Data Science and Interdisciplinary Research: Recent Trends and Applications
Description
Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields.
Key Themes:
Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting.
Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis.
Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity.
Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture.
Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities.
Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments.
Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face.
Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis.
Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data.
Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.
All prices
More details
Other editions
Additional editions
Content
- Cover
- Title
- Copyright
- End User License Agreement
- Contents
- Preface
- List of Contributors
- A Comprehensive Study and Analysis on Prediction of Rainfall Across Multiple Countries using Machine Learning
- C. Kishor Kumar Reddy1,*, P.R. Anisha1 and Nguyen Gia Nhu2
- INTRODUCTION
- RELEVANT WORK
- DISCUSSION
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- A Novel Approach for Clustering Large-scale Cloud Data using Computational Mechanism
- Zdzislaw Polkowski1, Jyoti Prakash Mishra2 and Sambit Kumar Mishra2,*
- INTRODUCTION
- REVIEW OF LITERATURE
- IMPLEMENTATION USING GENETIC ALGORITHM
- STRATEGIES OF EVALUATION OF QUERY PLANS RELATED TO LARGE SCALE DATA
- ALGORITHM
- EXPERIMENTAL ANALYSIS
- DISCUSSION AND FUTURE DIRECTION
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- Secure Communication Over In-Vehicle Network Using Message Authentication
- Manjunath Managuli1,*, Sudha Slake2, Pankaja S. Kadalgi2 and Gouri C. Khadabadi2
- INTRODUCTION
- Background for Vehicle Security
- Hacking Incidents on Vehicles
- Economic Value at Risk Due to Poor Security Investments
- Security Goals
- Security Attacks
- Techniques to Implement Security Mechanisms
- Network Security Model
- Security by Design
- Cybersecurity Concept for Connected Car
- Designing Secure Automotive Systems
- Security by Design across CAR Development Lifecycle
- Vehicle Communication Buses
- Format of Request and Response Messages
- Internal Key used for Decryption and Encryption
- AES Algorithm
- Sequence of Key Update Procedure
- Routine Control (31 hex) Service
- Steps Involved in Key Update
- Step 1:
- Step 2:
- Step 3:
- Step 4:
- STEP 5:
- Step 6:
- Step 7:
- Step 8:
- DESIGN AND IMPLEMENTATION
- Overview of the AUTOSAR Standard
- AUTOSAR Architecture Overview
- AUTOSAR Software Architecture and Features for Security
- Design Flow within AUTOSAR Security Software Modules
- Implementation of in-vehicle Message Authentication
- Sequence Diagram Authentication during Direct Transmission
- Sequence Diagram Verification during Direct Reception
- Introduction to DaVinci Developer tool
- A Decision Model for Reliability Analysis of Agricultural Sensor Data for Smart Irrigation 4.0
- Subhash Mondal1, Samrat Podder1 and Diganta Sengupta1,*
- INTRODUCTION
- LITERATURE SURVEY
- PROPOSED METHODOLOGY
- Dataset Acquisition
- Dataset Pre-Processing
- Framework
- Algorithm
- Parameter Estimation
- Modeling/Training Stage
- Hyper-Parameter Tuning
- EXPERIMENTAL RESULT & ANALYSIS
- Precision
- Recall
- F1. Score
- Comparative Analysis
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- Machine Learning based Smart Electricity Monitoring & Fault Detection for Smart City 4.0 Ecosystem
- Subhash Mondal1, Suharta Banerjee1, Sugata Ghosh1, Adrija Dasgupta1 and Diganta Sengupta1,*
- INTRODUCTION
- RELATED WORKS
- PROPOSED FRAMEWORK
- Electricity Prediction Module
- Threshold Calculation Module
- Fault Detection Module
- EXPERIMENTAL RESULT & ANALYSIS
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- Investigating the Effectiveness of Mobile Learning in Higher Education
- V. Kalaiarasi1,*, D. Alamelu2 and N. Venugopal3
- INTRODUCTION
- MODEL CONSTRUCTION AND DEVELOPMENT OF HYPOTHESIS
- Technology Acceptance and Learner Satisfaction
- System Success and Learner satisfaction
- Environmental Factors and Learner satisfaction
- Technology Acceptance and Learner Intention
- System Success and Learner Intention
- Environmental Factors and Learner Intention
- Learner Satisfaction and M-learning effectiveness
- Learner Intention and M-learning effectiveness
- METHODOLOGY
- Operational Design
- Data Collection
- Instrument Development
- RESULT
- Data Analysis and Results - Qualitative Study
- Technology Acceptance
- System Success
- Environmental Factors
- Learner Satisfaction
- Learner Intention
- M-Learning Effectiveness
- Data Analysis and Results - Quantitative
- SEM in VPLS
- Results of Hypothesis Testing
- DISCUSSION AND CONCLUSION
- ABBREVIATIONS
- REFERENCES
- Socio-Economy of Coastal Fishing Community of Southern Coast of Odisha: A Case Study
