Artificial Intelligence: Theory and Applications

Name: Artificial Intelligence: Theory and Applications | Proceedings of AITA 2023, Volume 1
Brand: Springer
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Proceedings of AITA 2023, Volume 1

Harish Sharma Antorweep Chakravorty Shahid Hussain Rajani Kumari(Editor)

Springer (Publisher)

1st Edition

Published on 27. February 2024

XIX, 489 pages

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978-981-99-8476-3 (ISBN)

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Harish Sharma is an associate professor at Rajasthan Technical University, Kota, in Department of Computer Science & Engineering. He has worked at Vardhaman Mahaveer Open University, Kota, and Government Engineering College Jhalawar. He received his B.Tech. and M.Tech. degrees in Computer Engg. from Government Engineering College, Kota, and Rajasthan Technical University, Kota, in 2003 and 2009, respectively. He obtained his Ph.D. from ABV-Indian Institute of Information Technology and Management, Gwalior, India. He is the secretary and one of the founder members of Soft Computing Research Society of India. He is a lifetime member of Cryptology Research Society of India, ISI, Kolkata. He is an associate editor of International Journal of Swarm Intelligence (IJSI) published by Inderscience. He has also edited special issues of the many reputed journals like Memetic Computing , Journal of Experimental and Theoretical Artificial Intelligence , Evolutionary Intelligence , etc. His primary area of interest is nature inspired optimization techniques. He has contributed in more than 105 papers published in various international journals and conferences.

Dr. Antorweep Chakravorty is an associate professor at the University of Stavanger. His current research and development work is in the field of applied Blockchains, Big Data, Large-Scale Machine Learning, and Data Privacy. He has an interest in real-world problems, especially development of privacy enabled data-driven services in smart energy, health care, and smart city domains. Antorweep completed his Ph.D. in 2015 with a thesis on Privacy Preserving Big Data Analytics at the University of Stavanger, Norway. Along with having a background in applied research in data-driven solutions, he is also involved in mentoring, teaching, and supervision.

Dr. Shahid Hussain is working at University of Canberra as an associate professor of Biomedical Robotics. Prior to that, he has workedas a lecturer at University of Wollongong, Australia. Dr. Hussain has obtained his Ph.D. in Mechanical Engineering from the University of Auckland, New Zealand, in 2013. His research interests include assistive and rehabilitation robotics, compliant actuation of robots, robot mechanism design and optimization, nonlinear dynamics and control of robotic systems, human-robot interaction, biomechanical modeling, engineering education, and micro-electro-mechanical systems (MEMS). Dr. Hussain has published more than 65 papers in the prestigious journals of the field.

Dr. Rajani Kumari is currently an assistant professor at IBS, Bangalore, Off-Campus Centre of ICFAI Foundation for Higher Education (IFHE) University, India. Previously she was an assistant professor at IIIM, Jaipur, and St. Xavier's College Jaipur, CHRIST University. She received the Ph.D. degree in computer science in 2015, the M.C.A. and B.C.A. from University of Rajasthan in 2010 and 2006, respectively. She has published more than forty research papers in various international journals/conferences and participated in many national and international conferences and workshops. She edited some special issue in Taylor & Francis and Inderscience journals including Journal of Information and Optimization Sciences (JIOS) and International Journal of Intelligent Information and Database Systems (IJIIDS) . Her research interests include Nature-Inspired Algorithms, Swarm Intelligence, Soft Computing, and Computational Intelligence.

