Advanced Computational Techniques for Sustainable Computing

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
1 Sustainable Computing—An Overview
1.1 What Is Sustainability?
1.2 Sustainable Development: Motivations and Obstacles
1.2.1 Present versus Future Generations
1.2.2 Economic versus Environmental Perspectives
1.3 Goals to Strive toward Sustainable Development
1.4 Sustainability and Computing
1.4.1 Product from Hardware Perspective
1.4.2 Product from Software Perspective
1.4.3 Production Processes from Hardware Perspective
1.4.4 Production Processes from Software Perspective
1.4.5 Consumption Processes from Hardware Perspective
1.4.6 Consumption Processes from Software Perspective
1.5 Computing Paradigms for Individual Sustainable Development Goals
1.5.1 No Poverty
1.5.2 Zero Hunger
1.5.3 Good Health and Well-Being
1.5.4 Quality Education
1.5.5 Gender Equality
1.5.6 Clean Water and Sanitation
1.5.7 Affordable/Clean Energy
1.5.8 Decent Work/Economic Growth
1.5.9 Industry, Innovation, and Infrastructure
1.5.10 Reduced Inequalities
1.5.11 Sustainable Cities/Communities
1.5.12 Responsible Consumption/Production
1.5.13 Climate Action
1.5.14 Life below Water
1.5.15 Life on Land
1.5.16 Peace/Justice/Strong Institutions
1.5.17 Partnerships for the Goals
1.6 Conclusion and Future Scope of Research
References
2 Ambient Air Quality Analysis and Prediction Using Air Quality Index and Machine Learning Models—The Case Study of Delhi
2.1 Introduction
2.2 Literature Survey
2.3 Materials and Methodology
2.3.1 Study Area
2.3.2 Dataset Description
2.3.3 Flowgraph
2.3.4 Data Preprocessing
2.4 Results and Analysis
2.5 Novelty
2.6 Conclusion
References
3 Assessing Land Cover and Drought Prediction for Sustainable Agriculture
3.1 Introduction
3.1.1 Contribution to Sustainable Development
3.2 Related Work
3.3 Case Study 1: Drought Prediction for Sustainable Agriculture
3.3.1 Dataset
3.3.2 Methodology
3.3.3 Implementation
3.4 Case Study 2: Assessing Land Cover
3.4.1 UC Merced
3.4.1.1 Dataset
3.4.1.2 Methodology
3.4.1.3 Implementation
3.4.2 Amazon Rainforest
3.4.2.1 Dataset
3.4.2.2 Dataset Analysis
3.4.2.3 Implementation
3.5 Conclusion
References
4 Electronic Health Record for Sustainable eHealth
4.1 Introduction
4.1.1 Significance of EHR
4.1.2 Health Analysis Benefits
4.2 Description
4.2.1 Dataset Descriptions and Parameters
4.2.2 Methodology
4.2.2.1 Imputation Methods
4.2.2.2 Machine Learning Algorithms
4.2.2.3 Clustering
4.2.2.4 Binning
4.2.2.5 Optimal Value of K for KNN
4.3 Experimental Results
4.3.1 Logistic Regression
4.3.2 Bayesian Logistic Regression
4.3.3 Comparative Study
4.3.4 Gender-Wise Distribution of Heart Diseases
4.3.5 Importance of Attributes Using Random Forest
4.3.6 Effect of Binning
4.4 Conclusion
References
5 Team Member Selection in Global Software Development—A Blockchain- Oriented Approach
5.1 Introduction
5.2 Related Work
5.3 Proposed Approach
5.4 System Architecture
5.5 Selection and Verification Process
5.6 Experimentation
5.6.1 Experiment
5.6.2 Result and Discussion of the Experiment Conducted
5.7 Conclusion
References
6 Machine Learning in Sustainable Healthcare
6.1 Introduction
6.2 Health Monitoring
6.2.1 Cyber-Physical System in Healthcare
6.2.2 Mobile Health Monitoring
