Machine Learning for Botnet Detection

2.3 Kernel Methods

2.4 Conformal Prediction

2.5 Deep Learning and Graph-Based Approaches

2.6 Challenges and Limitations

2.7 Motivation for the Proposed Approach

2.8 Emerging Trends and Research Directions

3 Methodology

3.1 Kernel Methods

3.2 Conformal Prediction

3.3 Proposed Approach: Kernel Methods for Conformal Prediction

3.4 Evaluation Metrics

3.5 Experimental Setup

4 Results

4.1 Dataset Description

4.2 Experimental Setup

4.3 Experimental Results

4.4 Analysis of Results

5 Conclusion

Biogas Generation from Animal Waste: A Case Study of Village Wazirpur

1 Introduction.

2 Biogas Production from Animal Waste

2.1 Factors Affecting Biogas Production

2.2 Sensors for Determining the Parameters Affecting Biogas Production

3 Area Under Study

4 Cost Analysis and Electricity Production

Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory

2 Problem Statement

3 Research Questions

4 KALSTM: A Hybrid Model

5 Results and Limitations

6 Conclusion

Modelling Stock Prices Prediction with Long Short-Term Memory (LSTM): A Black Box Approach

2 Methodology Based on LSTM

3 Description of Datasets

4 Results and Discussions

5 Conclusion and Future Work

Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning

2 Literature Survey

3.1 Dataset

3.2 Methodology

4 Architecture

5 Result Analysis

Application of Artificial Intelligence on Camera-Based Human Pose Prediction for Yoga: A Methodological Study

1.1 Scope

1.2 Challenges

1.3 Impact of Yoga [1]

2 Literature Review

3.1 Research Process

3.2 Key Point Detection Methods

3.3 Implementation Methodology [12, 13]

4 Datasets and Metrics

5 Results

7 Future Potential Development

Predicting of Credit Risk Using Machine Learning Algorithms

2 Review of Literature

2.1 Machine Learning Algorithms

2.2 Development of Credit Risk Model

3 Data and Methodology

3.1 Data

3.2 Variables

3.3 Machine Learning Models and Evaluation Parameters

3.4 Evaluation Parameters

3.5 Methodology

4 Empirical Findings

5 Conclusions and Implications

Study of Various Text Summarization Methods.

1 Introduction

3 Overview of Proposed Model

3.1 Proposed Methodology

3.2 Design of Model Architecture

3.3 Model Evaluation

Investigations on Deep Learning Pre-trained Model VGG-19 Using Transfer Learning for Remote Sensing Image Classification on Benchmark Datasets

3 Comparison of Performance Metrics of Machine Learning Methods on the PatterNet Dataset

4 Utilizing Pre-trained Models for Transfer Learning

5 Transfer Learning with Pre-trained Models Based on the Baseline ImageNet Dataset

6 Overview of VGG-19

7 Enabling Efficient Feature Reuse and Information Flow in Deep Neural Networks for Superior Performance

8 Deep Learning Surpassing Traditional Machine Learning Techniques

9 Setting Up Experiments: Feature Extraction and Classification for Remote Sensing Images with a Pre-trained VGG-19 Model

9.1 Dataset Description

9.2 Assessment Metrics Utilized for Model Evaluation in Image Classification and Retrieval

9.3 Research Findings: Investigating Test Accuracy and Test Loss Scores on Benchmark Datasets Using VGG-19 Pre-trained Model

10 Summarizing the Feature Extraction with Transfer Learning Approach in Deep Learning

Complexity Analysis of Legal Documents

2.1 NER for Indian Legal Documents

2.2 Information Extraction

2.3 Summarising in Legal Domain

2.4 Complexity of Legal Documents

3.1 Proposed Model

3.2 Analysis of Complexity

4 Result Analysis

5 Conclusion and Future Works

Predicting Virality of Tweets Using ML Algorithms and Analyzing Key Determinants of Viral Tweets

2 Theoretical Background and Related Work

4 Results and Discussion.

5 Conclusion, Limitations, and Future Scope

Review of Classification and Detection for Insects/Pests Using Machine Learning and Deep Learning Approach

1.1 Pictorial Representation of Classification and Detection of Pests and Comparison Between ML and DL

2 Material

2.1 Dataset Collection

3 Literature Work

3.1 Review of Different Machine Learning and Deep Learning Techniques for the Classification of Pests

Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Study

3 Sentiment Analysis

3.1 Sentiment Analysis Based on Machine Learning

3.2 Sentiment Analysis Based on Deep Learning

3.3 Sentiment Analysis Based on Transformer-Based Models

4 Implementation

4.1 Dataset

4.2 Data Pre-processing

4.3 Classification Models

5 Results and Discussions

5.1 Hyper Parameters Used

5.2 Performance Evaluation

Effect of Variation in Pause Times Over MANET Routing Protocols

2 MANET Routing Protocols and Literature Review

3 Environment Setup

4 Performance Metrics

5 Conclusions and Future Scope

DDCMR2: A Deep Detection and Classification Model with Resizing and Rescaling for Plant Disease

3 Proposed Methodology and Implementation

3.1 Data Collection

3.2 Data Cleaning, Preprocessing, and Visualization

3.3 Cache, Shuffle, and Prefetch

3.4 Model Building

3.5 Hyperparameters Choice

4 Results and Discussion

5 Conclusion and Future Scope

Leveraging Natural Language Queries for Effective Video Analysis

3 Methodology and Models

3.1 Uni-Modal Encoder

3.2 Cross-Modal Encoder.

3.3 Query Generator

3.4 Query Decoder

4 Experimental Analysis and Outcomes

An Experimental Study to Perform Bioinformatics Based on Heart Disease Case Study Using Supervised Machine Learning

2 Preliminaries

2.1 Machine Learning

2.2 Logistic Regression

2.3 Decision Tree

2.4 Support Vector Machine

3 Experimentation

3.1 Data Provenance

3.2 Flow Diagram of This Study

3.3 Correlation Matrix

3.4 Logistic Regression

3.5 Support Vector Machine (SVM)

3.6 Decision Tree

4 Results and Analysis

Empirical Analysis of Denoising Algorithms for CCTV Face Images

3 BM3D (Block-Matching and 3D Filtering)

3.1 Collaborative Filtering: It Takes Four Steps

3.2 Aggregation

3.3 Wiener Filtering Step

4 KSVD (k-Singular Value Decomposition)

5 WNNM (Weighted Nuclear Norm Minimization)

6 Results and Discussion

Content-Based Tagging and Recommendation System for Tamil Songs Based on Text and Audio Input

3 Proposed Methodology

3.1 Music Segmentation

3.2 Instrument Recognition

3.3 Lyric Collection and Translation

3.4 Lyric Tagging

3.5 Audio Prompt

3.6 Similarity-Based Retrieval

4 Datasets

4.1 MUSDB18 Dataset

4.2 Tamil Songs

4.3 AudioSet

5 Outcomes

5.1 Metrics for Evaluation

5.2 Summary of Metrics

6 Conclusions and Future Work

Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach

3.1 Preprocessing

3.2 Feature Extraction (BSIF)

3.3 Feature Level Fusion

3.4 Score Level Fusion

4 Experimental Results and Discussion

4.1 GTAV Dataset.

4.2 FEI Face Database.

Notes:

Includes bibliographical references and index.

ISBN:

981-9984-76-9