Applied Soft Computing and Communication Networks

Name: Applied Soft Computing and Communication Networks | Proceedings of ACN 2023
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Proceedings of ACN 2023

Sabu M. Thampi Jiankun Hu Ashok Kumar Das Jimson Mathew Shikha Tripathi(Editor)

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1st Edition

Published on 27. July 2024

XXVIII, 593 pages

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978-981-97-2004-0 (ISBN)

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Sabu M. Thampi is Professor at the School of Computer Science and Engineering, Digital University Kerala, Trivandrum, India. His current research interests include the Internet of Things (IoT), cognitive security, social networks, endpoint security, and smart cyber-physical systems. Sabu also coordinates the Connected Systems and Intelligence (CSI) Lab at the University. He holds a Ph.D. in Computer Engineering from the National Institute of Technology Karnataka. Dr. Sabu has been actively involved in funded research projects and published papers in book chapters, journals, and conference proceedings. He has authored and edited a few books and edited 45+ conference proceedings published by Springer in various series, as well as a few others published by IEEE, ACM, and Elsevier.

Dr. Jiankun Hu is a full professor of Cyber-Security at the School of Engineering and Information Technology, the University of New South Wales at the Australian Defence Force Academy (UNSW@ADFA), Australia. His major research interest is in computer networking and computer security, especially biometric security. He has been awarded ten Australia Research Council Grants. He was a program co-chair of the 2008 International Symposium on Computer Science and its Applications. He is serving as an associate editor of the following journals: Journal of Security and Communication Networks, Wiley; IEEE TIFS (Senior Area Editor); KSII TIIS (Area Editor); IET CPS; IEEE OJCS; and Wiley S&P.

Dr. Ashok Kumar Das is currently working as an associate professor in the Center for Security, Theory and Algorithmic Research (Department of Computer Science and Engineering) of the International Institute of Information Technology (IIIT), Hyderabad, India. He received his Ph.D. degree in Computer Science and Engineering, the M.Tech. degree in Computer Science and Data Processing, and the M.Sc. in Mathematics (Minor: Computer Science), all from Indian Institute of Technology (IIT) Kharagpur, India. His research interests include cryptography and network security. He has authored over 195 papers in international journals and conferences including 170 reputed journal papers (including IEEE Transactions). He is a senior member of the IEEE.

Dr. Jimson Mathew is an associate professor with Indian Institute of Technology in Patna, India. He received Doctorate from the University of Bristol, Bristol, UK. His research interests include but are not limited to fault-tolerant computing, VLSI design and methodologies, reliability-aware designs, hardware security, machine intelligence, and IoT. He is a senior member IEEE and a member of IET.

Dr. Shikha Tripathi holds a Ph.D. from BITS, Pilani, India, and a Master's degree from BIT, Mesra, Ranchi, India. Dr. Tripathi joined PES University, Bangalore, in July 2017. Currently, she is serving as a professor in Dept. of Electronics and Communication Engineering and research head of the Electronic City campus. She is also the director of Centre for Robotics, Automation and Intelligent Systems at PES University, RR Campus. Prior to this, she was serving as a professor and chairperson at Dept. of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, School of Engineering, Bangalore campus, since July 2009. She has worked at BITS Pilani, Pilani campus in various capacities before joining Amrita Vishwa Vidyapeetham from 1998 to 2009. She was heading the Dept. of Electronics and Instrumentation Engineering at BITS Pilani before relocating to Bangalore.

