Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- List of Contributors -- Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision -- 1.1 Introduction -- 1.2 Deep Learning Algorithms -- 1.2.1 Convolutional Neural Networks -- 1.2.2 Restricted Boltzmann Machines -- 1.2.3 Deep Boltzmann Machines -- 1.2.4 Deep Belief Networks -- 1.2.5 Stacked (de-Noising) Auto-Encoders -- 1.2.5.1 Auto-Encoders -- 1.2.5.2 Denoising Auto Encoders -- 1.3 Comparison of the Deep Learning Algorithms -- 1.4 Challenges in Deep Learning Algorithms -- 1.5 Conclusion and Future Scope -- References -- Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach -- 2.1 Introduction -- 2.2 Applications of Object Extraction -- 2.3 Edge Detection Techniques -- 2.3.1 Roberts Edge Detection -- 2.3.2 Sobel Edge Detection -- 2.3.3 Prewitt's Operator -- 2.3.4 Laplacian Edge Detection -- 2.4 Related Work -- 2.5 Proposed Model -- 2.6 Results and Discussion -- 2.7 Conclusion -- References -- Chapter 3 Deep Learning Techniques for Image Captioning -- 3.1 Introduction to Image Captioning -- 3.1.1 How Does Image Recognition Work? -- 3.2 Introduction to Deep Learning -- 3.2.1 Pros of the Deep Learning Algorithm -- 3.2.2 Customary / Traditional CV Methodology -- 3.2.3 Limitations/challenges of Traditional CV Methodology -- 3.2.4 Overcome the Limitations of Deep Learning -- 3.3 Deep Learning Algorithms for Object Detection -- 3.3.1 Types of Deep Models for Object Detection -- 3.4 How Image Captioning Works -- 3.4.1 Transformer Based Image Captioning -- 3.4.2 Visual Scene Graph Based Image Captioning -- 3.4.3 Challenges in Image Captioning -- 3.5 Conclusion -- References -- Chapter 4 Deep Learning-Based Object Detection for Computer Vision Tasks: A Survey of Methods and Applications -- 4.1 Introduction. , 4.2 Object Detection -- 4.3 Two-Stage Object Detectors -- 4.3.1 R-CNN -- 4.3.2 SPPNet -- 4.3.3 Fast RCNN -- 4.3.4 Faster RCNN -- 4.3.5 R-FCN -- 4.3.6 FPN -- 4.3.7 Mask RCNN -- 4.3.8 G-RCNN -- 4.4 One-Stage Object Detectors -- 4.4.1 YOLO -- 4.4.2 CenterNet -- 4.4.3 SSD -- 4.4.4 RetinaNet -- 4.4.5 EfficientDet -- 4.4.6 YOLOR -- 4.5 Discussion On Model Performance -- 4.5.1 Future Trends -- 4.6 Conclusion -- References -- Chapter 5 Deep Learning Algorithms for Computer Vision: A Deep Insight Into Principles and Applications -- 5.1 Introduction -- 5.2 Preliminary Concepts of Deep Learning -- 5.2.1 Artificial Neural Network -- 5.2.2 Convolution Neural Network (CNNs) -- 5.3 Recurrent Neural Network (RNNs) -- 5.4 Overview of Applied Deep Learning in Computer Vision -- 5.6 Industrial Applications of Computer Vision -- 5.7 Future Scope in Computer Vision -- 5.8 Conclusion -- References -- Chapter 6 Handwritten Equation Solver Using Convolutional Neural Network -- 6.1 Introduction -- 6.2 State-Of-The-Art -- 6.3 Convolutional Neural Network -- 6.3.1 Convolution Layer -- 6.3.2 Pooling Layer -- 6.3.3 Fully Connected Layer -- 6.3.4 Activation Function -- 6.4 Handwritten Equation Recognition -- 6.4.1 Dataset Preparation -- 6.4.2 Proposed Methodology -- 6.4.2.1 Dataset Acquisition -- 6.4.2.2 Preprocessing -- 6.4.2.3 Recognition Through CNN Model -- 6.4.2.4 Processing Inside CNN Model -- 6.4.3 Solution Approach -- 6.5 Results and Discussion -- 6.6 Conclusion and Future Scope -- References -- Chapter 7 Agriware: Crop Suggester System By Estimating the Soil Nutrient Indicators -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Proposed Methodology -- 7.4 Experimental Results and Discussion -- 7.5 Conclusion and Future Work -- References -- Chapter 8 A Machine Learning Based Expeditious Covid-19 Prediction Model Through Clinical Blood Investigations -- 8.1 Introduction. , 8.2 Literature Survey -- 8.3 Methodology -- 8.3.1 Dataset and Its Preparation -- 8.3.2 Classification Set Up -- 8.3.3 Performance Evaluation -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Chapter 9 Comparison of Image Based and Audio Based Techniques for Bird Species Identification -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Methodology -- 9.4 System Design -- 9.4.1 Dataset Used -- 9.4.2 Image Based Techniques -- 9.4.3 Audio Based Techniques -- 9.5 Results and Analysis -- 9.6 Conclusion -- References -- Chapter 10 Detection of Ichthyosis Vulgaris Using SVM -- 10.1 Introduction -- 10.2 Literature Survey -- 10.3 Types of Ichthyosis -- 10.3.1 Ichthyosis Vulgaris -- 10.3.2 Hyperkeratosis -- 10.4 Sex-Connected Ichthyosis -- 10.5 Symptoms -- 10.6 Complications -- 10.7 Diagnosis -- 10.8 Methodology -- 10.9 Results -- 10.10 Future Work -- 10.11 Conclusion -- References -- Chapter 11 Chest X-Ray Diagnosis and Report Generation: Deep Learning