Advanced Machine Intelligence and Signal Processing (Lecture Notes in Electrical Engineering, 858)

✍ Scribed by Deepak Gupta (editor), Koj Sambyo (editor), Mukesh Prasad (editor), Sonali Agarwal (editor)

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 859
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).

✦ Table of Contents

Preface
Contents
About the Editors
Leukocyte Subtyping Using Convolutional Neural Networks for Enhanced Disease Prediction
1 Introduction
2 Related Work and Motivation
3 Background
3.1 Convolutional Neural Network
3.2 Layers of CNN
3.3 Inception-v3
3.4 VGGNet
3.5 AlexNet
3.6 Transfer Learning
4 Proposed Work
4.1 Dataset
4.2 Simple CNN Model and Its Parameters
4.3 Inception-v3 CNN and Its Parameters
4.4 VGGNet CNN and Its Parameters
4.5 AlexNet CNN and Its Parameters
5 Results and Discussion
5.1 Evaluation Parameters
5.2 Simple CNN Results
5.3 Inception-v3 Results
5.4 VGGNet Results
5.5 AlexNet Results
5.6 Comparative Analysis
6 Conclusion
References
Analysis of Fifteen Approaches to Automated COVID-19 Detection Using Radiography Images
1 Introduction
1.1 Motivation
2 Related Work
3 Overview
4 Discussion and Comparative Analysis
4.1 Discussion
5 Conclusion and Future Work
5.1 Conclusion
5.2 Future Work
References
Human Emotion Classification Based on Speech Enhancement Using Neural Networks
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Proposed High-Pass Filter
3.2 Extracting Features Using MFCC
3.3 Neural Network Systems
3.4 Proposed Methodology for Emotion Recognition
4 Results
5 Conclusion and Future Scope
References
OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer
1 Introduction
2 Literature Review
3 Proposed Methodology
4 Working Model
4.1 Preprocessing
4.2 Feature Selection
4.3 Training and Test Datasets
4.4 XG Boost Classifier
4.5 Evaluation Metrics
5 Comparative Analysis
6 Comparative Analysis
References
Texture Unit Pattern Approach for Fabric Classification
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Results and Discussion
5 Conclusion
References
Skeleton-Based Human Action Recognition Using Motion and Orientation of Joints
1 Introduction
2 Related Work
3 Proposed System
3.1 Motion of Joints
3.2 Orientation of Joints
3.3 MOJ Descriptor
3.4 CNN Model
4 Experiments, Results, and Analysis
4.1 Datasets
4.2 Computation Complexity
5 Conclusion
References
An Empirical Study on Graph-Based Clustering Algorithms Using Schizophrenia Genes