Content

Intro
Conference Organization
Preface
Contents
Editors and Contributors
Artificial Intelligence and Human-Computer Interaction
Open Gaze: Open-Source Eye Tracker for Smartphone Devices Using Deep Learning
1 Introduction
2 Dataset
3 Data Splits
3.1 MIT Split
3.2 Exclusively Mobile Devices in All Orientations
3.3 Mobile Devices Used Solely in Portrait Orientation
3.4 Google's Data Division
4 Model Architecture
5 Training
5.1 Implementation
5.2 Refining Hyperparameters
6 Post-training SVR Personalization
6.1 Differential Impact Based on Dataset Characteristics
6.2 Segmentation According to 13-Point Calibration
6.3 Google's 70/30 Training and Testing Split Methods
7 Results
7.1 PyTorch Results
8 Individualization Using Transformations
8.1 SVR Results
8.2 Version of Google Split
8.3 Version of MIT Split
8.4 Unique
8.5 Random Data Points/Samples
8.6 Outcomes from MIT Split Evaluation
8.7 Results from Google Split Evaluation
9 Future Scope and Improvements
10 Availability of Code and Data
References
Comparison on the Metaverse Space Development in Spatial.io Platform and Monaverse Platform
1 Introduction
2 Background
2.1 Spatial.io: Platform Overview
2.2 Monaverse: Platform Overview
3 Comparative Analysis
3.1 Decentralization
3.2 Interoperability
3.3 Space Creation and Scalability
3.4 Project Setup and Space Components
3.5 Interaction Interface of the Metaverse Space
3.6 Examples of Monetization and Opportunities
4 Discussion and Future Work
References
Intelligent Holo-Assistant Avatar with Lip-Syncing
1 Introduction
2 Methodology
2.1 Designing of GUI of Holo-Assistant Applications
2.2 Developing Intelligent Assistant Avatar with Lip-Syncing
2.3 Integrate the Intelligent Holo-Assistant Avatar with Lip-Syncing
3 Implementation and Results
4 Conclusion
References
Historical Analysis of Financial Fraud and Its Future
1 Introduction
2 Historical Analysis of Financial Fraud
2.1 Early Modern Finance
2.2 Industrial Revolution and Capital Market Development
2.3 Post World War II
2.4 Globalization and Financial Deregulation
2.5 Digital Age and FinTech
3 Factors Contributing to Financial Fraud
3.1 The Human Factor
3.2 Weak Internal Controls
3.3 The Psychological Factor
4 Classification and Impacts of Financial Fraud
4.1 Direct Impact of Financial Fraud
4.2 Indirect Impact of Financial Fraud
5 The Future of Financial Fraud
5.1 The Technology Advancements
5.2 Dynamic Economic Circumstances
5.3 Regulatory Responses
5.4 Fraudster Adaptability
5.5 Ethical Considerations
6 Conclusion
References
A Study on Colour-Emotion Association for Happiness Among the Indian Youth Using Artificial Intelligence
1 Introduction
2 Methodology
2.1 Colour Extraction from T-Shirts
2.2 Facial Emotion Recognition
2.3 Data Collection for Colour-Emotion Mapping
3 Discussion
4 Conclusion and Future Directions
References
Comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in kk-Coverage and mm-Connectivity Problem
1 Introduction
2 Related Works
3 Problem Formulation
3.1 Multiobjective Optimization Formulation
4 Proposed Algorithm
4.1 ILA Versus CSO
4.2 Fitness Function for both Algorithm
5 Simulation Results
6 Conclusion
References
Security and Privacy in Emerging Technologies
Machine Learning-Based Detection of Attacks and Anomalies in Industrial Internet of Things (IIoT) Networks
1 Introduction
1.1 IIoT Network Security Challenges
1.2 Attack Detection and Anomaly Identification Using Machine Learning
2 Literature Review
2.1 Existing IDS Systems Based on GANs
2.2 G-IDS Proposal
3 Dataset Information
4 Proposed System
4.1 Data Preprocessing
4.2 Feature Selection Algorithm
4.3 Generative Adversarial Networks
5 Results and Discussion
6 Conclusion
References
Privacy in Data Handling in Agile Development Environments
1 Introduction
1.1 Motivation
1.2 Problem and Target Audience
2 Theoretical Reference
3 Related Work
4 Proposal Presentation
4.1 Methodological Structure
4.2 Setting Security and Privacy Controls
4.3 Proposed Instruction Set
5 Practical Application
6 Final Considerations
References
Feature Enriched Framework for Rumor Detection Using Tweets
1 Introduction
2 Related Work
2.1 Machine Learning Models/Techniques
2.2 Deep Learning Models/Techniques
2.3 Hybrid Techniques
