Machine Learning Algorithms and Applications

Name: Machine Learning Algorithms and Applications
Brand: Wiley
Price: 164.99 EUR
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Mettu Srinivas G. Sucharitha Anjanna Matta(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 10. August 2021

368 Seiten

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Inhalt

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Acknowledgments
Preface
Part 1: Machine Learning for Industrial Applications
1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services
1.1 Introduction
1.1.1 Open Government Data Initiative
1.1.2 Air Quality
1.1.3 Impact of Lockdown on Air Quality
1.2 Literature Survey
1.3 Implementation Details
1.3.1 Proposed Methodology
1.3.2 System Specifications
1.3.3 Algorithms
1.3.4 Control Flow
1.4 Results and Discussions
1.5 Conclusion
References
2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning
2.1 Introduction
2.2 Conventional Silkworm Egg Detection Approaches
2.3 Proposed Method
2.3.1 Model Architecture
2.3.2 Foreground-Background Segmentation
2.3.3 Egg Location Predictor
2.3.4 Predicting Egg Class
2.4 Dataset Generation
2.5 Results
2.6 Conclusion
Acknowledgment
References
3 A Wind Speed Prediction System Using Deep Neural Networks
3.1 Introduction
3.2 Methodology
3.2.1 Deep Neural Networks
3.2.2 The Proposed Method
3.2.2.1 Data Acquisition
3.2.2.2 Data Pre-Processing
3.2.2.3 Model Selection and Training
3.2.2.4 Performance Evaluation
3.2.2.5 Visualization
3.3 Results and Discussions
3.3.1 Selection of Parameters
3.3.2 Comparison of Models
3.4 Conclusion
References
4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections
4.1 Introduction
4.2 Related Work
4.3 Preliminaries
4.3.1 ResNet
4.3.2 Squeeze-and-Excitation Block
4.4 Proposed Model
4.4.1 Effect of Bridge Connections in ResNet
4.4.2 Res-SE-Net: Proposed Architecture
4.5 Experiments
4.5.1 Datasets
4.5.2 Experimental Setup
4.6 Results
4.7 Conclusion
References
5 Hitting the Success Notes of Deep Learning
5.1 Genesis
5.2 The Big Picture: Artificial Neural Network
5.3 Delineating the Cornerstones
5.3.1 Artificial Neural Network vs. Machine Learning
5.3.2 Machine Learning vs. Deep Learning
5.3.3 Artificial Neural Network vs. Deep Learning
5.4 Deep Learning Architectures
5.4.1 Unsupervised Pre-Trained Networks
5.4.2 Convolutional Neural Networks
5.4.3 Recurrent Neural Networks
5.4.4 Recursive Neural Network
5.5 Why is CNN Preferred for Computer Vision Applications?
5.5.1 Convolutional Layer
5.5.2 Nonlinear Layer
5.5.3 Pooling Layer
5.5.4 Fully Connected Layer
5.6 Unravel Deep Learning in Medical Diagnostic Systems
5.7 Challenges and Future Expectations
5.8 Conclusion
References
6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks
6.1 Introduction
6.1.1 Motivation
6.2 Literature Survey
6.3 Proposed Model for Credit Scoring
6.3.1 Stage-1: Feature Selection
6.3.2 Proposed Criteria Function
6.3.3 Stage-2: Ensemble Classifier
6.4 Results and Discussion
6.4.1 Experimental Datasets and Performance Measures
6.4.2 Classification Results With Feature Selection
6.5 Conclusion
References
7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization
7.1 Introduction
7.2 Related Works
7.3 Feature Agglomeration Clustering
7.4 Proposed Methodology
7.4.1 Pre-Processing
7.4.2 Modified Block Clustering Using Feature Agglomeration Technique
7.4.3 Post-Processing and Summary Generation
7.5 Results and Analysis
7.5.1 Experimental Setup and Data Sets Used
7.5.2 Evaluation Metrics
7.5.3 Evaluation
7.6 Conclusion
References
Part 2: Machine Learning for Healthcare Systems
8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier
8.1 Introduction
8.2 Materials and Methods
8.2.1 MIT-BIH Arrhythmia Database
8.2.2 Signal Pre-Processing
8.2.3 Feature Extraction
8.2.4 Classification
8.2.4.1 XGBoost Classifier
8.2.4.2 AdaBoost Classifier
8.3 Results and Discussion
8.4 Conclusion
References
9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data
9.1 Introduction
9.2 Related Works
9.3 An Overview of Gravitational Search Algorithm
9.4 Proposed Model
