Data Management, Analytics and Innovation

Name: Data Management, Analytics and Innovation | Proceedings of ICDMAI 2024, Volume 1
Brand: Springer
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Proceedings of ICDMAI 2024, Volume 1

Neha Sharma Amol C. Goje Amlan Chakrabarti Alfred M. Bruckstein(Editor)

Springer (Publisher)

1st Edition

Published on 22. July 2024

XIV, 673 pages

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978-981-97-3242-5 (ISBN)

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Dr. Neha Sharma is Data Science Crusader who advocates its application for achieving sustainable goals, solving societal, governmental, and business problems as well as promotes the use of open data. She has more than 24 years of experience and presently working with Tata Consultancy Services and is Founder Secretary, Society for Data Science. Prior to this, she has worked as Director of premier Institute of Pune that run post-graduation courses like MCA and MBA. She is Alumnus of a premier College of Engineering and Technology, Bhubaneshwar, and completed her Ph.D. from prestigious Indian Institute of Technology, Dhanbad. She is Senior IEEE Member, former Secretary-IEEE Pune Section and ACM Distinguished Speaker. She is Astute Academician and has organized several national and international conferences and published several research papers. She is Recipient of "Best Ph.D. Thesis Award" and "Best Paper Presenter at International Conference Award" at National Level.

Dr. Amol C. Goje is President of Society of Data Science and served as Director, Vidya Pratishthan's Institute of Information Technology (VIIT), Baramati, Pune, for last 19 Years. He has a total of over twenty five years of experience in the field of Information and Computer Technology (ICT). He has developed many systems for the University. His main area of interest is to work for underprivileged people in the rural part of India. In his nineteen years as Director, he has designed and implemented numerous path-breaking, innovative, and cost effective solutions. His main innovation is Computer Mobile Van. He has done lot of research work in Information Technology and its application for rural community. In appreciation to his exemplary work, he has received the Ashoka Fellow award in the year 2002. He is engaged as Technical Advisor on many government and non-government organizations.

Amlan Chakrabarti is presently Full Professor and Director of the A.K.Choudhury School of Information Technology, University of Calcutta. He is an M.Tech. from University of Calcutta and did his Doctoral research at the Indian Statistical Institute, Kolkata. He was Post-doctoral Fellow at the School of Engineering, Princeton University, USA, during 2011-2012. He is Recipient of DST BOYSCAST fellowship award in the area of Engineering Science in 2011, Indian National Science Academy Visiting Scientist Fellowship in 2014, JSPS Invitation Research Award from Japan in 2016, Erasmus Mundus Leaders Award from European Union in 2017, Hamied Visiting Fellowship University of Cambridge in 2018 and Shiksha Ratna Award from Govt. of West Bengal, in 2018. He is Team Leader of European Center for Research in Nuclear Science (CERN, Geneva) ALICE-India project for University of Calcutta and also Key Member of the CBM-FAIR project at Darmstadt Germany.

Professor Alfred M. Bruckstein, B.Sc., M.Sc. in EE from the Technion IIT, Haifa, Israel, and Ph.D. in EE, from Stanford University, Stanford, California, USA, is Technion Ollendorff Professor of Science, in the Computer Science Department there, and is Visiting Professor at NTU, Singapore, in the SPMS. He has done research on Neural Coding Processes, and Stochastic Point Processes, Estimation Theory, and Scattering Theory, Signal and Image Processing Topics, Computer Vision and Graphics, and Robotics. Over the years he held visiting positions at Bell Laboratories, Murray Hill, NJ, USA (1987-2001) and TsingHua University, Beijing, China (2002-2023) and made short time visits to many universities and research centers worldwide. At the Technion, he was Dean of the Graduate School and is currently Head of the Technion Excellence Program.