2.4 Word/Contextual Embedding Techniques and Transformer Models
3 Research Design
3.1 Feature Enriched Rumor Detection Framework
3.2 Perspectives Used for Rumor Modeling
4 Methodology
4.1 Models Used for Experimentation
4.2 Rumor Dataset Description
4.3 Experimentation
4.4 Evaluation Results
5 Analysis and Discussion
6 Conclusion
References
Enhancing KYC Verification: A Secure and Efficient Approach Utilizing Blockchain Technology
1 Introduction
2 Background
2.1 Traditional KYC Verification
2.2 Blockchain
2.3 KYC and Blockchain
3 Related Work
4 Enhancements
5 Proposed Approach
5.1 System Architecture
5.2 Proposed Algorithms
6 Results and Discussion
6.1 Scalability
6.2 Flexibility
6.3 Transparency
6.4 Privacy
6.5 History Tracking
7 Future Scope
8 Conclusion
References
A Smartphone Data Steganography Framework Based on K-mean Image Selection and Random Embedding in Three Image Channels
1 Introduction
2 Related Works
3 K-mean Select Random Insert (KSRI) Framework
3.1 The K-mean Selector Layer
3.2 The Random Inserter Layer
4 Experiment
4.1 The K-means Model Validation
4.2 The Data Embedding in Cover Images
5 Conclusion
References
Communication and Network Technologies
Comparative Analysis of LSTM and GRU for Uplink Data Rate Prediction in 5G Networks
1 Introduction
2 Background and State of the Art
3 Data and Methods
3.1 Data Collection
3.2 RNN Variants-LSTM and GRU
3.3 Performance Measures
4 Proposed Approach
4.1 Data Preparation
4.2 Experiments
4.3 Evaluation
5 Conclusion
References
Scheduling in Time-Sensitive Networks Using Deep Reinforcement Learning
1 Introduction
2 Related Work
3 The Solution Approach
3.1 Reinforcement Learning
3.2 Simulation
4 Results and Discussions
5 Conclusion and Future Work
References
Power Control for Collaborative Sensors in Internet of Things Environments Using upper KK-means Approach
1 Introduction
2 Channel and Signal Modeling of Collaborative Sensors
3 Power Control Strategy of Collaborative Sensors
3.1 Energy Consumption Model
3.2 Power Control Strategy
4 Implementing K-means
5 Simulation and Results
6 Conclusion
References
Optimizing the EDFA Gain for Optical WDM Network Using Simulation Method
1 Introduction
2 Simulative Setup
3 Results and Discussion
4 Conclusion
References
RFID-Based Smart Trolley with Mobile Application
1 Introduction
2 Literature Survey
3 Materials and Methods
3.1 Arduino UNO
3.2 EM-18 Reader Module
3.3 Bluetooth Module (HC-05)
3.4 RFID Tags
3.5 Voltage Divider Circuit
3.6 Android Studio
3.7 Radio Frequency Identification
4 Results and Discussions
5 Conclusion and Future Scope
References
Secure Authentication and Key Management
SAKM-ITS: Secure Authentication and Key Management Protocol Concerning Intelligent Transportation Systems
1 Introduction
2 Related Work
3 Threat Model
4 Proposed Model
4.1 Registration of Entities
4.2 Authentication and Key Exchange Phase
5 Security Analysis
5.1 Formal Verification Using Scyther
5.2 Formal Verification Using Tamarin Prover
5.3 Informal Security Analysis
6 Comparative Analysis
6.1 Communication Cost
6.2 Computation Cost
7 Conclusion
References
Addressing Single Point of Failure in Group Communication of Constrained Environments
1 Introduction
2 Background
2.1 Shamir's Secret Sharing
2.2 Lagrange Interpolation
2.3 ECC and ECDH
3 The Proposed Scheme
3.1 Sharing Shares
4 Analysis of the Scheme
5 Conclusion
References
An RFID-Based Authentication Protocol for Smart Healthcare Applications
1 Introduction
1.1 Motivation and Contribution
1.2 Organization of the Paper
2 Preliminaries
2.1 Basic Concept of Elliptic Curve over a Prime Field GF(p)
2.2 Cryptographic Hash Function
2.3 Threat Model
3 Proposed Protocol
3.1 Initialization Phase
3.2 Registration Phase
3.3 Login and Mutual Authentication Phase
4 Security and Performance Analysis
4.1 Formal Security Verification Using AVISPA Tool
4.2 Security Features Comparison
4.3 Performance Analysis
5 Conclusion
References
Advanced Threat Detection and Mitigation
On Credit Card Fraud Detection Using Machine Learning Techniques
1 Introduction
2 Machine Learning Techniques
2.1 Decision Tree
2.2 Random Forest
2.3 Isolation Forest
2.4 Support Vector Machine
3 Experimental Results
4 Conclusion
References
Refactoring Web Applications Using Functional Programming Principles to Improve Security
