Advanced Computing and Intelligent Technologies

Name: Advanced Computing and Intelligent Technologies | Proceedings of ICACIT 2023
Brand: Springer
Price: 255.73 EUR
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Proceedings of ICACIT 2023

Rabindra Nath Shaw Sanjoy Das Marcin Paprzycki Ankush Ghosh Monica Bianchini(Editor)

Springer (Publisher)

1st Edition

Published on 28. May 2024

XXIII, 643 pages

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978-981-97-1961-7 (ISBN)

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Dr. Rabindra Nath Shaw is currently working as an adjunct professor at Chandigarh University, Chandigarh, India, and also worked as the director of International Relations at Bharath Institute of Higher Education & Research (Deemed to be University), Chennai, India. Before joining BIHER, he has also served Galgotias University as Director, IR&C. He is an alumnus of the Applied Physics Department, University of Calcutta, India. He is a senior member of IEEE Industry Application Society, USA, and fellow of Nikhil Bharat Shiksha Parishad, India. Dr. Shaw is a global leader in organizing International conferences. His brand of world-leading conference series includes IEEE International Conference on Computing, Power and Communication Technologies (GUCON), IEEE International Conference on Computing, Communication and Automation (ICCCA), IEEE IAS Global Conference on Emerging Technologies (GlobConET), International Conference on Electronics & Electrical Engineering (ICEEE), InternationalConference on Advances in Computing and Information Technology (ICACIT), etc.

Dr. Sanjoy Das is currently working as a professor and head of Department of Computer Science, Indira Gandhi National Tribal University (A Central Government University), Amarkantak, M.P. (Manipur Campus)- India. He did his B.E. and M.Tech., Ph.D. in Computer Science. Before joining IGNTU, he has worked as an associate professor at School of Computing Science and Engineering, Galgotias University, India, from July 2016 to Sept 2017. He has worked as an assistant Prof. at Galgotias University from Sept 2012 to June 2016, and also as an assistant professor, G. B. Pant Engineering College, Uttarakhand, and Assam University, Silchar, from 2001-2008. He has 16+ years of experience in teaching and research. He has published more than 80+ research papers in Scopus/Web of Science/ SCI-indexed international journals, conference proceedings, and books. His current research interest includes mobile ad hoc networks and vehicular ad hoc networks, distributed systems, and data mining.

Dr. Marcin Paprzycki received the M.S. degree from Adam Mickiewicz University, Poznan, Poland, the Ph.D. degree from Southern Methodist University, Dallas, Texas, and the Doctor of Science degree from the Bulgarian Academy of Sciences, Sofia, Bulgaria. He is an associate professor with the Systems Research Institute, Polish Academy of Sciences. He is a senior member of the ACM, a senior Fulbright lecturer, and an IEEE Computer Society distinguished visitor. He has contributed to more than 500 publications and was invited to the program committees of more than 800 international conferences.

Prof. Ankush Ghosh is a senior member of IEEE, fellow of IETE currently working as an adjunct professor at Chandigarh University, Chandigarh, India. He has received his Ph.D. (Engg.) degree from Jadavpur University, India, in 2010. He was a research fellow of theAdvanced Technology Cell- DRDO, Govt. of India. He was awarded National Scholarship by HRD, Govt. of India. He is serving as an editorial board member of several international journals including Chief Editor. He has more than 15 years of experience in teaching, research as well as industry. His UG and PG teaching assignments include microprocessor and microcontroller, AI, IoT, embedded and real-time systems, etc. He has delivered keynote/invited lecture in a number of international seminar/conferences, refreshers courses, and FDPs. He has guided a large number of M.Tech and Ph.D. students. He is an editor and organizing committee member of the conference series GUCON, ICCCA, ICEEE, and ICACIT.

Dr. Monica Bianchini received the Laurea cum laude in Mathematics and the Ph.D. degree in Computer Science from the University of Florence, Italy, in 1989 and 1995, respectively. After receiving the Laurea, for two years, she was involved in a joint project of Bull HN Italia and the Department of Mathematics (University of Florence), aimed at designing parallel software for solving differential equations. From 1992 to 1998, she was a Ph.D. student and a postdoc fellow with the Computer Science Department.

