Handbook on Intelligent Healthcare Analytics E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Machine Learning in Biomedical Science and Healthcare Informatics
• Sprache: Englisch

Seitenzahl: 606

Veröffentlichungsjahr: 2022

Beschreibung

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: * Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; * An exploration of predictive analytics in healthcare; * The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

1 An Introduction to Knowledge Engineering and Data Analytics

1.1 Introduction

1.2 Knowledge and Knowledge Engineering

1.3 Knowledge Engineering as a Modelling Process

1.4 Tools

1.5 What are KBSs?

1.6 Guided Random Search and Network Techniques

1.7 Genetic Algorithms

1.8 Artificial Neural Networks

1.9 Conclusion

References

2 A Framework for Big Data Knowledge Engineering

2.1 Introduction

2.2 Big Data in Knowledge Engineering

2.3 Proposed System

2.4 Results and Discussion

2.5 Conclusion

References

3 Big Data Knowledge System in Healthcare

3.1 Introduction

3.2 Overview of Big Data

3.3 Big Data Tools and Techniques

3.4 Big Data Knowledge System in Healthcare

3.5 Big Data Applications in the Healthcare Sector

3.6 Challenges with Healthcare Big Data

3.7 Conclusion

References

4 Big Data for Personalized Healthcare

4.1 Introduction

4.2 Related Literature

4.3 System Analysis and Design

4.4 System Implementation

4.5 Results and Discussion

4.6 Conclusion

References

5 Knowledge Engineering for AI in Healthcare

5.1 Introduction

5.2 Overview

5.3 Applications of Knowledge Engineering in AI for Healthcare

5.4 Conclusion

References

6 Business Intelligence and Analytics from Big Data to Healthcare

6.1 Introduction

6.2 Related Works

6.3 Conceptual Healthcare Stock Prediction System

6.4 Implementation and Result Discussion

6.5 Comparisons of Healthcare Stock Prediction Framework

6.6 Conclusion and Future Enhancement

References

Books

Web Citation

7 Internet of Things and Big Data Analytics for Smart Healthcare

7.1 Introduction

7.2 Literature Survey

7.3 Smart Healthcare Using Internet of Things and Big Data Analytics

7.4 Security for Internet of Things

7.5 Conclusion

References

8 Knowledge-Driven and Intelligent Computing in Healthcare

8.1 Introduction

8.2 Literature Review

8.3 Framework for Health Recommendation System

8.4 Experimental Results

8.5 Conclusion and Future Perspective

References

9 Secure Healthcare Systems Based on Big Data Analytics

9.1 Introduction

9.2 Healthcare Data

9.3 Recent Works in Big Data Analytics in Healthcare Data

9.4 Healthcare Big Data

9.5 Privacy of Healthcare Big Data

9.6 Privacy Right by Country and Organization

9.7 How Blockchain is Big Data Usable for Healthcare

9.8 Blockchain Threats and Medical Strategies Big Data Technology

9.9 Conclusion and Future Research

References

10 Predictive and Descriptive Analysis for Healthcare Data

10.1 Introduction

10.2 Motivation

10.3 Conclusion

References

11 Machine and Deep Learning Algorithms for Healthcare Applications

11.1 Introduction

11.2 Artificial Intelligence, Machine Learning, and Deep Learning

11.3 Machine Learning

11.4 Advantages of Using Deep Learning on Top of Machine Learning

11.5 Deep Learning Architecture

11.6 Medical Image Analysis using Deep Learning

11.7 Deep Learning in Chest X-Ray Images

11.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval

11.9 Image Retrieval Performance Metrics

11.10 Conclusion

References

12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering

12.1 Introduction

12.2 Literature Review

12.3 AI in Healthcare

12.4 Data Science and Knowledge Engineering for COVID-19

12.5 Proposed Architecture and Its Implementation

12.6 Conclusions and Future Work

References

13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System

13.1 Introduction

13.2 Ongoing Research on Intelligent Decision Support System

13.3 Methodology and Architecture of the Intelligent Rule-Based System

13.4 Creating a Rule-Based System using Prolog

13.5 Results and Discussions

13.6 Conclusion

13.7 Acknowledgments

References

14 Big Data in Healthcare: Management, Analysis, and Future Prospects

14.1 Introduction

14.2 Breast Cancer: Overview

14.3 State-of-the-Art Technology in Treatment of Cancer

14.4 Early Diagnosis of Breast Cancer: Overview

14.5 Literature Review

14.6 Machine Learning Algorithms

14.7 Result and Discussion

14.8 Experimental Result and Discussion

14.9 Conclusion

References

15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System

15.1 Introduction

15.2 Machine Learning in Healthcare

15.3 Types of Learnings in Machine Learning

15.4 Types of Machine Learning Algorithms

15.5 Machine Learning for Information Extraction

15.6 Predictive Analysis in Healthcare

15.7 Conclusion

References

16 Knowledge Fusion Patterns in Healthcare

16.1 Introduction

16.2 Related Work

16.3 Materials and Methods

16.4 Proposed System

16.5 Results and Discussion

16.6 Conclusion and Future Work

References

17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells

17.1 Introduction

17.2 Materials and Methods

17.3 Results and Discussion

17.4 Conclusion

References

18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare

18.1 Introduction

18.2 Related Work

18.3 Convolutional Layer

18.4 Pooling Layer

18.5 Fully Connected Layer

18.6 Recurrent Neural Network

18.7 LSTM and GRU

18.8 Materials and Methods

18.9 Results and Discussions

18.10 Conclusion

18.11 Acknowledgement

References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1

Knowledge engineering.

