Predictive Methods in Next-Generation Computing : An Approach Toward Sustainability.

Format:

Book

Author/Creator:

Sathiyaraj, R.

Contributor:

Sathiyaraj, R.

Dhanaraj, Rajesh Kumar

Kumar, K. Arun

Jhaveri, Rutvij H.

Abbas, A. Mohamed

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Physical Description:

1 online resource (341 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

Predictive Methods in Next-Generation Computing is essential for anyone looking to understand how next-generation computing technologies are driving predictive models to create smarter, safer, and more sustainable solutions across diverse fields.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Chapter 1 Introduction to Intelligent Computational Technologies

1.1 Introduction

1.2 Literature Survey

1.3 Methodology

1.4 Simulation Metrics

1.4.1 Identification of E-Governance Adoption and Assessment Factors

1.4.2 Sample Data Collection Using Questionnaire

1.4.3 Respondent Details

1.4.4 Fuzzy Conjoint Model

1.4.4.1 Calculation of Weight for Each Respondent

1.4.4.2 Calculating the Similarity Degree

1.5 Computation

1.5.1 Computation of Fuzzy Vector of Responses

1.5.2 Computation of Similarity Degree

1.5.3 Result Analysis

1.5.4 Validation

1.5.5 Limitations and Future Study

1.6 Summary

Bibliography

Chapter 2 Design of Smart and Sustainable Applications Using Intelligent Computational Techniques

2.1 Introduction

2.2 Background

2.2.1 Adoption Models and Theories

2.3 Methodology

2.3.1 Simulation Metrics

2.3.2 Identification of E-Governance Adoption and Assessment Factors

2.3.3 Sample Data Collection Using Questionnaire

2.3.4 Respondent Details

2.3.5 Fuzzy Conjoint Model

2.3.5.1 Calculating the Similarity Degree

2.4 Result Analysis

2.5 Conclusion

References

Chapter 3 Intelligent Predictive Analysis for Sustainable Global Development

3.1 Introduction

3.2 Literature Survey

3.2.1 Concepts of ANN

3.3 Proposed Work

3.3.1 Layout of a Neural Network

3.3.2 Neural Network Structure

3.3.3 Back-Propagation Neural Network

3.3.4 SVM

3.3.5 Learning Sequence of NN

3.3.6 Forage Progressive Network

3.3.7 Neural Network Learning

3.3.8 Model Somatic Cell

3.3.9 Fabricated Visual Structure

3.3.10 Neuron Weight Adjustment

3.4 Results and Discussion

3.4.1 Normalization of Knowledge

3.4.2 Testing and Validation

3.4.3 Error Measures.

3.4.4 Prediction Analysis

3.4.5 Overall Prediction Analysis

3.4.6 Actual and Predicted Value Analysis

3.4.7 Overall Actual and Predicted Value Analysis

3.5 Summary

Chapter 4 Intelligent Transport System and Traffic Management Frameworks

4.1 Introduction

4.2 Background Study

4.3 Methodology

4.3.1 Segmentation-Fuzzy Clustering

4.3.2 Fuzzy Clustering

4.4 Artificial Neural Network (ANN)

4.5 Proposed Methodology

4.5.1 Detection and Extraction of Traffic Sign

4.5.1.1 Image Extraction and Pre-Processing Using YCbCr

4.5.1.2 Active Appearance Model (AAM)

4.5.1.3 Extracting the Region of Interest

4.5.1.4 Edge Detection Using Sobel Operator

4.5.1.5 Segmentation Using Adaptive Fuzzy Clustering

4.5.1.6 Tracking the Detected Sign

4.5.2 Recognition of Traffic Sign

4.5.2.1 MTANN Training Model for Classification

4.5.2.2 Multiple MTANN Training Models

4.5.2.3 MTANN Classification

4.6 Results and Discussion

4.6.1 LiU Traffic Sign Database

4.6.2 Investigations of Various Classification Techniques

4.7 Summary

Chapter 5 Internet of Things in Smart and Secure Applications Development-Based Sustainability

5.1 Introduction

5.2 Literature Survey

5.3 Methodology

5.3.1 Module for the Database

5.3.2 Information Preparation Section

5.3.3 Database Module

5.3.4 Data Preprocessing Module

5.3.5 Optimal Feature Selection Module

5.3.6 Classification Module

5.4 Generative Adversarial Network

5.5 Datasets Used in This Work

5.5.1 NSL-KDD Dataset

5.5.2 CIC-DDoS Dataset

5.6 Performance Measures Used for Evaluation

5.7 Conclusion

Chapter 6 Modern Application for Smart Applications in Traffic Management

6.1 Introduction

6.2 Related Work

6.3 Delimited Spaces: Proposed Method

6.3.1 Bag of Features (BoF).

6.4 Implementation Details

6.4.1 Datasets

6.4.2 Experiment 2: Original and Split Dictionaries

6.4.3 Experiment 3: Feature Fusion

6.4.4 Results: Delimited Spaces

6.5 Non-Delimited Spaces: Proposed Method

6.5.1 Background Subtraction for Hypothesis Generation

6.5.2 Results: Non-Delimited Spaces

6.6 Conclusion

Chapter 7 Artificial Intelligence and Deep Learning in Healthcare: Evaluation, Opportunities, Challenges and Future Prospects Technologies in Healthcare Systems