- T. Padmavati1,*
- INTRODUCTION
- INFORMATION AND METHODOLOGY
- RESULT AND DISCUSSION
- Overall population, geography, and literacy of Odisha
- Origin, present status, geography, and administrative classification of Ganjam
- Census (Govt. of India) 2011
- Ganjam District Population
- Ganjam District Population Growth Rate
- Ganjam District Density
- Ganjam Literacy Rate
- Ganjam Sex Ratio
- Ganjam Child Population
- Ganjam District Urban Population
- Ganjam District Rural Population
- Education Facilities
- Socio-economic status of the coastal total fishing community of Ganjam
- Fishing Activities
- Assets of the Fishermen
- Fishing Fleets
- Fishing craft
- Fishing gear and method
- Fish Harvest
- Fish Marketing and Preservation
- Problems Encountered in Fish Marketing
- Socio-economics
- Welfare Schemes
- Role of Different Banks in Financing Fishermen
- Fisheries Co-operatives
- Geomorphology
- Potential Fishing Zone (PFZ) Advisories using Remote Sensing Technology for Reduction of Fuel Consumption and Search Time and Improvement of Catch
- Socio-economic Situation of Fisherwomen in Ganjam District: A Case Study
- Significant Problems Associated with the Fisherwomen Community
- Lack of Empowerment among Women
- Inadequate Systems and Techniques to Support Fisher Women Micro-enterprises
- Lack of Capacity Building, Skills, and Institution
- Coastal Fishing Community at Gopalpur-on-sea (the Most Important Coastal Site for Fshing and Tourism of Ganjam District): A Particular Case Study
- Ongoing Problems and Subsequent Demands of the Coastal Fishing Community of Gopalpur-on-sea
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENTS
- REFERENCES
- Filtering Techniques for Removing Noise From ECG Signals
- K. Manimekalai1,* and A. Kavitha2
- INTRODUCTION
- ARTIFACTS
- Types of Artifact in ECG Signal
- Power Line Interference
- Muscle Contractions
- Electrode Motion Artifacts
- Baseline Wandering
- Reversed Lead
- ECG RECORDING CONDITIONS
- Calibration of the Equipment
- Recording Procedure
- ECG Signal Filtering
- Decomposition
- Discrete Wavelet Transform based Decomposition
- ALGORITHM: DWT DECOMPOSITION
- Denoising of ECG Signal
- Hard and Soft Thresholding
- Wavelet Thresholding
- EMD-Thresholding
- Wavelet-based Thresholding
- Wavelet Frequency Thresholding
- ECG Signal Filtering Techniques
- Derivative Base Filters
- EVALUATION CRITERIA FOR DENOISING
- Signal to Noise Ratio
- Mean Square Error
- EXPERIMENTAL RESULTS
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- Deep Learning Techniques for Biomedical Research and Significant Gene Identification using Next Generation Sequencing (NGS) Data: - A Review
- Debasish Swapnesh Kumar Nayak1,*, Jayashankar Das2 and Tripti Swarnkar3
- INTRODUCTION
- BACKGROUND
- DNA SEQUENCING
- Sanger Sequencing
- Next Generation Sequencing (The Rising Trend)
- NGS GENE EXPRESSION DATA (STRUCTURE, CHARACTER, AND CHALLENGES)
- QC TOOLS FOR NGS DATA PRE-PROCESSING
- MACHINE LEARNING TECHNIQUES FOR NGS DATA ANALYSIS
- Various Datamining Methods for Sequence data
- Taxonomy of Datamining, ML, and DL Techniques used for NGS data Analysis
- MACHINE LEARNING TECHNIQUES FOR NGS FEATURE SELECTION
- Filter Method
- Wrapper Method
- Embedded Method
- Hybrid Method
- Ensemble Method
- FEATURE EXTRACTION TECHNIQUES FOR NGS DATA
- Correlation-based Feature Selection (CFS)
- Fast Correlation-Based Filter (FCBF)
- INTERACT
- Information Gain
- ReliefF
- Minimum Redundancy Maximum Relevance (mRMR)
- LASSO (Least Absolute Shrinkage and Selection Operator)
- Elastic Net (E-Net)
- Random Forest (RF)
- ISSUES AND OPPORTUNITIES WITH TRADITIONAL MACHINE LEARNING
- DEEP LEARNING (THE EMERGING TREND)
- The Revolution of Deep Learning
- DEEP LEARNING APPROACH FOR NGS DATA ANALYSIS
- Artificial Neural Network (ANN)
- Convolutional Neural Network (CNN)
- Deep Neural Network (DNN)
- Feedforward Neural Network (FNN)
- Recurrent Neural Network (RNN)
- SIGNIFICANT GENE IDENTIFICATION AND ANNOTATION
- SUMMARY OF DL METHODS USED FOR NGS DATA ANALYSIS
- CRITICAL OBSERVATION
- Data Volume
- Data Quality
- The Curse of Dimensionality
- Interpretability
- Domain Complexity
- Biological Annotation
- CONCLUSION AND FUTURE SCOPE
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- Breast Cancer Detection Using Machine Learning Concepts
- Fahmina Taranum1,* and K. Sridevi1
- INTRODUCTION
- Background
- Undertaking Thorough Medical History
- Imaging Tests
- Advanced Test
- Classification Using the Techniques
- Dataset
- PROPOSED SYSTEM
- Problem Statement
- Objectives
- Why WDBC?
- LITERATURE SURVEY
- Technological Development
- Dataset used in the Research
- Related Work
- METHODOLOGIES
- Learning Algorithms
- Measuring the Effectiveness of the Models
- Processing of Patterns
- RESULTS AND DISCUSSION
- CONCLUSION
- CONSENT FOR PUBLICATION
- CONFLICT OF INTEREST
- ACKNOWLEDGEMENT
- REFERENCES
- Subject Index
- Back Cover
