Applied Computer Vision and Soft Computing with Interpretable AI

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1: Improved Healthcare Systems Using Artificial Intelligence: Technology and Challenges
1.1 Introduction
1.2 Motivation
1.3 Literature Review
1.4 Technology in Healthcare
1.4.1 Accurate Cancer Diagnosis
1.4.2 Premature Detection of Lethal Blood Diseases
1.4.3 Customer Service Chatbots
1.4.4 Treatment of Odd Diseases
1.4.5 Automation of Repetitive Jobs
1.4.6 Handling and Supervision of Medical Records
1.4.7 Development of New Medicines
1.4.8 Robot-assisted Surgery
1.4.9 Automation of Medical Image Diagnoses
1.5 Challenges and Solutions
1.5.1 AI Bias
1.5.2 Personal Security
1.5.3 Transparency
1.5.4 Data Formats
1.5.5 Societal Acceptance/Human Factors
1.6 Conclusion and Future Scope
References
Chapter 2: A Brain MRI Segmentation Method Using Feature Weighting and a Combination of Efficient Visual Features
2.1 Introduction: Background and Driving Forces
2.2 Proposed Framework
2.2.1 Approach Overview
2.2.2 Preprocessing
2.2.3 Feature Extraction
2.2.4 Clustering Step
2.2.5 Post-processing
2.3 Experiments
2.3.1 Dataset
2.3.2 Performance Metrics
Experiment 1: The Analysis of Extracted Features
Experiment 2: The Impact of the Feature Weighting Strategy
Experiment 3: The Proposed Method vs. Other Methods
2.4 Conclusion
Note
References
Chapter 3: Vision Based Skin Cancer Detection: Various Approaches with a Comparative Study
3.1 Introduction: Background and Driving Forces
3.1.1 Problem Formulation and Motivation
3.1.1.1 Proposed Solution
3.1.1.2 Scope of the Proposed Solution
3.1.2 Review of the Literature
3.1.2.1 Image Preprocessing and Enhancement
3.1.2.2 Image Segmentation
3.1.2.3 Feature Extraction
3.1.2.4 Classification
3.1.3 Algorithmic View with Implementation Details
3.1.3.1 Preprocessing
3.1.3.2 Segmentation
3.1.3.3 Feature Extraction
3.1.3.4 Classification
3.1.4 Results and Discussion
3.1.4.1 Performance of Eight-bins
3.1.4.2 Performance of CNN
3.2 Conclusion, Take-aways, and Future Directions
References
Chapter 4: MentoCare: An Improved Mental Healthcare System for the Public
4.1 Introduction
4.2 Related Work
4.3 Proposed Methodology
4.4 Results and Discussion
4.5 Conclusion and Future Scope
References
Chapter 5: An Employee Health Monitoring System Using Wireless Body Area Networks and Machine Learning
5.1 Introduction
5.2 Literature Survey
5.3 MI Theory
5.3.1 STEMI
5.3.2 NSTEMI
5.3.3 Angina
5.4 Proposed Methodology
5.4.1 Data creation
5.4.2 Authentication
5.4.3 Disease Prediction System (DPS)
5.5 Algorithm for DLNN
5.6 UML and Working
5.7 Working on the Proposed Project
5.7.1 Admin Interface
5.7.2 Medical Professional Interface
5.7.3 Employer Interface
5.8 Conclusion
Appendix A
References
Chapter 6: Monitoring Operational Parameters in the Manufacturing Industry Using Web Analytical Dashboards
6.1 Introduction
6.2 Challenges
6.3 Literature Review
6.4 Methodology
6.5 Datasets
6.6 Experimental Investigation
6.6.1 Need of Data Analytics
6.6.1.1 Steps of Data Analytics
6.6.1.2 Types of Data Analytics
6.6.1.3 Benefits of Data Analytics [ 23–29 ]
6.7 Tools and Technologies Used
6.8 Results and Discussion
6.9 Results
6.9.1 Daily Production Report Dashboard
6.9.2 MIS Production Report Dashboard (Sheet 1: FC-Machine Shop)
6.9.3 MIS Production Report Dashboard (Sheet 2: FC-Operations in House Production)
6.10 Future Directions
Acknowledgment
References
Chapter 7: Concurrent Line Perpendicular Distance Functions for Contour Point Analysis
7.1 Introduction
7.2 Background
7.3 Shape Descriptor
7.4 Scale Invariant Features
7.5 Experiments and Analysis
7.5.1 Kimia’s Dataset
7.5.2 MPEG-7 Dataset
7.6 Conclusions
References
Chapter 8: A Resemblance of Convolutional Neural Network Architectures for Classifying Ferrograph Images
8.1 Introduction
8.2 Dataset
8.3 Transfer Learning and Fine Tuning
