
Intelligent Systems Design and Applications
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This book highlights recent research on intelligent systems and nature-inspired computing. It presents 223 selected papers from the 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers, and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 65 countries, the book offers a valuable reference guide for all researchers, students, and practitioners in the fields of computer science and engineering.
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Content
- Intro
- Preface
- ISDA 2022-Organization
- Contents
- Machine Learning Approach for Detection of Mental Health
- 1 Introduction
- 2 Literature Review
- 3 Dataset Description
- 4 Proposed Model
- 5 Results and Discussion
- 6 Conclusion and Future Scope
- References
- U-Net as a Tool for Adjusting the Velocity Distributions of Rheomagnetic Fluids
- 1 Introduction
- 2 Theoretical Basics
- 2.1 Physics-Based Loss
- 2.2 Rheomagnetic Fluids
- 3 Simulation Modeling
- 4 Results and Discussion
- 5 Conclusions
- References
- Detection of Similarity Between Business Process Models with the Integration of Semantics in Similarity Measures
- 1 Introduction
- 2 Similarity Measures
- 2.1 Syntactic Measures
- 2.2 Semantic Measures
- 2.3 Structural Measures
- 2.4 Behavioral Measures
- 3 Problem Illustration
- 3.1 Similarity Measures
- 3.2 Dimensions of Semantic Similarity
- 3.3 Cardinality Problem
- 3.4 Genetic Algorithm
- 4 Related Work
- 5 Our Approach
- 5.1 Steps of Genetic Algorithm
- 6 Conclusion
- References
- Efficient Twitter Sentiment Analysis System Using Deep Learning Algorithm
- 1 Introduction
- 2 Literature Review
- 3 Proposed Method
- 3.1 Pre-processing
- 3.2 User-Mention
- 3.3 EMOJ Positive and Negative
- 3.4 Feature Selection
- 3.5 Classification
- 4 Experimental Results and Discussion
- 5 Conclusion
- References
- An Efficient Deep Learning-Based Breast Cancer Detection Scheme with Small Datasets
- 1 Introduction
- 1.1 Contributions
- 2 Proposed Method
- 2.1 Preprocessing
- 2.2 CNN Architecture
- 3 Datasets
- 4 Results and Discussion
- 5 Conclusion
- References
- Comparative Analysis of Machine Learning Models for Customer Segmentation
- 1 Introduction
- 2 Problem Statement
- 3 Literature Review
- 4 Algorithms for Customer Segmentation
- 4.1 Customer Segmentation Using K-Means
- 4.2 Customer Segmentation Using DBSCAN
- 4.3 Agglomerative Clustering (Using PCA)
- 4.4 K-Means Using PCA
- 5 Results and Discussion
- 5.1 K-Means Model
- 5.2 DBSCAN
- 5.3 Agglomerative Clustering with PCA
- 5.4 Kmeans with PCA
- 6 Conclusion
- References
- An Intelligent Approach to Identify the Eggs of the Insect Bemisia Tabaci
- 1 Introduction
- 2 Overview of Proposed Approach
- 2.1 Deep Learning Algorithm
- 2.2 Auto-encoder
- 2.3 Stacked Auto-encoder for Egg Classification
- 3 Results and Discussion
- 3.1 Databases Used
- 3.2 Result
- 3.3 Test Phase
- 4 Conclusion
- References
- Overview of Blockchain-Based Seafood Supply Chain Management
- 1 Introduction
- 2 Blockchain Technology: Overview and Adoption in Supply Chains Management
- 3 An Overview of Blockchain Based Seafood Supply Chain Management Systems
- 4 Discussion and Research Challenges
- 5 Conclusion
- References
- Synthesis of a DQN-Based Controller for Improving Performance of Rotor System with Tribotronic Magnetorheological Bearing
- 1 Introduction
- 2 System Description and Modeling
- 2.1 Rotor Model
- 2.2 Bearing Model
- 3 Model Verification
- 4 Designing a DQN Controller
- 5 Results and Discussion
- 6 Conclusion
- References
- Card-Not-Present Fraud Detection: Merchant Category Code Prediction of the Next Purchase
- 1 Introduction
- 2 State of the Art
- 2.1 What is a Card-Not-Present Transaction?