Editor

ISNI: 0000 0000 8431 8016

Content

Intro
Preface
Contents
About the Editors
A Comprehensive Literature Review on Emerging Potentials of Machine Learning Algorithms on Geospatial Platform for Medicinal Plant Cultivation Management in Existing Scenario
1 Introduction
2 Literature Review
2.1 Overview on Medicinal Plant Cultivation (MPC) Management
2.2 Descriptive Machine Learning (M/L) Techniques for Geospatial Data Analysis
2.3 Findings from Literature
3 Proposed Framework for Medicinal Plant Cultivation Using Machine Learning Approach on Geospatial Platform
3.1 Phases of Medicinal Plant Cultivation
3.2 Machine Learning Algorithms
3.3 Performance Evaluation
4 Conclusion
References
Facial Features Recognition and Classification Using Machine Learning Model
1 Introduction
2 Related Works
3 Datasets
4 Proposed Method
4.1 Training the Machine Learning Model
4.2 Real-Time Detection Using Haar Cascades
5 Results and Discussion
6 Future Scope and Conclusion
References
An Intelligent System for Prediction of Lung Cancer Under Machine Learning Framework
1 Introduction
2 Literature Survey
3 Methodology
3.1 Dataset Collection
3.2 Feature Selection
3.3 Split the Dataset
3.4 Model Building
4 Result Analysis
4.1 Confusion Matrix
5 Conclusion
References
Identification of Misinformation Using Word Embedding Technique Word2Vec, Machine Learning, and Deep Learning Models
1 Introduction
2 Related Work
2.1 Background Work
3 Proposed Methodology
3.1 Dataset Description
4 Results and Discussion
4.1 Data Collection and Cleaning
4.2 Data Visualization
4.3 Feature Extraction
4.4 Model Selection
4.5 Performance Measure
5 Conclusion and Future Scope
References
Multiscript Handwriting Recognition Using RNN Transformer Architecture
1 Introduction
2 Related Works
3 Proposed Method
3.1 Recurrent Neural Network (RNN)
3.2 Transformer Architecture
4 Result and Discussion
5 Conclusion and Future Work
References
Enhancing Agriculture Productivity with IoT-Enabled Predictive Analytics and Machine Learning
1 Introduction
2 Literature Survey
3 Agile Agriculture Prototype
3.1 SAPS (Smart Agricultural Production System)
3.2 ACIS (Automated Crop Irrigation System)
4 Agile Agriculture Workflow
5 Simulation
6 Conclusion
References
Gender Gaps in the Context of Cryptocurrency Literacy: Evidence from Survey Data in Europe and Asia
1 Introduction
2 Data
3 Results and Discussion
4 Conclusion
References
An Optimized Machine Learning Model for Crop Yield Predication by Applying Weighted Ensemble Technique
1 Introduction
2 Literature Survey
3 Proposed Optimized Ensemble Model (OEM)
3.1 Weight
3.2 Optimized Weight Calculation
3.3 A Weighted Optimized Ensemble Model Algorithm (OEM)
4 Experimental Setup
5 Result and Discussion
6 Conclusion
References
Crop Recommendation and Irrigation System Using Machine Learning with Integrated IoT Devices
1 Introduction
2 Literature Review
3 Problems with the Existing Methods
4 Proposed Methodology
5 Crop Recommendations Flow
6 Irrigation Flow
7 Implementation
8 Comparative Analysis
9 Results
10 Conclusion
References
Toward Space-Efficient Semantic Querying with Graph Databases
1 Introduction
2 Literature Survey
3 Methodology/Proposed Approach
3.1 Knowledge Graph Creation
3.2 Opportunity Identification
4 Challenges
5 Conclusion
References
Enhanced Artificial Neural Networks for Prostate Cancer Detection and Classification
1 Introduction
2 Literature Survey
3 Proposed Method
4 Results and Discussion
5 Conclusion
References
Agricultural Indicators as Predictors of Annual Water Quality: An Analysis of Interconnectedness and Prediction Using Machine Learning
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Data
3.2 Machine Learning Methods
4 Experiments and Results
4.1 Insights on Water Quality
4.2 Linear Regression
4.3 Prediction
5 Limitations
6 Conclusions and Future Work
References
Predicting Mental Health Disorders in the Technical Workplace: A Study on Feature Selection and Classification Algorithms
1 Introduction
2 Background
3 Data Exploration
4 Modeling of Feature Selection
4.1 Feature Selection Using LASSO
4.2 Feature Selection Using RFECV
4.3 Feature Selection Using RFE
5 Performance Evaluations
5.1 LASSO-Based Classification