8.4 Hardware and Convolutional Neural Network Architectures
8.4.1 VGG
8.4.2 ResNet
8.4.3 InceptionV3
8.4.4 Xception
8.4.5 MobileNet
8.4.6 DenseNet
8.4.7 MobileNetV2
8.4.8 EfficientNet
8.4.9 ConvNeXt
8.5 Model Configuration and Training
8.6 Results
8.7 Conclusion
References
Chapter 9: The Role of Artificial Intelligence and the Internet of Things in Smart Agriculture towards Green Engineering
9.1 Introduction
9.2 Artificial Intelligence in Agriculture
9.3 Precision Agriculture Artificial Intelligence
9.3.1 Geographic Information System (GIS)
9.3.2 Autosteer
9.4 Agricultural Robotics and Drones
9.4.1 Harvest CROO Robotics
9.4.2 Robot Drone Tractors
9.4.3 Farm Bots
9.4.4 Autonomous Tractors
9.4.5 Unmanned Aerial Vehicles (UAVs)
9.5 Image-based Insight Generation
9.6 Artificial Intelligence in Management Accounting
9.7 Agriculture and the Internet of Things
9.8 The Precision Farming Internet of Things (IoT)
9.8.1 Agriculture Sensors
9.8.2 Communication in Agriculture
9.9 The Internet of Things Cloud
9.9.1 Climate Change
9.9.2 Smart Greenhouses
9.9.3 Internet of Things-based Tractors
9.10 Challenges with the Internet of Things
9.10.1 Future Scope of the Internet of Things in Agriculture
9.11 Integrating Artificial Intelligence and the Internet of Things in Agriculture
9.12 Applications of Artificial Intelligence and the Internet of Things in Agriculture
9.13 Conclusion
References
Chapter 10: Intuitionistic Fuzzy Hypergraphs and Their Operations
10.1 Introduction
10.2 The Literature Review
10.3 Preliminaries
10.4 Different Types of Operations with Respect to IFHGs
10.4.1 Complement of an IFHG
10.4.2 Union of Two IFHGs
10.4.3 Intersection of Two IFHGs
10.4.4 Ring Sum of Two IFHGs
10.4.5 Join of Two IFHGs
10.4.6 Cartesian Product of Two IFHGs
10.4.7 Composition of Two IFHGs
10.5 Summary
List of Abbreviations
References
Chapter 11: Spammer Detection Based on a Heterogeneous Multiple-mini-graph Neural Network
11.1 Introduction
11.2 Literature Review
11.2.1 Existing Work
11.2.2 Summary of the Literature
11.3 Graph Terminologies
11.3.1 Graph Neural Networks
11.3.2 Graph Convolutional Networks
11.3.3 Heterogeneous GNNs
11.3.4 Vanilla Feature Embedding
11.3.5 Random Walk
11.4 Proposed Spammer Detection Methodology
11.4.1 Hypergraph Generation
11.4.2 Heterogeneous Graph Convolution
11.4.3 Model Training and Analysis
11.4.3.1 Model Training
11.4.3.2 Model Analysis
11.5 Experimental Setup and Results
11.5.1 Parameters Defined
11.5.2 Experimental Setting
11.5.2.1 Preprocessing Input
11.5.3 Performance Analysis
11.5.4 Performance Comparison
11.6 Conclusion
References
Chapter 12: Spam Email Classification Using Meta-heuristic Algorithms
12.1 Introduction
12.2 Related Work
12.3 Proposed System Architecture
12.3.1 Pre-processing
12.3.2 Horse Herd Optimization Algorithm
12.3.3 Multi-objective Opposition-based Binary HOA
12.3.4 Spam Detection Using MOBHOA
12.4 Results Analysis
12.5 Conclusion
Conflict of Interest
References
Chapter 13: A Blockchain Model for Land Registration Properties in Metro Cities
13.1 Introduction
13.1.1 Land Registration Types
13.1.2 Issues or Challenges in Land Registry, Maharashtra, India
13.1.3 Use of Blockchain Technology for These Issues
13.1.4 Structure of Blockchains
13.1.5 The Various Kinds of Agreement Conventions Utilized for Approving Exchanges on the Blockchain
13.2 Current Land Registration Procedure
13.2.2 Measures which Should be Taken to Avoid Bad Land Deals
13.2.3 Types of Blockchain Technology for Land Registration
13.2.4 Hybrid Blockchains
13.2.5 Case Study: Gujarat Land Registration
13.3 Proposed Hybrid Blockchain Model for Land Registry in Maharashtra, Pune
13.4 Future Scope and Conclusion
References
Chapter 14: A Review of Sentiment Analysis Applications and Challenges
14.1 Introduction
14.2 Sentiment Analysis: An Overview
14.2.1 Level of Aspect
14.2.2 Level of Sentence
