Advances in Computing and Data Sciences

Name: Advances in Computing and Data Sciences | 8th International Conference, ICACDS 2024, Vélizy, France, May 9-10, 2024, Revised Selected Papers
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8th International Conference, ICACDS 2024, Vélizy, France, May 9-10, 2024, Revised Selected Papers

Mayank Singh Vipin Tyagi P. K. Gupta Jan Flusser Tuncer Ören Amar Ramdane Cherif Ravi Tomar(Editor)

Springer (Publisher)

Published on 17. October 2024

XVI, 348 pages

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978-3-031-70906-7 (ISBN)

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Content

Intro
Preface
Organization
Contents
Advanced Computing
Exploring the Impact of KNN and MLP Classifiers on Valence-Arousal Emotion Recognition Using EEG: An Analysis of DEAP Dataset and EEG Band Representations
1 Introduction
1.1 DEAP Dataset
2 Methodology
3 Results
4 Conclusion
References
A Novel Container Based Computing Environment
1 Introduction
2 Literature Review
3 Proposed System Architecture
4 Results and Discussion
5 Conclusion
References
An Ensemble Deep Learning Framework for Enhancing Sentiment Analysis
1 Introduction
2 Literature Review
2.1 Attention Mechanism
2.2 Contextual Valance Shifter
2.3 Hybrid Approach
3 Research Method and Specification
3.1 Methodology and Materials
4 Experiments
4.1 Model Comparison
5 Discussion and Results
6 Conclusion
References
Performance Comparison of Julia with C and Python for Solving Computational Problems
1 Introduction
1.1 Background and Motivation
2 Brief About Computational Problems
2.1 1D Vector
2.2 Matrix Multiplication
2.3 2D Heat Equation
3 Experimental Results and Observations
3.1 Experiment-1: 1D Vector
3.2 Experiment-2: Matrix Multiplication
3.3 Experiment-3: 2D Heat Equation
4 Conclusion
5 Future Scope
References
An Optimized Approach Towards Malware Detection Using Java Microservices
1 Introduction
2 Related Work
3 Dataset
4 Methodology
5 Result
6 Conclusion
References
2DP-FHS: 2D Pareto Optimized Fog Head Selection for Multiple EEG Healthcare Data Analysis and Computations
1 Introduction
1.1 Motivation and Contribution
2 Proposed Methodology
2.1 Architectural View
2.2 Fog Head Selection
3 Simulation and Result Analysis
4 Conclusion
References
Geospatial Analysis and Machine Learning for Vehicular Mobility Patterns on Indian Two-Way Roads: Leveraging Geotagged Microphone Data and Modified CNN Classifier
1 Introduction
2 Research Gap
3 Objective
4 Methodology
4.1 Characterization of Mobility Patterns
4.2 Speed Estimation Using Manual Marker and M-CNN
4.3 Mapping Characterized Vehicles Over ArcGIS
5 Computations
6 Results
7 Conclusion
References
Security and Privacy Challenges of Metaverse in Education
1 Introduction
2 Foundations
2.1 Metaverse in Education
2.2 Metaverse Architecture
2.3 Metaverse Security Issues of Metaverse
3 Methodology
4 Challenges of Metaverse and Traditional e-Learning Platforms: A Comparison
5 Conclusion
6 Limitations and Future Scope
References
Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis
1 Introduction
2 Materials and Method
2.1 Object Detection
2.2 Model and Parameter
2.3 Dataset
2.4 Metric
3 Result and Discussion
4 Conclusion
References
Data Sciences
Integrated Model for the Prediction of Disease and Treatment Recommendation
1 Introduction
2 Related Work
3 Detailed Methodology
4 Result Analysis
4.1 Result and Discussion
5 Conclusion
References
Assessing Customer Retail Data Through the Application of Various Clustering Algorithms
1 Introduction
2 Customer Segmentation Dataset
3 Proposed Methodology
3.1 K-Means Clustering (K-MC) Algorithm
3.2 Agglomerative Hierarchical Clustering Algorithm