Content

Intro
Preface
Contents
Editors and Contributors
Development of CNN-Based Feature Extraction and Multi-layer Perceptron for Eye Disease Detection
1 Introduction
2 Existing Works
2.1 Related Works
2.2 Research Gaps and Challenges
3 Methodology of Multiple Eye Disease Detection Technique
3.1 Proposed Detection System for Multiple Eye Disorder
3.2 Details of Eye Image Collection
4 Feature Extraction and Multi-layer Perceptron Model for Multiple Eye Disease Detection
4.1 CNN-Based Feature Extraction
4.2 Multi-layer Perceptron for Classification
5 Result and Discussions
6 Conclusion
References
Optimal Relay Node Selection Using Machine Learning to Extend Coverage in Disaster Area Network
1 Introduction
2 Related Work
3 System Model
3.1 Network Architecture
3.2 Optimal Relay Node Selection Using Machine Learning Algorithm
3.3 Network Performance of the Proposed Model
3.4 Outage Probability Analysis
4 Result
5 Conclusion
References
A Structured Literature Review and Meta-analysis of Forecasting Methods for Energy Consumption in Smart Buildings
1 Introduction
2 Methodology
3 Energy Consumption in Smart Buildings
3.1 Need of Energy Consumption Forecasting in Smart Buildings
3.2 Energy Forecasting Methods
4 Black-Box Methods
5 White-Box Methods
6 Grey-Box Methods
7 Input Parameters
8 Prediction Intervals
9 Measures of Performance Analysis
10 Results and Discussion
11 Future Challenges
12 Conclusion
References
Document Summarization Leveraging Modified LexRank Algorithm
1 Introduction
1.1 Related Work
1.2 Contributions
2 Methodology
3 Case Studies and Results
4 Conclusion
References
A Blockchain-Based Approach to Improve Data Integrity in Federated Cloud Environment
1 Introduction
1.1 Federated Cloud Environments
1.2 Benefits of Cloud Federations
1.3 Data Integrity Challenges
1.4 Blockchain Overview
1.5 Blockchain Architecture
2 Literature Survey
2.1 Related Work
2.2 Comparative Study
3 Proposed Framework
3.1 Layer 1: Utilizing Permissioned Blockchain for Operation Tracking
3.2 Layer 2: Ensuring Integrity of Individual Data Items:
3.3 Elasticsearch
4 Strengths of the Proposed Framework
4.1 Enhanced Data Integrity
4.2 Efficient Consensus Mechanism
4.3 Mitigation of Side-Channel Attacks
4.4 Improved Leadership Selection
4.5 Robust Log Storage
4.6 Enhanced Security
4.7 Greater Transparency
4.8 Instant Traceability
5 Conclusion
References
Performance Analysis of Hybrid Cryptographic Algorithms in Serverless Platforms
1 Introduction
2 Background
2.1 Fernet Algorithm
2.2 RSA Algorithm
2.3 Triple DES Algorithm
3 Related Work
4 Proposed Hybrid Approach
4.1 Two-Tier Model
4.2 Three-Tier Model
5 Performance Analysis
5.1 Comparison with 3DES + RSA Model
6 Conclusion
References
Intensifying Cross Architecture Cyber-Resilience System with Descriptive Malware Analysis
1 Introduction
2 Literature Survey
3 Methodology
3.1 Dataset
3.2 Concept Used
3.3 Proposed Method
4 Result
5 Conclusion
6 Future Scope
References
Challenges and Solutions in Integrating Narrowband IoT with Edge Computing: Resource Constraints, Security, Latency, and IDS Deployment
1 Introduction
1.1 Background
1.2 Objectives of the Study
1.3 Scope and Limitations
2 Literature Review
2.1 Previous Work on IoT and Edge Computing
2.2 Identified Areas Requiring Improvement
3 Methodology
3.1 Data Collection
3.2 Data Analysis Resource Constraints
4 Steps Used to Study the Dataset
5 Data Analysis
5.1 Phase 1: Data Acquisition
5.2 Phase 2: Data Preprocessing
5.3 Phase 3: Exploratory Data Analysis (EDA)
5.4 Phase 4: Data Analysis
6 Conclusion
7 Areas for Further Research
References
An Automatic Brick Grading System Using Convolutional Neural Network: Bangladesh Perspective
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Splitting Data
3.4 Training
3.5 Evaluate Model
4 Experiment
4.1 Data Collection
4.2 Data Prepossessing
4.3 Libraries
4.4 Early Stopping
4.5 Convolutional Neural Network