Figure 1.2

Knowledge as modelling process.

Figure 1.3

KBE.

Chapter 2

Figure 2.1

Traditional Bayesian Neural Network disaster prediction from the data...

Figure 2.2

Proposed system for predicting disaster using improved Bayesian hidde...

Figure 2.3

Total number of disaster analysis using improved Bayesian Markov chai...

Figure 2.4

Changes from various impacts from natural disaster.

Figure 2.5

Economic damage changes a prediction analysis.

Figure 2.6

Boxplot view of natural disaster on various entity.

Chapter 3

Figure 3.1

Dimensions of big data.

Figure 3.2

Big data value creation flow.

Figure 3.3

Different sources of healthcare data.

Figure 3.4

Knowledge discovery process of big data in healthcare.

Chapter 4

Figure 4.1

Architecture diagram.

Figure 4.2

Functional block diagram.

Figure 4.3

Storage block.

Figure 4.4

Reporting block.

Figure 4.5

Analysis block.

Figure 4.6

Management block.

Figure 4.7

Use case diagram.

Figure 4.8

Sequence diagram.

Figure 4.9

Class diagram.

Figure 4.10

Cases of patients.

Figure 4.11

Notifications of medicines to endpoints.

Figure 4.12

Admin dashboard.

Chapter 5

Figure 5.1

Process of knowledge engineering.

Figure 5.2

Data science and knowledge engineering.

Chapter 6

Figure 6.1

Conceptual healthcare stock prediction system.

Figure 6.2

Overview of business intelligence and analytics framework.

Figure 6.3

Illustration of healthcare stock prediction system.

Figure 6.4

Prediction of the closing price using LR.

Figure 6.5

Prediction of the closing price using ARIMA.

Figure 6.6

Prediction of the closing price using LSTM.

Figure 6.7

Prediction of the closing price using LR.

Figure 6.8

Prediction of the closing price using ARIMA.

Figure 6.9

Prediction of the closing price using LSTM.

Figure 6.10

Prediction of the closing price using LR.

Figure 6.11

Prediction of the closing price using ARIMA.

Figure 6.12

Prediction of the closing price using LSTM.

Figure 6.13

Prediction of the closing price using LR.

Figure 6.14

Prediction of the closing price using ARIMA.

Figure 6.15

Prediction of the closing price using LSTM.

Figure 6.16

Prediction of the closing price using LR.

Figure 6.17

Prediction of the closing price using ARIMA.

Figure 6.18

Prediction of the closing price using LSTM.

Figure 6.19

Prediction of the closing price using LR.

Figure 6.20

Prediction of the closing price using ARIMA.

Figure 6.21

Prediction of the closing price using LSTM.

Figure 6.22

Prediction of the closing price using LR.

Figure 6.23

Prediction of the closing price using ARIMA.

Figure 6.24

Prediction of the closing price using LSTM.

Figure 6.25

Prediction of the closing price using LR.

Figure 6.26

Prediction of the closing price using ARIMA.

Figure 6.27

Prediction of the closing price using LSTM.

Figure 6.28

Prediction of the closing price using LR.

Figure 6.29

Prediction of the closing price using ARIMA.

Figure 6.30

Prediction of the closing price using LSTM.

Figure 6.31

Prediction of the closing price using LR.

Figure 6.32

Prediction of the closing price using ARIMA.

Figure 6.33

Prediction of the closing price using LSTM.

Chapter 7

Figure 7.1

Block diagram for smart diabetes prediction.

Figure 7.2

Decision tree diagram for attribute age.

Figure 7.3

Categorized into carbohydrate, protein, and fat.

Figure 7.4

Percentages of each category of persons identified from analyzed valu...

Figure 7.5

Conceptual diagram for prediction of ADHD/LD.

Figure 7.6

Decision tree for classification of learners.

Figure 7.7

Classification of learners.

Figure 7.8

Heart disease using naïve bayes classifier.

Figure 7.9

ECC k(binary) FSM.

Figure 7.10

k-NAF ECC processor.

Figure 7.11

k-NAF FSM.

Figure 7.12

k-NAF ECC FSM.

Figure 7.13

Battery charge level measurement in Java application using system pr...

Chapter 8

Figure 8.1

Framework of health recommendation system.

Figure 8.2

Flowchart of health recommendation system.

Figure 8.3

Personal information ontology.

Figure 8.4

SWRL rule for the HRS.

Figure 8.5

Cases of iris dataset.

Figure 8.6

Cases of liver disorder.

Chapter 9

Figure 9.1

Various large data healthcare stakeholders.

Figure 9.2

Benefits in adopting blockchain healthcare privacy information.

Figure 9.3

Various forms of big data tools for healthcare.

Figure 9.4

Electronic medical record (EMR).