Approach -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Proposed Methodology -- 11.3.1 Overview of Deep Learning Algorithms -- 11.3.2 Data -- 11.3.3 Feature Extraction -- 11.3.4 Report Generation -- 11.3.5 Evaluation Metrics -- 11.4 Results and Discussions -- 11.4.1 Feature Extraction -- 11.4.2 Report Generation -- 11.5 Conclusion -- References -- Chapter 12 Deep Learning Based Automatic Image Caption Generation for Visually Impaired People -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Methods and Materials -- 12.3.1 Data Set -- 12.3.2 Deep Neural Network Architectures -- 12.3.2.1 Convolution Neural Networks (CNNs) -- 12.3.2.2 Long Short-Term Memory (LSTM) -- 12.3.3 Proposed Model -- 12.3.3.1 Feature Extraction Models -- 12.3.3.2 Workflow for Image Caption Generation -- 12.4 Results and Discussion -- 12.4.1 Evaluation Metrics -- 12.4.2 Analysis of Results -- 12.4.3 Examples. , 12.5 Discussion and Future Work -- 12.6 Conclusions -- References -- Chapter 13 Empirical Analysis of Machine Learning Techniques Under Class Imbalance and Incomplete Datasets -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Class Imbalance -- 13.2.2 Missing Values -- 13.2.3 Missing Value in Class Imbalance Datasets -- 13.3 Methodology -- 13.4 Results -- 13.4.1 Overall Performance -- 13.4.2 Effect of Class Imbalance and Missing Values -- 13.5 Conclusion -- References -- Chapter 14 Gabor Filter as Feature Extractor in Anomaly Detection From Radiology Images -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Research Methodology -- 14.3.1 Data Set -- 14.3.2 Gabor Filter -- 14.4 Results -- 14.5 Discussion -- 14.6 Conclusion -- References -- Chapter 15 Discriminative Features Selection From Zernike Moments for Shape Based Image Retrieval System -- 15.1 Introduction -- 15.2 Zernike Moments Descriptor (ZMD) -- 15.2.1 Zernike Moments (ZMs) -- 15.2.2 Orthogonality -- 15.2.3 Rotation Invariance -- 15.2.4 Features Selection -- 15.3 Discriminative Features Selection -- 15.4 Similarity Measure -- 15.5 Experimental Study -- 15.5.1 Experiment Setup -- 15.5.2 Performance Measurement -- 15.5.3 Experiment Results -- 15.6 Discussions and Conclusions -- References -- Chapter 16 Corrected Components of Zernike Moments for Improved Content Based Image Retrieval: A Comprehensive Study -- 16.1 Introduction -- 16.2 Proposed Descriptors -- 16.2.1 Invariant Region Based Descriptor Using Corrected ZMs Features -- 16.2.2 Selection of Appropriate Features -- 16.2.3 Invariant Contour Based Descriptor Using HT -- 16.3 Similarity Metrics -- 16.4 Experimental Study and Performance Evaluation -- 16.4.1 Measurement of Retrieval Accuracy -- 16.4.2 Performance Comparison and Experiment Results -- 16.5 Discussion and Conclusion -- References. , Chapter 17 Translate and Recreate Text in an Image -- 17.1 Introduction -- 17.2 Literature Survey -- 17.3 Existing System -- 17.4 Proposed System -- 17.4.1 Flow Chart -- 17.4.2 Experimental Setup -- 17.4.3 Dataset -- 17.4.4 Text Detection and Extraction -- 17.4.5 Auto Spelling Correction -- 17.4.6 Machine Translation and Inpainting -- 17.5 Implementation -- 17.5.1 Text Detection and Extraction -- 17.5.2 Auto Spelling Correction -- 17.5.2.1 Simple RNN -- 17.5.2.2 Embed RNN -- 17.5.2.3 Bidirectional LSTM -- 17.5.2.4 Encoder Decoder With LSTM -- 17.5.2.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance -- 17.5.3 Machine Translation -- 17.5.4 Inpainting -- 17.6 Result Analysis -- 17.6.1 Simple RNN -- 17.6.2 Embed RNN -- 17.6.3 Bidirectional LSTM -- 17.6.4 Encoder Decoder With LSTM -- 17.6.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance -- 17.6.6 BLEU (Bilingual Evaluation Understudy) -- 17.7 Conclusion -- Acknowledgments -- References -- Chapter 18 Multi-Label Indian Scene Text Language Identification: Benchmark Dataset and Deep Ensemble Baseline -- 18.1 Introduction -- 18.2 Related Works -- 18.3 IIITG-MLRIT2022 -- 18.4 Proposed Methodology -- 18.4.1 Transfer Learning -- 18.4.1.1 ResNet50 [37] -- 18.4.1.2 XceptionNet [39] -- 18.4.1.3 DenseNet [38] -- 18.4.1.4 MobileNetV2 [36] -- 18.4.2 Convolutional Neural Network -- 18.4.3 Multi-Label Deep Ensemble Via Majority Voting -- 18.4.4 Weighted Binary Cross Entropy -- 18.5 Training and Experiment -- 18.6 Results and Discussion -- 18.7 Conclusion -- References -- Chapter 19 AI Based Wearables for Healthcare Applications: A Survey of Smart Watches -- 19.1 Introduction -- 19.2 Systematic Review -- 19.2.1 Criterion to Select Research -- 19.2.2 Source of Information -- 19.2.2.1 Search Plan -- 19.2.2.2 Data Abstraction -- 19.2.3 Outcomes -- 19.2.4 Healthcare Applications. , 19.2.4.1 Activity and Human Motion.