- 2.2 Card not Present Fraud Scenario
- 2.3 What is the Merchant Category Code?
- 2.4 Prediction of Merchant Category Code for the Next Buy
- 3 Related Works
- 3.1 Online Payment Fraud Detection AI Proposals
- 4 Conclusion
- References
- Fast Stroke Lesions Segmentation Based on Parzen Estimation and Non-uniform Bit Allocation in Skull CT Images
- 1 Introduction
- 2 Related Works: classical and Deep Learning Approaches
- 3 Materials and Methods
- 3.1 Level Set
- 3.2 Parzen Window
- 3.3 Non-uniform Bit Allocation: -law and A-law Algorithms
- 3.4 Datasets and Evaluation Metrics
- 4 LSBRD: An Approach Based on Parzen Estimation and Non-uniform Bit Allocation via -law and A-law
- 5 Results and Discussions
- 5.1 Algorithm Performance Analysis
- 6 Conclusion and Future Works
- References
- Methods for Improving the Fault Diagnosis Accuracy of Rotating Machines
- 1 Introduction
- 2 Intellectual Diagnostic Methods
- 2.1 Fully Connected Neural Networks
- 2.2 Generative Adversarial Network
- 3 Results and Discussion
- 3.1 Data Collection
- 3.2 Fully Connected Neural Networks to Rotor Diagnostic Defects
- 3.3 Generative Adversarial Network to Increasing the Volume and Variety of Training Data
- 4 Conclusion
- References
- Heuristics Assisted by Machine Learning for the Integrated Production Planning and Distribution Problem
- 1 Introduction
- 2 Problem Definition
- 3 Proposed Algorithms
- 3.1 Decoding Algorithms
- 3.2 Initial Solution
- 3.3 Neighborhood Search Heuristics
- 3.4 Framework
- 4 Computational Experiments
- 4.1 Computational Results
- 5 Conclusions
- References
- LSTM-Based Congestion Detection in Named Data Networks
- 1 Introduction
- 2 Background and Related Works
- 2.1 Long Short Term Memory Background
- 2.2 Related Works
- 3 LSTM-Based Congestion Detection
- 4 Performance Evaluation
- 5 Conclusion
- References
- Detection of COVID-19 in Computed Tomography Images Using Deep Learning
- 1 Introduction
- 2 Related Work
- 3 Materials and Methods
- 3.1 Image Acquisition
- 3.2 Pre-processing
- 3.3 Data Augmentation
- 3.4 Evaluated Architectures
- 3.5 Proposed Method
- 4 Experimental Results
- 4.1 Transfer Learning Results
- 4.2 Fine-Tuning Results
- 5 Discussion
- 6 Conclusion
- References
- Abnormal Event Detection Method Based on Spatiotemporal CNN Hashing Model
- 1 Introduction
- 2 Related Work
- 3 Proposed Approach
- 3.1 Spatiotemporal Stream
- 3.2 Network Architecture
- 4 Experiments Results
- 4.1 Network Architecture
- 4.2 Datasets
- 4.3 Evaluation
- 5 Conclusion
- References
- A Multi-objective Iterated Local Search Heuristic for Energy-Efficient No-Wait Permutation Flowshop Scheduling Problem
- 1 Introduction
- 2 Problem Description
- 3 Multi-objective Iterated Local Search Heuristic
- 3.1 Multi-objective Local Search and Perturbation
- 4 Computational Experiments
- 4.1 Obtained Results
- 5 Conclusions
- References
- An Elastic Model for Virtual Computing Labs Using Timed Petri Nets
- 1 Introduction
- 2 Cloud Computing
- 2.1 Cloud Service Models
- 2.2 Cloud Deployement Models
- 2.3 Benefits and Challenges of Cloud Computing
- 3 Related Works
- 4 Background
- 4.1 Timed and Colored Petri Nets
- 4.2 Cloud Elasticity
- 5 Proposed Approach
- 5.1 The Challenge for Moving to VCL
- 5.2 Model Description
- 5.3 Vertical Elasticity Algorithm
- 5.4 Proposed Solution
- 5.5 Support Tools
- 6 Conclusion
- References
- A Decision Support System Based Vehicle Ontology for Solving VRPs
