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Smart and Sustainable Intelligent Systems.

By: Contributor(s): Material type: TextTextSeries: Sustainable Computing and Optimization SeriesPublisher: Newark : John Wiley & Sons, Incorporated, 2021Copyright date: ©2021Edition: 1st edDescription: 1 online resource (576 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119752127
Subject(s): Genre/Form: Additional physical formats: Print version:: Smart and Sustainable Intelligent SystemsDDC classification:
  • 006.33
LOC classification:
  • QA76.76.E95 .S637 2021
Online resources:
Contents:
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Organization of the Book -- Acknowledgement -- Part 1: MACHINE LEARNINGAND ITS APPLICATION -- 1 Single Image Super-Resolution Using GANs for High-Upscaling Factors -- 1.1 Introduction -- 1.2 Methodology -- 1.2.1 Architecture Details -- 1.2.2 Loss Function -- 1.3 Experiments -- 1.3.1 Environment Details -- 1.3.2 Training Dataset Details -- 1.3.3 Training Parameters -- 1.4 Experiments -- 1.5 Conclusions -- 1.6 Related Work -- References -- 2 Landmark Recognition Using VGG16 Training -- 2.1 Introduction -- 2.2 Related Work -- 2.2.1 ImageNet Classification -- 2.2.2 Deep Local Features -- 2.2.3 VGG Architecture -- 2.3 Proposed Solution -- 2.3.1 Revisiting Datasets -- 2.4 Results and Conclusions -- 2.5 Discussions -- References -- 3 A Comparison of Different Techniques Used for Classification of Bird Species From Images -- 3.1 Introduction -- 3.2 CUB_200_2011 Birds Dataset -- 3.3 Machine Learning Approaches -- 3.3.1 Softmax Regression -- 3.3.2 Support Vector Machine -- 3.3.3 K-Means Clustering -- 3.4 Deep Learning Approaches -- 3.4.1 CNN -- 3.4.2 RNN -- 3.4.3 InceptionV3 -- 3.4.4 ImageNet -- 3.5 Conclusion -- 3.6 Conclusion and Future Scope -- References -- 4 Road Lane Detection Using Advanced Image Processing Techniques -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Proposed Approach -- 4.4 Analysis -- 4.4.1 Dataset -- 4.4.2 Camera Calibration and Distortion Correction -- 4.4.3 Threshold Binary Image -- 4.4.4 Perspective Transform -- 4.4.5 Finding the Lane Lines-Sliding Window -- 4.4.6 Radius of Curvature and Central Offset -- 4.5 Annotation -- 4.6 Illustrations -- 4.7 Results and Discussions -- 4.8 Conclusion and Future Work -- References -- 5 Facial Expression Recognition in Real Time Using Convolutional Neural Network -- 5.1 Introduction.
5.1.1 Need of Study -- 5.2 Related Work -- 5.3 Methodology -- 5.3.1 Applying Transfer Learning using VGG-16 -- 5.3.2 Modeling and Training -- 5.4 Results -- 5.5 Conclusion and Future Scope -- References -- 6 Feature Extraction and Image Recognition of Cursive Handwritten English Words Using Neural Network and IAM Off-Line Database -- 6.1 Introduction -- 6.1.1 Scope of Discussion -- 6.2 Literature Survey -- 6.2.1 Early Scanners and the Digital Age -- 6.2.2 Machine Learning -- 6.3 Methodology -- 6.3.1 Dataset -- 6.3.2 Evaluation Metric -- 6.3.3 Pre-Processing -- 6.3.4 Implementation and Training -- 6.4 Results -- 6.4.1 CNN Output -- 6.4.2 RNN Output -- 6.4.3 Model Analysis -- 6.5 Conclusion and Future Work -- 6.5.1 Image Pre-Processing -- 6.5.2 Extend the Model to Fit Text-Lines -- 6.5.3 Integrate Word Beam Search Decoding -- References -- 7 License Plate Recognition System Using Machine Learning -- 7.1 Introduction -- 7.1.1 Machine Learning -- 7.2 Related Work -- 7.3 Classification Models -- 7.3.1 Logistic Regression -- 7.3.2 Decision Trees -- 7.3.3 Random Forest -- 7.3.4 K Means Clustering -- 7.3.5 Support Vector Machines -- 7.4 Proposed Work and Methodology -- 7.4.1 Detect License Plate -- 7.4.2 Segmentation -- 7.4.3 Training the Model -- 7.4.4 Prediction and Recognition -- 7.5 Result -- 7.6 Conclusion -- 7.7 Future Scope -- References -- 8 Prediction of Disease Using Machine Learning Algorithms -- 8.1 Introduction -- 8.2 Datasets and Evaluation Methodology -- 8.2.1 Datasets -- 8.3 Algorithms Used -- 8.3.1 Decision Tree Classifier -- 8.3.2 Random Forest Classifier -- 8.3.3 Support Vector Machines -- 8.3.4 K Nearest Neighbors -- 8.4 Results -- 8.5 Conclusion -- References -- Part 2: DEEP LEARNING AND ITS APPLICATION -- 9 Brain Tumor Prediction by Binary Classification Using VGG-16 -- 9.1 Introduction -- 9.2 Existing Methodology.
9.2.1 Dataset Description -- 9.2.2 Data Import and Preprocessing -- 9.3 Augmentation -- 9.3.1 For CNN Model -- 9.3.2 For VGG 16 Model -- 9.4 Models Used -- 9.4.1 CNN Model -- 9.4.2 VGG 16 Model -- 9.5 Results -- 9.6 Comparison -- 9.7 Conclusion and Future Scope -- References -- 10 Study of Gesture-Based Communication Translator by Deep Learning Technique -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 The Proposed Recognition System -- 10.3.1 Image Acquisition -- 10.3.2 Pre-Processing -- 10.3.3 Classification and Recognition -- 10.3.4 Post-Processing -- 10.4 Result and Discussion -- 10.5 Conclusion -- 10.6 Future Work -- References -- 11 Transfer Learning for 3-Dimensional Medical Image Analysis -- 11.1 Introduction -- 11.2 Literature Survey -- 11.2.1 Deep Learning -- 11.2.2 Transfer Learning -- 11.2.3 PyTorch and Keras (Our Libraries) -- 11.3 Related Works -- 11.3.1 Convolution Neural Network -- 11.3.2 Transfer Learning -- 11.4 Dataset -- 11.4.1 Previously Used Dataset -- 11.4.2 Data Acquiring -- 11.4.3 Cleaning the Data -- 11.4.4 Understanding the Data -- 11.5 Description of the Dataset -- 11.6 Architecture -- 11.7 Proposed Model -- 11.7.1 Model 1 -- 11.7.2 Model 2 -- 11.7.3 Model 3 -- 11.8 Results and Discussion -- 11.8.1 Coding the Model -- 11.9 Conclusion -- 11.10 Future Scope -- Acknowledgement -- References -- 12 A Study on Recommender Systems -- 12.1 Introduction -- 12.2 Background -- 12.2.1 Popularity-Based -- 12.2.2 Content-Based -- 12.2.3 Collaborative Systems -- 12.3 Methodology -- 12.3.1 Input Parameters -- 12.3.2 Implementation -- 12.3.3 Performance Measures -- 12.4 Results and Discussion -- 12.5 Conclusions and Future Scope -- References -- 13 Comparing Various Machine Learning Algorithms for User Recommendations Systems -- 13.1 Introduction -- 13.2 Related Works -- 13.3 Methods and Materials -- 13.3.1 Content-Based Filtering.
