Artificial Intelligence: Theory and Applications

Name: Artificial Intelligence: Theory and Applications | Proceedings of AITA 2023, Volume 2
Brand: Springer
Price: 223.63 EUR
Availability: OnlineOnly

Proceedings of AITA 2023, Volume 2

Harish Sharma Antorweep Chakravorty Shahid Hussain Rajani Kumari(Editor)

Springer (Publisher)

1st Edition

Published on 2. January 2024

XVIII, 532 pages

E-Book

PDF with digital watermarking

System requirements

978-981-99-8479-4 (ISBN)

€223.63incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Persons

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 Engineering 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 worked as 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
Spam Email Image Detection Using Convolution Neural Network and Convolutional Block Attention Module
1 Introduction
2 Related Works
3 Convolution Neural Network
4 Convolutional Block Attention Model
5 Experimental Result
5.1 Dataset
5.2 Evaluation Metrics
5.3 Discussion
6 Conclusion
References
Identifying Fake Twitter Trends with Deep Learning
1 Introduction
2 Related Work
2.1 PolitiFact Dataset
2.2 PHEME Dataset
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Modeling
3.4 Experiments
4 Results and Analysis
5 Conclusion and Future Work
References
An Intelligent Agent Framework for Resilient Deployment in the Internet of Things Environment
1 Introduction
2 Intelligent Agent Framework
3 Proposed Deployment Algorithm
4 Results and Discussion
5 Conclusions
References
The Impact of Financial Ratios and Pandemic on Firm Performance: An Indian Economic Study
1 Introduction
2 Literature Review and Hypotheses Development
2.1 Debt and Firm Performance
2.2 Working Capital and Firm Performance
2.3 Financial Autonomy and Firm Performance
2.4 Quick Ratio and Firm Performance
3 Sample
4 Research Methodology
5 Results
5.1 Trend Analysis
5.2 Regression Results
6 Conclusion
References
An Enhanced BERT Model for Depression Detection on Social Media Posts
1 Introduction
2 Related Work
3 Methodology
3.1 Enhanced BERT Classical
4 Results and Discussion
4.1 Dataset
4.2 Comparison with the Existing System
5 Conclusion
References
Quality Prediction of a Stack Overflow Question Using Machine Learning
1 Introduction
2 Literature Review
3 Techniques
3.1 Naïve Bayes (NB)
3.2 Support Vector Classifier (SVC)
3.3 Decision Tree (DT)
3.4 Logistic Regression (LR)
3.5 K-Nearest Neighbors (KNN)
3.6 Random Forest (RF)
3.7 Natural Language Processing (NLP)
3.8 Bag of Words (BoW)
3.9 Word Cloud Analysis
4 Proposed Methodology and Implementation
4.1 Importing Libraries
4.2 Description of Dataset
4.3 Cleaning Textual Data
4.4 Tokenization, Lemmatization, and PoS Tagging
4.5 Lexicon-Based Sentiment Analysis
4.6 Word Embedding
4.7 Splitting the Dataset
4.8 Model Building and Performance Evaluation
5 Experimental Results and Analysis
6 Conclusion
References
Decision-Making Framework for Supplier Selection Using an Integrated Approach of Dempster-Shafer Theory and Maximum Entropy Principle
1 Introduction
2 Preliminaries
2.1 Dempster-Shafer Evidence Theory
2.2 Cobb-Douglas Utility Function
2.3 Maximum Entropy Method
3 Proposed Methodology
3.1 Evaluate Criteria Weights
3.2 Evaluate Alternative Ranking
4 Numerical Example
4.1 Problem Description
4.2 Evaluation of Criteria Weights
4.3 Ranking of Alternatives
5 Comparative Analysis and Discussions
5.1 Comparative Analysis of Weight Determination Method
5.2 Comparative Analysis of Ranking Method