Figure 9.5

Different forms of strategies for security.

Chapter 10

Figure 10.1

Different types of data analytics. (a) Percentage (%). (b) Types wit...

Figure 10.2

Disease categorization by age.

Figure 10.3

Disease categorization by age.

Figure 10.4

Challenges in healthcare.

Chapter 11

Figure 11.1

Schematic representation of computer science subfields.

Figure 11.2

Methods of machine learning algorithms.

Figure 11.3

Neural network architecture.

Figure 11.4

Deep learning architecture with multiple layers.

Figure 11.5

Block diagram of the CBIR system.

Chapter 12

Figure 12.1

Comparative study of number of positive COVID-19 cases in various co...

Figure 12.2

Comparison of number of COVID-19 deaths in various countries.

Figure 12.3

COVID-19 statistics worldwide based on total cases, recovered, death...

Figure 12.4

Architecture of the proposed methodology.

Figure 12.5

Complete flow of the proposed methodology.

Figure 12.6

Statistics of COVID-19 recovered patients (male).

Figure 12.7

Statistics of COVID-19 recovered patients (female).

Figure 12.8

Analysis of real time data collected.

Figure 12.9

Comparison of various machine learning algorithms.

Chapter 13

Figure 13.1

Diabetes survey as per the category.

Figure 13.2

Diabetes survey as per the age range.

Figure 13.3

Architecture diagram of the intelligent system for diabetes.

Figure 13.4

Process flow of proposed intelligent system for diabetes.

Figure 13.5

Facts for type_one_diabetes.

Figure 13.6

Rules for type_one_diabetes.

Figure 13.7

Predicted output for type_one_diabetes.

Figure 13.8

Intelligent system’s complete output for type_one_diabetes.

Chapter 14

Figure 14.1

Prediction of breast cancer using machine learning algorithms using ...

Figure 14.2

Prediction of breast cancer using machine learning algorithms.

Figure 14.3

Mitoses distribution in PCA and K-means algorithm.

Figure 14.4

Mitoses distribution in machine learning algorithms.

Figure 14.5

Performance comparison of various machine learning algorithms.

Chapter 15

Figure 15.1

Healthcare data sources.

Figure 15.2

Process of data handling.

Figure 15.3

Applications of ML.

Figure 15.4

Types of learning in ML.

Figure 15.5

Example for KNN.

Figure 15.6

Categories of hyperplane.

Figure 15.7

Process of predictive analytics.

Chapter 16

Figure 16.1

Data fusion hierarchical framework for big data and IoT devices.

Figure 16.2

Proposed architecture TLCA in healthcare ecosystem.

Figure 16.3

Comparison of features to calculate the prediction of data fusion ac...

Figure 16.4

Data fusion along with sensor fusion using TLCA healthcare system.

Figure 16.5

Comparison of IoT devices count based on data aggregation.

Figure 16.6

Number of procedure based on hierarchical ecosystem vs frequency.

Figure 16.7

Accuracy, precision and recall (%) based on distributed framework.

Chapter 17

Figure 17.1

Normal cell and Abnormal cell as viewed under microscope. (Courtesy ...

Figure 17.2

Neural network architecture.

Figure 17.3

The predicted normal red blood cell.

Figure 17.4

The graphs of training losses against epoch numbers.

Chapter 18

Figure 18.1

Deep learning–based absence seizure detection work flow.

Figure 18.2

First eight segments of single instances after augmentation.

Figure 18.3

Feature extraction process with its parameters.

Figure 18.4

Convolution layer output of absence seizure pattern in time and freq...

Figure 18.5

Working of GRU-SVM.

Figure 18.6

Performance of the classifiers.

List of Tables

Chapter 2

Table 2.1

Entities from weather forecasting dataset.

Table 2.2

Sample dataset for predicting weather forecasting.

Chapter 3

Table 3.1

Dimensions of big data.

Table 3.2

Big data technologies [12, 14, 26].

Table 3.3

Difference between electronic health record and electronic medical rec...

Table 3.4

Summary of different sources of healthcare data [13].

Table 3.5

Patient health checking devices.

Chapter 6

Table 6.1

Comparison of LR, ARIMA, and LSTM of MSE and RMSE.

Chapter 7

Table 7.1

Results of learners.

Chapter 8

Table 8.1

Dataset with cases.

Chapter 9

Table 9.1

Knowledge security laws from numerous countries and organizations.

Table 9.2

Blockchain flexibility concerning threats.

Chapter 11

Table 11.1

Comparison of different DNN architectures.

Table 11.2

Different gaps in CBIR systems [36].

Chapter 12

Table 12.1

Characteristics details of COVID-19 in various organs [2].

Table 12.2

AI methods and big data for health care sector.

Chapter 14

Table 14.1

Performance of the various machine learning algorithms.

Chapter 15

Table 15.1

Classification and prediction of customer data set.

Table 15.2

Clustering and association of customer data set.

Chapter 16

Table 16.1

Classification of healthcare data for accurate multiple data integrit...

Table 16.2

Healthcare sample dataset for preprocessing and data fusion.

Chapter 17

Table 17.1

Performance estimation for accuracy.

Chapter 18

Table 18.1

Seizure detection based pre-processing, input formulation, feature ex...