Content

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
1 Introduction
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
References
Kernel Methods for Conformal Prediction to Detect Botnets
1 Introduction
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
References
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
5 Conclusion
References
Volume of Imbalance Container Prediction using Kalman Filter and Long Short-Term Memory
1 Introduction
2 Problem Statement
3 Research Questions
4 KALSTM: A Hybrid Model
5 Results and Limitations
6 Conclusion
References
Modelling Stock Prices Prediction with Long Short-Term Memory (LSTM): A Black Box Approach
1 Introduction
2 Methodology Based on LSTM
3 Description of Datasets
4 Results and Discussions
5 Conclusion and Future Work
References
Agricultural Crop Yield Prediction for Indian Farmers Using Machine Learning
1 Introduction
2 Literature Survey
3 Methodology
3.1 Dataset
3.2 Methodology
4 Architecture
5 Result Analysis
6 Conclusion
References
Application of Artificial Intelligence on Camera-Based Human Pose Prediction for Yoga: A Methodological Study
1 Introduction
1.1 Scope
1.2 Challenges
1.3 Impact of Yoga [1]
2 Literature Review
3 Methodology
3.1 Research Process
3.2 Key Point Detection Methods
3.3 Implementation Methodology [12, 13]
4 Datasets and Metrics
5 Results
6 Conclusion
7 Future Potential Development
References
Predicting of Credit Risk Using Machine Learning Algorithms
1 Introduction
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
References
Study of Various Text Summarization Methods
1 Introduction
2 Literature Review
3 Overview of Proposed Model
3.1 Proposed Methodology
3.2 Design of Model Architecture
3.3 Model Evaluation
4 Results
5 Conclusion
References
Investigations on Deep Learning Pre-trained Model VGG-19 Using Transfer Learning for Remote Sensing Image Classification on Benchmark Datasets
1 Introduction
2 Literature Review
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
References
Complexity Analysis of Legal Documents
1 Introduction
2 Related Works
2.1 NER for Indian Legal Documents
2.2 Information Extraction
2.3 Summarising in Legal Domain
2.4 Complexity of Legal Documents
3 Methodology
3.1 Proposed Model
3.2 Analysis of Complexity
4 Result Analysis
5 Conclusion and Future Works
References
Predicting Virality of Tweets Using ML Algorithms and Analyzing Key Determinants of Viral Tweets
1 Introduction
2 Theoretical Background and Related Work
3 Methodology
4 Results and Discussion
5 Conclusion, Limitations, and Future Scope
References
Review of Classification and Detection for Insects/Pests Using Machine Learning and Deep Learning Approach
1 Introduction
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
4 Conclusion
References
Sentiment Analysis of Product Reviews Using Deep Learning and Transformer Models: A Comparative Study
1 Introduction
2 Literature Review
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
6 Conclusion
References
Effect of Variation in Pause Times Over MANET Routing Protocols
1 Introduction
2 MANET Routing Protocols and Literature Review
3 Environment Setup
4 Performance Metrics
5 Conclusions and Future Scope
References
DDCMR2: A Deep Detection and Classification Model with Resizing and Rescaling for Plant Disease
1 Introduction
2 Literature Review
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
References
Leveraging Natural Language Queries for Effective Video Analysis
1 Introduction
2 Related Work
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
5 Conclusion
References
An Experimental Study to Perform Bioinformatics Based on Heart Disease Case Study Using Supervised Machine Learning
1 Introduction
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
5 Conclusion
References
Empirical Analysis of Denoising Algorithms for CCTV Face Images
1 Introduction
2 Related Work
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
7 Conclusion
References
Content-Based Tagging and Recommendation System for Tamil Songs Based on Text and Audio Input
1 Introduction
2 Literature Survey
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
References
Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach
1 Introduction
2 Literature Review
3 Proposed Methodology
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
4.3 RR Database
4.4 Analysis
5 Conclusion
References
Classification of Dementia Detection Using Hybrid Neuro Multi-kernel SVM (NMKSVM)
1 Introduction
2 Existing Techniques of Classification
3 Materials and Methods
3.1 Data Description
3.2 Classifier
4 Performance Metrics
5 Results and Discussion
6 Conclusion
References
A Literature Review on Monitoring and Control Strategies in Smart Agriculture Using IoT
1 Introduction
2 Literature Review
3 Smart Farming System
4 Soil Moisture and Water Level Monitoring
5 Sensors