5.2 Classification with RFECV
5.3 Classification with RFE
6 Results and Discussions
7 Conclusion
References
Enhancing the Detection of Fake News in Social Media: A Comparison of Support Vector Machine Algorithms, Hugging Face Transformers, and Passive Aggressive Classifier
1 Problem Description
2 Introduction
3 Survey of Literature
3.1 Support Vector Machine (SVM) [1-4]
3.2 Hugging Face Transformers [6, 7]
3.3 Passive Aggressive Classifier [8]
4 Comparison of Various Algorithms
4.1 Proposed Comparison
4.2 Proposal for the New Work
5 Methodology
6 Algorithm Summary
7 Execution Environment
7.1 Powerful Processing
7.2 Scalability
7.3 Flexibility
7.4 Ease of Use
7.5 Cost-Effective
8 Inference Based on Test Results and Outputs from Table 1
8.1 Hugging Face Model
8.2 Passive Aggressive Classifier
8.3 Support Vector Machine (SVM)
9 Model Fine-Tuning for Hugging Face Model
10 Conclusion
References
A Multifactor Authentication Framework for Usability in Education Sectors in Uganda
1 Introduction
2 Related Works
3 Research Methodology
4 Results and Discussion of Findings
4.1 Username/Passwords
4.2 Face Recognition
4.3 Fingerprint Authentication
5 Framework Design for E-MuAF
6 E-Assessment Multifactor Authentication Framework
7 Conclusion
References
Rank Prediction for Indian Universities Based on National Institutional Ranking Framework
1 Introduction
2 Literature Review
3 Problem Statement
4 Research Method
4.1 NIRF Ranking Parameters and Sub-Parameters
4.2 ML Algorithms Used
5 Result and Discussion
6 Conclusion
References
Research Paper Summarization Using Extractive Approach
1 Introduction
2 Literature Review
3 Design and Implementation
4 Results and Discussion
5 Conclusion
References
Safarnaama: User Experience-Based Travel Recommendation System
1 Introduction
2 Review of Literature
2.1 Gaps in Literature Survey
3 Design of Safarnaama
3.1 Architectural Design
3.2 Data Description
3.3 Cold Start Problem
3.4 User Interface Design
3.5 Algorithms and Methods Used
4 Performance Analysis
4.1 Data Collection and Pre-processing
4.2 Formula for Custom Recommendations
4.3 App Development
4.4 Feedback Analysis
4.5 Result Analysis
5 Conclusion and Future Scope
References
Handling Missing Data in Longitudinal Anthropometric Data Using Multiple Imputation Method
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Data Source
3.2 Imputation Methods
4 Pre-processing and Analysis
5 Experimental Results and Evaluation
5.1 Comparative Analysis of Imputation Methods
6 Conclusion
References
From Pixels to Insight: Enhancing Metallic Component Defect Detection with GLCM Features and AI Explainability
1 Introduction
2 Methods and Methodology
2.1 Dataset
2.2 Feature Extraction
2.3 Training and Testing
3 Explainability and Shapley Additive Explanations (SHAP) Plots
3.1 Explainability
3.2 SHAP and SHAP Plots
3.3 Results and Discussion
4 Conclusions
References
Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning
1 Introduction
2 Comprehensive Review
3 Proposed Framework
4 Experiment and Performance Evaluation
5 Performance Evaluation Metrics
6 Conclusions
References
Analyzing UNO Statistics on Land Use of Agricultural Practices by Using k-Means Clustering and SARIMA: Irrigated, Organic, and Overall Agricultural Activities on a Global Scale
1 Introduction
2 Literature Review
3 Data and Methods
4 Analysis
4.1 Silhouette Score
4.2 Limitations of k-Means Clustering
4.3 k-Means Clustering
4.4 SARIMA Prediction
5 Alternative Explanations for the Observed Results
6 Conclusion
References
Fruit and Vegetable Segmentation with Decision Trees
1 Introduction
2 Related Literature
3 Data Acquisition
4 Image Preprocessing
4.1 Image Segmentation
4.2 Feature Extraction
5 Classification
6 Results
7 Summary and Conclusion
References
Chatbot Development Simplified: An In-Depth Look at JIGYASABOT Platform and Alternatives
1 Introduction
2 Literature Survey
3 Problem Statement
4 Proposed Solution
4.1 Architecture
4.2 Survey Platforms Ratings and Results
5 Building Chatbots with JIGYASABOT
5.1 Design of JIGYASABOT Chatbot
6 Comparative Market Analysis
7 Use Cases and Innovations
8 Conclusion
9 Future Scope
References
Guarding the Gateway: Data Privacy and Security in Metaverse Tourism