Table 18.2

Details of normal, abnormal, and absence subject.

Table 18.3

Schematics of convolution layer.

Table 18.4

Schematics of GRU-SVM layers.

Table 18.5

p-value of a classification model.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Index

End User License Agreement

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Scrivener Publishing

100 Cummings Center, Suite 541J

Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

Handbook of Intelligent Healthcare Analytics

Knowledge Engineering with Big Data Analytics

Edited by

A. Jaya

Department of Computer Application, B.S. Abdur Rahman Crescent Institute of Science, Technology, Chennai, India

K. Kalaiselvi

Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai, India

Dinesh Goyal

Poornima Institute of Engineering & Technology, Jaipur, India

and

Dhiya AL-Jumeily

Faculty of Engineering and Technology, Liverpool John Moores University, UK

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-79179-9

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Preface

The power of healthcare data analytics is being increasingly used in the industry. With this in mind, we wanted to write a book geared towards those who want to learn more about the techniques used in healthcare analytics for efficient analysis of data. Since data is generally generated in enormous amounts and pumped into data pools, analyzing data patterns can help to ensure a better quality of life for patients. As a result of small amounts of health data from patients suffering from various health issues being collectively pooled, researchers and doctors can find patterns in the statistics, helping them develop new ways of forecasting or diagnosing health issues, and identifying possible ways to improve quality clinical care. Big data analytics supports this research by applying various processes to examine large and varied healthcare data sets. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. This book covers both the theory and application of the tools, techniques and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, this book also explores various sources of personalized healthcare data.

For those who are healthcare researchers, this book reveals the innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets. Since machine learning algorithms play a major role in analyzing the volume, veracity and velocity of big data, the scope of this book focuses on various kinds of machine learning algorithms existing in the areas such as supervised, unsupervised, semi-supervised, and reinforcement learning. It guides readers in implementing the Python environment for machine learning in various application domains. Furthermore, predictive analytics in healthcare is explored, which can help to detect early signs of patient deterioration from the ICU to a general ward, identify at-risk patients in their homes to prevent hospital readmissions, and prevent avoidable downtime of medical equipment.

Also explored in the book are a wide variety of machine learning techniques that can be applied to infer intelligence from the data set and the capabilities of an application. The significance of data sets for various applications is also discussed along with sample case studies. Moreover, the challenges presented by the techniques and budding research avenues necessary to see their further advancement are highlighted.

Patient’s healthcare data needs to be protected by organizations in order to prevent data loss through unauthorized access. This data needs to be protected from attacks that can encrypt or destroy data, such as ransomware, as well as those attacks that can modify or corrupt a patient’s data. Security is paramount since a lot of devices are connected through the internet of things and serve many healthcare applications, including supporting smart healthcare systems in the management of various diseases such as diabetes, monitoring heart functions, predicting heart failure, etc. Therefore, this book explores the various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., which create a new burden for providers to maintain compliance with healthcare data security.

In addition to inferring knowledge fusion patterns in healthcare, the book also explores the commercial platforms for healthcare data analytics. The new benefits that healthcare data analytics brings to the table, run analytics and unearth information that could be used in the decision-making of practitioners by providing insights that can be used to make immediate decisions. Also investigated are the new trends and applications of big data analytics for medical science and healthcare. Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Editors

Dr. A. Jaya

Dr. K. Kalaiselvi*

Dr. Dinesh Goyal

Prof. Dhiya AL-Jumeily

*Corresponding Editor

1An Introduction to Knowledge Engineering and Data Analytics

D. Karthika* and K. Kalaiselvi†

Department of Computer Applications, Vels Institute of Science, Technology & Advanced Studies (Formerly Vels University), Chennai, Tamil Nadu, India

Abstract

In recent years, the philosophy of Knowledge Engineering has become important. Information engineering is an area of system engineering which meets unclear process demands by emphasizing the development of knowledge in a knowledge-based system and its representation. A broad architecture for knowledge engineering that manages the fragmented modeling and online learning of knowledge from numerous sources of information, non-linear incorporation of fragmented knowledge, and automatic demand-based knowledge navigation. The project aims to provide petabytes in the defined application domains with data and information tools. Knowledge-based engineering (KBE) frameworks are based on the working standards and core features with a special focus on their built-in programming language. This language is the key element of a KBE framework and promotes the development and re-use of the design skills necessary to model complex engineering goods. This facility allows for the automation of the process preparation step of multidisciplinary research (MDA), which is particularly important for this novel. The key types of design rules to be implemented in the implementation of the KBE are listed, and several examples illustrating the significant differences between the KBE and the traditional CAD approaches are presented. This chapter discusses KBE principles and how this technology will facilitate and enable the multidisciplinary optimization (MDO) of the design of complex products. This chapter discusses their reach goes beyond existing CAD structure constraints and other practical parametric and space exploration approaches. There is a discussion of the concept of KBE and its usage in architecture that supports the use of MDO. Finally, this chapter discusses on the key measures and latest trends in the development of KBE.