4.6 Adam Optimizer
4.7 Hyperparameters
4.8 Training
4.9 Testing
5 Result and Discussion
5.1 Experimental Result
5.2 Evaluation Matrix
5.3 Training and Validation Loss
5.4 Training and Validation Accuracy
5.5 Confusion Matrix
5.6 Comparison
6 Conclusion
6.1 Limitations and Future Work
References
Optimization, Modelling and Evaluation of Marshall Stability of Asphaltic Concrete with Agricultural and Industrial Wastes Through Response Surface Method
1 Introduction
2 Materials and Methods
3 Results and Discussion
3.1 Evaluation of the Fitted Model Parameters
3.2 Interactive Effect of Significant Factors on Marshall Stability
4 Conclusion
References
A Query-Based Approach to Mitigate the Shortcomings of Widely Used Learning Methods Through E-Learning
1 Introduction
2 Literature Review
2.1 Shortcomings of Existing Methods
2.2 E-Learning and the Evolution of Technology
2.3 The Art of Questioning in Learning
2.4 Questions as a Learning Tool
2.5 Query-Based Access to Neurons
3 Methodology
3.1 E-Learning Using QuBAN Method
4 Result and Discussion
4.1 Effectiveness Across Courses
4.2 Effect on Confidence and Motivation
4.3 Impact on Questioning Habits
4.4 Student's Attitude Towards QuBAN
5 Conclusion
References
Navigating the Waters of Image Watermarking: A Neural Network-Centric Review
1 Introduction
1.1 Image Watermarking Using Neural Networks
1.2 Types of Neural Networks Used in State-of-the-Art Image Watermarking Techniques
1.3 Performance Metrics for Different Watermarking Techniques
2 Literature Review
3 Future Scope
4 Conclusion
References
Evaluation of Group Chats Using Exploratory Data Analysis
1 Introduction
2 Literature Work
3 Working Methodology
3.1 Data Collection
3.2 Data Pre-processing
3.3 Data Preparation and Cleaning
3.4 Analysis and Interpretation of Results
3.5 Data Visualization
4 Result and Discussion
5 Conclusion
References
FinTech Revolution in Bharat
1 Introduction
2 Evolution of FinTech
2.1 Pre-digitization Period
2.2 Digitization Period
2.3 Post-digitization Period
3 The India Stack
3.1 Identity Layer
3.2 Payments Layer
3.3 Data Layer
4 Financial Inclusion and Solutions
4.1 Development and Financial Inclusion
4.2 FinTech Solutions
5 FinTech Adoption and Investments
5.1 Trends Driving FinTech Adoption
5.2 FinTech Investments
6 Challenges in FinTech
6.1 Lack of Trust and Awareness
6.2 Formal Credit
6.3 Rigorous Regulatory System
6.4 Skill-Based Talent Acquisition
6.5 Cybersecurity
6.6 Global Economics and Recession
7 Conclusion
References
A Review of 3D Avatar Reconstruction for Virtual Conferencing
1 Introduction
2 Problem Background
3 3D Reconstruction
4 3D Avatar
4.1 Classification of 3D Avatars
4.2 Application of 3D Avatar in Virtual Conference
5 3D Avatar Reconstruction
5.1 Traditional Reconstruction Pipeline
5.2 Reconstruction Based on Deep Learning
6 Conclusion
References
Real-Time Food Simulation Using Real Hand Gesture for Malaysia Cultural Heritage
1 Introduction
2 Designing Hand Gestures
3 Proposed VR Pulut Panggang
3.1 Test Application
4 Conclusion
References
Feature Selection Using Chi-Squared Feature-Class Association Model for Fake Profile Detection in Online Social Networks
1 Introduction
2 Related Works
3 Feature Extraction
4 Proposed Chi-Squared Feature-Class Association Model
5 Result and Analysis
5.1 Dataset and Description
5.2 Evaluation Metrics
6 Results
7 Conclusion
References
Classification and Identification of Weeds Using Gradient Boosting Classifiers
1 Introduction
2 Related Work
3 Convolutional Neural Network
3.1 Convolutional Layers
3.2 Pooling Layers
3.3 Global Average Pooling Layer
3.4 Fully Connected Layers
4 Boosting Algorithms
4.1 Gbm
4.2 Lgbm
4.3 CatBoost
4.4 AdaBoost
4.5 XGBoost
5 Dataset
6 Methodology
7 Experimental Results
7.1 Assessment Metrics
8 Results and Discussion
8.1 Comparison with Other Studies
8.2 Limitations and Complexity of Proposed Model
9 Conclusion
References
