Data Science and Interdisciplinary Research : Recent Trends and Applications / edited by Brojo Kishore Mishra.

Format:

Book

Author/Creator:

Mishra, Brojo Kishore, Author.

Contributor:

Mishra, Brojo Kishore, editor.

Series:

Advances in Computing Communications and Informatics Series

Advances in Computing Communications and Informatics Series ; Volume 5

Language:

English

Subjects (All):

Big data.

Data mining.

Machine learning.

Physical Description:

1 online resource (260 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2023.

Summary:

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.

Contents:

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,*

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

Secure Communication Over In-Vehicle Network Using Message Authentication

Manjunath Managuli1,*, Sudha Slake2, Pankaja S. Kadalgi2 and Gouri C. Khadabadi2

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,*

LITERATURE SURVEY

PROPOSED METHODOLOGY

Dataset Acquisition

Dataset Pre-Processing

Framework

Algorithm

Parameter Estimation

Modeling/Training Stage

Hyper-Parameter Tuning

EXPERIMENTAL RESULT &amp

ANALYSIS

Precision

Recall

F1. Score

Comparative Analysis

Machine Learning based Smart Electricity Monitoring &amp

Fault Detection for Smart City 4.0 Ecosystem

Subhash Mondal1, Suharta Banerjee1, Sugata Ghosh1, Adrija Dasgupta1 and Diganta Sengupta1,*

RELATED WORKS

PROPOSED FRAMEWORK

Electricity Prediction Module

Threshold Calculation Module

Fault Detection Module

Investigating the Effectiveness of Mobile Learning in Higher Education

V. Kalaiarasi1,*, D. Alamelu2 and N. Venugopal3

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

Socio-Economy of Coastal Fishing Community of Southern Coast of Odisha: A Case Study

T. Padmavati1,*

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

ACKNOWLEDGEMENTS

Filtering Techniques for Removing Noise From ECG Signals

K. Manimekalai1,* and A. Kavitha2

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

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

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

Breast Cancer Detection Using Machine Learning Concepts

Fahmina Taranum1,* and K. Sridevi1

Background

Undertaking Thorough Medical History

Imaging Tests

Advanced Test

Classification Using the Techniques

Dataset

PROPOSED SYSTEM

Problem Statement

Objectives

Why WDBC?

Technological Development

Dataset used in the Research.

Related Work.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9789815079005

981507900X

OCLC:

1402817368