Data Science and Security

Name: Data Science and Security | Proceedings of IDSCS 2023
Brand: Springer
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Proceedings of IDSCS 2023

Samiksha Shukla Hiroki Sayama Joseph Varghese Kureethara Durgesh Kumar Mishra(Editor)

Springer (Publisher)

1st Edition

Published on 30. May 2024

XI, 579 pages

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978-981-97-0975-5 (ISBN)

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Samiksha Shukla is currently employed as an Associate Professor at the Department of Computer Science and Engineering, Christ(Deemed to be University), Bangalore. Her research interests include computational security, artificial intelligence, machine learning, data science, and big data. She is a certified AWS Educator. She has presented and published several research papers in reputed journals and conferences. She has co-edited Data Science and Security books (Springer, 2020, 2021 & 2022) and also co-authored Springer Brief on Data Ethics and Challenges and Data Economy in the Digital Age . She has 18 years of academic and research experience. She is a reviewer for the Inderscience Journal, Springer Nature's International Journal of Systems Assurance Engineering and Management (IJSA), and IEEE and ACM conferences. Dr. Shukla is an experienced and focused teacher committed to promoting students' education and well-being. She is passionate about innovation and good practices in teaching. She has constantly engaged in continuous learning to broaden her knowledge and experience. Her core expertise lies in Computational Security, Artificial Intelligence, and Healthcare-related projects. She is skilled at adopting a pragmatic approach to improvising solutions and resolving complex research problems. Dr. Shukla possesses an integrated set of competencies encompassing teaching, Mentoring, Strategic Management, and establishing a Centre of Excellence via Industry tie-ups. She has a track record of driving unprecedented research and development projects with international collaboration and has been instrumental in organizing various National and International Events.

Hiroki Sayama is a Professor in the Department of Systems Science and Industrial Engineering and Director of the Center for Collective Dynamics of Complex Systems (CoCo) at Binghamton University, State University of New York, USA. He also serves as a Non-tenured Professor in the School of Commerce at Waseda University, Japan, as well as an External Faculty Member of the Vermont Complex Systems Center at the University of Vermont. He received his B.Sc., M.Sc., and D.Sc. in Information Science, all from the University of Tokyo, Japan. He did his postdoctoral work at the New England Complex Systems Institute in Cambridge, Massachusetts. His research interests include complex dynamical networks, human and social dynamics, collective behaviors, artificial life/chemistry, interactive systems, and complex systems education, among others. He is an Expert in mathematical/computational modeling and analysis of various complex systems. He has published more than 200 peer-reviewed journal articles and conference proceedings papers and has written or edited 14 books and conference proceedings about complex systems-related topics. His open-access textbook on complex systems modeling and analysis has been downloaded more than 68,000 times globally and has become one of the standard textbooks on this subject. He currently serves as Elected Council and Executive Committee Member of the Complex Systems Society (CSS), Board member of the Network Science Society (NetSci) and the International Society for Artificial Life (ISAL), Chief Editor of Complexity (Wiley/Hindawi), Associate Editor of Artificial Life (MIT Press), and as Editorial Board Member for several other journals.

Joseph Varghese Kureethara is a Professor of Mathematics at Christ University. He was the Director of the Centre for Research there. He has over seventeen years of experience in teaching and research at Christ University, Bengaluru. He has published around 230 articles in Graph Theory, Number Theory, History, Religious Studies, and Sports, both in English and Malayalam. Dr. Kureethara co-edited five books and authored six books. His blog articles, comments, facts, and poems have earned about 170 thousand pageviews. He has delivered invited talks at over fifty conferences and workshops. He is a Member of the Editorial Board and Reviewer of several journals. He has worked as a member of several institutions' boards of studies, boards of examiners, and management committees. He has supervised 8 Ph.Ds., 12 M.Phils., and supervising 8 Ph.Ds.

Durgesh Kumar Mishra has received an M.Tech. degree in Computer Science from DAVV, Indore, in 1994 and Ph.D. in Computer Engineering in 2008. Presently, he is working as a Professor (CSE) and Director of the School of Computer Science and Information Technology, Symbiosis University of Applied Science, Indore, MP, India. He has around three decades of teaching experience and 18 years of research experience. His research topics are Secure Multi-party Computation, Image Processing, and Cryptography. He has published around 100 papers in refereed international/national journals and conferences, including IEEE and ACM. He isa Senior Member of IEEE, the Computer Society of India, and ACM. He has played a very important role in professional society as Chairman. He has been a Consultant to industries and government organizations like the Sales Tax and Labor Department of the Government of Madhya Pradesh, India.

