Deep Sciences for Computing and Communications

Name: Deep Sciences for Computing and Communications | Second International Conference, IconDeepCom 2023, Chennai, India, April 20-22, 2023, Proceedings, Part I
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Second International Conference, IconDeepCom 2023, Chennai, India, April 20-22, 2023, Proceedings, Part I

Annie Uthra R.Kottilingam Kottursamy Gunasekaran Raja Ali Kashif Bashir Utku Kose Revathi Appavoo Vimaladevi Madhivanan(Editor)

Springer (Publisher)

Published on 28. September 2024

XX, 518 pages

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978-3-031-68905-5 (ISBN)

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This two-volume set, CCIS 2176-2177, constitutes the proceedings from the Second International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023, held in Chennai, India, in April 2023.

The 74 full papers and 8 short papers presented here were thoroughly reviewed and selected from 252 submissions. The papers presented in these two volumes are organized in the following topical sections:

Part I: Applications of Block chain for Digital Landscape; Deep Learning approaches for Multipotent Application; Machine Learning Techniques for Intelligent Applications; Industrial use cases of IOT; NLP for Linguistic Support; Convolution Neural Network for Vision Applications.

Part II: Optimized Wireless Sensor Network Protocols; Cryptography Applications for Enhanced Security; Implications of Networking on Society; Deep Learning Model for Health informatics; Web Application for Connected Communities; Intelligent Insights using Image Processing; Precision Flood Prediction Models.