13.3.2 Collaborative Filtering -- 13.3.3 User-User Collaborative Filtering -- 13.3.4 Item-Item Collaborative Filtering -- 13.3.5 Random Forest Algorithm -- 13.3.6 Neural Networks -- 13.3.7 ADA Boost Classifier -- 13.3.8 XGBoost Classifier -- 13.3.9 Trees -- 13.3.10 Regression -- 13.3.11 Dataset Description -- 13.4 Experiment Results and Discussion -- 13.5 Future Enhancements -- 13.6 Conclusion -- References -- 14 Indian Literacy Analysis Using Machine Learning Algorithms -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Solution Approaches -- 14.3.1 Preparation of Dataset -- 14.3.2 Data Reduction -- 14.3.3 Data Visualization -- 14.3.4 Prediction Models -- 14.4 Proposed Approach -- 14.5 Result Analysis -- 14.6 Conclusion and Future Scope -- 14.6.1 Conclusion -- 14.6.2 Future Scope -- References -- 15 Motion Transfer in Videos using Deep Convolutional Generative Adversarial Networks -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Methodology -- 15.3.1 Pre-Processing -- 15.3.2 Pose Detection and Estimation -- 15.4 Pose to Video Translation -- 15.5 Results and Analysis -- 15.6 Conclusion and Future Scope -- References -- 16 Twin Question Pair Classification -- 16.1 Introduction -- 16.2 Literature Survey -- 16.2.1 Duplicate Quora Questions Detection by Lei Guo, Chong Li &amp -- Haiming Tian -- 16.2.2 Natural Language Understanding with the Quora Question Pairs Dataset by Lakshay Sharma, Laura Graesser, Nikita Nangia, Utku Evci -- 16.2.3 Duplicate Detection in Programming Question Answering Communities by Wei Emma Zhang and Quan Z. Sheng, Macquarie University -- 16.2.4 Exploring Deep Learning in Semantic Question Matching by Arpan Poudel and Ashwin Dhakal [1] -- 16.3 Methods Applied for Training -- 16.3.1 Count Vectorizer -- 16.3.2 TF-IDF Vectorizer -- 16.3.3 XG Boosting -- 16.3.4 Random Forest Classifier -- 16.4 Proposed Methodology.
16.4.1 Data Collection -- 16.4.2 Data Analysis -- 16.4.3 Data Cleaning and Pre-Processing -- 16.4.4 Embedding -- 16.4.5 Feature Extraction -- 16.4.6 Data Splitting -- 16.4.7 Modeling -- 16.5 Observations -- 16.6 Conclusion -- References -- 17 Exploration of Pixel-Based and Object-Based Change Detection Techniques by Analyzing ALOS PALSAR and LANDSAT Data -- 17.1 Introduction -- 17.2 Classification of Pixel-Based and Object-Based Change Detection Methods -- 17.2.1 Image Ratio -- 17.2.2 Image Differencing -- 17.2.3 Image Regression -- 17.2.4 Vegetation Index Differencing -- 17.2.5 Minimum Distance Classification -- 17.2.6 Maximum Likelihood Classification -- 17.2.7 Spectral Angle Mapper (SAM) -- 17.2.8 Support Vector Machine -- 17.3 Experimental Results -- 17.3.1 Omission Error -- 17.3.2 Commission Error -- 17.3.3 User Accuracy -- 17.3.4 Producer Accuracy -- 17.3.5 Overall Accuracy -- 17.4 Conclusion -- Acknowledgment -- References -- 18 Tracing Bad Code Smells Behavior Using Machine Learning with Software Metrics -- 18.1 Introduction -- 18.2 Related Work and Motivation -- 18.3 Methodology -- 18.3.1 Data Collection -- 18.3.2 Static Code Analysis -- 18.3.3 Sampling -- 18.3.4 Machine Learning Approach -- 18.4 Result Analysis and Manual Validation -- 18.5 Threats, Limitation and Conclusion -- References -- 19 A Survey on Various Negation Handling Techniques in Sentiment Analysis -- 19.1 Introduction -- 19.2 Methods for Negation Identification -- 19.2.1 Bag of Words -- 19.2.2 Contextual Valence Shifters -- 19.2.3 Semantic Relations -- 19.2.4 Relations and Dependency-Based or Syntactic-Based -- 19.3 Word Embedding -- 19.4 Conclusion -- References -- 20 Mobile-Based Bilingual Speech Corpus -- 20.1 Introduction -- 20.2 Overview of Multilingual Speech Corpus for Indian Languages -- 20.3 Methodology for Speech Corpus Development -- 20.3.1 Recording Setup.