1 Introduction
2 Motivation of the Work
3 Related Work
4 Graph-Based Clustering
4.1 Intuitive Idea
4.2 Column-Stochastic Matrix
4.3 Matrix Operations
4.4 Markov Clustering Algorithm
4.5 Regularized Markov Clustering
4.6 Variable Inflation Rate MCL
5 How to Construct PPI Network?
6 Datasets and Technical Details of the Simulations
7 Simulation Environment
8 Results
8.1 Runtime comparison
8.2 Histogram of the size of generated clusters
8.3 Selection Criteria for Hyper Parameters
8.4 Cluster Validity Index
8.5 Density of Matrix
8.6 Comments and Discussion
References
Hybrid Model of Multifactor Analysis with RNN-LSTM to Predict Stock Price
1 Introduction
2 Related Work
3 Proposed Method
3.1 Hybrid Model of RNN-LSTM to Predict Stock Price
3.2 MLPRegressor Model to Predict Stocks of SBI by Using Other Related Company Stocks
3.3 ARIMA-GRU Model for Prediction of Stock Price by Using Historical Data
4 Results
4.1 Experimental Dataset
4.2 Experimental Environment
4.3 Experimental Results
5 Conclusion
References
Deep Learning Approach to Deal with E-Waste
1 Introduction
2 Related Work
3 Methodology
3.1 Preprocessing
3.2 Network Architecture
4 Result and Discussion
5 Conclusion and Future Work
References
Comparative Study on Different Classifiers for Gait-Based Human Identification
1 Introduction
2 Related Works
3 Overview of System
3.1 Gait Energy Image (GEI)
3.2 Features Extraction
3.3 Features Selection
3.4 Classification
4 Experiment and Result
5 Conclusion
References
Internet of Things: A Survey on Fused Machine Learning-Based Intrusion Detection Approaches
1 Introduction
1.1 Motivation
2 Literature Review
2.1 DL Techniques for IDS
3 Dataset
4 Challenges and Future Scope
5 Conclusion
References
Image Fusion-Based Watermarking in IWT-SVD Domain
1 Introduction
2 Literature Survey
3 The Proposed Scheme
3.1 Procedure for Image Fusion
3.2 Procedure of Finding Embedding Strength (α)
3.3 Procedure for Embedding and Recovery Mark
4 Experimental Results
5 Conclusions
References
On Twin Bounded Support Vector Machine with Pinball Loss
1 Introduction
2 Related Work
2.1 Support Vector Machine with Hinge Loss (SVM)
2.2 Twin Bounded Support Vector Machine (TBSVM)
2.3 Pinball Loss Support Vector Machine (Pin-SVM)
3 Pinball Loss Twin Bounded Support Vector Machine (Pin-TBSVM)
3.1 Linear Pin-TBSVM
3.2 Non-linear Pin-TBSVM
4 Experimental Results
5 Conclusion
References
Traffic Rule Violation Detection System: Deep Learning Approach
1 Introduction
2 Literature Review
3 Proposed Solution
3.1 Helmet Detection
3.2 Triple Seat Detection
3.3 Number Plate Detection
4 Implementation
5 Results and Discussion
6 Conclusion
References
Template-Based Thinning Method for Handwritten Gujarati Character’s Strokes and its Classification for Writer-Dependent Gujarati Font Synthesis
1 Introduction
2 Proposed Solution
2.1 Image Acquisition
2.2 Character Segmentation
2.3 Stroke Segmentation
2.4 Improved Thinning Algorithm
2.5 Feature Extraction and Feature Vector Generation
2.6 Classification
3 Results and Discussion
3.1 Strokes Extraction and Verification
3.2 Classification
4 Conclusion
References
A Model on Intrusion Detection Using Firefly Algorithm and Classical Machine Learning
1 Introduction
2 Related Works
3 Firefly
4 The Proposed Model
4.1 Firefly Algorithm Encoding
4.2 Objective Function
4.3 Flowchart
4.4 The Algorithm
5 Performance Analysis
5.1 Experiment 1
5.2 Experiment 2
5.3 Comparative Analysis
6 Conclusion
References
Deep Learning Framework Based on Audio–Visual Features for Video Summarization
1 Introduction
1.1 Research Contributions
2 Related Work
2.1 Outcome of Literature Survey
3 Methodology
3.1 Audio Extraction and Processing