- 1 Introduction
- 2 Literature Review
- 2.1 Classification of Vehicle Routing Problems
- 2.2 Ontologies for Vehicle Domain
- 3 Decision Support System
- 4 Proposed VRP-Vehicle Ontology
- 5 Conclusion
- References
- Web API Service to RDF Mapping Method for Querying Distributed Data Sources
- 1 Introduction
- 2 Related Work
- 2.1 Smart City Platforms
- 2.2 Relational Databases
- 3 Accident Card Analysis System
- 3.1 R2RML Mapping
- 3.2 Data Quality
- 4 Web API Service to RDF Mapping Method Description
- 4.1 Weather Data Sources Specific
- 4.2 W2RML Scheme
- 5 Conclusion
- References
- Risk Management in the Clinical Pathology Laboratory: A Bayesian Network Approach
- 1 Introduction
- 2 Research Design
- 3 Results
- 3.1 Literature Review
- 3.2 Risk Model
- 4 Conclusions
- References
- Leveraging Sequence Mining for Robot Process Automation
- 1 Introduction
- 2 Related Works
- 3 The Proposed Approach
- 3.1 FEM-M: Frequent Episode Miner
- 3.2 TEF-M: Target Episode Finder
- 4 Experimental Results
- 5 A Case Study
- 6 Conclusions
- References
- Intelligent Agents System for Intention Mining Using HMM-LSTM Model
- 1 Introduction and Motivation
- 2 Related Works
- 3 Architecture of Multi Intelligent Agents System Approach
- 3.1 Description of Agents
- 3.2 Hybrid Model
- 4 Experimentation and Validation
- 4.1 Dataset
- 4.2 Evaluation Metrics
- 4.3 Result
- 5 Conclusion
- References
- Unsupervised Manipulation Detection Scheme for Insider Trading
- 1 Introduction
- 2 Literature Review
- 3 Proposed Methodology
- 3.1 Feature Characterisation
- 3.2 Kernel Principal Component Analysis (KPCA)
- 4 Results and Discussion
- 5 Conclusion
- References
- A Comparative Study for Modeling IoT Security Systems
- 1 Introduction
- 1.1 Originality and Objectives
- 1.2 Outline
- 2 IoT and Modeling Languages
- 2.1 IoT Background
- 2.2 Overview of UML
- 2.3 Overview of SysML
- 3 Related Works
- 4 Proposed New IoT Security Modeling
- 4.1 IoT Architecture and Security Requirements
- 4.2 Modeling the Security of the Physical Layer and the Network Layer of IoT Systems with UML Language: Use Case Diagram
- 4.3 Modeling IoT Security Systems Using SysML Language: Requirement Diagram
- 5 Analysis and Discussion
- 6 Conclusion
- References
- Improving the Routing Process in SDN Using a Combination of the Evidence Theory and ML
- 1 Introduction
- 2 Related Work
- 3 Overview of the Trust-Based Routing Scheme
- 4 Global Trust (GT) Vector Computation
- 4.1 Trust Factors Extraction
- 4.2 Computation of the GT Vector
- 5 Multi-class SVM Classification Model
- 6 Trust Graph Establishment
- 7 TPF: Trusted Path First Algorithm
- 8 Simulation Results
- 8.1 Evaluation of the Classification Model
- 8.2 Evaluation of the TPF Algorithm
- 9 Conclusion and Future Work
- References
- GANASUNet: An Efficient Convolutional Neural Architecture for Segmenting Iron Ore Images
- 1 Introduction
- 2 Related Work
- 3 Our Proposal: GANASUNet
- 3.1 Data Collection and Acquisition
- 3.2 Model Architecture Using Genetic Algorithms
- 4 Experimental Results
- 4.1 Settings
- 4.2 Results
- 5 Conclusion
- References
- Classifying 2D ECG Image Database Using Convolution Neural Network and Support Vector Machine
- 1 Introduction
- 2 Method
- 2.1 Database
- 2.2 Pre-processing
- 2.3 ECG Signal Transformation Using CWT
- 2.4 CNN-SVM Model Architecture
- 3 Result and Discussions
- 3.1 Comparison Result Between CNN-SVM and Normal CNN