3.3 Divisive Hierarchical Clustering Algorithm
3.4 Density Based Clustering Algorithm (DBSCAN)
4 Simulation Results and Discussion
5 Conclusion
References
Unveiling a Cutting-Edge Living Style-Based Neural Network Boost Model for Early Heart Disease Prediction
1 Introduction
2 Methods and Materials
2.1 Description of Dataset
2.2 Data Normalization
3 Proposed Model
3.1 Data Balancing Using SMOTE
3.2 Proposed NeNBoost Model
4 Results and Discussion
4.1 Performance Comparison with Some Existing Studies
5 Conclusion
References
Zero-Shot Learning in Cybersecurity: A Paradigm Shift in Attack and Defense Strategies
1 Introduction
2 Background
3 Literature Review
4 ZERO-SHOT Learning in Cybersecurity
5 Novel Attack Methodologies
5.1 Semantic Mapping Attack
5.2 Dynamic Threat Evolutional Attack
5.3 Zero-Knowledge Attack
5.4 Stealth Reconnaissance Attack
6 ZERO-SHOT Learning for Defense
6.1 Preceding and Managing Threats
6.2 Dynamic System Defense Adaptation
6.3 Predicting Unknown Vulnerabilities
6.4 Detecting Behavioral Anomalies
6.5 Self-Healing Networks
7 Interdisciplinary Perspectives
7.1 Social Engineering View
7.2 Behavioral Psychology View
7.3 AI Governance View
8 Quantitative Modeling and Prototyping
8.1 Quantitative Zero-Shot Learning Defenses and Attacks Modeling
8.2 Zero-Shot Learning Attack Pretype
9 Comparison with Traditional Methods
10 Limitations and Mitigations
11 Conclusion
References
Data Engineering for Nonverbal Expression Analysis - Case Studies of Borderline Personality Disorder
1 Introduction
2 Background
2.1 Data Engineering
3 Case Studies and Social Tasks
3.1 Subjects
3.2 Materials
3.3 Procedure
4 Analysis and Results
4.1 Extract
4.2 Transform
4.3 Load
5 Discussion
6 Conclusion
Appendix A
Appendix B
References
SGDR-YOLOv8: Training Method for Rice Diseases Detection Using YOLOv8
1 Introduction
1.1 Leaf Blast
1.2 Leaf Folder
1.3 Brown Spot
2 Proposed Method
2.1 Introduce to Different Versions of YOLOv8
2.2 Data Preparation
2.3 Augmentation Hyperparameters
2.4 YOLOv8 Model
3 Result and Discussion
4 Conclusion
References
Comparative Analysis of Speech Emotion Recognition Models
1 Introduction
2 Related Work
3 Dataset and Methods
3.1 Dataset Description
3.2 Models And Methods Used
4 Comparative Analysis
4.1 Data Preprocessing
4.2 Feature Extraction
4.3 Computing Environment
4.4 Comparative Analysis
5 Equations
6 Figures
7 Conclusion
References
Estimating the Concrete Compressive Strength of Regression Model for Machine Learning
1 Introduction
2 Literature Review
3 Methods
3.1 Regression Model and Their Purpose
3.2 Data Collection and Description
3.3 Data Cleaning and Data Preparation Challenges
4 Model Development
4.1 Model Evaluation
5 Result and Discussion
5.1 Model Evaluation Result
5.2 Hypothesis Testing
6 Conclusion, Limitation, and Future Scope
References
Model Evaluation and Selection for Robust and Efficient Advertisement Detection in Print Media
1 Introduction
2 Background
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Model Selection
3.4 Training the Model
3.5 Evaluation and Validation
4 Results
5 Discussion
6 Conclusion
References
LatentNeuroNet: A Text-Conditioned Stable Diffusion Framework for Reconstructing Visual Stimuli from fMRI
1 Introduction
2 Related Work
3 Dataset
4 Methodology
4.1 Overview
4.2 Extraction of Intermediate Latent(Stage 1)
4.3 Generation of Captions Using the BLIP Model (Stage 2)
4.4 Generation of the Reconstructed Stimuli (Stage 3)
4.5 System Requirements
5 Evaluation Metrics
5.1 Frechet Inception Distance
5.2 Compute Identification Accuracy
6 Results
7 Discussion
8 Conclusion
9 Future Work
References
Proposal of Indicators for the Design of an App for Teaching Learning in Children with Autism