Content

Intro
Preface
Contents
Fear and Finance: An Unsupervised Machine Learning Study on Credit-Averse Households in the U.S
1 Introduction
1.1 Background on Credit Fear and Its Impact on Households
1.2 Purpose of the Research
1.3 Overview of the Methodology and Data Sources
2 Literature Review
2.1 Previous Research on Credit Fear and Credit Constraints
2.2 Overview of the Survey of Consumer Finances (SCF)
2.3 Overview of Unsupervised Machine Learning Method with Clustering
3 Methodology
3.1 K-means Clustering
4 Analysis and Results
4.1 Data Preparation
4.2 Feature Selection Approach
4.3 Principal Component Analysis
5 Conclusions
5.1 Limitations and Future Research Directions
References
OGIA: Ontology Integration and Generation Using Archaeology as a Domain
1 Introduction
2 Related Works
3 Proposed System Architecture
4 Results and Performance Evaluation
5 Conclusions
References
Data: A Key to HR Analytics for Talent Management
1 Introduction
2 Conventional Talent Management Techniques
2.1 Comparison Between the Traditional Performance Management System and the Unconventional Performance Management System
3 Framework for Data-Driven Talent Management
4 Parameters Used in HR Analytics
5 Tools Used for HR Analytics
6 Challenges of Conventional Talent Management Techniques
7 Importance of Data and HR Analytics in Workforce Planning
7.1 Analyzing Workforce Turnover Patterns Using Data Analytics: A Case Study
8 Application of Data-Oriented HR Analytics
8.1 Healthcare Sector
8.2 Academics
8.3 IT Sector
9 Limitations
10 Conclusion and Future Recommendations
References
An Early Lumpy Skin Disease Detection System Using Machine Learning
1 Introduction
2 Literature Review
3 Data Collection
3.1 Data Pre-processing
4 Model Training
4.1 Decision Tree
4.2 Random Forest
4.3 Logistic Regression
4.4 Support Vector Machine
4.5 KNN
5 Results and Discussion
6 Conclusion
References
Extracting Network Structures from Corporate Organization Charts Using Heuristic Image Processing
1 Introduction
2 Dataset
3 Method
4 Results
5 Conclusions
References
Generating Equations for Mathematical Word Problems Using Multi-head Attention Transformer
1 Introduction
2 Literature Review
3 Methodology
3.1 Preprocessing
3.2 Baseline Model (Bi-LSTM Model with Attention Layers)
3.3 Transformer Model with Attention
4 Results and Conclusions
5 Future Scope
References
Autonomous System Enabling Node and Edge Detection, Path Optimization, and Effective Color-Coded Box Management in Diverse Robotic Environments
1 Introduction
2 Literature Review
3 Methodology
3.1 Extracting Necessary Features and Information from the Given Image
3.2 Path Planning and Navigation
4 Implementation Using LiDAR
5 Comparative Study
6 Results
7 Future Scope and Conclusion
References
GLANCE-Guided Language Through Autoregression Establishing Natural and Classifier-Free Editing
1 Introduction
2 Related Work
3 Methodology
3.1 Scene Tokenizing and Representation
3.2 Prioritizing Human Attention in Tokenization
3.3 Vector Quantization of Faces
3.4 Enhancing Facial Emphasis Within the Scene Context
3.5 Object Vector Quantization
3.6 Transformer-Scene Based
3.7 Pioneering Classifier-Free Transformer Guidance
4 Results
4.1 Dataset
4.2 Metrics
4.3 Previous Work Comparison
4.4 Experimental Setting with Result Analysis for Storytelling
5 Conclusion
References
Method for Design of Magnetic Field Active Silencing System Based on Robust Meta Model
1 Introduction
1.1 Introduction to the Problem
1.2 Contribution
1.3 Paper Organization
2 Related Works
3 Exact Model Design
4 Robust Meta Model Design
5 Active Silencing Robust System Design
6 Games Solutions Calculation
7 Numerical Study
8 Experimental Study
9 Conclusions
References
Ontology Integration for Cultural Landscape Management Using ML and Assistive Artificial Intelligence
1 Introduction
2 Related Works
3 Proposed System Architecture
4 Performance Evaluation and Results
5 Conclusion
References