6 Sensitivity Analysis
7 Conclusions
References
Improved Accuracy of Robotic Arm Using Virtual Environment
1 Introduction
2 Literature Review and Related Work
3 Proposed Method, Tools, and Techniques Used
3.1 Forward Kinematics
3.2 Inverse Kinematics
3.3 Technique
4 Methodology
4.1 Design of the Robotic Arm
4.2 Addition of Revolute Joints
4.3 Importing the Model to MATLAB
4.4 Defining the Robot Environment
4.5 Defining the Desired Trajectory
4.6 Working of Inverse Kinematic Block
4.7 Working of Forward Kinematic Block
4.8 Data Collection and Analysis
5 Result
6 Conclusion
References
Human Activity Recognition a Comparison Between Residual Neural Network and Recurrent Neural Network
1 Introduction
2 HAR Using ResNet50
3 HAR Using RNN
4 Conclusion
References
Using AI Planning to Automate Cloud Infrastructure
1 Introduction
2 Related Work
2.1 Cloud Infrastructure
2.2 Prototype Model
3 Problem Formulation
4 Proposed Solution
5 Testing and Result Evaluations
5.1 Test Case 1: Empty Environment
5.2 Test Case 2: No Application Module
5.3 Test Case 3: Neither the Database nor the Application Module
5.4 Test Case 4: Inconsistent Database
5.5 Test Case 5: Application Module in Unhealthy Status
5.6 Test Case 6: Application Out of Capacity
6 Conclusions and Future Scope
References
Using Historical Trip Information to Determine the Waiting Time Required for Taxi Services
1 Introduction
2 Related Work
3 Problem Formulation
4 Proposed Solution: Design and Implementation
5 Results' Evaluation
6 Conclusions and Future Scope
References
The Impact of Cesarean Section Trends and Associated Complications in the Current World: A Comprehensive Analysis Using Machine Learning Techniques
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Decision Tree (DT)
3.2 Random Forest (RF)
3.3 K-Nearest Neighbour (KNN)
3.4 Support Vector Machine (SVM)
3.5 AdaBoost (AB)
4 Results
4.1 Decision Tree
4.2 Random Forest
4.3 K-Nearest Neighbour
4.4 Support Vector Machine
4.5 AdaBoost
5 Conclusion
References
Novel Hybrid Methods for Journal Article Summarization Combining Graph Method and Rough Set TFIDF Method with Pegasus Model
1 Introduction
2 Related Works and Summarization Datasets
3 Motivation and Challenges in Article Summarization
4 Methodology
4.1 Extractive Summarization Methods
4.2 Abstractive Summarization Methods
4.3 Hybrid Summarization Methods
5 Results and Discussion
5.1 Rouge Score for Word Overlap over Annotated Summary
5.2 BERT Score for Similarity Score
6 Conclusion and Future Scope
References
Differential Evolution Wrapper-Based Feature Selection Method for Stroke Prediction
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset
3.2 Proposed System
4 Result Analysis
5 Discussion
6 Conclusion
References
Parkinson's Disease Identification from Speech Signals Using Machine Learning Models
1 Introduction
2 Literature Review
2.1 Machine Learning and Neural Network Techniques for PD Classification
2.2 Hybrid Models with Machine Learning Techniques and Neural Network
3 System Architecture
3.1 Dataset Details
3.2 Proposed Approach
4 Results and Discussions
4.1 Performance Matrices
4.2 Experimental Analysis
5 Conclusion
References
Performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification
1 Introduction
2 Literature Review
3 Objective, Motivation and Challenges
4 Methodology
4.1 Dataset Description
5 Implementation
5.1 Convolution Neural Network
5.2 Convolutional Layer
5.3 Pooling Layer
5.4 Fully Connected Layer
5.5 Creating a CNN Model
5.6 You Only Look Once (YOLO)