6 Issues and Challenges in Smart Agriculture
7 Proposed Technique
8 Discussion and Analysis
9 Conclusions and Future Directions
References
Speech Emotion Recognition Using Deep Learning
1 Introduction
2 Literature Survey
3 Proposed System
4 Methodology
4.1 Data Collection
4.2 Data Pre-processing
4.3 Deep Learning Algorithms
4.4 Evaluation Parameters
5 Implementation and Results
5.1 CNN
5.2 GRU
5.3 DANN
5.4 TCN 25
5.5 TCN 40
5.6 TCN 58
6 Conclusion
References
Real-Time Inferencing Using Transfer Learning for a Screening of Depression Detection Using Actigraphy
1 Introduction
2 Related Work
3 Methods
3.1 Data Collection-Primary and Secondary
3.2 Problem Construct and Target Variable
3.3 Feature Engineering
3.4 Modeling Approach
3.5 Deployment
4 Results and Discussion
5 Conclusion and Future Work
References
Offline Signature Verification Using Neural Network Technology
1 Introduction
2 Literature Review
3 Concepts and Methodology
3.1 Dataset
3.2 CNN Algorithm
3.3 Feature Extraction
3.4 Columns Separations
3.5 Loss Function
4 Experiments
4.1 Data Preparation and Preprocessing
4.2 Model Architecture
4.3 Networks Training and Validation
5 Results and Discussion
5.1 Discussion
6 Conclusion and Future Work
References
Decision Tree-Based Electricity Optimization Using Intelligent Appliance Controller
1 Introduction
2 Literature Survey
3 Proposed System
4 Implementation Details
5 Result
6 Conclusions and Future Scope
References
Aruco Marker-Based Pick and Place Approach Using a UR5 Robotic Arm and Vacuum Gripper
1 Introduction
2 Aruco Markers
2.1 Encoding
2.2 Decoding
2.3 Pose Estimation
3 Robotic Operating System and Motion Planning of UR5 Robotic Arm
3.1 Simulation Setup
3.2 UR5 Robotic Arm
4 Working and Application
5 Conclusion
References
Smart Watch Assisted Multi-disease Detection Using Machine Learning: A Comprehensive Survey
1 Introduction
2 Literature Survey
2.1 Selection Criteria for Survey Papers
2.2 Objectives of Literature Survey
2.3 Algorithm and Methodologies Used
2.4 Data Flow
2.5 Challenges
3 Conclusion
References
A Comparative Review of Convolutional Neural Networks, Long Short-Term Memory, and Recurrent Neural Networks in Recommendation Systems
1 Introduction
1.1 A Research Gaps and Challenges in Deep Learning Model-Based Recommendation Systems
2 Literature Review
2.1 Deep Learning for Recommender Systems
2.2 Convolution Neural Networks (CNNs)
2.3 Long Short-Term Memory (LSTM)
2.4 Recurrent Neural Networks (RNNs)
3 Discussion
4 Conclusion and Future Direction of Research
References
In Silico Molecular Docking Analysis of Myricetin, Fisetin, and Kaempferol Against Spike Protein SARS-CoV-2 Omicron: Opening Possibilities for the Drug Discovery Against SARS-CoV-2 Omicron
1 Introduction
2 Material and Methods
2.1 Docking Interaction Software
2.2 Three-Dimensional Structure Preparation
2.3 Preparation of Ligands with Active Site Determination
2.4 Molecular Docking Research
3 Results and Discussion
4 Conclusions
References
Study on the Potential of E-Commerce in Growing India and Its Use in a Green Initiative
1 Introduction
2 Literature Review
3 Methodology
4 Figures and Tables
5 Results and Findings
5.1 A Great Concern
5.2 Big Social Change in India
5.3 Potential of India
5.4 Covid-19
5.5 Covid Brought Pandemic as Well as Opportunities
5.6 India and ASEAN
5.7 Safety of Consumers
5.8 Green Marketing
5.9 Potential Toward a Green Cause
6 Conclusion and Scope
References
Big Mart Sales Prediction Using Machine Learning
1 Introduction
2 Problem Statement and Proposed System
2.1 Proposed System
2.2 Hypotheses About Factors Affecting Sales
2.3 Produce-Level Hypotheses
3 Background Knowledge and Related Work
4 Proposed Methodology
4.1 Linear Regression
4.2 XGBoost Regressor
4.3 Random Forest
4.4 Data Exploration
4.5 Data Cleaning
4.6 Feature Engineering
5 Experimental Result Analysis and Discussions
5.1 Dataset Description
5.2 Result Discussions
6 Conclusion and Future Works
References
Enriching Big Data Intrusion Detection and Service Through Mapping and Parallel Computation
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Result and Discussions
5 Conclusion and Future Scope
References
Stock Market Prediction Using ML Module
1 Introduction
2 Related Work
2.1 Motivation
3 Methodology
4 Implementation
4.1 Stock Market Forecast
4.2 Experimental Results
5 Conclusion
References
Rainfall Prediction Using Fuzzy Systems
1 Introduction
2 Related Work
3 Experiments and Result Analysis
4 Conclusion
References
Enhancing Customer Experience: Exploring Deep Learning Models for Banking Customer Journey Analysis
1 Introduction
2 Literature Studies
3 Data and Methodology
4 Results
5 Discussions and Conclusions
References
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Artificial Intelligence: Theory and Applications

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