Keywords: Data analytics, knowledge, knowledge engineering, principles, knowledge acquisition

1.1 Introduction

1.1.1 Online Learning and Fragmented Learning Modeling

Applied artificial intelligence (AI) was defined as knowledge engineering [1], with three major scientific questions: knowledge representation, the use of information, and the acquisition of knowledge. In the big data age, the three fundamental problems must evolve with the basic characteristics of the complex and evolving connections between data objects, which are autonomous sources of information. Big data not only rely on domain awareness but also distribute knowledge from numerous information sources. To have knowledge of engineering tools for big data, we need tons of experience. Three primary research issues need to be addressed for the 54-month, RMB 45-million, 15-year Big Data Knowledge Engineering (BigKE) project sponsored by China’s Ministry of Science and Technology and several other domestic agencies: 1) online learning and fragmented learning modeling; 2) nonlinear fusion of fragmented information; and 3) multimedia fusion of knowledge. Discussing these topics is the main contribution of this article. With 1), we examine broken information and representation clusters, immersive online content learning with fragmented knowledge, and simulation with spatial and temporal characteristics of evolving knowledge. Question 2) will discuss connections, a modern pattern study, and dynamic integration between skills subsections of fragmented information. The key issues mentioned in Figure 1.1 will be collaborative, context-based computing, information browsing, route discovery, and the enhancement of interactive knowledge adaptation.

Figure 1.1 Knowledge engineering.

Due to these features of several channels, traditional offline data mining methods cannot stream data since it is important to reform the data. Online learning methods help solve this issue and adapt easily to the drift of the effects of streaming. But typical methods to online learning are explicitly configured for single-source info. Thus, the maintenance of these features concurrently provides great difficulties and opportunities for large-scale data production. Big data starts with global details, tackles dispersed data like data sources and function streams, and integrates diverse understanding from multiple data channels, as well as domain experience in personalized demand-driven knowledge services. In the age of big data, many data sources are usually heterogeneous and independent and require evolving, complex connections among data objects. These qualities are considered by substantial experience. Meanwhile, major suppliers of information provide personalized and in-house demand-driven offerings through the usage of large-scale information technologies [2].

Centered on the characteristics of multiple data sets, the key to a multisource retrieval of information is fragmented data processing [3]. To create global awareness, local information pieces from individual data points can be merged. Present online learning algorithms often use linear fitting for the retrieval of dispersed knowledge from local data sources [4]. In the case of fragmented knowledge fusion, though, linear fitting is not successful and may even create problems of overfitting. Several studies are ongoing to improve coherence in the processing and interpretation of fragmented knowledge [6], and the advantage of machine learning for large data interpreting is that most samples are efficient, thus eliminating the possibility of over-adjustment at any rate [7]. Big data innovation acquires knowledge mostly from user-produced content, as opposed to traditional information engineering’s focused on domain experience, in addition to authoritative sources of knowledge, such as technical knowledge bases. The content created by users provides a new type of database that could be used as a main human information provider as well as to help solve the problem of bottlenecks in traditional knowledge engineering. The information created by the consumer is broad and heterogeneous which leads to storage and indexing complexities [5], and the knowledge base should be able to build and disseminate itself to establish realistic models of data relations. For instance, for a range of reasons, clinical findings in survey samples can be incomplete and unreliable, and preprocessing is needed to improve analytical data [8].

Both skills are essential for the creation of personalized knowledge base tools as the knowledge base should be targeted to the needs of individual users. Huge information reinforces distributed expertise to develop any ability. Big data architecture also requires a customer interface to overcome user-specific problems. With the advent of science and innovations, in the fast-changing knowledge world of today, the nature of global economic growth has changed by introducing more communication models, shorter product life cycles, and a modern product production rate. Knowledge engineering is an AI field that implements systems based on information. Such structures provide computer applications with a broad variety of knowledge, policy, and reasoning mechanisms that provide answers to real-world issues. Difficulties dominated the early years of computer technology. Also, knowledge engineers find that it is a very long and expensive undertaking to obtain appropriate quality knowledge to construct a reliable and usable system. The construction of an expert system was identified as a knowledge learning bottleneck. This helped to gain skills and has been a big area of research in the field of information technology.

The purpose of gathering information is to create strategies and tools that make it as simple and effective as possible to gather and verify a professional’s expertise. Experts tend to be critical and busy individuals, and the techniques followed would also minimize the time expended on knowledge collection sessions by each expert. The key form of the knowledge-based approach is an expert procedure, which is intended to mimic an expert’s thinking processes. Typical examples of specialist systems include bacterial disease control, mining advice, and electronic circuit design assessment. It currently refers to the planning, administration, and construction of a system centered on expertise. It operates in a broad variety of aspects of computer technology, including data baselines, data collection, advanced networks, decision-making processes, and geographic knowledge systems. This is a big part of software computing. Wisdom engineering also falls into connection with mathematical reasoning as well as a strong concern with cognitive science and social-cognitive engineering, where intelligence is generated by socio-cognitive aggregates (mainly human beings) and structured according to the way humans thought and logic operates. Since then, information engineering has been an essential technology for knowledge incorporation. In the end, the exponentially increasing World Wide Web generates a growing market for improved usage of knowledge and technological advancement.