9.4.1 Pre-Processing
9.4.2 Proposed GSA-Based Feature Selection
9.5 Simulation Results
9.5.1 Biological Analysis
9.6 Conclusion
References
Part 3: Machine Learning for Security Systems
10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching
10.1 Introduction
10.1.1 Related Works
10.2 Preliminary Details
10.2.1 Fusion
10.3 Experiments and Results
10.3.1 Databases
10.3.2 Experimental Results
10.3.2.1 Same Spectral Matchings
10.3.2.2 Cross Spectral Matchings
10.3.3 Feature-Level Fusion
10.3.4 Score-Level Fusion
10.4 Conclusions
References
11 Fake Social Media Profile Detection
11.1 Introduction
11.2 Related Work
11.3 Methodology
11.3.1 Dataset
11.3.2 Pre-Processing
11.3.3 Artificial Neural Network
11.3.4 Random Forest
11.3.5 Extreme Gradient Boost
11.3.6 Long Short-Term Memory
11.4 Experimental Results
11.5 Conclusion and Future Work
Acknowledgment
References
12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks
12.1 Introduction
12.2 Related Work
12.3 Methods and Materials
12.3.1 Feature Extraction Using SURF
12.3.2 Feature Extraction Using Conventional Methods
12.3.2.1 Local Orientation Estimation
12.3.2.2 Singular Region Detection
12.3.3 Proposed CNN Architecture
12.3.4 Dataset
12.3.5 Computational Environment
12.4 Results
12.4.1 Feature Extraction and Visualization
12.5 Conclusion
Acknowledgements
References
13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features
13.1 Introduction
13.2 Related Work
13.3 Proposed Method
13.3.1 Convolutional Neural Network
13.3.1.1 Convolution Layer
13.3.1.2 Pooling Layer
13.3.1.3 ReLU Layer
13.3.1.4 Fully Connected Layer
13.3.2 Histogram of Gradient
13.3.3 Facial Landmark Detection
13.3.4 Support Vector Machine
13.3.5 Model Merging and Learning
13.4 Experimental Results
13.4.1 Datasets
13.5 Conclusion
Acknowledgement
References
Part 4: Machine Learning for Classification and Information Retrieval Systems
14 AnimNet: An Animal Classification Network using Deep Learning
14.1 Introduction
14.1.1 Feature Extraction
14.1.2 Artificial Neural Network
14.1.3 Transfer Learning
14.2 Related Work
14.3 Proposed Methodology
14.3.1 Dataset Preparation
14.3.2 Training the Model
14.4 Results
14.4.1 Using Pre-Trained Networks
14.4.2 Using AnimNet
14.4.3 Test Analysis
14.5 Conclusion
References
15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis
15.1 Introduction
15.2 Related Work
15.3 The Proposed System
15.3.1 Feedback Collector
15.3.2 Feedback Pre-Processor
15.3.3 Feature Selector
15.3.4 Feature Validator
15.3.4.1 Removal of Terms From Tentative List of Features on the Basis of Syntactic Knowledge
15.3.4.2 Removal of Least Significant Terms on the Basis of Contextual Knowledge
15.3.4.3 Removal of Less Significant Terms on the Basis of Association With Sentiment Words
15.3.4.4 Removal of Terms Having Similar Sense
15.3.4.5 Removal of Terms Having Same Root
15.3.4.6 Identification of Multi-Term Features
15.3.4.7 Identification of Less Frequent Feature
15.3.5 Feature Concluder
15.4 Result Analysis
15.5 Conclusion
References
16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding
16.1 Introduction
16.2 Related Work
16.3 Proposed Approach
16.3.1 Phrase Extraction
16.3.2 Corpus Annotation
16.3.3 Phrase Embedding
16.4 Experimental Setup
16.4.1 Dataset Preparation
16.4.2 Parameter Setting
16.5 Results
16.5.1 Phrase Extraction
16.5.2 Phrase Embedding
16.6 Conclusion
References
17 Image Anonymization Using Deep Convolutional Generative Adversarial Network
17.1 Introduction
17.2 Background Information
17.2.1 Black Box and White Box Attacks
17.2.2 Model Inversion Attack
17.2.3 Differential Privacy
17.2.3.1 Definition
17.2.4 Generative Adversarial Network
17.2.5 Earth-Mover (EM) Distance/Wasserstein Metric
17.2.6 Wasserstein GAN
17.2.7 Improved Wasserstein GAN (WGAN-GP)
17.2.8 KL Divergence and JS Divergence
17.2.9 DCGAN
17.3 Image Anonymization to Prevent Model Inversion Attack
17.3.1 Algorithm
17.3.2 Training
17.3.3 Noise Amplifier
17.3.4 Dataset
17.3.5 Model Architecture
17.3.6 Working
17.3.7 Privacy Gain
17.4 Results and Analysis
17.5 Conclusion
References
Index
EULA