5.7 LeNet
6 Result and Discussion
7 Conclusions
References
Data-Driven Decision Support Systems in E-Governance: Leveraging AI for Policymaking
1 Introduction
2 Related Work
3 Proposed Work
3.1 Research Objectives
3.2 Research Methodology
3.3 Proposed Contributions
4 Experimental Results
4.1 Accuracy
4.2 Precision
4.3 Recall (Sensitivity)
4.4 F1 Score
4.5 Mean Absolute Error (MAE)
4.6 Root Mean Square Error (RMSE)
5 Conclusions
References
The Infrastructure Development of Contemporary Medical Devices Based on Internet of Things Technology
1 Introduction
2 IoT Infrastructure in Health Care
3 IoT Wireless Standards
4 IoT Network Architecture
4.1 Availability
4.2 Scalability
4.3 Network Performance
4.4 Manageability
4.5 Affordability
4.6 Adaptability
4.7 Usability
5 Cloud Computing in IoT
6 Contemporary Applications of IoT
6.1 Smart IoT Sensors
6.2 Smart Pill Containers
6.3 Smart Hospital Beds
6.4 Temperature Sensors
6.5 Wearable Devices
6.6 Future of IoT Medical Instruments
7 Conclusions
References
Business Intelligence System Adoption Project in the Area of Investments in Financial Assets
1 Introduction
2 Understanding the Significance and Functionality of Business Intelligence Systems
3 Review of Business Intelligence Literature
4 Traditional Information Systems on Investments in Financial Assets
5 Business Intelligence for Investments in Financial Assets-The Case of an IT Industry Organization
5.1 Preliminary Information
5.2 Original Business Intelligence System Employed in the Organization
6 Conclusions
References
Feature Selection Techniques to Enhance Prediction of Clinical Appointment No-Shows Using Neural Network
1 Introduction
2 Related Studies
3 Model Development
4 Analysis of Performance Measures
5 Conclusion
References
A Simple Recommendation Model Using the Item's Global Popularity and Frequency-Based User Preference
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Formalizations
3.2 Model
4 Experimental Results
5 Conclusion
References
An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset
1 Introduction
2 The Next Step in Comprehending the Brain and Psychiatric Diseases is Machine Learning, Deep Learning, and Predicting Disease States
3 ABIDE Dataset Classification
4 Deep Learning Algorithms and Imaging of the Brain
5 Resources and Techniques
5.1 Participants
5.2 Resting State and Feature Selection
5.3 Data Preprocessing
5.4 Connectivity of Different Functions (Selections of Features)
5.5 Classification Methods
5.6 Classifier Evaluation
6 Results and Discussion
7 Autism Brain Neural Patterns Connectivity
8 Conclusion
References
Comparison of Machine Learning-Based Intrusion Detection Systems Using UNSW-NB15 Dataset
1 Introduction
2 Security Policies
3 Dataset Description
3.1 Objectives
4 Literature Review
4.1 Inferences Drawn from Literature Review
5 Problem Formulation and Proposed Work
5.1 Implementation
6 Results and Discussions
6.1 Linear Regression
6.2 Logistic Regression
6.3 Linear SVM
6.4 KNN
6.5 Random Forest
6.6 Decision Tree
6.7 Multilayer Perceptron
6.8 Multiclass Classifiers
7 Conclusion and Future Scope
References
Comparative Analysis of Face Recognition Models for Criminal Detection
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection and Preprocessing
3.2 Architecture
4 Models
4.1 VGGFace [6]
4.2 FaceNet [7]
4.3 OpenFace [8]
4.4 DeepFace [7]
5 Implementation and Results
5.1 Comparison Between Models
5.2 Results
5.3 Limitations
6 Key Findings and Contributions
6.1 Future Scope