1.2 Knowledge and Knowledge Engineering

1.2.1 Knowledge

Knowledge is characterized as abilities, (i) which the person gains in terms of practice or learning; theoretical or practical knowledge of a subject; (ii) what is known inside or as a whole; facts and information; or (iii) knowing or acquainted with the experience of life or situation. This knowledge is defined accordingly. The retrieval of information requires complex cognitive processes including memory, understanding, connectivity, association, and reasoning. Knowledge of a subject and its capacity for usage for a specific purpose are also used to suggest trustworthy comprehension. Information may be divided into two forms of knowledge: tacit and clear. Tacit knowledge is the awareness that people have and yet cannot get. Tacit knowledge is more relevant since it gives people, locations, feelings, and memories a framework. Efficient tacit information transfer typically requires intensive intimate correspondence and trust. The tacit understanding is not easily shared. Still, consciousness comprises patterns and culture that we still cannot comprehend. On the other hand, information, which is easy to articulate, is called explicit knowledge. Coding or codification is the tool used to translate tacit facts into specific details. The awareness expressed, codified, and stored in such media is explicitly facts. Explicit information. The most common simple knowledge sources are guides, manuals, and protocols. Audio-visual awareness may also be an example of overt intelligence, which is based on the externalization of human skills, motivations, and knowledge.

1.2.2 Knowledge Engineering

Edward Feigenbaum and Pamela McCorduck created Knowledge Engineering in 1983: To address difficult issues that typically need a great deal of human experience in the fields of engineering, knowledge engineering needs the integration of information of computer systems. In engineering, design information is an essential aspect. If the information is collected and held in the knowledge base, important cost and output gains may be accomplished. In a range of fields, information base content may be used as to reuse information in other ways for diverse goals, to employ knowledge to create smart systems capable of carrying out complicated design work. We shall disseminate knowledge to other individuals within an organization. While the advantages of information capture and usage are obvious, it has long been known in the AI world that knowledge is challenging to access from specialists. Second, the specialists do not remember and describe “tacit knowledge” effectively, and this operates subconsciously and, where it is not impossible, is difficult to overcome the problems arising from several subject matters that they learn. To elaborate, they have to know what it is called. Third, there are various prospects and points of view which include aggregation to provide a coherent view. Last, professionals create abstract concepts and shortcuts for which they cannot communicate. The area of information technology was created some 25 years ago to address such problems, and the role of the knowledge engineer was born. Since then, computer engineers have developed a variety of principles, methods, and tools that have improved the acquisition, use, and implementation of knowledge considerably.

1.3 Knowledge Engineering as a Modelling Process

There is also a consensus that the KBS construction approach may be used as a modeling operation. It is not intended to construct a cognitively appropriate model, but to build a model that offers similar results for problem-solving problems in the area of concern as seen in Figure 1.2.

Figure 1.2 Knowledge as modelling process.

Building a KBS requires building a programming model to acquire problem-solving capabilities like those of a domain specialist. This material is not directly available; therefore, it needs to be created and arranged.

1.4 Tools

Intelligence engineers make the more efficient and less bogus use of dedicated computational tools for the acquisition, simulation, and handling of intelligence. PC PACK is a versatile compilation of this programmed, commercially available as a package of knowledge technology tools that are designed to be tested on a wide range of projects. The aim is to consider the key characteristics of the domain. The method simulates how anyone should label a text page with different colors such as green for suggestions and yellow for attributes. The labeled text would immediately be placed in the PCPACK database to be applied to all other resources when the user has highlighted a document. The MOKA and Popular KADS Methodologies are supported by the CFPACK. It is also fully compliant with information engineering approaches and techniques. The CFPACK is a software suite that includes the following

(i) Protocol tool: It enables the discovery, recognition, and definition of interview transcripts, conclusions, and documentation that may be included in the knowledge base.

(ii) Ladder Tool: This allows hierarchies of knowledge elements such as meanings, features, procedures, and specifications to be developed.

(iii) Chart Tool: This allows users to build mobile networks of connections between data elements, such as process maps, maps of ideas, and cutting-edge diagrams.

(iv) Matrix Tool: This allows grids that show the connection and attributes of the elements to be developed and edited.

(v) Annotation tools: This facilitates the creation of sophisticated HTML annotations, with links to other sites and other knowledge templates automatically generated in the CFPACK.

(vi) Tool publisher: This allows the creation from a knowledge base of a website or some other information resource using a model-driven approach to optimize re-usability. MOKA, CommonKADS, and the 47-step protocol provide approaches to run a project from beginning to completion, as well as maintaining best practice.

1.5 What are KBSs?

A knowledge-based framework is a system that utilizes AI tools in problem-solving systems to assist human decision-making, understanding, and intervention.

There are two core components of the KBSs:

• Information base (consists of a collection of details and a set of laws, structures, or procedures).

• Inference engine (Responsible for the extension of the information base to the issue at hand).

In contrast to human expertise, there are pros and cons to utilizing KBSs.