1 Introduction
2 Background of the Study
3 Methodology
4 Data Collection and Analysis
5 Results and Discussion
6 Conclusion
7 Recommendation
References
Quantum Computing-Based Banking System
1 Introduction
2 Literature Survey
3 Proposed Work
4 Simulation Results
4.1 Transaction Authenticity
4.2 Transaction Uniqueness
5 Conclusion
6 Future Work
References
Design and Analysis of Quantum Transfer Fractal Priority Replay and Mirdad Priority Loss Algorithms for Quantum Reinforcement Learning
1 Introduction
2 Related Works
3 Problem Definition
4 Objective of Research
5 Scope of Research
6 Proposed Methodology
6.1 Working Principle
6.2 Algorithm Design
6.3 Experimental Setup
7 Evaluation Metrics and Interpretive Analysis with Existing Algorithm
7.1 Convergence Speed: QTFPR-QQL Versus QQL
7.2 Rewards: QTFPR-QQL Versus QQL
7.3 Experience: QTFPR-QQL Versus QQL
7.4 Priority: QTFPR-QQL Versus QQL
8 Hyperparameter Tuning of QTFPR-QQL and QQL
8.1 Interpretation for QQL Algorithm
8.2 Interpretation for QTFPR-QQL Algorithm
9 Result and Discussion
9.1 Challenges of QTFPR Implementation
9.2 Limitations of QTFER
10 Conclusion
References
Smart Farming Based on IoT-Edge Computing: Exploiting Microservices' Architecture for Service Decomposition
1 Introduction
2 Literature Survey
2.1 IoT Architecture
2.2 Microservices' Architecture
3 Proposed Framework
3.1 Device Layer
3.2 Edge Computing Layer
3.3 Cloud Computing Layer
4 System Setup and Results
4.1 System Setup
4.2 Results
5 Conclusion
References
Li-Fi for Secured Access to Wireless Network During Online Examination in Classrooms
1 Introduction
2 Proposed Framework
3 Requirement Analysis and Implementation
4 Experimental Setup and Results
5 Conclusion
References
Maximizing Cloud Resource Utility: Region-Adaptive Optimization via Machine Learning-Informed Spot Price Predictions
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Random Forest Regression
3.3 XGBoost Regression
4 Results and Discussion
4.1 Price Prediction
4.2 Discussion
4.3 Comparison with Existing Approaches
4.4 Using Trained Model for Prediction
4.5 Dynamic Resource Provisioning-Practical Use Case
5 Conclusion
References
A Comparative Analysis of Top NIRF-Ranked Universities and International Universities Using Search Engine Optimization Tools and Techniques
1 Introduction
2 Literature Review
2.1 NIRF as a Benchmark
2.2 SEO Strategies in the Indian Context
2.3 Correlation Between Visibility and Recognition
2.4 Research Objectives
3 Background of Research
4 Methodology
5 SEO Results
6 Conclusion
References
Unbreakable Passwords: Fortifying Cryptographic Security with Derangement Keys
1 Introduction
1.1 Motivation
1.2 Literature Survey
2 Proposed Methodology
3 Result and Analysis
4 Conclusion
References
A Simplified Hasse Diagram for Visualizing Large Datasets
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Case Study 1: Retail Sales Analysis
3.2 Case Study 2: Healthcare Data Visualization
3.3 Case Study 3: Social Network Analysis
4 Results and Analysis
5 Conclusion
References
Harnessing the Power of LSTM-XGBoost Ensemble Model for Prediction of Sea Surface Temperature Anomalies in the Indian Ocean
1 Introduction
2 Preliminaries
2.1 Essential Prerequisites for Appraisal of Related Work
2.2 Related Work
3 Dataset and Geographical Area of Research
3.1 Description of the Data
3.2 Sample Dataset Analysis
3.3 Area of Geographical Research
4 Proposed Method
4.1 Nine-Layer Long Short-Term Memory (LSTM) Model
4.2 XGBoost Ensemble Model
4.3 LSTM and XGBoost Ensemble Model
5 Experimental Results and Discussion
5.1 Data Preprocessing
5.2 Normalization of Data
5.3 Results and Comparative Analysis
6 Conclusion and Future Work
References
H-Index Analysis of Research Paper Using Web Crawling Techniques
1 Introduction
2 Technology Used for H Index Analysis
2.1 Techniques Used in Web Crawling
2.2 Existing Web Crawling Techniques for H-index Analysis
3 Proposed Methodology for H Index Using Web Crawling
3.1 Flow Chart
3.2 Developed Code
3.3 Observation
4 Conclusion
References
A Sustainable Antenna Design to Enhance Precision Beamforming Capabilities
1 Introduction
2 Potential Drawbacks of MU-MIMO
3 Challenges for Wi-Fi Antenna Design