3.2 Low-Level Refining
3.3 High-level Refining
3.4 Static Video Summarization
3.5 Dynamic Video Summarization
4 Experimental Results and Analysis
4.1 Effect of Including Audio
4.2 Evaluation Using Standard Metrics
4.3 Static Video Summary
4.4 Dynamic Video Summary
4.5 Evaluation of Event Coverage
5 Conclusion and Future Work
References
A Comparative Study of Deep Learning Models for Word-Sense Disambiguation
1 Introduction
2 Literature Survey
3 Methodology
3.1 Text Preprocessing
3.2 Assigning Word ID and Sense IDs and the Dataset Used
3.3 Marking the Word to Disambiguate
3.4 Using Deep Learning Models
3.5 Determining Sense and Evaluation
4 Implementation Specifics and System Architecture
5 Results
5.1 Analysis of CNN and LSTMs
5.2 Analysis of Bidirectional LSTM and CNN + LSTM
5.3 Analysis on Use of Embeddings and TCN
6 Conclusion
References
MultiYOLO: Learning New YOLO Categories Without Full Retraining
1 Introduction
2 YOLO Description
3 The MultiYOLO Approach
3.1 MultiYOLO Training
3.2 MultiYOLO Detection
4 Implementation Details
5 Experimental Evaluation
5.1 An Illustrative Example: Adding the New Object Category ‘door’
5.2 Leave-One-Class-Out Experiments
6 Conclusions
References
Vehicle Routing Problem Using Reinforcement Learning: Recent Advancements
1 Introduction
2 Vehicle Routing Problem
2.1 Unit-Square Vehicle Routing Problem
3 Reinforcement Learning and Pointer Networks
3.1 Reinforcement Learning
3.2 Pointer Networks
4 Review Research
5 Major Issues and Challenges
6 Conclusion
References
Disease Detection of Plant Leaves with the Aid of Region Growing and Neural Network: A Comparative Analysis
1 Introduction
2 Literature Survey
2.1 Related Works
2.2 Review
3 Plant Leaf Disease Detection Using Machine Learning Strategy
3.1 Proposed Model
3.2 Image Scaling, Contrast Enhancement and Histogram Equalization for Pre-processing
4 Leaf Segmentation and Abnormality Segmentation for Plant Leaf Disease Detection
4.1 Leaf Segmentation
4.2 Abnormality Segmentation
5 Neural Network-Based Plant Leaf Disease Detection
5.1 GLCM Feature Extraction
5.2 NN-Based Classification
6 Results and Discussions
6.1 Experimental Setup
6.2 Dataset Description and Segmentation Results
6.3 Performance Analysis
6.4 Overall Classifier Analysis
7 Conclusion
References
A Web Application for Early Prediction of Diabetes Using Artificial Neural Network: A Comparative Study
1 Introduction
2 Brief Overview of Literature
3 Proposed Methodology
3.1 Algorithms Used
3.2 Proposed Work
3.3 Dataset Description
3.4 Data Preprocessing
3.5 Data Scaling
3.6 Data Mining
4 Experimental Results
4.1 Neural Network
4.2 Logistic Regression
4.3 Naive Bayes
4.4 K-Nearest Neighbors
4.5 Support Vector Machine
4.6 Model Prediction Comparison
5 Conclusions
References
Machine Learning Equipped Web-Based Disease Prediction and Recommender System
1 Introduction
2 Motivation
3 Literature Review
3.1 Classification Strategies
4 Proposed Methodology
4.1 Web Application
4.2 Disease Prediction System
5 Results
5.1 Experimental Analysis
6 Conclusion
References
Comparing the Predictive Accuracy of Machine Learning Algorithms for Neonatal Mortality Risk Classification
1 Introduction
1.1 Working with the Dataset—Flowchart
2 Literature Review
3 Dataset Description and Exploratory Analysis
4 Proposed Method and Data Mining Techniques
4.1 Data Cleansing and Sampling Approach
4.2 Data Mining Techniques
5 Experiments and Results
5.1 Computational Environment Setup
5.2 Results—Base Classifiers
5.3 Results—Ensemble Classifiers
5.4 Ensemble Classifiers—Suggested Framework
6 Discussions and Future Enhancements
References
Extraction of Waterbody Using Object-Based Image Analysis and XGBoost
1 Introduction