Enhancing Accuracy and Efficiency in Diabetic Retinopathy Detection: A Deep Learning Framework for Fundus Image Analysis
1 Introduction
2 Methodology of the Proposed System
3 Various Machine Learning Algorithm
4 Result and Discussion
5 Conclusion
References
Enhancing Brain MRI Tumor Detection: Exploring Vision Transformers and Fine-Tuned Convolutional Neural Network Architecture for Improved Performance
1 Introduction
1.1 Convolution Neural Networks
1.2 Vision Transformers
2 Dataset Analysis
3 Methodology
3.1 Convolution Neural Networks
3.2 Proposed Model (Vision Transformers)
4 Experimental Results
5 Conclusion
References
Bitcoin Price Prediction Using Sentiment Analysis
1 Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Scope of the Study
2 Literature Review
2.1 Property Research Distribution
3 Methodology
3.1 Introduction
3.2 Methodology
4 Implementation
4.1 Introduction
4.2 Data Loading
4.3 The Splitting of Data
4.4 Tweet Analysis
4.5 Daily Price Trend Prediction
4.6 Daily Price Change Magnitude Prediction
5 Results and Discussions
6 Conclusion
References
A Blockchain-Based Model to Enhance the Traceability and Transparency of Drug Supply Chain
1 Introduction
2 Literature Review
3 The Proposed Work
3.1 Data Construction
3.2 Smart Contract and Its Mechanism
4 Discussion and Performance Evaluation of the Proposed Work
4.1 Discussion
4.2 Performance Analysis
5 Conclusion
References
Optimizing Supply Chain Operations in the Electronics Industry Using Machine Learning and Integer Linear Programming
1 Introduction
1.1 Background
1.2 Problem Statement
1.3 Research Objectives
1.4 Significance of the Study
2 Literature Review
2.1 Supply Chain Optimization
2.2 Challenges in the Electronics Supply Chain
2.3 Integration of ML and ILP
3 Methodology
3.1 Data Collection and Analysis
3.2 Model Development and Implementation
4 Results and Analysis
4.1 Demand Forecasting
4.2 Inventory Management
4.3 Transportation Logistics Optimization
5 Conclusion
References
Automatic Brain Tumor Segmentation from MRI Images Using Variants of U-Net Model
1 Introduction
2 U-Net-Based Models
2.1 U-Net
2.2 U-Net++
2.3 Attention U-Net
2.4 U-Net 3+
2.5 ELU-Net
3 Dataset and Evaluation Metrics
4 Training and Result
4.1 Result
5 Conclusion
References
Analyzing Abusive Comments in Bangla: Machine Learning Study of Feminism on Social Media ,
Abstract
1 Introduction
2 Related Work
3 Methodology
3.1 Data Collection
3.2 Dataset Preprocessing
3.3 Feature Selection
3.4 Model Training
3.5 Model Evaluation
4 Results and Discussion
4.1 Results of Manually Annotated Dataset
4.2 Results of Keyword-Based Dataset
4.3 Error Analysis
5 Conclusion and Future Work
References
Forecasting Health Impacts of Air Pollution with Deep Learning Models
1 Introduction
2 Methodology
2.1 Model Development
2.2 Pollution Mitigation and Traffic Management
2.3 Various Machine Learning Approaches
3 Result and Discussion
4 Conclusion
References
An Ensemble Feature Selection Approach for Intrusion Detection Systems
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Preprocessing Module
3.2 Ensemble Feature Selection Module (EFS)
3.3 Performance Analysis Module
4 Experiment and Results
4.1 Experimental Setup
4.2 Dataset
4.3 Results and Discussion
5 Conclusion and Future Scope
References
Navigating the Landscape of Ransomware Detection Methods: A Review
1 Introduction
2 Ransomware Background
2.1 Ransomware Impact
2.2 Type of Ransomware
2.3 Ransomware Lifecycle
2.4 Infection Methods
2.5 Evolution Ransomware
3 Evaluation Metrics
4 Anti-ransomware Detection Techniques
4.1 Machine Learning-Based
4.2 Non-machine Learning-Based
5 Survey Analysis
5.1 Major Themes
5.2 Limitations
5.3 Limitation of Our Survey
6 Conclusion and Future Work
References
Methods and Datasets for Detecting Hate Speech in Textual Content
1 Introduction
2 Related Work
3 Methodology
4 Experimental Evaluation
5 Results
6 Conclusion
References