1
A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services

Priyank Jain* and Gagandeep Kaur┼

Dept. of CSE & IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

Abstract

Air pollution has become a major concern in many developing countries. There are various factors that affect the quality of air. Some of them are Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter 10 (PM10), Particulate Matter 2.5 (PM2.5), Sulfur Dioxide (SO2), and Carbon Monoxide (CO). The Government of India under the Open Data Initiative provides data related to air pollution. Interpretation of this data requires analysis, visualization, and prediction. This study proposes machine learning and visualization techniques for air pollution. Both supervised and unsupervised learning techniques have been used for prediction and analysis of air quality at major places in India. The data used in this research contains the presence of six major air pollutants in a given area. The work has been extended to study the impact of lockdown on air pollution in Indian cities as well.

Keywords: Open Data, JSON API, OpenAQ, clustering, SVM, LSTM, prediction, Heat Map visualizations

1.1 Introduction

1.1.1 Open Government Data Initiative

These days, Open Government Data (OGD) is gaining momentum in providing sharing of knowledge by making public data and information of governmental bodies freely available to private citizens in system processable formats so as to reuse it for mutual benefits. OGD is a global movement and has its roots in the initiative started in 2009 by the US President as a Memorandum on Transparency and Open Government providing transparency in government projects and collaborations through sharing of data by public administration and industry to private citizens. Indian Government also has joined this initiative and provides free access to the data for development of applications, etc., so as to be able to reuse the information for mutual growth of industry and government. Open Data is the raw data made available by governments, industry, as well as NGOs, scientific institutions, and educational organizations and as such is not an individual's property.

The growth in the field of Open Data surely asks for new tools and techniques that can support it. Digital transformation needs companies to look out for new tools and techniques so as to be able to support the increasing need for faster delivery of services at large numbers of delivery points. Technologies like SaaS, mobile, and Internet of Things are gaining grounds in providing increase in endpoints and thus enabling the success of Open Data Initiative.

1.1.2 Air Quality

A report, State of Global Air 2017, by Institute for Health Metrics published recently [1] stated that, in the year 2015, there have been 1,090,400 deaths in India only due to an increase in PM2.5. High concentration of PM2.5 in the air is majorly caused by burning of petroleum fuels, household fuels, wooden fuels, agricultural fires, and industry related pollutants and contaminants. In 2015, India and Bangladesh came next to North African and Middle East countries in terms of places with high concentration of PM2.5 in air.

The report compares the ambient concentrations to the air quality guidelines established by the WHO in 2005. Based on the report by WHO, in the year 2015, 92% of the world's population and 86% of Indian population lived in unsafe areas exceeding safe limits. It is therefore need of the hour to develop tools that can provide better forecasting and easy understanding of the surrounding environment to naive users with lowest cost possible. Air Quality Index (AQI) is a commonly used index by agencies to provide information about quality of air in the vicinity to its residents.