1.1 Water Indices for Water Body Extraction
2 Study Area
3 Proposed Method
3.1 Assessment of Accuracy
4 Results and Discussion
5 Conclusion
References
Deep Learning-Based Image Retrieval in the JPEG Compressed Domain
1 Introduction
2 Related Work
2.1 Deep Local and Global Features (DELG)
2.2 Deep Local Features (DELF)
2.3 Discrete Cosine Transform in Computer Vision
3 Methodology
3.1 The JPEG Encoder
3.2 Proposed Model
4 Experiments and Results
4.1 Pre-processing of Data in the Compressed Domain
4.2 Static Frequency Channel Selection
4.3 Model Implementation
4.4 Training Details
4.5 Evaluation Dataset
4.6 Feature Extraction and Matching
4.7 Results
5 Conclusion
References
Fake News Detection Using Genetic Algorithm-Based Feature Selection and Ensemble Learning
1 Introduction
2 Literature Survey
3 Methodology
3.1 Data Extraction
3.2 Feature Selection Using Genetic Algorithm
3.3 Models
3.4 Ensemble Methods
3.5 Evaluation Metrics
4 Implementation
4.1 Data
4.2 Feature Extraction
4.3 Feature Selection Using Genetic Algorithm
4.4 Model Training
5 Results
6 Conclusion
References
Application of SPSS for Forecasting of Renewable Energy as Future Energy in India
1 Introduction
2 Methodology
2.1 Forecasting Method
2.2 Forecasting Tool
3 Result and Discussion
3.1 Mini Hydro Energy
3.2 Solar Energy
3.3 Wind Energy
3.4 Biomass Energy
3.5 Other Energy
4 Conclusion
References
Robust Multi-task Least Squares Twin Support Vector Machines for Classification
1 Introduction
2 Related Work
2.1 Multi-task Twin Support Vector Machine
2.2 Multi-task Least Squares Twin Support Vector Machine
3 Robust Multi-task Least Squares Twin Support Vector Machines
3.1 Linear Robust Multi-task Least Squares Twin Support Vector Machine
3.2 Nonlinear Robust Multi-task Least Squares Twin Support Vector Machine
4 Computational Complexity
5 Experimental Results
5.1 Benchmark Data Sets
6 Conclusions
References
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain Using Transfer Learning Technique
1 Introduction
2 Related Literature
3 Proposed Methodology
3.1 Background of DCT Compression
3.2 Transfer Learning
3.3 Proposed Approach
3.4 Network Architecture
4 Experimental Results
4.1 Dataset
4.2 Results
5 Conclusion
References
Angle-Based Feature Learning in GNN for 3D Object Detection Using Point Cloud
1 Introduction
2 Related Work
3 Methodology
3.1 Methods
3.2 Dataset
4 Experiments
5 Results
6 Analysis
7 Conclusions
References
Increasing the Versatility of Leaky ReLU Using a Nonlinear Function
1 Activation Function
2 Rectified Linear Activation Function
3 Leaky Rectified Linear Activation Function
4 Removing the Negative Sign of Leaky ReLU
5 Result and Discussion
6 Conclusion
References
Automatic Recognition of Road Cracks Using Gray-Level Co-occurrence Matrix and Machine Learning
1 Introduction
2 Proposed Methodology
3 Results and Analysis
3.1 Linear Crack: Image Collected from China
3.2 Complex Alligator Crack: Image Captured from India
3.3 Longitudinal Crack: Image Captured from Japan Using a Smartphone Mounted Moving Vehicle
3.4 Qualitative Analysis
4 The Proposed Machine Learning-Based Algorithm Utilizing the Extracted Features
5 Conclusion
References
Feature Extraction and Fusion of Multiple Convolutional Neural Networks for Firearm Detection
1 Introduction
2 Related Work
3 Proposed Model
3.1 The Network Architecture and Feature Extraction
3.2 Fusion Mechanism and Dimensionality Reduction
3.3 Image Classification Using SVM
3.4 Bounding Box Regression Using LSTM
4 Experiments and Analysis
4.1 Dataset
4.2 Implementation Details and Results
4.3 Comparison of Experimental Results
5 Conclusion
References
Real-Time Speech Recognition Using Convolutional Neural Network