1.5.1 What is KBE?

It starts with a discussion about what KBE is in this book and begins with a simple definition: Knowledge-based Engineering (KBE) uses the knowledge of product and operation, which was collected and retained in specific software applications, to enable its direct usage and reuse in the development of new products and variants. KBE’s implementation consists of applying a specific class of computing tools, called the KBE systems, which enable engineers to acquire and reuse engineering knowledge using methods and methodologies. The name of the KBE architecture is derived from the mixture of KBS and engineering, which are one of the major outcomes of AI. In the 1970s, KBE systems demonstrate the advancement of the KBS by applying the special engineering industry requirements. KBE systems combine KBS rule-based logic technologies with engineering data analysis and geometry like CAD.

For these reasons, a traditional KBE architecture provides the user with a programming language that is generally object-oriented and one (something more) embedded or closely connected CAD engine. The vocabulary of programming enables the user to capture and reuse rules and procedures in engineering, while the object-oriented approach of design corresponds well with how engineers see the world: systems abstract assets of objects, defined by parameters and behaviors, linked by relationships. Access and management by the programming language of the CAD engine meet the geometry handling requirements characteristic of the engineering architecture. The MIT AI laboratories and the Computer Vision CAD Group developed the first commercially available KBE system named ICAD1984 (now PTC). Fortunately, this asks the first inquiry you will hear in Figure 1.3 about KBE.

It will be useful at this stage to quickly clarify what we refer to as information and how we use this term to describe concepts other than data and evidence. Both terms are sometimes misused in the traditional spoken language; truth and knowledge are often interchangeably used. The hierarchy of data and intellect (and knowledge) is the subject of long-term disputes between epistemologists and IT experts. Since this subject goes well beyond the scope of this chapter, this is our definition of data. Data are objects that have no meaning before they are put in form like symbols, statistics, digits, and indications. The information consists of important processed data. The context in which the data are collected gives it meaning, importance, and intent. Human and electronic information can be collected, shared, and processed. The knowledge is encrypted by code, normally organized in a structure or size, and stored in hard or soft media to accomplish this. Awareness is the condition of information and awareness processing, which requires the chance to act.

New information may be produced as a result of the application of knowledge. The IGES file with a geometrical definition of the surface as a piece of information is an example of this. IGES files will be encoded with numbers and symbols (i.e., data) and will only provide knowledge useful if they understand the meaning (i.e., the fact they are the data of an IGES file). The information which can be collected with a KBE method is regarded as a simple example of the algorithm that reads such an IGES file, reconstructs a specified surface model, intersects it with a floor plane, and, if the crossroad is non-zero, calculates the length of the corresponding curve. It is also sensible to ask why geometry varies from the standard CAD paradigm and enables the creation and manipulation of geometry. Owing to the varying scopes of these systems, the differences are important. Digitized drawing systems, which allow programmers to catch their ideas have been designed to create CAD systems. They build and store the results using the CAD framework’s geometry simulation functions. A set of points, lines, planes, and solids with reference and note are an almost all-inclusive link to the structure. These data provide enough information for the creation of a system that can be used to build a specification by production engineers. In doing so, creators store the specifics of “what,” but they retain the “how” and “why.” In a sense, the CAD approach can be considered a system “posterior,” because before it can be moved to the system it is necessary to know what the principle is like. It can be argued that CAD is geometry or drawing/drawing engineering to distinguish this approach from KBE.

Figure 1.3 KBE.

KBE-supported technology is different. Technology Instead of shifting “what,” engineering experts are trying to move “how” and “why,” encapsulating in the KBE process knowledge and thinking instead of geometry in the CAD framework. Not only does this work by manipulating geometric structures, but programming is needed rather than writing. The “how” and “why” in engineering are in some cases used in textbooks, databases, tip sheets, and several other outlets. Much of the knowledge is held up by engineers, mostly in a manner that is strongly compiled and not specifically suitable for translation to the KBE procedure. This experience should be sufficiently transparent to create a KBE program to be codified into a software application capable of producing all kinds of product specifics, including geometry templates, scores, and data that are not associated with the geometry. Because of its capacity to generate a specification rather than simply text, it is widely referred to as a generative model.

1.5.2 When Can KBE Be Used?

How easy is the use of KBE? It is only necessary to rapidly generate different configurations and variants of a given variable. In certain practical cases, this is not essential, so it may be a wrong expenditure to try to clarify details and program a KBE system. One-off prototypes and designs that need not be optimized or prototype versions for space travel are usually outside the scope of KBE implementation.

It explores the design field through the development of different design variants within the product family and evaluates its performance compared to previously tested versions with the multidisciplinary optimization (MDO) application. KBE will assist in many respects in this case.

It enables stable product parametric models to be generated which make topology changes and the freedom to make adaptation changes usually impossible for those built with a conventional CAD framework. This is important when considering broad variations like those which occur when a yacht manufacturer decides to accept one or more hull settings. It supports the integration into MDO through automation of the generation of necessary disciplinary abstractions of heterogeneous sets of analytical methods (low and high fidelity, in-house and off-shelf). It removes the optimizer from the challenge of managing the spatial integration constraints that generative models should guarantee. This is essential because the user does not need to specify constraints on configuration variables or restrictions to avoid intersection of two elements; or because a certain structural element does not need to remain beyond the same outer mold line; or because, during optimization, two products are expected to have a certain relative position apart.