7 Conclusion
References
Transcription of Ancient Indian Manuscripts Through Artificial Intelligence-Current Status of Technology and the Way Forward
1 Motivation for the Study
2 Handwriting Recognition Systems-A Brief Overview
3 Problems and Issues Related to Handwritten Character Recognition
4 Literature Survey-Developments in Machine Transcription of Old Manuscripts in European Scripts
4.1 Deciphering Unknown Scripts
4.2 Decoding the Voynich Script
4.3 Start-Flow-Read (SFR) Model
4.4 'In Codice Ratio' Project to Transcribe Vatican Secret Archives
4.5 'Normalized', 'Semi-diplomatic', and 'Diplomatic' Manuscripts-Guidelines for Transcribing the Paris Bibles
4.6 Transcribing Documents from the Abbey Library of St. Gall
5 Literature Survey-Academic Research Papers on Handwritten Character Recognition of Indian Scripts
6 AI-Powered Platforms for Text Recognition, Transcription, and Historical Document Search-An Overview
6.1 Workings of an AI-Powered HWR Platform
6.2 Benefits of an AI-Powered HWR Platform
6.3 Challenges of Using an AI-Powered HWR Platform
7 Advantages of Deep Learning Techniques Over Regular Neural Networks
8 Prominent Handwriting Text Recognition Systems in Actual Use
9 OCRopus API
10 eScriptorium
11 Transkribus
11.1 Benefits of Using Transkribus
11.2 Limitations of Transkribus
11.3 Some Tips for Using Transkribus Effectively
12 Nanonets
12.1 Advantages of Nanonets
12.2 Limitations of Nanonets
13 Comparison Between Transkribus and Nanonets
14 Conclusions
15 Exhibits
15.1 Figure 1
15.2 Figure 2
15.3 Figure 3
15.4 Figure 4
References
Computer Vision and Convolutional Neural Network for Dense Crowd Count Detection
1 Introduction
2 Literature Survey
2.1 Detection-Based Approaches
2.2 Regression-Based Approaches
2.3 Density Estimation-Based Approaches
2.4 Crowd Detection Using CNN
3 Proposed Model
3.1 Congested Scene Recognition Network (CSRNet)
3.2 VGG-16
3.3 Dilated Convolution
3.4 Setting Up Network
4 Experiments and Results
4.1 Dataset Overview
4.2 Performance Analysis
5 Conclusion
References
Evaluation Methods of CDIO Project at Duy Tan University
1 Introduction
2 Methodology
2.1 The Period Before the COVID-19 Pandemic
2.2 The Period in the COVID-19 Pandemic
2.3 The Period After the COVID-19 Pandemic
3 Discussion
4 Conclusion
References
Deciphering Stem Cell Pluripotency Using a Machine Learning Clustering Approach
1 Introduction
2 Methodology
2.1 Collection of Data
2.2 Preprocessing of Data and Analysis
2.3 Building Accuracy of the Data
2.4 Model for Machine Learning
3 Results and Discussion
3.1 Identification of DEGs
3.2 Variables and Clusters
3.3 PPI Network Analysis
3.4 Statistical Analysis and Clustering Model Using Machine Learning (ML)
4 Discussion
5 Conclusion
6 Future Aspects
References
Extremal Trees of the Reformulated and the Entire Zagreb Indices
1 Introduction
2 Reformulated and Entire Zagreb Indices
3 Some Graph Operations
4 Some Properties of Zagreb Indices
5 Main Results
6 Conclusion
References
Sign Language Interpreter Using Stacked LSTM-GRU
1 Introduction and State of the Art in the Detection of the Sign Language
1.1 Benefits of LSTM-GRU Classification Over Other Deep Learning-Based Techniques
2 The Proposed Sign Language Interpreter
2.1 Preprocessing
2.2 Stacked LSTM-GRU Classifier
3 Experimental Results and Discussions
4 Conclusion and Perspectives
References
Can Learning Games Facilitate Open Innovation Capacity in IT Industry? The Case of Resilience
1 Introduction
2 Related Work
3 Method
3.1 Study Design