Early Phase Detection of Bacterial Blight in Pomegranate Using GAN Versus Ensemble Learning
1 Introduction
2 Literature Review
2.1 Comparison Table of Previous Existing Techniques and Methods
3 Methodology
3.1 Steps Involved
3.2 Cycle-GAN Generated Images of Different Classes
3.3 Ensemble Learning Model
4 Graphs
5 Results
6 Conclusion
References
Classification of Diseased Leaves in Plants Using Convolutional Neural Networks
1 Introduction
2 Literature Review
3 Research Methodology
4 Data Analysis
5 Results, Discussion, and Conclusion
References
Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Dataset
3.2 Experimental Setup
3.3 MobileNet-V3
3.4 EfficientNetV2L
3.5 The Ensemble Model
3.6 Fast Marching Segmentation
4 Results and Discussions
4.1 MobileNetV3L
4.2 EfficientNetV2
4.3 Ensemble Model
4.4 Segmentation
5 Conclusion
References
Spectrum and Energy of the Mobius Function Graph of Finite Cyclic Groups
1 Introduction
2 Preliminaries
3 Adjacency Spectrum of upper M left parenthesis normal upper G right parenthesis M(G)
4 Laplacian Spectrum of upper M left parenthesis normal upper G right parenthesis M(G)
5 Energy of upper M left parenthesis normal upper G right parenthesis M(G) and the Laplacian Energy of upper M left parenthesis normal upper G right parenthesis M(G)
6 Conclusion
References
Analysis of Multinomial Classification for Legal Document Categorization
1 Introduction
2 Dataset Preparation and Methodology
2.1 Dataset Preprocessing
2.2 Methodology
3 Discussions
4 Conclusion
References
Pioneering Image Analysis with Hybrid Convolutional Neural Networks and Generative Adversarial Networks for Enhanced Visual Perception
1 Introduction
1.1 Scope
2 Literature Review
3 Proposed Methodology
3.1 Problem Definition
3.2 Data Acquisition and Pre-processing
3.3 Hybrid Deep CNN Architecture
3.4 GAN Architecture
3.5 Hybrid Integration
3.6 Training
3.7 Evaluation
4 Result and Discussion
4.1 Accuracy
4.2 Loss
5 Conclusion
References
Enhancing Medical Decision Support Systems with the Two-Parameter Logistic Regression Model
1 Introduction
2 Methodology
3 Empirical Study
4 Conclusion
5 Future Research
References
BI-RADS Score Prediction Using AI for Breast Cancer Screening
1 Introduction
2 Literature Survey
3 Materials and Methods
3.1 Collection of Mammograms
3.2 Data Pre-processing
3.3 Transforming DICOM
3.4 DICOM Annotation
3.5 Model Building
4 Results and Discussion
5 Conclusion
References
Modeling and Analysis of the Lead-Lag Network of Economic Indicators
1 Introduction
2 Methodology
3 Results
4 Conclusion and Limitations
References
Predictive Maintenance Model for Industrial Equipment
1 Introduction
2 Literature Review
3 Methodology
4 Result and Discussion
5 Conclusion
References
Taming the Complexity of Distributed Lag Models: A Practical Approach to Multicollinearity, Outliers, and Auto-Correlation in Finance
1 Introduction
2 Methodology
3 Empirical Application
4 Conclusions
5 Future Scope
References
Algorithm of Robust Control for Multi-stand Rolling Mill Strip Based on Stochastic Multi-swarm Multi-agent Optimization
1 Introduction
1.1 Introduction to the Problem
1.2 Contribution
1.3 The Organization of the Paper
2 Literature Review and Problem Statement
3 Design of an Exact Model of the Random Processes of the Longitudinal Variation in Strip Thickness
4 Design of a Robust Meta-Model of the Random Processes of the Longitudinal Variation in Strip Thickness
5 Model Description for Multi-stand Rolling Mill
6 Robust System Design
7 Calculation of Vector Games Solutions
8 Simulation Results
9 Conclusions
References
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
1 Introduction
2 Related Work
2.1 Inference from Literature Review
3 The Materials and the Method
3.1 Data Set
3.2 Data Preprocessing
3.3 Experimental Setup
3.4 Data Exploration and Analysis
3.5 Machine Learning Models for Data Classification
3.6 Performance Evaluation
4 Research Results and Discussions
5 Conclusion
References