- 3.2 Performance Comparison with Other Published Methods
- 3.3 Discussion
- 4 Conclusion
- References
- Conceptual Model of a Data Visualization Instrument for Educational Video Games
- 1 Introduction
- 2 The Proposed Conceptual Model
- 2.1 User Functionalities Model of the Instrument
- 2.2 Conceptual Model of the Data Visualization Instrument for the System Login and Visualize Data Functionalities
- 3 Conclusion and Future Work
- References
- Mobile and Cooperative Agent Based Approach for Intelligent Integration of Complex Data
- 1 Introduction
- 2 Overview About Related Works
- 3 A Mobile Cooperative Agent Based Approach for Intelligent Integration of Complex Data
- 4 Discussion
- 5 Conclusion and Future Scope
- References
- Euler Transformation Axis Method for Online Virtual Trail Room Using Fusion of Images
- 1 Introduction
- 2 Literature Review
- 3 Proposed Methodology
- 3.1 Algorithm
- 3.2 Methodology
- 4 Implementation Details
- 5 Conclusion
- References
- A Novel Approach for Classification of Real Time Data Stream to Reduce Query Processing Time
- 1 Introduction
- 1.1 Data Streaming
- 1.2 Streamed Data Model
- 2 Literature Review
- 3 Proposed System
- 3.1 Gather Data Stream
- 3.2 Classification of Data
- 3.3 Gather Data Stream
- 3.4 Gather Data Stream
- 4 Result Analysis
- 4.1 Accuracy
- 4.2 Error Rate
- 4.3 Memory Utilization
- 4.4 Time Consumption
- 5 Conclusion
- References
- A Review on Machine Learning and Blockchain Technology in E-Healthcare
- 1 Introduction
- 2 Machine Learning for E-Healthcare
- 2.1 Machine Learning (ML) Models in E-Healthcare
- 3 Blockchain Technology in E-Healthcare
- 3.1 EHR Security
- 3.2 Applications of Blockchain Technology in E-Healthcare
- 4 Data Collection for E-Healthcare
- 5 Research Findings and Issues
- 6 Conclusion
- References
- Machine Learning Models for Toxicity Prediction in Chemotherapy
- 1 Introduction
- 2 Related Works
- 3 Machine Learning Methods for Toxicity Prediction
- 3.1 Linear Discriminant Analysis
- 3.2 Naïve Bayes Model
- 3.3 Decision Trees Model
- 4 Evaluation of Methods
- 5 Characteristics of the DataSet
- 6 Data Preprocessing
- 7 Prediction Process
- 7.1 Linear Discriminant Analysis
- 7.2 Naïve Bayes Model
- 7.3 Decision Tree Model
- 8 Comparative Study
- 9 Conclusions
- References
- Underwater Acoustic Sensor Networks: Concepts, Applications and Research Challenges
- 1 Introduction
- 2 Basics of Acoustic Communication
- 3 UASNs Architecture
- 3.1 1-D Architecture
- 3.2 2-D Architecture
- 3.3 3-D Architecture
- 3.4 4-D Architecture
- 4 Related Works
- 5 Open Issues and Research Challenges
- 5.1 Void Node Problem
- 5.2 Secure Routing
- 5.3 Optimal Energy Efficient Route
- 5.4 Hotspot Problem
- 5.5 Link Stability
- 5.6 Network Partitioning
- 5.7 Sensor Node Movement Model
- 5.8 Network Coverage and Connectivity
- 6 Conclusion
- References
- A Step-To-Step Guide to Write a Quality Research Article
- 1 Introduction
- 1.1 For Science
- 1.2 For Engineering
- 2 Collecting Quality Research Articles
- 2.1 Previously Published Articles
- 2.2 Segregating Unused/duplicate Articles
- 3 Reading Articles
- 4 Summarizing all Works for Literature Review
- 5 Finding a Feasible Problem
- 6 Solving the Identified Problem
- 7 Comparing Your Results with Existing Results
- 8 Publishing Your Research Work
- 8.1 Journals
- 8.2 Conferences
- 8.3 Chapters
- 8.4 Other
- 9 Challenges Faced During Conducting/Implementing Research