6.2.3 Internet of Things in Healthcare
6.2.4 Wearable Computing for Health Monitoring
6.2.5 Ambient Assisted Living
6.2.6 Body Area Network
6.3 Significance of Machine Learning in Sustainable Healthcare
6.4 Case Studies
6.5 Conclusion
References
7 Multimedia Audio Signal Analysis for Sustainable Education
7.1 Introduction
7.2 Related Work
7.3 Proposed Methodology and Model Assumptions
7.4 Results and Observations
7.5 Conclusion
References
8 Smart Health Analytics for Sustainable Energy Monitoring Using IoT Data Analytics
8.1 Introduction
8.2 Internet of Things
8.3 Data Science vs. Data Analytics
8.4 Time-Series Data
8.4.1 Univariate Time Series
8.4.2 Multivariate Time Series
8.5 Aspects of Time Series
8.6 Predictive Analytics with Time-Series Streaming Data
8.7 Long Short-Term Memory
8.8 Optimizers
8.8.1 Root Mean Square Propagation
8.9 Comparison of SGD and ADAM
8.10 Performance Metrics
8.11 Result Analysis
8.12 Conclusion
References
9 Customer Analytics for Purchasing Behavior Prediction
9.1 Introduction
9.2 Literature Survey
9.3 Description and Experimentation
9.4 Implementation
9.4.1 Data Analysis
9.4.2 Customer Analytics and Loyalty Prediction
9.4.3 Analyzing Profitable Customers
9.4.4 Customer Segmentation
9.4.4.1 k-Means/Centroid-Based Clustering
9.4.5 Purchasing Behavior Prediction
9.5 Result and Analysis
9.6 Conclusion
References
10 Discernment of Malaria-Infected Cells in the Blood Streak Images Using Advanced Learning Techniques
10.1 Introduction
10.2 Related Work
10.3 Basic Methodology
10.3.1 Preprocessing
10.3.1.1 Noise Removal and Filtering
10.3.1.2 RGB to Gray/Binary Conversion
10.3.2 Segmentation
10.3.2.1 Watershed Segmentation
10.3.3 Feature Extraction
10.3.4 Classification
10.3.4.1 Convolution
10.3.4.2 Max Pooling
10.3.4.3 Flattening
10.3.4.4 Full Connection
10.4 Results
10.5 Conclusion
10.6 Future Scope
References
11 Handwritten Text Recognition with IoT Devices
11.1 Introduction
11.1.1 Supervised Learning
11.1.2 Unsupervised Learning
11.1.3 Reinforcement Learning
11.2 Related Work
11.3 Observations
11.4 Open Challenges
11.5 Proposed Solutions
11.5.1 Input
11.5.2 Additional Sensors
11.5.3 Machine Learning
11.5.4 Output
11.5.5 Dataset
11.5.6 Implementation
11.6 Conclusion
References
12 Circadian Rhythm and Lifestyle Diseases
12.1 Introduction
12.2 Characteristic Features of Mammalian CC
12.2.1 Architecture of the Biological Clock
12.2.2 SCN: Anatomy and Molecular Oscillations
12.2.3 Molecular Mechanism of Core Circadian Genes
12.3 Hormonal Mechanism of Sleep–Wake Cycle
12.4 CR and Sleep-Related Disorders
12.4.1 Intrinsic CRSW Disorders
12.4.1.1 Delayed SW-Phase Disorder
12.4.1.2 Advanced SW-Phase Disorder
12.4.1.3 Non-Twenty-Four-Hour SW Rhythm Disorder
12.4.1.4 Irregular SW Rhythm Disorder
12.4.2 Extrinsic or Environmentally Influenced CRSWDs
12.4.2.1 Jet Lag
12.4.2.2 Shift Work
12.4.3 Therapeutic Options for CRSWDs
12.5 Circadian Regulation of Metabolism
12.6 Circadian Disruption and Neurodegeneration
12.6.1 Oxidative Stress
12.6.2 Neuroinflammation
12.6.3 Neurodegenerative Disorders
12.6.4 AD and Related Dementias
12.6.5 Parkinson’s Disease
12.7 Role of CR in Cancer
12.8 Conclusion
References
13 Deep Learning for Automated Disease Detection