An Enhanced Power Management and Prediction for Smart Grid Using Machine Learning
1 Introduction
2 Related Work
3 Comfort Level Prediction
4 Proposed Algorithm
5 Simulation and Experimental Result
5.1 Simulation Analysis
6 Conclusion
References
Feature Reduction Set for the Prediction of Renal Disease Using Ensemble Methods and Optimal Hyperplane Algorithms
1 Introduction
2 Literature Survey
3 Data Pre-processing
4 Data Reduction
5 Supervised and Ensemble Learning Approach
5.1 Optimal Hyperplane (SVM)
6 Adaptive Boosting
7 Results and Discussion
8 Conclusion
References
Domain-Driven Summarization: Models for Diverse Content Realms
1 Introduction
2 Previous Studies and Approaches
3 Proposed Methodology
3.1 Quantitative Metrics: ROUGE Scores as the Yardstick
3.2 Domain-Specific Performance Comparison
3.3 Models Under Study
3.4 Description of the Dataset
4 Experiments and Results
4.1 Cross-Domain Consistency and Generalizability
5 Conclusion
References
File Validation in the Data Ingestion Process Using Apache NiFi
1 Introduction
2 Selection of Validations
3 Processor Design and Implementation
3.1 File Size Validation Processor
3.2 Ingestion Frequency Validation Processor
4 Testing and Validation
4.1 File Size Validation
4.2 Ingestion Frequency Validation
4.3 Observations
5 Conclusions
References
An Analysis of Manufacturing Machine Failures and Optimization Using Replacement Year Prediction
1 Introduction
2 Methodology
3 Modules Description
3.1 Data Preprocessing and Loading
3.2 Calculation of Overall Failure Rate and Lines Failure Rate
3.3 Identifying Production Lines with High Failure Rate
3.4 Model Training and Evaluation
3.5 Replacement Year Prediction
4 Results and Discussion
5 Conclusion
References
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
1 Introduction
2 Literature Review
2.1 Geographical Features Segmentation Techniques
2.2 Evolution of U-Net Models in Image Segmentation
3 Methodology
3.1 Data Collection and Preprocessing
4 U-Net Model Architectures
4.1 Standard U-Net Model
4.2 U-Net with Skip Connections UNet++
4.3 U-Net with Attention Mechanisms UNetFormer
5 Experimental Results
6 Conclusion
References
Maximizing Blogging Impact: A Unified Interface for Multichannel Bloggers
1 Introduction
2 Proposed Model
3 Proposed Architecture
4 Methodology
5 Results
6 Conclusion
References
Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection
1 Introduction
2 Literary Review
2.1 Performance Evaluation of ML Algorithms
2.2 Application of ML in Intrusion Detection
2.3 Pattern-Based Intrusion Detection
3 Methodology
3.1 Gathering of Data
3.2 Preprocessing the Data Using Robust Scaler, One-Hot Encoding, and Principal Component Analysis
3.3 Machine Learning Models for Intrusion Detection
4 Results
5 Conclusion
References
A Comprehensive Review of Linear Regression, Random Forest, XGBoost, and SVR: Integrating Machine Learning and Actuarial Science for Health Insurance Pricing
1 Introduction
2 Literature Survey
3 Methodology
3.1 Data Preprocessing
3.2 Machine Learning Algorithms Selection and Justification
3.3 Investigating Hypotheses
3.4 Metrics for Model Assessment
3.5 Analysis Using Multiple Linear Regression
4 Results and Analysis
4.1 Dataset Overview
4.2 Model Evaluation
4.3 Attribute Comparison
5 Conclusion
References
Efficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization
1 Introduction
2 Related Work and Motivation
3 Problem Definition
3.1 Preliminaries
3.2 Problem Statement
4 The Proposed Algorithm
4.1 Creation of Bit-Vectors
4.2 Clustering of Transactions
4.3 Generation of HUIs
5 Experimental Evaluation
6 Conclusion
References
Synthesis of Online Criminal User Behaviours Disseminating Bengali Fake News Using Sentiment Analysis
1 Introduction
2 Literature Survey
3 Existing Methodology
3.1 Machine Learning Models
4 Proposed Methodology
4.1 Experimental Setup
4.2 Model Process
5 Implementation
6 Result Analysis
7 Conclusion
8 Limitations and Future Work
References
Text-to-Face Generation with Novel Fusion Mechanism Using DCGAN