20.3.2 Capturing.
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Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Organization of the Book -- Acknowledgement -- Part 1: MACHINE LEARNINGAND ITS APPLICATION -- 1 Single Image Super-Resolution Using GANs for High-Upscaling Factors -- 1.1 Introduction -- 1.2 Methodology -- 1.2.1 Architecture Details -- 1.2.2 Loss Function -- 1.3 Experiments -- 1.3.1 Environment Details -- 1.3.2 Training Dataset Details -- 1.3.3 Training Parameters -- 1.4 Experiments -- 1.5 Conclusions -- 1.6 Related Work -- References -- 2 Landmark Recognition Using VGG16 Training -- 2.1 Introduction -- 2.2 Related Work -- 2.2.1 ImageNet Classification -- 2.2.2 Deep Local Features -- 2.2.3 VGG Architecture -- 2.3 Proposed Solution -- 2.3.1 Revisiting Datasets -- 2.4 Results and Conclusions -- 2.5 Discussions -- References -- 3 A Comparison of Different Techniques Used for Classification of Bird Species From Images -- 3.1 Introduction -- 3.2 CUB_200_2011 Birds Dataset -- 3.3 Machine Learning Approaches -- 3.3.1 Softmax Regression -- 3.3.2 Support Vector Machine -- 3.3.3 K-Means Clustering -- 3.4 Deep Learning Approaches -- 3.4.1 CNN -- 3.4.2 RNN -- 3.4.3 InceptionV3 -- 3.4.4 ImageNet -- 3.5 Conclusion -- 3.6 Conclusion and Future Scope -- References -- 4 Road Lane Detection Using Advanced Image Processing Techniques -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Proposed Approach -- 4.4 Analysis -- 4.4.1 Dataset -- 4.4.2 Camera Calibration and Distortion Correction -- 4.4.3 Threshold Binary Image -- 4.4.4 Perspective Transform -- 4.4.5 Finding the Lane Lines-Sliding Window -- 4.4.6 Radius of Curvature and Central Offset -- 4.5 Annotation -- 4.6 Illustrations -- 4.7 Results and Discussions -- 4.8 Conclusion and Future Work -- References -- 5 Facial Expression Recognition in Real Time Using Convolutional Neural Network -- 5.1 Introduction.

5.1.1 Need of Study -- 5.2 Related Work -- 5.3 Methodology -- 5.3.1 Applying Transfer Learning using VGG-16 -- 5.3.2 Modeling and Training -- 5.4 Results -- 5.5 Conclusion and Future Scope -- References -- 6 Feature Extraction and Image Recognition of Cursive Handwritten English Words Using Neural Network and IAM Off-Line Database -- 6.1 Introduction -- 6.1.1 Scope of Discussion -- 6.2 Literature Survey -- 6.2.1 Early Scanners and the Digital Age -- 6.2.2 Machine Learning -- 6.3 Methodology -- 6.3.1 Dataset -- 6.3.2 Evaluation Metric -- 6.3.3 Pre-Processing -- 6.3.4 Implementation and Training -- 6.4 Results -- 6.4.1 CNN Output -- 6.4.2 RNN Output -- 6.4.3 Model Analysis -- 6.5 Conclusion and Future Work -- 6.5.1 Image Pre-Processing -- 6.5.2 Extend the Model to Fit Text-Lines -- 6.5.3 Integrate Word Beam Search Decoding -- References -- 7 License Plate Recognition System Using Machine Learning -- 7.1 Introduction -- 7.1.1 Machine Learning -- 7.2 Related Work -- 7.3 Classification Models -- 7.3.1 Logistic Regression -- 7.3.2 Decision Trees -- 7.3.3 Random Forest -- 7.3.4 K Means Clustering -- 7.3.5 Support Vector Machines -- 7.4 Proposed Work and Methodology -- 7.4.1 Detect License Plate -- 7.4.2 Segmentation -- 7.4.3 Training the Model -- 7.4.4 Prediction and Recognition -- 7.5 Result -- 7.6 Conclusion -- 7.7 Future Scope -- References -- 8 Prediction of Disease Using Machine Learning Algorithms -- 8.1 Introduction -- 8.2 Datasets and Evaluation Methodology -- 8.2.1 Datasets -- 8.3 Algorithms Used -- 8.3.1 Decision Tree Classifier -- 8.3.2 Random Forest Classifier -- 8.3.3 Support Vector Machines -- 8.3.4 K Nearest Neighbors -- 8.4 Results -- 8.5 Conclusion -- References -- Part 2: DEEP LEARNING AND ITS APPLICATION -- 9 Brain Tumor Prediction by Binary Classification Using VGG-16 -- 9.1 Introduction -- 9.2 Existing Methodology.

