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Table of Contents
Intro
Acknowledgements
Editor biographies
Sachin Taran
Chhavi Dhiman
Manjeet Kumar
List of contributors
Contributor biographies
Mohit Aggarwal
Ashwini M Bakde
Manisha Das
Sanjoli Goyal
Deep Gupta
Mohammad Farukh Hashmi
Muskan Jain
Ruchi Jain
Kranti Kamble
Vikram Singh Kardam
Anahita Karthik
Basavaraj G Katageri
P S Kavimandan
Paramkusham Venkata Keshava Krishna
Avinash G Keskar
Rajashri Khanai
Smith K Khare
Amit Kukker
Bhagyashree V Lad
Ruchika Malhotra
Seema Mehla
Virender Kumar Mehla
Om Mishra
Rajan Mishra
Sukumar Nagineni
Chandan Nayak
Krishna Pai
Deepak Parashar
Amit Patil
J Persiya
P Prakash
Kemal Polat
Rajkumar V Raikar
Aditi Rao
S Mohamed Mansoor Roomi
Neetu Sardana
A Sasithradevi
Joydeep Sengupta
Ananya Srivastava
Komal Tahiliani
Dattaprasad Torse
Abhuday Tripathi
Deepika Varshney
M Vijayalakshmi
Mayank Yadav
Chapter A precise ECG QRS complex detector using a WOA optimized fractional-order digital differentiator
1.1 Introduction
1.2 Materials and methods
1.2.1 Introduction to the metaheuristic algorithm
Algorithm 1:
1.2.2 Proposed fifth-order FODD design
1.2.3 Whale optimization algorithm (WOA)
1.2.4 Proposed QRS complex identification scheme
1.2.5 ECG databases and the performance measurement
1.3 Results
1.3.1 Simulation results of the proposed FODD
1.3.2 Sensitivity analysis
1.3.3 Comparison with existing FODDs
1.3.4 Simulation results of the proposed fifth-order FODD for the HOD based QRS detector
1.4 Conclusion
References
Chapter Focal and non-focal EEG signal classification using the Wigner-Ville distribution and deep feature extraction
2.1 Introduction
2.2 Methodology
2.2.1 Dataset.
2.2.2 Optimum allocation sampling (OAS)
2.2.3 Wigner-Ville distribution
2.2.4 Deep learning model
2.2.5 ELM classifier
2.3 Results and discussion
2.4 Conclusion
References
Chapter Multi-channel EEG-based affective emotion identification using a dual-stage filtering approach
3.1 Introduction
3.2 Method
3.2.1 Database
3.2.2 Dual-stage CIF filtering with EMD-NCVMD integration
3.2.3 Feature extraction
3.2.4 Classification
3.3 Result and discussion
3.4 Conclusion
References
Chapter Variational mode decomposition based entropy features for classification of myopathy, neuropathy, and normal EMG signals
4.1 Introduction
4.2 Methodology
4.2.1 Dataset11https://physionet.org/physiobank/database/emgdb/
4.2.2 Variational mode decomposition
4.2.3 Feature extraction
4.2.4 Random forest (RF) classifier
4.2.5 Distance-weighted k-nearest neighbour classifier (WKNN)
4.3 Results and discussion
4.4 Conclusion
References
Chapter Epileptic EEG signal classification using wavelet transform and SVM
5.1 Introduction
5.2 Proposed method
5.2.1 Wavelet transform
5.2.2 Feature extraction
5.2.3 Feature selection
5.3 Classifications
5.3.1 Support vector machine
5.3.2 k-nearest neighbors
5.3.3 Decision tree
5.3.4 Ensemble bagged random forest
5.4 Result and discussion
5.5 Conclusion
References
Chapter Imbalanced class problem analysis for lung cancer detection using convolutional neural networks