1 Introduction
2 Literature Review
3 Objectives
4 Methodology/Experimental
5 Results and Discussions
References
SSAT: Scientific Storyboarding Framework Using Artificial Intelligence Techniques
1 Introduction
2 Related Works
3 Proposed System Architecture
4 Results
5 Conclusion
References
Comparative Analysis of Facial Expression Recognition Algorithms
1 Introduction
2 Various Facial Expression Recognition Algorithms
2.1 ANN
2.2 ANN Utilizing Harmony Search Algorithm
2.3 AdaBoost with Haar Cascade Classifier and PCA Along with LDR
2.4 Binary Neural Networks (BNN)
2.5 Convolutional Neural Networks (CNN)
2.6 CNN, XGBoost, and Model-Fusion
2.7 Recurrent Neural Network (RNN)
2.8 Support Vector Machine
3 Comparative Analysis
4 Conclusion
References
Driver Drowsiness Detection Using CNN and ESP32 CAM
1 Introduction
2 Literature Review
3 Methodology
3.1 Architecture
3.2 Using IOT Devices
4 Results
5 Conclusion
6 Limitation and Future Scope
References
Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
1 Introduction
2 SPX-CerebroPredict Technique
2.1 Data Standardization
2.2 Data Normalization
2.3 Feature Selection
2.4 Class Imbalance
3 Results and Discussion
3.1 Experiments Setup
3.2 Evaluation
4 Conclusion
References
RMIKD: An RDF and Metadata-Driven Scheme for Recommending Web Images for E-Commerce Fashion and Products Using Incremental Knowledge Derivation
1 Introduction
2 Related Works
3 Proposed System Architecture
4 Implementations, Results, and Performance Evaluation
5 Conclusion
References
Unlocking the Future: Graphical Passwords with Flask Framework
1 Introduction
2 Literature Review
3 Methodology
3.1 Aim of the Project
3.2 Tools Used in the System
3.3 Setting Up the Graphical Password
3.4 Signing in Process
3.5 Encrypting the Password
3.6 Hint Feature
3.7 Password Space of the System
4 Results and Discussion
5 Conclusion
References
MIWE: Multimodal Indexing of Web Entities Incorporating Semantic Artificial Intelligence
1 Introduction
2 Related Works
3 Proposed System Architecture
4 Implementation, Evaluation, and Results
5 Conclusions
References
Stealth Cruiser the Spy Bot
1 Introduction
2 Literature Review
3 Methodology
4 Results and Discussions
5 Conclusion
References
An Efficient Detection and Prediction of Intrusion in Smart Grids Using Artificial Neural Networks
1 Introduction
2 Related Work
3 Data Extraction and Preparation
3.1 Cleaning the Data
3.2 Choosing the Features
4 Method for Detecting Fraud
4.1 Selection of Input/Output Parameters for the Configuration of ANN Architecture
4.2 Instruct the ANN on Its Operational Procedures
4.3 Prospects for the Future
4.4 Identifying a Deviation from the Established Standard
5 Simulation Results and Analysis
5.1 Setting Up the Experiment
6 Simulation Results
6.1 The Initial Examination
6.2 The Second Examination
6.3 The Third Examination
7 Conclusion
References
Data Economy: Data and Money
1 Introduction
2 Evolution of Data Economies
3 Various Domains of Data Economy
4 Need for Data Economy
5 Challenges in Data Economy Practices
6 Conclusion and Future Scope
References
MANET's Dependable Data Delivery Systems Through the Use of Global Timing Protocols
1 Introduction
2 Literature Survey
3 Proposed Work
4 Result Analysis Simulation Environment and Results
5 Conclusion
References
Emotion Detection Using Machine Learning Technique
1 Introduction
2 Related Work
3 Proposed Model
3.1 Data Collection and Processing
3.2 Feature Selection
3.3 Feature Extraction
3.4 Model Training
4 Proposed Methodology
4.1 Face Emotion Detection Using CNN
5 Dataset
6 Results and Discussions
7 Conclusions
References
A Machine Learning-Based Approach for Network Optimization in WSNs
1 Introduction
2 Energy Consumption and Latency in WSNs
3 Related Work
4 Proposed Algorithm
5 Experimental Results
6 Conclusion
References
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Data Science and Security

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