13.1 Introduction: Background and Driving Forces
13.2 Dataset Description
13.3 Data Processing and Techniques
13.3.1 Electronic Health Record
13.3.2 Image Data
13.3.3 Skin Cancer
13.3.4 Cardiac MRI Segmentation
13.3.5 Lung Cancer Detection
13.4 Conclusion
References
14 Time Series Analysis and Trend Exploration of Stock Market
14.1 Introduction and Related Study
14.2 Dataset Description
14.3 Requirement Analysis and Solution Approach
14.4 Modeling and Implementation Details
14.5 Conclusion
References
15 Medical Search Engine
15.1 Introduction
15.2 Literature Survey
15.3 Methodology
15.3.1 Dataset Description
15.3.2 Approach
15.3.3 Artificial Neural Network
15.3.4 System Architecture
15.3.5 Proposed Solution
15.3.6 Overfitting
15.3.7 Validating Model’s Effectiveness
15.4 Heart Rate Calculator
15.5 Results and Analysis
15.5.1 Data Visualization
15.6 Conclusion
15.7 Future Work
References
16 Assessing Impact of Global Terrorism Using Time Series Analysis
16.1 Introduction
16.2 Related Work
16.3 Dataset Description and Analysis
16.3.1 Global Terrorism Database (GTD)
16.3.2 GeoEPR Dataset
16.3.3 G-Econ
16.3.4 Nighttime Lights
16.3.5 Population Density
16.3.6 Topography
16.3.7 Happy Index
16.3.8 Overall Analysis
16.4 Proposed Methodology
16.4.1 Data Sampling
16.4.2 Normalizing
16.4.3 Dataset Design
16.4.4 Machine Learning Algorithms
16.5 Result Analysis
16.6 Conclusion
References
17 Sustainable Statistics for Death Cognizance Analysis
17.1 Introduction
17.2 Literature Review
17.3 Problem Formulation
17.4 Observations from the Dataset
17.5 Experimental Results
17.6 Conclusion
17.7 Future Scope
References
18 Modeling the Immune Response of B-Cell Receptor Using Petri Net for Tuberculosis
18.1 Introduction
18.2 Biological Background
18.3 Methodology
18.4 Structural and Behavioral Properties of PN with Its Application in Modeling Biological Processes
18.5 PN Modeling of BCR- Signaling Pathways
18.6 Results and Discussions
18.7 Validation and Scope
18.8 Conclusion
Acknowledgments
Abbreviations
References
19 Crop Prediction and the Sustainability of Farming
19.1 Introduction
19.2 Related Work
19.3 Methodology
19.3.1 Dataset Description
19.3.2 Techniques Used
19.3.3 Proposed Prediction Model
19.4 Results and Analysis
19.5 Conclusion
References
20 Personalized Heart Disease Framework for Health Sustainability
20.1 Introduction
20.1.1 Basics
20.1.2 Background Study
20.1.2.1 Decision Tree
20.1.2.2 Logistic Regression
20.1.2.3 Neural Network
20.1.2.4 Support Vector Machine
20.1.2.5 Item-Based Collaborative Filtering
20.2 Literature Survey
20.3 Methodology
20.3.1 Predictive Model
20.3.2 Recommendation System
20.4 Results
20.5 Conclusion
References
21 Sports Analytics for Classifying Player Actions in Basketball Games
21.1 Introduction
21.2 Related Work
21.3 Methodology
21.3.1 Dataset
21.3.2 Use of OpenPose for Getting Player’s Coordinates
21.3.3 Court Segmentation to Optimize Number of Detected Players
21.3.4 Player Tracking
21.3.5 Proposed Model Consisting of C3D Model and FCNN
21.3.5.1 C3D
21.3.5.2 FCNN
21.3.5.3 Hybrid Model
21.4 Results
21.4.1 OpenPose Results
21.4.2 Court Segmentation Results
21.4.3 Player Tracking Results
21.4.4 Hybrid Model Results
21.5 Conclusion
References
Index