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Content

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Applications of Block Chain for Digital Landscape
SFLAB: Smart FIR Lodging Architecture and Solution Using Blockchain and IPFS Technology
1 Introduction
1.1 Major Contributions
2 Related Works
3 Proposed Methodology
4 Proposed Algorithm
4.1 Implementation Results and Discussion
5 Conclusion
References
AJAZ
1 Introduction
2 Literature Survey
3 Proposed System
4 Architecture Diagram
5 Methodology
6 Advantage
7 Results and Conclusion
8 Conclusion
References
Digital Identity System for Real World Asset Using Blockchain
1 Introduction
2 Related Works
3 Scope and Application of Blockchain
4 Working Methodology
4.1 User Dashboard
4.2 Creating NFT Token
4.3 Ownership Access
5 System Architecture
6 NFT
7 Smart Contract
7.1 Creating Transaction
7.2 View UTXO Array
7.3 Generate RSA Key
7.4 Track an Item
7.5 Mining Block
7.6 Verify Block
8 Conclusion
References
Blockchain-Driven Framework for Fake Product Detection
1 Introduction
1.1 Motivation
1.2 Research Contributions
1.3 Paper Structure
2 Literature Review
3 Adoption of Blockchain in Fake Product Detection
4 Proposed Framework
4.1 Manufacturer Layer
4.2 E-commerce Layer
4.3 Blockchain Layer
4.4 Consumer Layer
5 Result Discussion
5.1 Experimental Setup and Tools
5.2 Smart Contract Implementation Interface for Fake Product Detection
5.3 Performance Analysis of the Proposed Framework
6 Opportunities and Challenges
7 Conclusion
References
Blockchain Powered Real-Estate Management System
1 Introduction
2 Background
3 Blockchain, Smart Contract and Ethereum
3.1 Blockchain
3.2 Smart Contract
3.3 Ethereum
4 Problem Statement
5 Proposed Solution
6 Working
7 Future Work
8 Conclusion
References
A Block-Chain Based Decentralized Mechanism to Ensure the Security of Electronic Voting System Using Solidity Language
1 Introduction
2 Literature Survey
3 Methodology
3.1 Smart Contract
3.2 Consensus Algorithm
3.3 Cryptographic Techniques
4 Proposed System
5 Results
6 Conclusion
References
A Secure Persistent Health Care System Using Blockchain Smart Contract
1 Introduction
1.1 Blockchain Technology
1.2 Internet of Things
1.3 Blockchain and IoT Integration
2 Related Study
3 Proposed Work
3.1 Performance Analysis
4 Conclusion
5 Future Enhancements
References
Deep Learning Approaches for Multipotent Application
Multimodal Fake News Detection Using Deep Learning Techniques
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 BERT
3.4 Robustly Optimized BERT Pre-training Approach
3.5 Capsule Neural Network
3.6 Performance Evaluation
4 Conclusion
References:
Maritime Human Drowning Detection Using Intelligent Deep Learning Algorithm
1 Introduction
1.1 Related Work
1.2 Research Gap
1.3 Organization of the Paper
2 System Architecture
2.1 Problem Statement
2.2 Project Objectives
3 Methodology
3.1 DL Model
3.2 Pre-processing
3.3 Workflow of the Model
4 Model Implementation
4.1 Dataset Description
4.2 Model Fitting
5 Results and Analysis
5.1 Model Results
6 Conclusion and Future Scope
References
Development of a Pothole Detection System Using Deep Learning Techniques and Depth Estimation
1 Introduction
2 Literature Survey
3 Methodology
3.1 Estimation of Depth of Pothole
3.2 Estimation of Depth of Pothole
3.3 Dataset Description
4 Results and Discussion
4.1 Results from YOLOv8
4.2 Results from YOLOV5
4.3 Training Graphs
5 Conclusion
6 Future Work
6.1 Incorporation of Model into a Product
6.2 Feedback Received from Blind Person's Association, Kolkata
References
Machine Learning and Deep Learning Algorithms for Breast Cancer Prediction
1 Introduction
2 Machine Learning Algorithm for Breast Cancer
2.1 Logistic Regression
2.2 KNN
2.3 Decision Tree
2.4 SVM
2.5 Naïve Bayes
3 Deep Learning Algorithms for Breast Cancer Prediction
3.1 CNN
3.2 ANN
4 Methodology and Implementation
4.1 Dataset
4.2 Data Pre-processing
5 Results and Discussion
6 Conclusion and Future Scope
References
Leaf Disease Detection Using Deep Learning Approach
1 Introduction
2 Methodology and Materials
2.1 Data Used
2.2 Proposed Model
2.3 CNN Model
3 Results and Discussion
4 Conclusion
References
Robust Traffic Sign Recognition Using CNN YOLOv5 Model
1 Introduction
2 Literature Survey
3 Datasets
4 Proposed Work
4.1 Abbreviations and Acronyms
4.2 Image Processing
4.3 Convolutional Neural Network
5 Methodology and Implementation
5.1 Collection of Dataset
5.2 Image Processing and Classification
5.3 CNN-YOLOv5 Model Training and Testing
5.4 Traffic Sign Detection
6 Results
7 Conclusion
References
Fusion Emotion Prediction Using the CEFER Algorithm
1 Introduction
2 Literature Review
3 Proposed Methodology
3.1 Image Pre-processing
3.2 Detect Face, Facial Point Localization and Extraction of Facial Point
3.3 CEFER Algorithm
3.4 Combined Emotions Using CEFER Algorithm
4 Data Classification and Reduction
5 Results and Discussion
5.1 Experimental Setup
5.2 Dataset
6 Conclusions and Future Research
References
Machine Learning Techniques for Intelligent Applications