9.2.1 Dataset Description -- 9.2.2 Data Import and Preprocessing -- 9.3 Augmentation -- 9.3.1 For CNN Model -- 9.3.2 For VGG 16 Model -- 9.4 Models Used -- 9.4.1 CNN Model -- 9.4.2 VGG 16 Model -- 9.5 Results -- 9.6 Comparison -- 9.7 Conclusion and Future Scope -- References -- 10 Study of Gesture-Based Communication Translator by Deep Learning Technique -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 The Proposed Recognition System -- 10.3.1 Image Acquisition -- 10.3.2 Pre-Processing -- 10.3.3 Classification and Recognition -- 10.3.4 Post-Processing -- 10.4 Result and Discussion -- 10.5 Conclusion -- 10.6 Future Work -- References -- 11 Transfer Learning for 3-Dimensional Medical Image Analysis -- 11.1 Introduction -- 11.2 Literature Survey -- 11.2.1 Deep Learning -- 11.2.2 Transfer Learning -- 11.2.3 PyTorch and Keras (Our Libraries) -- 11.3 Related Works -- 11.3.1 Convolution Neural Network -- 11.3.2 Transfer Learning -- 11.4 Dataset -- 11.4.1 Previously Used Dataset -- 11.4.2 Data Acquiring -- 11.4.3 Cleaning the Data -- 11.4.4 Understanding the Data -- 11.5 Description of the Dataset -- 11.6 Architecture -- 11.7 Proposed Model -- 11.7.1 Model 1 -- 11.7.2 Model 2 -- 11.7.3 Model 3 -- 11.8 Results and Discussion -- 11.8.1 Coding the Model -- 11.9 Conclusion -- 11.10 Future Scope -- Acknowledgement -- References -- 12 A Study on Recommender Systems -- 12.1 Introduction -- 12.2 Background -- 12.2.1 Popularity-Based -- 12.2.2 Content-Based -- 12.2.3 Collaborative Systems -- 12.3 Methodology -- 12.3.1 Input Parameters -- 12.3.2 Implementation -- 12.3.3 Performance Measures -- 12.4 Results and Discussion -- 12.5 Conclusions and Future Scope -- References -- 13 Comparing Various Machine Learning Algorithms for User Recommendations Systems -- 13.1 Introduction -- 13.2 Related Works -- 13.3 Methods and Materials -- 13.3.1 Content-Based Filtering.