1 Introduction
2 Background
2.1 Web Application Security
2.2 Functional Programming
2.3 Refactoring
3 Problem
4 Methodology
4.1 Development of the Methodology
4.2 Description of Methodological Approach
4.3 Applying the Methodology to Web Application Code
4.4 Summary of Applying the Methodology
4.5 Testing Approach
5 Case Study
5.1 Step One
5.2 Step Two
5.3 Step Three
5.4 Repeating Steps Two and Three
6 Case Study Results
6.1 Discussion of Case Study Results
7 Limitations
7.1 Performance
7.2 Validation
7.3 Complexity
8 Future Work
8.1 Further Validation
8.2 Broader Scope
8.3 Automation
9 Conclusion
References
Private Contact Tracing on Trajectory Data
1 Introduction
1.1 Related Works
2 The Proposed PCT Scheme
2.1 Setup
2.2 The Scheme
3 Security Analysis
4 Experimental Result
5 Conclusion
References
Innovating Threat Detection: Behavioral Rule Generators for Malware Families
1 Introduction
2 Motivation
3 Requirements of the Behavioral Rule Generator
3.1 Generic Requirements
3.2 Specific Requirements
4 High-Level Architecture
4.1 Extractor Component
4.2 Sandbox Instances
4.3 Sandbox Orchestrator/Monitor
4.4 Aggregator and Decoder
4.5 Test Action Generator
4.6 Test Action Script
5 Application of Test Action Scripts
6 Advantages of BRG and Dynamic Rules
7 Limitations of BRG and Dynamic Rules
8 Future Work
9 Conclusion
References
Stratifying Malware Clusters: A Solution Mapping Paradigm
1 Introduction
2 Current Problems and Limitations
3 Solution Mapping Framework
4 Solution Mapping
5 Challenges and Limitations
6 Future Work
7 Conclusion
References
Signal Processing in Imaging and Sensing
A Multimodal Deep Learning Approach for High-Resolution Land Surface Temperature Estimation
1 Introduction
2 Methodology
2.1 Data Collection
2.2 Data Preprocessing
2.3 Data Preparation
2.4 cGANs Training
3 Results
3.1 Urban Zone
3.2 Extended Zone
4 Discussion
5 Conclusion
References
Image Super-Resolution by Augmentation of Region Information by Rapid Segmentation
1 Introduction
2 Dataset, Model and Data Augmentation
2.1 Image Dataset
2.2 Deep Learning Model
2.3 Data Augmentation
3 Implementation Details
4 Results and Analysis
5 Conclusion and Future Scope
References
Characterization of Heart-Centric Nanoscale Communication at Terahertz and Optical Bands
1 Introduction
2 Theoretical Modeling of the Channel
3 Numerical Results
3.1 Terahertz Band
3.2 Optical Band
4 Conclusion
References
Comparison of MobileNetV2 and VGG19 for the Categorization of Thermal Images
1 Introduction
2 Related Work
3 Research Method
3.1 Data Acquisition
3.2 Image Preprocessing
3.3 Contrast Limited Adaptive Histogram Equalization (CLAHE)
3.4 Deep Learning
4 Hyperparameter
5 Performances Metrics
5.1 Accuracy
5.2 Precision
5.3 Sensitivity (Recall)
5.4 F1 Score
6 Results
7 Conclusion and Future Work
References
MRI Denoising with Residual Connections and Two-Way Scaling Using Unsupervised Swin Convolutional U-Net Transformer (USCUNT)
1 Introduction
2 Related Woks
3 Problem Statement
4 USCUNT Model for MRI Denoising
4.1 Swin Transformer
4.2 U-Net Architecture
5 Result Analysis
5.1 Data Set Description
5.2 Analysis of Denoising
6 Conclusion
References
Segmentation of Mammogram Images Using U-Net with Fusion of Channel and Spatial Attention Modules (U-Net CASAM)
1 Introduction
2 Related Works
3 Problem Statement
4 U-Net CASAM Model for Mammogram Semantic Segmentation
5 Result Analysis
5.1 Dataset Description
5.2 Analysis of Segmentation
6 Conclusion
References
Detecting Human Walking Direction Using Wi-Fi Signals
1 Introduction
2 Related Work
2.1 Channel State Information of Wi-Fi Signals
2.2 Literature Review
3 System Design
3.1 Experiment Setup
3.2 Collecting the Data
3.3 Preprocessing the Data
3.4 Recognizing Activities
4 Assessing Performance
4.1 Different Metrics
4.2 Effect of Changes in the Environment
4.3 Effect of a Changing Number of Individuals
4.4 Robustness
5 Conclusion and Future Work
References
Intelligent Systems and Learning Algorithms
Size and Inference Time Optimized Automatic Speech Recognition Model
1 Introduction
2 Dataset Description
3 Proposed Methodology
4 Model Architecture
5 Results and Discussion
5.1 Feature Extraction
6 Conclusion
References
