Intelligent Computing : Proceedings of the 2024 Computing Conference, Volume 1 / edited by Kohei Arai.

Format:

Book

Author/Creator:

Arai, Kohei.

Series:

Lecture Notes in Networks and Systems, 2367-3389 ; 1016

Language:

English

Subjects (All):

Computational intelligence.

Telecommunication.

Computational Intelligence.

Communications Engineering, Networks.

Local Subjects:

Computational Intelligence.

Communications Engineering, Networks.

Physical Description:

1 online resource (644 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

Explore the forefront of computing with the proceedings of the Computing Conference 2024. Featuring 165 carefully selected papers from a pool of 457 submissions, this collection encapsulates the cutting-edge research and innovation presented during the conference. Delve into a diverse range of topics, insights, and methodologies that shape the future of computing. Whether you're an academic, researcher, or enthusiast, this concise volume offers a snapshot of the dynamic and collaborative spirit defining the Computing Conference 2024.

Contents:

Intro

Preface

Contents

Analyzing the Effect of Cyber Attacks on Zoned-Microgrid's Voltage Stability

1 Introduction

2 Background on Modeling of Inverter-Based Microgrids

2.1 Voltage Source Inverter (VSI) Modeling

2.2 PQ Inverter Modeling

2.3 Secondary Voltage Control

2.4 Average Consensus Algorithm (ACA)

2.5 Zone Dedication/Sensitivity Analysis

3 Zone Dedication to PQ Inverters

4 Simulation Results

4.1 Case 1: Effect of Zone Dedication on Improving the Microgrid Response

4.2 Case 2: Effect of False Data Cyber Attack on Zone Dedicated Microgrid

5 Conclusions

References

Enhancing Pilgrim Safety During Hajj: A Smart Healthcare Solution with MYPARAMEDIC App and Vital Sign Monitoring Bracelet

1.1 Context and Motivation

1.2 Problem Statement

1.3 Aim and Objectives

1.4 Proposed Solution

1.5 Scope

1.6 Framework

1.7 Methodology

2 Literature Review

2.1 Health Issues in Hajj

2.2 IoT Overview

2.3 IoT in Healthcare

3 Related Work

3.1 Smart Bracelet for Temperature Monitoring and Movement Tracking Analysis

3.2 AI Overview

3.3 AI in Medicine

3.4 Chatbot

3.5 An Overview of Chatbot Technology

3.6 Detection Abnormal Cases Model

3.7 Reinforcement Learning

3.8 Deep Learning

3.9 GPS

4 Analysis

4.1 Data Gathering Method

4.2 Requirement Specification

4.3 Comparison of Supervised Models

4.4 Chatbot Implementation

4.5 Chatbot Tools

4.6 Backend Implementation

5 Result

5.1 API Implementation

5.2 Integrate API with Flutter

5.3 Database Implementation

5.4 Types of Testing

5.5 Unit Testing

6 Conclusion

7 Future Work

Hybrid CNN Models for Plant Species Recognition and Disease Detection

2 Related Works.

3 Proposed Work for Plant Species Recognition and Disease Detection Model

3.1 Image Pre-processing

3.2 Architecture I: CSN and Multi-learners

3.3 Architecture II: CNN and Multi-learners

4 Comparative Study

5 Conclusion

Exploring Perceptions of Children's Learning Stress for Stress Management

2 Related Work

2.1 Stress Display and Management

2.2 Intervention for Improving Children's Self-awareness

3 System Design and Implementation

3.1 Objective and Design Consideration

3.2 How It Works

4 User Study

4.1 Study Participant

4.2 Methods

4.3 Data Collection and Analysis

5 Results

5.1 Perceptions of Children and Parents

5.2 HRV Data and Self-evaluation

6 Discussion and Future Work

7 Conclusion

Systematic Review of Machine Learning in Recommendation Systems Over the Last Decade