6.1 Introduction
6.1.1 Problem definition
6.1.2 Literature survey and research gap
6.1.3 Motivation and contribution
6.2 Methods and materials
6.2.1 Dataset
6.2.2 ResNet50 and AlexNet
6.2.3 DenseNet
6.2.4 SMOTE analysis to oversample the data
6.2.5 Class-weighted approach
6.2.6 Data augmentation
6.3 Results and discussion.
6.3.1 Implementation of the DenseNet-121 model
6.3.2 Implementation of the AlexNet model
6.3.3 Implementation of the ResNet50 model
6.3.4 Performance evaluation parameters
6.4 Conclusion
Acknowledgments
References
Chapter An end-to-end content-aware generative adversarial network based method for multimodal medical image fusion
7.1 Introduction
7.2 Proposed fusion method
7.3 Experimental results and discussion
7.3.1 Dataset and implementation details
7.3.2 Performance analysis
7.4 Conclusion
References
Chapter Infrared thermography in diagnosing macular edema
8.1 Introduction
8.2 Physical principles of infrared thermal imaging
8.3 Infrared thermal imaging camera
8.4 Infrared thermal imaging in medicine
8.5 Procedure for infrared imaging
8.6 Classification algorithms used for thermal imaging
8.6.1 Artificial neural network (ANN)
8.6.2 Support vector machine (SVM)
8.6.3 Naive Bayes (NB)
8.6.4 AdaBoost
8.6.5 k-nearest neighbors (k-NN)
8.6.6 Decision tree (DT)
8.6.7 Random forest
8.7 Discussion
8.8 Conclusion
References
Chapter Variants of generative adversarial networks for underwater image enhancement
9.1 Introduction
9.2 Background and key concepts
9.2.1 The atmospheric model for image formation
9.3 Generative adversarial network
Algorithm
9.4 Variants of GAN for image enhancement
9.4.1 DenseGAN
9.4.2 Fusion generative adversarial network
9.4.3 WaterGAN
9.4.4 UWGAN
9.4.5 MCycleGAN
9.4.6 UIE-sGAN
9.4.7 UGAN
9.4.8 AquaGAN
9.5 Underwater image datasets
9.6 Performance standards
9.7 Future scope
9.8 Conclusion
References
Chapter Leveraging knowledge graphs for the analysis and recommendation of jobs
10.1 Introduction
10.2 Related work
10.3 Proposed methodology
10.4 Experimental results.
10.4.1 Dataset used
10.4.2 Evaluation parameters
10.4.3 Tools used for evaluation and benefits
10.4.4 Experimental results
10.5 Conclusion
References
Chapter A recommender system based on variants of singular value decomposition
11.1 Introduction
11.2 Limitations of the recommender system
11.3 Literature review
11.4 Singular value decomposition
11.4.1 Generation of predictions
11.4.2 Recommendation generation
11.4.3 Performance evaluation
11.5 Experiment
11.6 Conclusion and future scope
Acknowledgments
References
Chapter Misleading multimodal news dataset for detecting fraudulent content
12.1 Introduction
12.2 Misleading multimodal dataset
12.2.1 Dataset collection and annotation
12.2.2 Dataset description
12.2.3 Proposed dataset analysis and evaluation
12.3 Experimental analysis and results
12.4 Conclusion
References
Chapter Structural crack detection, segmentation, and classification: a review
13.1 Introduction
13.2 Methodology
13.3 Literature and experimental review
13.4 Results and discussion
13.5 Conclusion
References
Chapter A systematic review of fault detection in hardware and software systems
14.1 Introduction
14.2 Literature review
14.2.1 Data based and signal model based fault detection
14.2.2 Process model based fault detection
14.2.3 Knowledge model based fault detection
14.3 Methodology and implementation
14.3.1 Hardware fault detection (faulty electrical lines)
14.3.2 Software fault detection
14.4 Results and outcomes
14.5 Conclusion
Acknowledgments
References
Chapter ACPSOD-Net: a deep atrous convolution pooling based network for salient object detection
15.1 Introduction
15.2 Proposed method
15.2.1 Encoders
15.2.2 The atrous convolution-based pooling (ACP) module.
15.2.3 Fusion module
15.2.4 Decoders
15.2.5 Saliency map fusion
15.2.6 Loss function
15.3 Experimental findings
15.3.1 Implementation details and SOD datasets
15.3.2 Results and discussion
15.4 Conclusion
References.