7.1 Introduction

7.1.1 Artificial Intelligence

7.1.2 Deep Intelligence

7.2 Applications of AI and Deep Learning in Healthcare

7.2.1 Medical Imaging

7.2.2 Personalized Medicine

7.2.3 Precision Diagnostics

7.2.4 Predictive Analytics

7.2.5 Drug Discovery

7.2.6 Drug Development

7.2.7 Remote Patient Monitoring

7.2.8 Electronic Health Records

7.3 Development of Deep Learning and Artificial Intelligence in Healthcare Sector

7.4 Analytics of Healthcare Data Through AI And DL

7.4.1 Machine Learning Models

7.4.1.1 Supervised Learning

7.4.1.2 Unsupervised Learning

7.4.2 Reinforcement Learning

7.4.3 Natural Language Processing (NLP)

7.4.4 Machine Vision

7.4.5 Data Mining

7.4.6 Artificial Neural Networks

7.4.7 Fuzzy Logic

7.4.8 Expert System

7.5 Deep Learning Models

7.5.1 Convolutional Neural Networks (CNNs)

7.5.2 Network Based on Long Short-Term Memory (LSTM)

7.5.3 Recurrent Neural Networks (RNNs)

7.5.4 Generative Adversarial Network (GANs)

7.5.5 Radial Basis Function Networks (RBFNs)

7.5.6 Multilayer Perceptrons (MLPs)

7.5.7 Self-Organizing Maps (SOMs)

7.6 Potential of AI and Deep Learning Models in Healthcare

7.7 The Rise of AI and DL in Drug Discovery

7.8 Application of AI in Drug Discovery

7.9 Challenges of AI And DL Models.

7.10 Future Vision in Developing Rural Health

7.10.1 Telemedicine

7.10.2 Disease Prediction and Prevention

7.10.3 Resource Allocation

7.10.4 Personalized Medicine

7.11 Conclusion

Chapter 8 Heart CAP: Heart Disease Classification: Autoencoders and Principal Components

8.1 Introduction

8.2 Related Study

8.3 Model Architecture

8.3.1 Cleveland Heart Disease Dataset

8.3.2 Data Preparation

8.3.3 Feature Scaling

8.3.4 Dimensionality Reduction Techniques

8.3.4.1 Principal Component Analysis (PCA)

8.3.4.2 Autoencoder Architecture

8.4 Results

8.4.1 Classification Performance

8.4.2 Analysis of Principal Component and Feature Coefficients

8.4.3 Analysis of Autoencoder Features

8.4.4 Analysis of Receiver Operating Characteristics

8.5 Conclusion

Chapter 9 Application of Intelligent Computational Techniques in the Development of Smart Cities

9.1 Introduction

9.1.1 Organization of the Chapter

9.2 Motivation and Justification

9.3 Iris Recognition System

9.4 Algorithm for Iris Recognition System

9.5 Block Diagram of Iris Recognition System

9.5.1 Edge Detection

9.5.2 Variance

9.5.3 Support Vector Machine

9.6 Performance Analysis of Various Biometric Method

9.6.1 Performance Evaluation

9.6.1.1 False Acceptance Ratio (FAR)

9.6.1.2 False Rejection Ratio (FRR)

9.6.2 Result and Discussion

9.7 Summary

Chapter 10 Security and Privacy Issues in Data Processing with Predictive Models

10.1 Introduction

10.2 Literature Survey

10.3 System Model

10.3.1 Cryptic Framework

10.3.2 Prediction Framework

10.4 System Algorithm

10.5 Results and Discussion

10.5.1 Examining the Security Model's Performance

10.5.2 Duration of Key Generation

10.5.3 Performance Analysis of Prediction Model.

10.5.3.1 Description of the Dataset

10.5.4 Performance Metrics

10.5.4.1 Sensitivity

10.5.4.2 Specificity

10.5.4.3 F-Measure

10.5.4.4 Diabetes Prediction

10.6 Summary of Contributions

Chapter 11 SmartMed: A Blockchain-Based Intelligent System for Managing Patient Data

11.1 Introduction

11.1.1 The Need for the Digitization of Medical Records

11.1.2 What is Blockchain?

11.1.3 How to Use Blockchain to Digitize Medical Records

11.2 Literature Survey

11.2.1 State-of-the-Art

11.2.2 Research Gap

11.3 SmartMed: Proposed System

11.4 SmartMed: Model Implementation

11.4.1 Software Requirements

11.4.2 User Registration

11.4.3 Login

11.4.4 Upload Records

11.4.5 View Records

11.4.6 Grant/Revoke Permissions

11.4.7 Encryption of Medical Records

11.4.8 Need for Encryption

11.4.9 Symmetric vs. Asymmetric Encryption

11.5 Result Analysis Using Hybrid Model

11.5.1 Scenario 1: Patient Uploads a Record

11.5.2 Scenario 2: Patient Views the Uploaded Record

11.5.3 Scenario 3: Patient Grants Access to His/Her Records to a Doctor

11.5.4 Scenario 4: Patient Revokes Access to His/Her Records from a Doctor

11.5.5 Scenario 5: Doctor Views the Patient Record

11.5.6 Scenario 6: Doctor Uploads a Record for the Patient

11.6 SmartMed: Performance Evaluation

11.6.1 Latency/Delay

11.6.2 Resource Utilization

11.7 Conclusion and Perspective

Chapter 12 Trinity: A Blockchain-Based Stablecoin Lending Protocol Using Decentralized Credit Default Swaps

12.1 Introduction

12.1.1 Background and Related Work

12.2 Motivation and Problem Statement

12.3 Methodology

12.3.1 Option Fees Exchanged Among the Participants

12.4 Discussions

12.4.1 Stakeholders in Protocol

12.4.2 Tokens in Protocol

12.4.3 Components of the Protocol.

12.4.4 Option Price Calculation.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-24882-2

1-394-24881-4

9781394248810

OCLC:

1577545873