- 10 Conclusion and Future Scope
- References
- A Survey on 3D Hand Detection and Tracking Algorithms for Human Computer Interfacing
- 1 Introduction
- 2 Methodology and Paper Selection
- 3 3D Hand Detection and Tracking Algorithms
- 4 Hardware Implementation of 3D Hand Detection and Tracking Algorithms
- 5 3D Hand Detection and Tracking Algorithms for Human Computer Interfacing
- 6 Major Findings and Future Challenges
- 7 Conclusions
- References
- Multi-level Image Segmentation of Breast Tumors Using Kapur Entropy Based Nature-Inspired Algorithms
- 1 Introduction
- 2 Related Works
- 3 Proposed Work
- 3.1 Kapur Entropy
- 3.2 Dragonfly Algorithm
- 3.3 Crow Search Algorithm
- 4 Experimental Setup
- 5 Results
- 6 Conclusion
- References
- Interference Detection Among Secondary Users Deployed in Television Whitespace
- 1 Introduction
- 2 Methodology
- 2.1 Detection Problem
- 2.2 Proposed System Model
- 3 Results and Discussion
- 4 Conclusion
- References
- Sampling Imbalanced Data for Multilingual Machine Translation: An Overview of Techniques
- 1 Introduction
- 1.1 Organization of the Overview
- 2 Static Methods
- 2.1 Proportional Sampling
- 2.2 Uniform Sampling
- 2.3 Temperature-Based Sampling
- 2.4 Oversampling and Downweighting
- 3 Dynamic Methods
- 3.1 MultiDDS. Miltilingual Differentiable Data Selection
- 3.2 MultiUAT. Uncertainty-Aware Training
- 3.3 Adaptive Scheduling for Multi-task Learning
- 3.4 CCL-M. Competence-Based Curriculum Learning
- 3.5 IBR. Iterated Best Response
- 3.6 CATS. Curvature Aware Task Scaling
- 3.7 Multi-arm Bandits
- 3.8 LSSD. Language-Specific Self-distillation
- 4 Conclusion
- References
- Digital Twin-Based Fuel Consumption Model of Locomotive Diesel Engine
- 1 Introduction
- 2 Methodology
- 2.1 Physical Model
- 2.2 Digital Twin Model
- 3 Results and Discussion
- 4 Conclusion
- References
- Centrality of AI Quality in MLOPs Lifecycle and Its Impact on the Adoption of AI/ML Solutions
- 1 Introduction
- 2 MLOps
- 2.1 Visualization in MLOps
- 2.2 MLOps Monitoring
- 3 AI Risk Assessment
- 4 Proposed Integrated Framework
- 4.1 AI Quality Criteria
- 5 Conclusion and Future Work
- References
- A Survey on Smart Home Application: The State-of-the-Art and Future Research Trends
- 1 Introduction
- 2 Smart Home Technology
- 2.1 Smart Devices
- 2.2 Smart Home Network
- 2.3 Smart Home Controller
- 2.4 Smart Kitchen
- 3 Smart Home Energy Management System (SHEMS)
- 3.1 Generation and Storage of Renewable Energy Sources
- 3.2 Category of Energy Usage in Residue
- 3.3 Implementation of SHEMS
- 4 Survey Results
- 5 Conclusion
- References
- A Survey on Currency Recognition Method
- 1 Introduction
- 2 Method for Coin Currency Recognition
- 3 Methods for Paper Currency Recognition
- 4 Literature Survey
- 5 Conclusion and Future Scope
- References
- Cryptocurrencies: An Epitome of Technological Populism
- 1 Introduction
- 2 Related Work
- 3 Objectives of the Study
- 4 Research Methodology
- 4.1 ARCH Model
- 4.2 GARCH Model
- 5 Analysis and Findings
- 5.1 Correlation Matrix
- 5.2 DCC-GARCH Result
- 6 Conclusion
- References
- Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research
- 1 Introduction
- 2 Related Work
- 3 Research Methodology
- 4 Result and Discussion
- 4.1 Bubble Detection
- 4.2 ARIMA
- 4.3 Prophet Model
- 5 Conclusion
- References
- Performance Evaluation of Signature Based and Anomaly Based Techniques for Intrusion Detection
- 1 Introduction
- 2 Literature Survey