1 Introduction
2 Literature Survey
3 Dataset and Methodology
4 Result and Discussion
5 Conclusion
References
Hybrid Combination of Machine Learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System
1 Introduction
2 Dataset
3 Materials and Methods
4 Proposed Work
5 Numerical Experiments
6 Experiment’s Outcome and Discussion
7 Conclusion and Work for Future
References
Legal Text Analysis Using Pre-trained Transformers
1 Introduction
2 Literature Survey
2.1 Background
2.2 Outcome of Literature Survey
3 Methodology
3.1 Datasets
3.2 Pre-trained Models
4 Experiment and Results
4.1 Metrics
4.2 Classification
4.3 Similarity Scores
5 Conclusion and Future Work
References
Price Prediction of Agricultural Products Using Deep Learning
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset Description
3.2 CNN Model Description
3.3 LSTM Model
3.4 Bidirectional LSTM Model
4 Experiment Analysis
4.1 CNN Model
4.2 LSTM Model
4.3 Bidirectional LSTM Model
5 Results
6 Conclusion and Future Work
References
Classification of Brain Hemorrhage Using Fine-Tuned Transfer Learning
1 Introduction
2 Related Works
3 Background Study
3.1 Transfer Learning
3.2 VGG 16
3.3 Inception V3
4 Proposed Methodology
4.1 Dataset
4.2 Pre-processing
4.3 Network Training
5 Experimental Results and Discussions
5.1 Results
5.2 Analysis
6 Conclusions and Future Scope
References
Deep Learning-Based Emotion Classification of Hindi Text from Social Media
1 Introduction
2 Related Works
3 Methodology
3.1 Corpus Creation
3.2 Pre-processing
3.3 Feature Extraction
3.4 Classification
4 Results and Observation
5 Conclusion
References
Multilingual Speech Recognition for Indian Languages
1 Introduction
2 Related Work
3 Approach
4 Dataset Preparation
4.1 Common Voice Dataset
4.2 Microsoft Dataset
4.3 IIT Madras Dataset
5 Experiments and Observations
6 Conclusion
References
Study of Machine Learning Techniques to Mitigate Fraudulent Transaction in Credit Cards
1 Introduction
1.1 Objectives in the Paper
1.2 Organization of the Paper
2 Related Works
2.1 Application Dataset
2.2 Brief Review of State-of-the-Art Classification Algorithms
3 Results and Discussion
4 Concluding Remarks
References
Exploring Unet Architecture for Semantic Segmentation of the Brain MRI Scans
1 Introduction
2 Background
3 Methodology
3.1 Dataset
3.2 Outline
3.3 Workflow
4 Experimental Results
5 Conclusion
References
Analysis of Machine Learning Model-Based Cardiovascular Disease Prediction
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Overview of Cardiovascular Disease
3.2 Dataset
3.3 Data Pre-processing
3.4 Implementation of Computational Intelligence
3.5 Classification
4 Result Analysis
5 Conclusion
References
Cluster-Based Probabilistic Neural Networks for Outlier Detection Via Autoencoder Variants
1 Introduction
2 Sources of Outliers in WSNs
3 Related Work
4 Outlier Detection Process
4.1 Improved Energy-Efficient Recursive Clustering Method (IEERCM)
4.2 Autoencoder
4.3 Denoising Autoencoder
4.4 Autoencoder with Dropout
4.5 Probabilistic Neural Network (PNN)
4.6 Outlier Detection Process with AE-PNN, DAE-PNN, Dropout with PNN
5 Experimental Design
6 Conclusion
References
Long-Term Average Spectral Feature-Based Parkinson’s Disease Detection from Speech
1 Introduction
2 LTAS Feature
2.1 LTAS Feature Extraction Framework
2.2 Filterbanks
3 Experimental Setup
3.1 Database
3.2 Feature Extraction
3.3 Classifiers
4 Experimental Results and Discussion
5 Conclusion
References
Automatic Detection of Lung Cancer from Lung CT Images Using 3D Convolution Neural Network
1 Introduction
2 Materials and Methodology
2.1 Data Sets
2.2 Convolutional Neural Network
2.3 ReLU
2.4 Softmax Layer