2.1 Approach Overview

2.2 Type of Machine Learning

3 Results

4 Conclusion

AI's Influence on Non-Player Character Dialogue and Gameplay Experience

3 Methodology

3.1 Research Hypothesis

3.2 Research Questions

3.3 Participants

3.4 Data Collection

3.5 Data Analysis

3.6 Experiment

3.7 Artefact Design and Development

4 Results

4.1 NPC Interaction Data

4.2 NPCs with Similar Ratings

4.3 NPCs with Divergent Ratings

An Investigation into AI-Generated Art Through GANs and ML Neural Network

1.1 Scope and Objectives

2 Background

2.1 Generative Adversarial Networks

2.2 Zero Shot Learning

2.3 Natural Language Supervision

2.4 Multimodal Learning

3 Literature Review

3.1 GAN Technology and Architectures for AI Art Generation

3.2 Neural Style Transfer Techniques.

3.3 Application of CLIP in AI Art Generation

4 Methodology

4.1 VQGAN + CLIP Architecture

4.2 Neural Style Transfer

4.3 Evaluation Techniques

5 Evaluation

5.1 VQGAN + CLIP Styling vs. Using NST

5.2 Qualitative User Evaluation Strategy

5.3 Summary of Results

6.1 Challenges and Limitations

6.2 Recommendations

6.3 Future Works

6.4 Summary

Trustworthy AI: Deciding What to Decide

1.1 Research Question

1.2 Research Method

1.3 Main Contributions

1.4 Scope of the Research

2.1 Types of Representation Space

2.2 Loss Function on Data

2.3 Optimizer

2.4 CDS and Strategic Decision-Making

2.5 Trustworthy AI (TAI) and Explainable AI (XAI)

3 A Brid's Eye View of the Dataset and Model Environment

3.1 Sub-dataset for Technology Sector

3.2 Model Environment and Context

3.3 Selection of Algorithm for Optimization

4 Experimental Setup, Assumptions and Results

4.1 GBM Experimental Results

4.2 Xgbm Experimental Results on HPC

4.3 Transformer Models

4.4 Variable Importance or Influence (VI) Results

4.5 Partial Dependent Plot (PDP) Results

4.6 Individual Conditional Expectation (ICE) Results

4.7 Local Interpretable Model-Agnostic Explanations (LIME) Results

4.8 Shapley Values (SHAP) Results

5 Results Analysis and Discussion

5.1 Results Summary

5.2 Result Analysis

6 Conclusions and Future Works

Towards Automatic Document Classification Using a Fuzzy Logic Approach

3.1 Data Gathering

3.2 Data Preparation

3.3 Data Models

4 Implementation

6 Conclusion and Future Work

Enhancing Email Urgency Reply Prediction with ATAN-Transformer Fusion

1 Introduction.

1.1 Adaptive Temporal Attention Transformer Fusion (ATATFUSION)

1.2 Why Combine ATAN and Transformer?

1.3 Fusion Approach

1.4 Benefits of ATAN-Transformer Fusion

2 Related Works

3 The Proposed Approach: Adaptive Temporal Attention Transformer Fusion (ATATFUSION)

3.1 Data Processing

3.2 ATAN Component

3.3 Transformer Component

3.4 Fusion Mechanism

3.5 Classification Layer

3.6 Training and Evaluation

3.7 Experiments and Hyperparameter Tuning

4 Adaptive Temporal Attention and Transformer Fusion (ATATFUSION)

4.1 Algorithm Description and Data Pre-processing

4.2 Mathematical Model

4.3 Novelty of the Approach

5 Dataset and Experimental Setup

5.1 Datasets

5.2 Preprocessing

5.3 Model Configuration

5.4 Evaluation Metrics

5.5 Experimental Design

5.6 Dataset Description

5.7 Hyperparameter Tuning

6 Comparison with Other Deep Learning Models

6.1 Analysis of Experimental Results

6.2 Significance of ATATFUSION in Email Urgency Reply Prediction

6.3 Comparison with Other Models

8 Future Directions

Challenges of Deepfakes

2.1 Misinformation and Disinformation

2.2 Financial Fraud

2.3 Election Interference

2.4 National Security Threats

2.5 Erosion of Trust

2.6 Impersonation and Social Engineering

2.7 National Intelligence Warning

2.8 Election Warning

3 Potential Mitigations and Technological Solutions

3.1 Enhance Resilience and Awareness

3.2 Detection

3.3 Technological Solutions

Designing a Technical Framework for Enabling Enterprise AI Adoption

2 Related Research

3 Research Approach

4 Artifact Development

4.1 Awareness of the Problem

4.2 Suggested Solution

4.3 Development of the Solution

4.4 Evaluation.

4.5 DSR Cycle Conclusion

5 Implications for Research and Practice

Predicting Suicide Cases Using Deep Neural Network

2 Method

3.1 Suicide Trends

3.2 Methodology and Model Performance

3.3 Model Validation

3.4 Evaluation Metrics

4 Discussion and Conclusion

4.1 Study Limitations and Strengths

6 Future Works

Head Tail Open: Open Tailed Classification of Imbalanced Document Data

2.1 Document Image Classification

2.2 Open World Classification

4 Experiments

4.1 Initial Experiment with Closed World Assumption

4.2 Dataset

4.3 Hyperparameters

4.4 Baseline

4.5 Experiment Results and Analysis

5 Discussions and Future Work

Saudi Arabic Multi-dialects Identification in Social Media Texts

3 Data Collection

4 Data Pre-processing and Annotation

5 Dataset Preparation for ChatGPT

6 Experiments and Results

Deep Feature Discriminability as a Diagnostic Measure of Overfitting in CNN Models

2 Related Studies

3 Framework

4 Datasets and Deep Learning Models

5 Results and Discussions

5.1 Cluster Discriminability Score

5.2 Cluster-Class Similarity Score

5.3 Effect Dropout on the Deep Feature Separability

A Meta-VAE for Multi-component Industrial Systems Generation

3 Research Methodology

3.1 From Complex Assemblies to a Foundational Dataset: A Multi-layered Industrial Use Case with Contact Constraint and Performance Measure

3.2 Evaluation Metrics

3.3 The Meta-VAE

3.4 Experiments

4 Results and Discussion

References.

Analysis of the Computational Complexity of Backpropagation and Neuroevolution.

Other Format:

Print version: Arai, Kohei Intelligent Computing

ISBN:

9783031622816

OCLC:

1441797533