- 3 Research Methodology
- 3.1 Signature-Based Intrusion Detection System
- 3.2 Anomaly-Based Intrusion Detection System
- 4 Results
- 5 Conclusion
- References
- Comparative Study on Black Hole Attack in Mobile Ad-Hoc Networks
- 1 Introduction
- 1.1 Manet Susceptibility
- 2 Literature Review
- 3 Comparisons of Some Existing Black Hole Attack Prevention Techniques
- 4 Conclusion and Future Scope
- References
- Machine Learning-Based Approach to Analyze Students' Behaviour in Digital Learning Systems
- 1 Introduction
- 2 Literature Survey
- 3 Basic Architecture of the Proposed Model
- 4 Methodology
- 4.1 Technology Used
- 4.2 Use of Machine Learning
- 5 Proposed Method
- 5.1 Learner Interface
- 5.2 Implementation
- 5.3 User Interface
- 6 Result and Discussion
- 7 Conclusion
- References
- Fake Review Prediction Using Machine Learning
- 1 Introduction
- 2 Related Works
- 3 Dataset
- 4 Methodology and Implementation
- 4.1 Classifiers
- 4.2 Prerocessing
- 4.3 Feature Extraction
- 5 Result and Discussion
- 6 Conclusion and Future Work
- References
- Context-Aware QoS Prediction for Web Services Using Deep Learning
- 1 Introduction
- 2 Related Work
- 2.1 Neighbourhood-based CF
- 2.2 Model-Based CF
- 2.3 Context Aware Neural Collaborative Filtering
- 2.4 Limitations of CF Techniques
- 2.5 Deep Neural Networks
- 3 The Proposed Method
- 3.1 Data Preprocessing
- 3.2 FCM Clustering
- 3.3 Training Data Preparation
- 3.4 Neural Network Algorithms
- 4 Results and Discussion
- 4.1 Performance Analysis
- 5 Conclusion and Future Scope
- References
- An Efficient Resource Allocation Technique in a Fog Computing Environment
- 1 Introduction
- 2 General Concepts Related to Fog Computing
- 2.1 Concept of Fog Computing
- 2.2 Related Work on Resource Management in Fog Environments
- 3 Stable Matching Based Resources Allocation (SMRA)
- 3.1 Hypotheses of SMRA
- 3.2 SMRA Architecture
- 3.3 Algorithmic Description of the Scenario
- 4 Implementation and Experimental Results
- 5 Conclusion and Future Work
- References
- Comparative Study of Various Pattern Recognition Techniques for Identifying Seismo-Tectonically Susceptible Areas
- 1 Introduction
- 2 Data Set
- 3 Pattern Recognition and Related Literature
- 3.1 Linear Discriminant Analysis (LDA)
- 3.2 Support Vector Machine (SVM)
- 3.3 K-Nearest Neighbour (K-NN)
- 3.4 Artificial Neural Networks (ANN)
- 4 Methodology and Comparative Analysis
- 4.1 Steps
- 5 Results and Discussion
- 6 Conclusion
- References
- Intelligent Diagnostic System for the Sliding Bearing Unit
- 1 Introduction
- 2 Experimental Research
- 3 Processing of the Obtained Results
- 4 Conclusion
- References
- A Systematic Review on Security Mechanism of Electric Vehicles
- 1 Introduction
- 2 Literature Survey
- 3 Existing Method
- 4 Proposed Model
- 5 Conclusion
- References
- Experimental Investigation of CT Scan Imaging Based COVID-19 Detection with Deep Learning Techniques
- 1 Introduction
- 2 Related Work
- 3 Proposed Work
- 3.1 Image Pre-processing
- 3.2 Augmentation
- 3.3 Convolution Neural Network Model
- 3.4 Dropout
- 3.5 Model Compilation and Training
- 4 Experimental Setup
- 5 Results and Discussion
- 5.1 Experimental Results on Dataset-I
- 5.2 Experimental Results on Dataset-II
- 5.3 Evaluation with Classification by SVM Trained on Handcrafted Features
- 6 Conclusion and Future Work
- References
- Author Index
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