Acknowledgements
Editor biographies
Sachin Taran
Chhavi Dhiman
Manjeet Kumar
List of contributors
Contributor biographies
Mohit Aggarwal
Ashwini M Bakde
Manisha Das
Sanjoli Goyal
Deep Gupta
Mohammad Farukh Hashmi
Muskan Jain
Ruchi Jain
Kranti Kamble
Vikram Singh Kardam
Anahita Karthik
Basavaraj G Katageri
P S Kavimandan
Paramkusham Venkata Keshava Krishna
Avinash G Keskar
Rajashri Khanai
Smith K Khare
Amit Kukker
Bhagyashree V Lad
Ruchika Malhotra
Seema Mehla
Virender Kumar Mehla
Om Mishra
Rajan Mishra
Sukumar Nagineni
Chandan Nayak
Krishna Pai
Deepak Parashar
Amit Patil
J Persiya
P Prakash
Kemal Polat
Rajkumar V Raikar
Aditi Rao
S Mohamed Mansoor Roomi
Neetu Sardana
A Sasithradevi
Joydeep Sengupta
Ananya Srivastava
Komal Tahiliani
Dattaprasad Torse
Abhuday Tripathi
Deepika Varshney
M Vijayalakshmi
Mayank Yadav
Chapter A precise ECG QRS complex detector using a WOA optimized fractional-order digital differentiator
1.1 Introduction
1.2 Materials and methods
1.2.1 Introduction to the metaheuristic algorithm
Algorithm 1:
1.2.2 Proposed fifth-order FODD design
1.2.3 Whale optimization algorithm (WOA)
1.2.4 Proposed QRS complex identification scheme
1.2.5 ECG databases and the performance measurement
1.3 Results
1.3.1 Simulation results of the proposed FODD
1.3.2 Sensitivity analysis
1.3.3 Comparison with existing FODDs
1.3.4 Simulation results of the proposed fifth-order FODD for the HOD based QRS detector
1.4 Conclusion
References
Chapter Focal and non-focal EEG signal classification using the Wigner-Ville distribution and deep feature extraction
2.1 Introduction
2.2 Methodology
2.2.1 Dataset.
2.2.2 Optimum allocation sampling (OAS)
2.2.3 Wigner-Ville distribution
2.2.4 Deep learning model
2.2.5 ELM classifier
2.3 Results and discussion
2.4 Conclusion
References
Chapter Multi-channel EEG-based affective emotion identification using a dual-stage filtering approach
3.1 Introduction
3.2 Method
3.2.1 Database
3.2.2 Dual-stage CIF filtering with EMD-NCVMD integration
3.2.3 Feature extraction
3.2.4 Classification
3.3 Result and discussion
3.4 Conclusion
References
Chapter Variational mode decomposition based entropy features for classification of myopathy, neuropathy, and normal EMG signals
4.1 Introduction
4.2 Methodology
4.2.1 Dataset11https://physionet.org/physiobank/database/emgdb/
4.2.2 Variational mode decomposition
4.2.3 Feature extraction
4.2.4 Random forest (RF) classifier
4.2.5 Distance-weighted k-nearest neighbour classifier (WKNN)
4.3 Results and discussion
4.4 Conclusion
References
Chapter Epileptic EEG signal classification using wavelet transform and SVM
5.1 Introduction
5.2 Proposed method
5.2.1 Wavelet transform
5.2.2 Feature extraction
5.2.3 Feature selection
5.3 Classifications
5.3.1 Support vector machine
5.3.2 k-nearest neighbors
5.3.3 Decision tree
5.3.4 Ensemble bagged random forest
5.4 Result and discussion
5.5 Conclusion
References
Chapter Imbalanced class problem analysis for lung cancer detection using convolutional neural networks