4 Antenna Design Considerations
5 Implementation
5.1 Hardware
5.2 Results
6 Sustainable Antenna Design Process
7 Advantages of Biofiber-Based Antenna Enhancements
8 Conclusion
References
Single Person Occupancy Detection Using PIR Sensors
1 Introduction
2 Methodology
2.1 Circuit Connection
2.2 Operation
2.3 Significance and Limitations
2.4 Algorithm 1: Single Person Occupancy Detection Algorithm with Two PIR Sensors and Room Constraints
3 Result and Discussion
3.1 Statistics
4 Limitations and Assumptions
5 Conclusion and Future Work
References
Investigation of Various Data-Driven Modeling Techniques for an Industrial Heat Exchanger
1 Introduction
2 Process Description and Data Acquisition
3 Methodology
3.1 Traditional Modeling Technique
3.2 Polynomial Modeling Technique
3.3 Nonlinear System Identification
4 Results and Discussion
4.1 Performance Evaluation of Traditional Models
4.2 Performance Evaluation of Polynomial Models
4.3 Performance Evaluation of Non-linear Models
5 Conclusion
References
Green Banking Awareness: A Study on Tier 3 Locations-With Special Reference to Kerala
1 Introduction
1.1 Objectives
2 Literature Review and Hypothesis Development
2.1 Conventional Banking Versus Green Banking Practices: A Gap Analysis
2.2 Green Banking Coverage
2.3 Customer Awareness on Green Banking
3 Methodology
3.1 Research Design
3.2 Data Analysis Techniques
3.3 Sampling and Participants
3.4 Conceptual Framework
4 Empirical Analysis
4.1 Demographic Profile and Frequency
4.2 Factor Analysis
4.3 Ordinal Regression
4.4 Correlation Analysis
5 Findings and Limitations
6 Conclusion
References
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
1 Introduction
2 Literature Survey
3 Proposed Methodology
3.1 Homomorphic Encryption
3.2 CKKS Scheme
3.3 Overview of the Original Unencrypted ELM
3.4 Proposed Chaotic Extreme Learning Machine and Privacy-Preserving Chaotic Extreme Learning Machine
4 Dataset Description
4.1 Health Care Datasets
4.2 Financial Datasets
5 Results and Discussions
5.1 Health-Care Dataset Results
5.2 Finance Dataset Results
6 Conclusions
References
Quantum Smart World Era-A Digital Innovative Perspective
1 Introduction
2 Literature Survey
3 Proposed Work
3.1 Quantum Smart Agriculture Farms
3.2 Quantum Smart Educational Institutions
3.3 Quantum Smart Traffic Control
3.4 Quantum Smart Hospitals
3.5 Quantum Smart Governance
4 Simulation Results
4.1 Inference Reliability
4.2 Processing Time
5 Conclusion
6 Future Work
References
Network Slicing and Traffic Classification in 5G Networks with Explainable Machine Learning
1 Introduction
2 Literature Review
3 Background Fundamentals
3.1 Overview of Explainability of ML Models
3.2 Lime
3.3 SHAP
4 Proposed Methodology
4.1 Novel Feature Selection
5 Dataset Description
5.1 Moore Dataset
5.2 Network Slice Recognition Dataset
6 Experimental Analysis
6.1 Local Explanation View Component to Explain Single Predictions
6.2 Explainable Artificial Intelligence (XAI) Component
6.3 Counterfactuals for Network Slice Data
6.4 XAI View (Partial Dependence Plot) for Moore Data and Network Slice Data
7 Conclusions
References
A Study of Multimodal Sentiment Analysis and Design of an Architecture
1 Introduction
2 Fundamental Elements and Challenges in Multimodal Sentiment Analysis
2.1 Combining Different Data Sources
2.2 Feature Extraction
2.3 Deep Learning Model Integration
2.4 Domain-Specific Turning
3 Deep Learning for Multimodal Sentiment Analysis
4 Typical Architecture
5 Differential Privacy and Multimodal Sentiment Analysis
6 Impact on Society
7 Research Directions
7.1 Security and Privacy of Data and Individuals
7.2 Massive Parallel and Real-Time Processing of Multimodal Data
7.3 Handling Noise and Missing Data in Multimodal Dataset
7.4 Adapting to Cultural and Language Differences
7.5 Better Integration of Modalities and Model Fusion
7.6 Domain-Specific Models and Transfer Learning
7.7 Incorporating User Engagement and Feedback
7.8 Handling Sarcasm and Ambiguity
7.9 Energy-Efficient Micro Models for IoT Devices
8 Conclusion
References
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Data Management, Analytics and Innovation

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