Performance Analysis of Various Machine Learning Techniques for Mental Health Tracking
1 Introduction
2 Literature Review
3 Overview of the Machine Learning Techniques
4 Methodology
4.1 Data Collection and Features
4.2 Preprocessing of the Data
4.3 Relationship Between the Data
5 Result Analysis
6 Conclusion and Future Scope
References
Chronic Kidney Disease (CKD) Detection Analysis Using Machine Learning
1 Introduction
2 Literature Review
3 Descriptive Features and Comparison
4 Analysis of the Literature
5 Conclusion
References
Revolutionizing MS Rehabilitation with Digital Twins and Machine Learning: A Promising Path to Precision Medicine
1 Introduction
2 Related Works
3 Digital Twin
3.1 Healthcare Upgrading via a Digital Twin
4 The Potential of AI in Digital Twin Development
5 Concept and Methodology
5.1 Modelling Healthcare Digital Twin (HDT) for MS Therapy
5.2 Data Acquisition
5.3 Cloud Storage and Processing
5.4 Feature Extraction and Selection
5.5 Data Analysis with ML Algorithms
5.6 AI Supported Clinical Decision Making
6 Results and Discussion
7 Conclusion and Future Work
References
Empirical Evaluation of Machine Learning Techniques for Car Price Prediction
1 Introduction
1.1 Prediction of Used Car Prices
2 Problem Statement
3 Literary Survey
4 Methods Used for Analysis
4.1 Statistics and Dataset
4.2 Preprocessing of the Data
4.3 Model and Approach
5 Result Analysis
6 Conclusion
References
Water Quality Analysis Using Machine Learning Techniques
1 Introduction
2 Literature Survey
3 Methodology
3.1 Collection of Datasets and Dataset Understanding
3.2 Data Preprocessing
3.3 Creation of Classification Models
3.4 Classification Model Evaluation
4 Results and Discussions
5 Conclusion
References
Design and Development of Human Identification and Obstacle Detection System for Blind Using Machine Learning
1 Introduction
2 Related Works
3 Architectural Design
4 System Implementation
4.1 MODULES Description
4.2 Object Detection and Face Recognition Module
5 Performance Analysis
6 Results and Discussions
7 Conclusion
References
Effective Parkinson Disease Detection and Prediction Using Voting Classifier in Machine Learning
1 Introduction
2 Literature Review
3 Proposed Work
3.1 Overview
3.2 Data pre-processing
3.3 Principal Component Analysis
3.4 Classification and Prediction
3.5 Logistic Regression
3.6 K-Neighbours Classifier
3.7 Support Vector machine
3.8 Random Forest Classifier
3.9 Proposed System Architecture
3.10 Decision Tree
3.11 XGBOOST Classifier
4 Results and discussions
4.1 Dataset Description
4.2 Kernel Distribution Plot
4.3 Proposed Ensemble Classifier
5 Conclusion
References
Inflow and Infiltration Water Problem Detection Using Machine Learning
1 Introduction
2 Machine Learning Algorithms
2.1 Logistic Regression
2.2 XG Boost Regression
2.3 SVM (Support Vector Machine)
2.4 Decision Trees
3 Implementation
3.1 Methodology
3.2 Workflow
3.3 Data Pre-processing
4 Results and Discussion
4.1 Feature Review
4.2 Prediction Result
5 Conclusion
References
Crop Recommendation and Production Prediction
1 Introduction
2 Literature Review
3 Background
4 Motivation and Purpose
5 Software Requirements
6 Approach
6.1 Dataset's Description
6.2 Normalization
6.3 Proposed Framework
6.4 Algorithm
7 Results and Discussion
7.1 Classifiers Used
8 Conclusion
9 Future Scope
References
A Comparison of Cox Model and Machine Learning Techniques in the High-Dimensional Survival Data
1 Introduction
2 Methods
2.1 Cox Regression Model
2.2 Regularized Regression Methods
2.3 Machine Learning Models
3 Result and Discussion
4 Conclusion
References
IV Industrial Usecases of IOT
Smart Farming: Using IoT and AI to Improve Crop Yield in Aeroponics System
1 Introduction
2 Related Research
3 Key Components of Aeroponics System
4 Design and Implementation of Aeroponics System
5 Conclusion
References
IoT Based Waste Segregation System for Small and Large-Scale Sectors
1 Introduction
1.1 Introduction to Waste Management
1.2 Introduction to Internet of Things
2 Related Works
3 Proposed System
4 Architectural Diagram and Components
4.1 Components Required
5 Arduino Program
6 Implementation and Results
6.1 Implementation
6.2 Results
7 Conclusion and Future Work
References
Assisting Paralyzed Patients with Iot Based Mobile Application Using Hand Gestures
1 Introduction
2 Related Works
3 Proposed System
4 Components
4.1 Flex Sensor
4.2 Arduino UNO
4.3 Resistor
4.4 Bluetooth Circuit
5 Working
6 Experiments and Results
7 Conclusion
References
NLP for Linguistic Support
Classification of Toxicity in Social Media Comments Using the Binary Relevance - Logistic Regression and BERT Model
1 Introduction
2 Literature Survey
2.1 Problem Statement
2.2 Motivation
2.3 Data Set
2.4 Analysis Charts and Graphs
3 Proposed System Model
3.1 Data Pre-processing
3.2 Extraction of Emojis Using the Spacymoji Extension
3.3 Comparing Vectorization Techniques
3.4 Comparing Different Modeling Techniques
3.5 Comparing Different Classifier Models
3.6 Bert Language Model
4 Results and Discussion
4.1 For Vectorization - Bag of Words
4.2 For Word Embeddings
5 Conclusion and Future Scope