6.1 Introduction
6.1.1 Problem definition
6.1.2 Literature survey and research gap
6.1.3 Motivation and contribution
6.2 Methods and materials
6.2.1 Dataset
6.2.2 ResNet50 and AlexNet
6.2.3 DenseNet
6.2.4 SMOTE analysis to oversample the data
6.2.5 Class-weighted approach
6.2.6 Data augmentation
6.3 Results and discussion.
6.3.1 Implementation of the DenseNet-121 model
6.3.2 Implementation of the AlexNet model
6.3.3 Implementation of the ResNet50 model
6.3.4 Performance evaluation parameters
6.4 Conclusion
Acknowledgments
References
Chapter An end-to-end content-aware generative adversarial network based method for multimodal medical image fusion
7.1 Introduction
7.2 Proposed fusion method
7.3 Experimental results and discussion
7.3.1 Dataset and implementation details
7.3.2 Performance analysis
7.4 Conclusion
References
Chapter Infrared thermography in diagnosing macular edema
8.1 Introduction
8.2 Physical principles of infrared thermal imaging
8.3 Infrared thermal imaging camera
8.4 Infrared thermal imaging in medicine
8.5 Procedure for infrared imaging
8.6 Classification algorithms used for thermal imaging
8.6.1 Artificial neural network (ANN)
8.6.2 Support vector machine (SVM)
8.6.3 Naive Bayes (NB)
8.6.4 AdaBoost
8.6.5 k-nearest neighbors (k-NN)
8.6.6 Decision tree (DT)
8.6.7 Random forest
8.7 Discussion
8.8 Conclusion
References
Chapter Variants of generative adversarial networks for underwater image enhancement
9.1 Introduction
9.2 Background and key concepts
9.2.1 The atmospheric model for image formation
9.3 Generative adversarial network
Algorithm
9.4 Variants of GAN for image enhancement
9.4.1 DenseGAN
9.4.2 Fusion generative adversarial network
9.4.3 WaterGAN
9.4.4 UWGAN
9.4.5 MCycleGAN
9.4.6 UIE-sGAN
9.4.7 UGAN
9.4.8 AquaGAN
9.5 Underwater image datasets
9.6 Performance standards
9.7 Future scope
9.8 Conclusion
References
Chapter Leveraging knowledge graphs for the analysis and recommendation of jobs
10.1 Introduction
10.2 Related work
10.3 Proposed methodology
10.4 Experimental results.
10.4.1 Dataset used
10.4.2 Evaluation parameters
10.4.3 Tools used for evaluation and benefits
10.4.4 Experimental results
10.5 Conclusion
References
Chapter A recommender system based on variants of singular value decomposition
11.1 Introduction
11.2 Limitations of the recommender system
11.3 Literature review
11.4 Singular value decomposition
11.4.1 Generation of predictions
11.4.2 Recommendation generation
11.4.3 Performance evaluation
11.5 Experiment
11.6 Conclusion and future scope
Acknowledgments
References
Chapter Misleading multimodal news dataset for detecting fraudulent content
12.1 Introduction
12.2 Misleading multimodal dataset
12.2.1 Dataset collection and annotation
12.2.2 Dataset description
12.2.3 Proposed dataset analysis and evaluation
12.3 Experimental analysis and results
12.4 Conclusion
References
Chapter Structural crack detection, segmentation, and classification: a review
13.1 Introduction
13.2 Methodology
13.3 Literature and experimental review
13.4 Results and discussion
13.5 Conclusion
References
Chapter A systematic review of fault detection in hardware and software systems
14.1 Introduction
14.2 Literature review
14.2.1 Data based and signal model based fault detection
14.2.2 Process model based fault detection
14.2.3 Knowledge model based fault detection
14.3 Methodology and implementation
14.3.1 Hardware fault detection (faulty electrical lines)
14.3.2 Software fault detection
14.4 Results and outcomes
14.5 Conclusion
Acknowledgments
References
Chapter ACPSOD-Net: a deep atrous convolution pooling based network for salient object detection
15.1 Introduction
15.2 Proposed method
15.2.1 Encoders
15.2.2 The atrous convolution-based pooling (ACP) module.
15.2.3 Fusion module
15.2.4 Decoders
15.2.5 Saliency map fusion
15.2.6 Loss function
15.3 Experimental findings
15.3.1 Implementation details and SOD datasets
15.3.2 Results and discussion
15.4 Conclusion
References.