14.2.3 Level of Document
14.3 Challenges
14.3.1 Unstructured Data
14.3.2 Aspect Identification
14.3.3 Sentiment Identification
14.3.3.1 Sentiment Recognition Using Supervised Methods
14.3.3.2 Sentiment Recognition Using Unsupervised Methods
14.3.3.3 Lexical Analysis for Sentiment Recognition
14.3.4 Topic Model-Based Approaches
14.4 Applications of Sentiment Analysis
14.4.1 Business Intelligence
14.4.2 Review Analysis
14.4.3 The Stock Market
14.4.4 Healthcare
14.4.5 Behavior Analysis
14.4.6 Social Media Analysis
14.4.7 Email Mining
14.5 Performance Evaluation Parameters
14.6 Conclusions
14.7 Further Research
Conflict of Interest
References
Chapter 15: Handling Skewed Datasets in Computing Environments: The Classifier Ensemble Approach
15.1 Building a Classifier Ensemble
15.1.1 Diversity among Different Classifiers
15.2 Base Classifiers for Classifier Ensembles
15.2.1 Support Vector Machine (SVM)
15.2.2 Decision Tree
15.2.3 Multilayer Perceptron (MLP)
15.3 Ensemble Combination Strategy
15.3.1 Classifier Fusion
15.3.1.1 Voting
15.3.2 Classifier Selection
15.4 Concluding Remarks
References
Chapter 16: Diagnosis of Dementia Using MRI: A Machine Learning Approach
16.1 Introduction
16.1.1 Alzheimer’s Disease (AD)
16.1.1.1 Early-stage Alzheimer’s (Mild)
16.1.1.2 Middle-stage Alzheimer’s (Moderate)
16.1.1.3 Late-stage Alzheimer’s (Severe)
16.1.2 Vascular Dementia (VD)
16.1.3 Lewy Body Dementia (LBD)
16.1.4 Frontotemporal Dementia (FTD)
16.1.5 Mixed Dementia
16.2 Literature Survey
16.3 Algorithmic Survey
16.3.1 Support Vector Machine (SVM)
16.3.2 Convolutional Neural Network (CNN)
16.3.3 Naïve Bayes
16.3.4 Decision Tree
16.3.5 Logistic Regression
16.3.6 Multilayer Perceptron (MLP)
16.3.7 Voting Based Classifiers
16.3.8 K-Nearest Neighbour
16.3.9 Extreme Gradient Boosting (XGB)
16.3.10 Kernel Support Vector Machine
16.3.11 Radial Basis Function
16.3.12 Gaussian Mixture Model
16.4 Proposed Methodology
16.4.1 Introduction
16.4.2 Dataset
16.4.3 Data Pre-processing
16.4.4 Visualizing Data
16.4.5 Feature Extraction
16.4.6 Applying ML and DL Techniques
16.4.7 Classification
16.4.8 Prediction
16.5 Results
16.6 Conclusion and Future Work
Acknowledgement
References
Chapter 17: Optimized Student’s Multi-Face Recognition and Identification Using Deep Learning
17.1 Introduction
17.2 The Literature
17.2.1 Technical Survey
17.2.2 Non-Technical Survey
17.3 Common Findings from the Survey
17.4 Results and Discussion
17.5 Conclusion
References
Chapter 18: Impact of Fake News on Society with Detection and Classification Techniques
18.1 Introduction
18.2 Research Methodology and Algorithm Design
18.2.1 Machine Learning Models
18.2.2 Machine Learning Model Evaluation
18.2.3 Algorithm Design for Proposed Model
18.3 Results and Discussion
18.4 Conclusion
References
Chapter 19: Neurological Disorder Detection Using Computer Vision and Machine Learning Techniques
19.1 Introduction
19.2 Literature Review
19.3 Methodology
19.3.1 Thresholding
19.3.2 Segmentation
19.3.3 Edge Based Segmentation Method
19.3.4 Region-Dependent Segmentation Approach
19.3.5 Convolution Neural Networks (CNNs)
19.3.6 KNN Algorithm
19.4 System Architecture
19.5 Results and Discussion
19.6 Conclusion
References
Chapter 20: Deep Learning for Tea Leaf Disease Classification: Challenges, Study Gaps, and Emerging Technologies
20.1 Introduction
20.2 Motivation
20.3 Literature Review
20.4 Challenges in DL for Tea Leaf Disease Classification
20.4.1 Variations in Symptoms
20.4.2 Interclass Similarities
20.4.3 Image Background
20.4.4 Other Problems
20.5 A Review of Recent CNN Architectures for Tea Leaf Disease Classification
20.5.1 GoogleNet
20.5.2 AlexNet
20.5.3 VGG16
20.5.4 ResNet50
20.5.5 LeafNet
20.5.6 MergeModel
20.5.7 Xiaoxiao SUN1’s CNN Architecture
20.5.8 LeNet
20.6 Trending Models and Techniques Used in This Field
20.7 Conclusion
References
Index