13.3.2 Collaborative Filtering -- 13.3.3 User-User Collaborative Filtering -- 13.3.4 Item-Item Collaborative Filtering -- 13.3.5 Random Forest Algorithm -- 13.3.6 Neural Networks -- 13.3.7 ADA Boost Classifier -- 13.3.8 XGBoost Classifier -- 13.3.9 Trees -- 13.3.10 Regression -- 13.3.11 Dataset Description -- 13.4 Experiment Results and Discussion -- 13.5 Future Enhancements -- 13.6 Conclusion -- References -- 14 Indian Literacy Analysis Using Machine Learning Algorithms -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Solution Approaches -- 14.3.1 Preparation of Dataset -- 14.3.2 Data Reduction -- 14.3.3 Data Visualization -- 14.3.4 Prediction Models -- 14.4 Proposed Approach -- 14.5 Result Analysis -- 14.6 Conclusion and Future Scope -- 14.6.1 Conclusion -- 14.6.2 Future Scope -- References -- 15 Motion Transfer in Videos using Deep Convolutional Generative Adversarial Networks -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Methodology -- 15.3.1 Pre-Processing -- 15.3.2 Pose Detection and Estimation -- 15.4 Pose to Video Translation -- 15.5 Results and Analysis -- 15.6 Conclusion and Future Scope -- References -- 16 Twin Question Pair Classification -- 16.1 Introduction -- 16.2 Literature Survey -- 16.2.1 Duplicate Quora Questions Detection by Lei Guo, Chong Li &amp -- Haiming Tian -- 16.2.2 Natural Language Understanding with the Quora Question Pairs Dataset by Lakshay Sharma, Laura Graesser, Nikita Nangia, Utku Evci -- 16.2.3 Duplicate Detection in Programming Question Answering Communities by Wei Emma Zhang and Quan Z. Sheng, Macquarie University -- 16.2.4 Exploring Deep Learning in Semantic Question Matching by Arpan Poudel and Ashwin Dhakal [1] -- 16.3 Methods Applied for Training -- 16.3.1 Count Vectorizer -- 16.3.2 TF-IDF Vectorizer -- 16.3.3 XG Boosting -- 16.3.4 Random Forest Classifier -- 16.4 Proposed Methodology.

16.4.1 Data Collection -- 16.4.2 Data Analysis -- 16.4.3 Data Cleaning and Pre-Processing -- 16.4.4 Embedding -- 16.4.5 Feature Extraction -- 16.4.6 Data Splitting -- 16.4.7 Modeling -- 16.5 Observations -- 16.6 Conclusion -- References -- 17 Exploration of Pixel-Based and Object-Based Change Detection Techniques by Analyzing ALOS PALSAR and LANDSAT Data -- 17.1 Introduction -- 17.2 Classification of Pixel-Based and Object-Based Change Detection Methods -- 17.2.1 Image Ratio -- 17.2.2 Image Differencing -- 17.2.3 Image Regression -- 17.2.4 Vegetation Index Differencing -- 17.2.5 Minimum Distance Classification -- 17.2.6 Maximum Likelihood Classification -- 17.2.7 Spectral Angle Mapper (SAM) -- 17.2.8 Support Vector Machine -- 17.3 Experimental Results -- 17.3.1 Omission Error -- 17.3.2 Commission Error -- 17.3.3 User Accuracy -- 17.3.4 Producer Accuracy -- 17.3.5 Overall Accuracy -- 17.4 Conclusion -- Acknowledgment -- References -- 18 Tracing Bad Code Smells Behavior Using Machine Learning with Software Metrics -- 18.1 Introduction -- 18.2 Related Work and Motivation -- 18.3 Methodology -- 18.3.1 Data Collection -- 18.3.2 Static Code Analysis -- 18.3.3 Sampling -- 18.3.4 Machine Learning Approach -- 18.4 Result Analysis and Manual Validation -- 18.5 Threats, Limitation and Conclusion -- References -- 19 A Survey on Various Negation Handling Techniques in Sentiment Analysis -- 19.1 Introduction -- 19.2 Methods for Negation Identification -- 19.2.1 Bag of Words -- 19.2.2 Contextual Valence Shifters -- 19.2.3 Semantic Relations -- 19.2.4 Relations and Dependency-Based or Syntactic-Based -- 19.3 Word Embedding -- 19.4 Conclusion -- References -- 20 Mobile-Based Bilingual Speech Corpus -- 20.1 Introduction -- 20.2 Overview of Multilingual Speech Corpus for Indian Languages -- 20.3 Methodology for Speech Corpus Development -- 20.3.1 Recording Setup.

20.3.2 Capturing.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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