1 Introduction
2 Methodology
3 Method to Validate Development Indicators
4 Criteria for an Application (App)
5 Results
5.1 Results of the Factor Analysis
5.2 Results of the Expert Analysis
6 Conclusions
References
Automatic Speaker Recognition Using Hybrid Parameters Based on Machine Learning Applied on Two Dataset
1 Introduction
2 Experimental Corpus and Development Environment
3 Process of Automatic Speaker Recognition
3.1 The KNeirest Neighbour (KNN) Algorithm
3.2 Performance Evaluation of the Classifiers
3.3 Feature Extraction
3.4 MFCC Vector
4 Results and Discussion
4.1 Performance Evaluation of the Classifiers (K-NN)
4.2 Performance Evaluation of the Classifiers (K-NN)
4.3 Performance Evaluation of the Classifiers (Multi Layer Perceptron, SMOSVM, Fonction.Logistique, Naïve Bayes)
5 Conclusion and Future Work
References
Discovering Personal Data Security Issues: Insights from "Have I Been Pwned"
1 Introduction
2 Literature Review
3 Research Methodology
4 Results and Discussion
5 Conclusion
References
An Advanced Deep Learning Detection of Rice Plant Diseases Based on Residual Neural Networks
1 Introduction
2 Methodology
2.1 Transfer Learning
2.2 Residual Neural Networks
2.3 DenseNet Model
2.4 Data Augmentation
3 Proposed Model and Deep Learning Scenario
4 Experimental Results and Discussion
5 Performance Metrics
6 Conclusion
References
An Analysis for Pre-consultations on the Korean Government-Managed Information System Projects
1 Introduction
2 Current Status of the Pre-consultation Body
3 Analysis of Submitted Projects
4 Analysis Methods
5 Discussion
6 Conclusion
References
Social Media Communication of Abu Dhabi HEIs Across Facebook and Twitter: A Comparative Analysis
1 Introduction
2 Literature Review
3 Research Methodology
4 Results
4.1 HEIs Posts Per Hour
4.2 HEIs' Posts Per Day
4.3 HEIs' Posts Per Month
4.4 HEIs' Posts and Media
4.5 HEIs' Posts and Language
5 Conclusion
References
Lightweight Encryption Scheme for Bio-metric 3-Plane Image Encryption Based on L-System Fractal and 2-D Chaotic ACM
1 Introduction
2 Literature Review
3 Description of the Proposed Research Work
3.1 Fractal Geometry
3.2 Arnold's CAT Map
4 Proposed 3-Plane Encryption and Decryption Scheme
4.1 Proposed Encryption Scheme
5 Encryption Results
6 Assessment of Proposed Work Through Experiments
6.1 Assessment Through Histograms
6.2 Assessment Through Scatter Plots of Adjoining Pixels
6.3 Assessment Through Information Entropy
6.4 Assessment Through Mean Squared Error and the Pearson Correlation Coefficient Between Images Before and After Encryption
7 Resistance Against Differential Attack
8 Resistance Against Noise Attacks
9 Resistance Against Cutting Plane Attack
10 Conclusion
References
Detecting Faulty Steel Plates Using Machine Learning
1 Introduction
2 Material and Methods
2.1 Random Forest
2.2 AdaBoost
2.3 Decision Tree
2.4 SVMRBF
2.5 Naive Bayes
2.6 Proposed Framework for Steel Plate Fault Detection
3 Results and Discussion
3.1 Data Description and Analysis
3.2 Classifying Faulty Steel Plates Using ML Methods
4 Conclusion
References
Revolutionize Infectious Prevention Using Artificial Intelligence and Deep Learning
1 Introduction
2 Methods
2.1 Diagnostics in Imaging and Laboratories
2.2 Medical Judgment Assistance
2.3 Public Health and the Surveillance of Infectious Diseases
2.4 Artificial Intelligence (AI) in the Study of Infections
2.5 Infectious Studies and Medical Care Using Artificial Intelligence (AI) Generative Models
3 Discussion
4 Conclusion
References
Author Index

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Advances in Computing and Data Sciences

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