2.5 Performance Metrics
3 Results and Discussion
4 Conclusion
References
An Experiment on Speech-to-Speech Translation of Hindi to English: A Deep Learning Approach
1 Introduction
2 Related Work
3 Automatic Speech Recognition
4 Neural Machine Translation
5 Speech Synthesis
6 Proposed Model
6.1 Modules of the Automatic Speech Recognition
6.2 Modules of Neural Machine Translation
6.3 Modules of Text-to-Speech
7 Experimental Setup
7.1 Automatic Speech Recognition
7.2 Neural Machine Translation
7.3 Speech Synthesis
8 Results
9 Conclusion
References
Identification of Biomarker Genes for Human Immunodeficiency Virus Using Ensemble Approach
1 Introduction
2 Methodology
2.1 Dataset
2.2 Pre-processing
2.3 Biclustering Algorithms
2.4 Mapping-Based Ensemble
2.5 Cluster Quality Indexing
2.6 Clusters with Primary Gene
2.7 Pearson’s Correlation
2.8 Ranking of Hub Genes
2.9 Comparison of Primary Genes with Hub Genes
2.10 Declaration of Newly Detected Biomarker
3 Discussion
4 Conclusion
References
Machine Learning Approaches for Rumor Detection on Social Media Platforms: A Comprehensive Survey
1 Introduction
2 Terminology
3 Datasets
4 Approaches for Detection of Rumors
5 Algorithm-Based Approach of Rumor Detection
6 Rumor Veracity Detection
7 Early Detection of Rumors
8 Discussion and Conclusion
References
Characterization of Common Thoracic Diseases from Chest X-ray Images Using CNN
1 Introduction
1.1 Overview
1.2 Related Work
2 Proposed Approach
2.1 Preprocessing on the Data
2.2 Detection Phase
2.3 Classification Phase
3 Dataset
4 Comparison with Previous Research
5 Result
5.1 Accuracy of Detection Phase
5.2 Accuracy of Classification Phase
6 Conclusion
References
Deep Learning Models for Tomato Plant Disease Detection
1 Introduction
1.1 Motivation
1.2 Literature Review
2 Materials and Methods
2.1 Technology Description
2.2 Dataset Description
2.3 Methodology
2.4 Convolutional Neural Network Models
3 Results and Discussion
3.1 VGG Results
3.2 GoogLeNet and AlexNet Results
4 Conclusions
References
A Deep Learning Framework for Anaphora Resolution from Social Media Text
1 Introduction
2 Challenges in Anaphora Resolution
2.1 The Language-Related Issues
2.2 The Technical Issues
3 Existing Work in Anaphora Resolution
4 Results and Discussions
5 Conclusion and Future Works
References
Autoencoder-Based Speech Features for Manipuri Dialect Identification
1 Introduction
2 Speech Dataset
3 Dialect Identification System
3.1 Feature Extraction
3.2 Reduction of Dimensions
3.3 Classifiers
4 Experimental Results
5 Conclusion and Future Work
References
A Survey on Image Processing and Machine Learning Techniques for Detection of Pulmonary Diseases Based on CT Images
1 Introduction
2 Segmentation Based on Image Processing Techniques
3 Segmentation Based on Deep Learning Techniques
4 Deep Learning Techniques for Detection of Lung Disease
5 Datasets
6 Discussion
6.1 Overview
6.2 Future Scope
7 Limitations
8 Conclusion
References
Emotion Classification Using Xception and Support Vector Machine
1 Introduction
2 Related Works
2.1 Xception
2.2 Discrete Wavelet Transform
2.3 Support Vector Machine
3 Materials and Methods
3.1 Proposed Methodology
3.2 Dataset
3.3 Implementation
4 Results
5 Conclusion
References
Energy-Based Least Squares Projection Twin SVM
1 Introduction
2 Related Work
2.1 Twin Support Vector Machines (TWSVM) ch57khemchandani2007twin
2.2 Least Squares Twin Support Vector Machines (LSTSVM) ch57kumar2009least
2.3 Energy-Based Least Squares Twin Support Vector Machines (ELSTSVM) ch57nasiri2014energy
3 Energy-Based Least Squares Projection Twin Support Vector Machines (ELSPTSVM)
3.1 Linear ELSPTSVM
3.2 ELSPTSVM for Multiple Projection Directions