Development and Evaluation of a Comprehensive Dataset for Pothole Depth Estimation of Indian Roads Using Smartphone Camera Approach
1 Introduction
2 Related Work
2.1 Existing Datasets
2.2 Object Detection Models
2.3 Depth Estimation Models
3 Dataset Creation
3.1 Data Categorization
3.2 Camera Placement and Video Collection Standards
3.3 Data Summary
3.4 Privacy Matters
4 Results
5 Conclusion
6 Future Works
References
Comprehensive Dataset Building of Isolated Handwritten Sanskrit Characters
1 Introduction
2 About Sanskrit Text
2.1 Structure of Sanskrit Language
2.2 Challenges in Sanskrit Character Recognition
2.3 A Survey of Existing Datasets in Sanskrit
3 Dataset
3.1 Framework for Collecting the Requisite Data
3.2 Demographic Particulars of the Writers
4 Approach to the Construction of the Dataset
4.1 Isolating Individual Characters
4.2 Data Pre-processing
4.3 Results
4.4 Addressing Data Scarcity
5 Conclusion
References
Comparative Analysis of Deep Machine Learning Models for Identification of Glaucoma from Fundus Images
1 Introduction
2 Background Study
2.1 Role of AI in Glaucoma Detection
2.2 Convolutional Neural Network (CNN)
3 Methodology
3.1 Approach
3.2 Dataset Description
3.3 Data Pre-processing
3.4 Data Augmentation
3.5 Model Implementation
4 Results and Discussion
5 Conclusion and Future Scope
References
Emotion Recognition from Speech, Text, and Facial Expressions Using Meta-Learning
1 Introduction
1.1 Meta-Learning
1.2 Emotion Recognition Using Meta-Learning
1.3 Audio Emotion Recognition
1.4 Face Emotion Recognition
1.5 Verbal Emotion Recognition
2 Literature Review
3 System Architecture
4 System Implementation and Testing
4.1 Module Description
4.2 Audio Emotion Recognition Using CNN and Siamese
4.3 Face Emotion Recognition
4.4 Verbal Emotion Recognition Using CNN and Siamese
5 Results and Analysis
5.1 Audio Emotion Recognition Analysis
5.2 Face Emotion Recognition Analysis
5.3 Verbal Emotion Recognition Analysis
6 Conclusion and Future Scope
References
A Comprehensive Literature Survey on Federated Learning
1 Introduction
2 Open-Source Framework for Federated Learning
2.1 FATE
2.2 TFF
2.3 Flower
2.4 PySyft
3 Device Organization
3.1 Cross-silo Interactions
3.2 Cross-device Interactions
4 Network Communication
4.1 Centralized Federated Learning (With Server)
4.2 Decentralized Federated Learning
5 Data Partition
5.1 Horizontal FL
5.2 Vertical FL
5.3 Transfer FL
6 Privacy Challenges in Federated Learning
6.1 Differential Privacy (DP)
6.2 Homomorphic Encryption (HE)
6.3 Secure Multiparty Computation (SMPC)
7 Security Challenges in Federated Learning
7.1 Cryptographic Protections in FL
7.2 Robust Aggregation Techniques
7.3 Techniques for Adversarial Robustness in FL
8 Model Aggregation
8.1 FedAvg (Federated Averaging)
8.2 FedProx (Federated Proximal)
9 Optimization Techniques in FL
9.1 Why Optimization Is Crucial in FL
9.2 Gradient Descent and Its Variants in FL
9.3 Adaptive Learning Rates in FL
9.4 Specialized Techniques for FL
10 Conclusion
References
Development of Low-Cost Wearables for Fall Detection and Prevention to Enhance Geriatric Care
1 Introduction
2 Methodology
2.1 Thresholding
3 Components Used
3.1 ESP 8266 Microcontroller
3.2 MPU6050 Motion Sensor
3.3 Wearable Device
3.4 Power Supply
3.5 Communication Interface
3.6 Alarm or Alert System
3.7 False Alarm Detection
4 Implementation
4.1 Placement
4.2 IOT Platform Interface
4.3 Interface for Fall Alert
5 Conclusion
References
A Systematic Overview of Meta-pruning Strategies in Deep Learning
1 Introduction
2 Model Pruning
2.1 Filter Pruning
2.2 Channel Pruning
2.3 Weight Pruning
2.4 Layer Pruning
2.5 Node Pruning
3 Meta-learning
3.1 Model-Agnostic Meta-learning (MAML)
3.2 Reinforcement Learning-Based Meta-learning
3.3 Memory-Augmented Meta-learning
3.4 Enhancement-Based Meta-learning
3.5 Transfer Learning-Based Meta-learning
3.6 Few-Shot and One-Shot Learning
4 Meta-pruning
5 Meta-pruning Strategies
5.1 Meta Channel Pruning
5.2 Meta Filter Pruning
5.3 HyperNetworks for Differentiable Meta-pruning
5.4 Prospect Pruning
6 Results
7 Future Work
8 Conclusion
References
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Applied Soft Computing and Communication Networks

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