The irony of today's Internet world is that even when we are inundated with large quantities of data or information, we as humans still struggle with its rightful interpretation. Extracting meaningful information from plain textual data in old tabular formats is an extraneous task. It is under these circumstances that data visualizations play a vital role.

The objective of this work was to build a machine learning-based visualization app for air quality evaluation and air pollution assessment by assessing various parameters by which air is getting polluted. Existing approaches did not account for variations in values of parameters at different locations. That is why we have trained different models for different locations to capture the trends better.

1.1.3 Impact of Lockdown on Air Quality

COVID-19 is a highly infectious disease caused by a newly discovered Coronavirus which was firstly identified in Wuhan, Central China. It has taken more than 460,000 lives as on 20th June, 2020, around the world. Due to this pandemic, a nationwide lockdown was imposed in India from 24th March, 2020, which extended up to several weeks. It is observed that lockdown could help in reducing pollution levels to a certain extent. This study tries to capture the variations in air pollution levels with and without lockdown.

1.2 Literature Survey

Air pollution occurs when particulates (pm2.5 and pm10), biological molecules, and other harmful substances are introduced into Earth's atmosphere. Natural processes and human activities can both generate air pollution. Air pollution can be further classified into two sections: visible air pollution and invisible air pollution.

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest [2]. A sliding window approach that employs the Multilayer Perceptron model to predict short-term PM 2.5 pollution situations is integrated into the proactive monitoring and control framework [2]. Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers, etc. [3]. A spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN) has been proposed [3]. A generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm in [4]. Air pollution data are available to the public as numeric values on the concentration of pollutants in the air on a web page [5, 6]. The numeric information is not conducive to determining the air pollution level intuitively [6]. To address this problem, the study developed and implemented a program for visualizing the air pollution level for six pollutants by obtaining real-time air pollution data using API and generating a keyhole markup language (KML) file defined to visualize the data on Google Earth intuitively [6]. Visualization method is intuitive and reliable through data quality checking and information sharing with multi-perspective air pollution graphs [7]. This method allows the data to be easily understood by the public and inspire or aid further studies in other fields [7]. As the tools are invented using spatial-temporal visualization and visual analytics for general visualization purposes of geo-referenced time series data of air quality and environmental data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations [8].

According to a survey mentioned in [9], pollution levels in many cities across the country reduced down drastically only after a few days of imposing lockdown. Also, as discussed in the study [10], lockdown could be the effective alternative measure to be implemented for controlling air pollution.

The results above show us that all these machine learning techniques can be used for prediction and evaluating air pollution thereafter. Implementation details are described in the next section.

1.3 Implementation Details

There are several paradigms that can be implemented to classify the quality of air. The novelty of the application is to predict the future air quality of different places in detail with estimated values of various parameters along with its air quality and AQI. The application is able to visualize data in an efficient and descriptive way which is hard to analyze numerically in its raw form.

1.3.1 Proposed Methodology

Our proposed methodology steps have been discussed as follows:

1. Fetch real-time air quality data through an API of Open Data.
2. Clustering of air quality data based on AQI and assigning classes of air quality from good to severe.
3. Train a Support Vector Machine (SVM) model on the previously clustered data.
4. Train different time series Long Short-Term Memory (LSTM), a Recurrent Neural Network (RNN) model for different places to predict the future air quality of that place based on the previous trend.
5. Assign air quality and AQI to the observed/predicted values of the parameters. AQI is assigned based on the worst 24-hour average of all the parameters.
6. Different visualizations of the past data and future predictions using Heat Maps, Graphs, etc.
7. Compare variations in different parameters contributing toward air pollution at different places.
8. Provide a user-friendly web app to predict and analyze air quality.

Figure 1.1 Workflow of...

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Machine Learning Algorithms and Applications

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Inhalt

1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services

1.1 Introduction

1.1.1 Open Government Data Initiative

1.1.2 Air Quality

1.1.3 Impact of Lockdown on Air Quality

1.2 Literature Survey

1.3 Implementation Details

1.3.1 Proposed Methodology

Systemvoraussetzungen

1
A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services