3.3 Nonlinear ELSPTSVM
4 Experiments
5 Conclusion
References
Task Scheduling Using Deep Q-Learning
1 Introduction
2 Literature Survey
3 Proposed Work
3.1 Environment
3.2 Agent
3.3 GUI Simulator
4 Results and Analysis
5 Conclusion and Future Work
References
Land Use Land Cover Classification Using Different ML Algorithms on Sentinel-2 Imagery
1 Introduction
2 Literature Review
3 Material and Data Used
3.1 Study Area
3.2 Sentinel-2 Satellite Imagery
4 Classification
4.1 Naive Bayes (1763)
4.2 CART (Between 1970–1980)
4.3 Gradient Tree Boost (1999)
4.4 Random Forest
5 Results
6 Discussion
7 Conclusion
References
Convolution Filter-Based Deep Neural Networks for Timely Diagnosis of COVID-19 Disease with Chest Radiographs
1 Introduction
2 Related Work
3 Methodology
3.1 Data Collection and Preprocessing
3.2 Architecture Used
3.3 Evaluation Parameters
4 Results
5 Conclusion
References
DNN Based Short Term Load Forecasting of Individual Household with Real and Synthetic Data-Set
1 Introduction
2 Basics of Deep Learning Models
2.1 Pooling Load Profiles
2.2 Choice of Optimizer
2.3 Input–Output Window Modeling
3 Implementation Details
3.1 Data-Set Preparation
3.2 Generating Data-Set with SRLS
3.3 Program Implementation
4 Results and Discussions
4.1 Performance of Load-Predictions on Real-Household Data
4.2 Performance of Load-Predictions on SRLS-Based Simulated Data
5 Conclusion
References
Indian Currency Classification and Counterfeit Detection Using Deep Learning and Image Processing Approach
1 Introduction
2 Literature Survey
3 Terminologies
3.1 Convolution Neural Network (CNN)
3.2 Image Augmentation
4 Methodology
4.1 Dataset Description
4.2 Deep Learning Models for Currency Classification and Fake Currency
4.3 Fake Currency Detection Using Image Processing
5 Experimental Results
5.1 Performance Analysis of Image Processing Approach for Fake Currency Detection
6 Comparative Analysis
7 Conclusion
References
Deep Residual Network-Based Sentiment Analysis of Amazon Cell Phone Reviews
1 Introduction
2 Motivation
2.1 Literature Review
2.2 Challenges
3 Proposed DRN for the Sentiment Analysis of Amazon Cell Phone Reviews
3.1 Review Acquisition
3.2 Preprocessing
3.3 Feature Extraction
3.4 Sentimental Classification
4 Results and Discussion
4.1 Experimental Setup
4.2 Dataset Description
4.3 Performance Metrics
4.4 Comparative Methods
4.5 Comparative Analysis
5 Conclusion
References
Deep Learning-Based Topic Categorization of Tamil Social Media Text Content
1 Introduction
2 Related Works
3 Methodology
4 Dataset
5 Approach
6 Experiments and Results
7 Error Analysis
8 Conclusion
References
Geometrical Feature Extraction of CAD Models with Fully Convolutional Networks
1 Introduction
2 Related Work
3 Background
3.1 Sparse Tensor and Convolution
3.2 Architecture
4 Materials and Methods
4.1 Dataset
4.2 Methodology
4.3 Frobenius Norm
5 Results and Analysis
6 Conclusion and Future Work
References
Image Forgery Detection Using Multi-Layer Convolutional Neural Network
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Data Pre-processing
3.2 Feature Extraction Module
3.3 Classification Module
4 Experimental Results and Discussion
4.1 Dataset
4.2 Data Pre-processing and Data Augmentation
4.3 Training and Implementation
4.4 Performance Evaluation Parameters
4.5 Result Visualization
4.6 Comparative Analysis
5 Conclusion
References
Feature Extraction from Plant Leaves and Classification of Plant Health Using Machine Learning
1 Introduction
2 Related Works
3 Methodology
3.1 Data
3.2 Data Pre-processing
3.3 Feature Extraction
3.4 Training, Testing, Validation and Prediction
4 Results and Discussion
4.1 Findings
5 Conclusion
References