Catalyzing Security and Efficiency: Blockchain's Integration with IoT and Cloud Computing
1 Introduction
2 Literature Review
3 Methodology
4 Result
5 Conclusion
References
A Deep Learning-Based Model for Indian Food Image Classification
1 Introduction
2 Literature Review
3 Materials and Methodology
3.1 Dataset
3.2 The Preparation of Images
3.3 Testing and Validation
3.4 Machine Learning Classifier
3.5 Convolutional Neural Network Model
3.6 Performance Metrix
4 Performance Analysis
5 Conclusion and Future Work
References
Annotating Fashion Images and Scraping Similar Products Over Web
1 Introduction
2 Literature Survey
3 Proposed Methodology
4 Implementation
5 Results
6 Conclusion
References
Adaptive Loss and Deep Convolutional Neural Networks: A Blending Approach to Self-adaptive Deep Learning Models for Brain Tumor Classification
1 Introduction
2 Literature Review
3 Proposed Methodology
4 Results and Discussion
5 Conclusion
References
Enhancing Heart Disease Prediction with Ensemble Deep Learning and Feature Fusion in a Smart Healthcare Monitoring System
1 Introduction
2 Methodology of the Proposed System
2.1 Various Sensor Used in This Research
3 Various Machine Learning Algorithm
3.1 Convolutional Neural Network
3.2 Support Vector Machine
3.3 Feature Selection
4 Result and Discussion
5 Conclusion
References
AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*
1 Introduction
2 Methodology
3 Result and Discussion
3.1 FEA of Spur Gear for Design-1, 2, and 3 at 350 N-m
4 Conclusion
References
Improving Automated Diagnosis of Diabetic Retinopathy: Exploring the Influence of Segmented Retinal Blood Vessel Images Through Deep Learning
1 Introduction
2 Research Problem
3 Creation of Dataset
4 Methodology of the Research
5 Result and Discussion
6 Conclusion
References
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in Online Food Delivery Organizations
1 Introduction
2 Related Work
3 Proposed Work with Factors Impacting the Delivery Window
4 Examining Different Online Food Delivery Models
5 Conclusions
References
Enhancing Urban Waste Management Through Smart Dustbins and IoT-Based Technology
1 Introduction
2 Related Work
3 Resources
3.1 Dataset
3.2 Software/Tools Requirements
4 Proposed System
4.1 System Analysis
4.2 System Design
4.3 ML Model Architecture
5 Results and Discussion
6 Conclusion
References
Hand Gesture Recognition with Audible Feedback for Deaf and Dumb Using ML
1 Introduction
2 Literature Review
3 Proposed Method
4 Flowchart
5 Results and Discussion
6 Conclusion
References
Leveraging Generative AI for Personalized Recommendation System
1 Introduction
2 Literature Review
3 Methodology Used
3.1 Data Preprocessing
3.2 Cosine Similarity Score Generation
3.3 Generative AI Modelling
3.4 Recommendation
4 Results and Discussions
5 Conclusion
References
Multi-class Skin Lesion Classification Using Intelligent Techniques
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Workflow
4 Results
5 Conclusion
References
Collaborative Smart Parking System
1 Introduction
1.1 Motivation
1.2 Major Contribution
2 Literature Survey
3 Proposed System
3.1 IoT Module
3.2 Cloud Storage
3.3 ALPR
3.4 Use Cases
4 Results and Analysis
5 Conclusion
References
Crowdfunding Platform Based on Blockchain Technology
1 Introduction
2 Literature Survey
3 Methodology
4 Result
5 Conclusion
6 Future Scope
References
Comparative Performance Evaluation of Breast Cancer Detection Techniques
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Acquisition and Data Preprocessing
3.2 Data Classification
3.3 Feature Selection
4 Data Visualization
5 Processing the Data
6 Predictive Modeling
6.1 SVM
6.2 KNN
6.3 Decision Tree
6.4 Random Forest
6.5 Hybrid Model
7 Results and Discussion
8 Conclusion and Future Directions
References

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Advanced Computing and Intelligent Technologies

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