References
Two Fold Clustering Schema (TFCS) for Acquisition of Authentic Reviews in Web Crawlers
1 Introduction
2 Literature Survey
3 System Architecture
3.1 Fold 1 Web Crawling
3.2 Fold 2 Fake Review Identification
4 Experimental Analysis
5 Conclusion
References
Introduction of the Rudiments of NLP: A Survey of Methods and Approaches to Natural Language Processing
1 Introduction
2 Literature Review
3 Importance of NLP
4 Applications of Natural Language Processing
5 Challenges Involved in Natural Language Processing
6 Language Learning Applications that Use NLP Technique
7 NLP-Empowered Language Learning Applications
8 English Language Learning on the NLP-Empowered Applications
9 Conclusion
References
Email Phishing Detection Using AI and ML
1 Introduction
1.1 Common Types of Phishing
2 Effects of Email Phishing
3 Related Work
3.1 Discussion on the Previous Proposed and Work and Improvement
4 Methodology and Working of the Model for Detecting Phishing Emails
4.1 Proposed Model
5 Performance Metrics
5.1 Precision
5.2 Recall
5.3 Accuracy
6 Proposed System and Method
7 Method
8 Dataset and Implementation
9 Algorithms Used
10 Result and Discussion
11 Conclusion
References
Computer Vision-Based Speed Tracking and Dynamic Exceed Limit System
1 Introduction
2 Literature Review
3 Existing System
4 Proposed System
5 Methodology
6 Results
7 Conclusion and Future Work
References
Speech Recognition Using RNN, DNN and Web Services
1 Introduction
2 Related Works
3 Methodology
3.1 Data Acquisition
3.2 Data Preprocessing
3.3 Acoustic Model
3.4 Language Model
3.5 Pattern Classification and Conversion
4 Performance Analysis
5 Result Analysis
5.1 Accuracy
5.2 Performance Specific to Language
5.3 Speed and Effectiveness
5.4 Resilience to Ambient Noise
5.5 User Satisfaction
6 Conclusion
References
A Transformer Based Medicine Recommendation System that Uses Drug Reviews
1 Introduction
2 Literature Survey/ Related Works
3 Proposed Architecture
4 Methodology
4.1 Dataset Insights
4.2 Data Pre-processing
4.3 Review Analysis
4.4 Recommendation Factors
4.5 Summary Score
4.6 Recommendation Data
4.7 Recommendation System
5 Results and Discussion
6 Conclusion
References
Voice Over Phonetic Medical Prescriptions for Diagnosing Diseases Using Random Forest Classifier
1 Introduction
1.1 Clinical Motivation
1.2 Innovation Idea
1.3 Requirement Gathering
2 Literature Survey
3 Machine Learning and System Architecture
3.1 Machine Learning Architecture
3.2 System Architecture
4 Module Description
4.1 Converting Audio to Text
4.2 Extracting Symptoms Using NLP
4.3 Machine Learning Model
4.4 Converting Extracted Symptoms to Binary and Sending It to Machine Learning Model
4.5 Showcasing Output Results
5 Results
6 Conclusion
References
Real-Time Phonic Decipherer
1 Introduction
2 Literature Review
3 Existing Work
4 Proposed Methodology
5 Results and Discussion
6 Conclusion
References
Hidden Markov Model Based Text to Speech Synthesis for Afan Oromo
1 Introduction
2 Statement of the Problem
3 Speech Synthesis System Based on HMMs
4 Related Works
5 System Architecture
5.1 Training Phase
5.2 Spectrum Modeling
5.3 Decision Tree-Based Context Clustering
5.4 Synthesis Phase
5.5 Speech Corpora
5.6 Normalization
5.7 Segmentation and Labeling
6 Results and Discussions
7 Conclusion
References
Convolution Neural Network for Vision Applications
Pothole Detection from an Enhanced Aerial Image Using CNN Model
1 Introduction
2 Literature Review
3 Proposed Methodology
4 Results and Discussion
5 Conclusion
References
Colorizing Images with Split-Brain Autoencoders and Convolutional Neural Networks
1 Introduction
2 Literature Survey
3 Proposed Approach
3.1 Design
3.2 Model Functionality and Implementation
3.3 Model Architecture
3.4 Dataset and Training Parameters
4 Analysis
4.1 Evaluation Metrics
4.2 Comparative Analysis with the Existing Models
5 Result
5.1 Findings Produced by the Study
6 Conclusion
7 Future Work
References
Real Time Face Emotion Detection with CNN
1 Introduction
2 Related Works
3 Implementation
3.1 Algorithm and Data Acquisition
3.2 Architecture Diagram
4 Proposed Work
4.1 Convolution Layer
4.2 ReLU Layer
4.3 Pooling Layer
4.4 Fully Connected Layer
5 Experiment
5.1 Comparative Analysis of the Work
5.2 Result Analysis Experiment
6 Conclusion
References
Tiny-Ml Model for Pugilism Sport Gesture Classification and Its Potential over Computer Vision
1 Introduction
2 Literature Study
3 Proposed Tinyml Model for Arduino Nano Sense
3.1 Implementation
4 Traditional Computer Vision vs Tinyml
5 Conclusion and Inference
References
Real-Time Hand Gesture Calculator Using Convolution Neural Network
1 Introduction
2 Related Work
3 Proposed System
3.1 System Overview
3.2 System Description
3.3 System Implementation
3.4 System Testing
4 Result and Conclusion
References
Correction to: Design and Development of Human Identification and Obstacle Detection System for Blind Using Machine Learning
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Deep Sciences for Computing and Communications

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