3.2 Description of the Game
3.3 Data Analysis and Results
3.4 2 × 2 ANOVAs
4 Discussion
5 Conclusion
References
Investigating Role of SVM, Decision Tree, KNN, ANN in Classification of Diabetic Patient Dataset
1 Introduction
1.1 Diabetes
1.2 Types of Diabetes
1.3 Machine Learning
1.4 SVM
1.5 Decision Tree
1.6 K-Nearest Neighbors
1.7 Artificial Neural Network
2 Literature Review
3 Problem Statement
4 Dataset Used
5 Proposed Research Methodology
6 Comparison of Accuracy of Conventional ML Approaches
7 Conclusion
8 Future Scope
References
Optimal Resource Allocation in Cloud Computing Using Novel ACO-DE Algorithm
1 Introduction
2 Literature Review
3 Methodologies of Problem Statement
3.1 Job Scheduling Methods
3.2 Ant Colony Optimisation Methods
3.3 Differential Evolution Methods
3.4 Objective Function
4 Proposed Algorithm
4.1 Procedure for ACO-DE Hybridisation Algorithm
5 Result and Analysis
5.1 Simulation of Experiments
6 Conclusion
References
Prediction of Cardiovascular Disease by Feature Selection and Machine Learning Techniques
1 Introduction
2 Literature Review
3 Feature Selection Techniques
3.1 EFS Feature Selection Technique
3.2 SFS Feature Selection Technique
4 Performance Measure Indices
5 Proposed Approach for Classification
5.1 Dataset Details
5.2 KNN (K-Nearest Neighbors) [9]
5.3 Logistic Regression [15]
5.4 Decision Tree [16]
5.5 Support Vector Machine (SVM) [14]
5.6 Bagging Method
6 Implementation
6.1 Different Machine Learning Libraries and Feature Selection Techniques Used
6.2 A Summary of Data Preprocessing and Cleaning Techniques
7 Results
7.1 Outcomes of Feature Processes Selection
7.2 A Comparison Based on Accuracy of Different Methods
7.3 A Comparison Based on Precision of Different Methods
7.4 A Comparison Based on Recall of Different Methods
7.5 A Comparison Based on F1-Score of Different Methods
7.6 Accuracy Comparison Table of Proposed Models
8 Discussion
9 Conclusion
References
Performance Evaluation and Comparative Analysis of Machine Learning Techniques to Predict the Chronic Kidney Disease
1 Introduction
1.1 Contribution
2 Review and Related Literature
3 Proposed Methodology
3.1 Parameter Information
3.2 Exploratory Data Analysis
4 Results and Discussion
4.1 Confusion Matrix
4.2 Accuracy of the Models
4.3 Other Statistical Measurements
4.4 AUC-ROC Curve
5 Comparative Analysis
6 Conclusion
References
Designer Face Mask Detection Using Marker-Based Watershed Transform and YOLOv2 CNN Model
1 Introduction
2 Preliminaries
2.1 Marker-Controlled Watershed Segmentation
2.2 YOLOv2
3 Proposed Work
4 Results
5 Confusion Matrix
6 Conclusions
References
Drought Prediction Using Machine Learning Forecasting Model in the Context of Bangladesh During 1981-2018
1 Introduction
2 Related Works
3 Dataset and Proposed Method
3.1 Proposed Model
3.2 Data Processing
3.3 Equations
4 Results and Accuracies
4.1 Interpreting SPI on Different Timescales
4.2 FB Prophet Prediction
5 Conclusion and Future Endeavors
References
A Survey on Various Aspects of Recommendation System Based on Sentiment Analysis
1 Introduction
2 Background
2.1 Sentiment Analysis (SA)
2.2 Recommender System
3 Recommendation Techniques
4 Literature Review
4.1 Online Restaurant Reviews
4.2 Sentiment Analysis Method
5 Applications
6 Conclusion
References
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Artificial Intelligence: Theory and Applications

Description

More details

Other editions

Additional editions

Persons

Content

System requirements