KBE generative models can be a secret in producing MDO systems that are not multidisciplinary in return for adherence to science and that can handle complex problems reflecting actual industrial circumstances. We discuss the different models of current MDO systems and compare them to advanced KBE implementations in the next section to clarify this claim.

The third set of MDO structure implementation is available to overcome the weaknesses of the two approaches described earlier in this section by introducing generative models into the system. One advantage of this approach is that the exact geometry representations normally used for the use of high faithfulness analysis instruments may serve as a basis for the disciplinary study. It is therefore well adapted to the geometric nuances that are not included in a few general criteria of modern products. This geometry depiction is generated following individual tools of multidisciplinary system analyzers (BB SA) along with others, usually not schematic, product abstractions, and are systematically updated after each optimization loop. These MDO systems can fully resolve multidisciplinary cases without penalizing the degree of faithfulness and can contribute to the early stages of the design phase by addressing substantial changes in the shape and topology. They may also support a more sophisticated modeling method in which complex and accurate geometric models for high fidelity analysis are needed. These functions allow the early use of highly reliable testing approaches to be implemented with novel prototypes that do not have correct or unavailable semi-empirical and predictive technologies. The product modeling scheme which is the key feature of the MDO system is undermined by this approach.

1.5.3 CAD or KBE?

It is a mistake to know whether KBE is greater than CAD or vice versa. One is in the whole sense no bigger than the other, and we argue here that KBE should replace CAD. In certain circumstances, the KBE programming process is more suitable than the interactive application of the CAD platform, given that MDO supports one of the interests of this novel. This chapter is beyond the scope of a general debate about the suitability of one option for the next. The suggestions are as follows:

Where the focus is only on geometry development and manipulation; where considerations such as direct interaction with geometric models are important, graphical rendering and inspection are essential; if uniform, aesthetic, and heuristic design are the guides behind modeling, rather than engineering laws.

When it comes to design purposes, vocabulary is required instead of design results. The programming method of KBE systems offers the best solution in this case. Although CAD systems are committed to better documenting the results of the human design phase, KBE systems are designed to report the design procedure (i.e., the purpose of the design) and not just the results.

• A language is needed to promote automation while preserving continuity. Whenever the generative model is “played.” The same protocol (i.e., the same rules and logic processes are applied) is constantly repeated with different appropriate inputs regardless of which operator and of how many replays. In some engineering design cases, one of which is the optimization of design, an obstacle to automation is placed in the loop (except process supervision).

• A vocabulary offers a competitive advantage when it comes to the ease of interaction with external modeling and simulation applications. Usually, both CAD and KBE systems link (to each other and) through standard data interchange formats including IGES and Move. In times of ad hoc interchange files that are dependent on ASCIIs, the most useful approach to dedicated writers and parser production is full-function language programming. Also, the KBE system can detect and largely simplify these processes where the tool to be connected is required by complicated and knowledge-intensive pre-processing operations to schedule the input.

• Where there is an aesthetic facet of the architecture and details are produced, but at the same time an multidisciplinary research (MDA) and an optimization approach are used in the design and size of a given product, the best possible solution is provided by combined applications of CAD and KBE. In this case, the CAD process geometry would become the KBE application’s feedback. This implementation will support the complex MDA structure and return the material (partially or fully) to the CAD system, where comprehensive work can take place more immersive.

At the end of the day, the heuristic and non-respecting, geometric or non-repeatable, one-off, and repetitive aspects of the design phase coexist and are interlinked: Both CAD and KBE can contribute to this step, which must be the focus of both the creators of CADs and KBE’s smooth integration.

1.6 Guided Random Search and Network Techniques

There are some methods designed to find suitable designs using techniques that avoid the use of the pursuit of gradients or almost gradients. A system that either uses random variations in design variables or avoids direct variations in design variables by the use of learning networks supplements the use of directional searches. We have selected the genetic algorithm (GA) as a representative in the first category, RAT (GRS), and we have selected the Artificial NERN (ANN) as a regular illustration in the second category, network-based learning methods.

1.6.1 Guide Random Search Techniques

Without stringent enumeration, guide random search technique (GRST) methods are attempting to seek a whole feasible design field and, in principle, have a global optimum. If this optimum is not suitable internationally, then traditional exploration procedures provide no underlying means to move away from the optimal local field to continue the search for the optimum global setting. However, it should be borne in mind that there can be no confidence that a GRST algorithm can solve a complex design problem globally and, as mentioned elsewhere in the book, no answer can be challenged to ensure that a global solution has been discovered. The methods, though, are rigorous and usually will include a solution that significantly improves on any initial concept put forward by the design team.

GRST methods can deal with problems with the architecture of undistinguished functions and with many local improvements. The ability to deal with non-differentiable functions makes it easy to address problems related to distinct design variables, which are common aspects of structural design. Many GRST methods are well adapted for parallel processing, in particular the evolutionary algorithms mentioned in the next section. The number of implementing variables would allow concurrent processing to be used to respond within a reasonable period if every MDO problem is resolved by the GRST method rather than trivial.

Evolutionary algorithms are a subset of GRST techniques that employ very special approaches that focus on evolutionary concepts seen in nature. This approach also exposes some designs to spontaneous variations and offers anyone with a practical advantage an increased opportunity to produce “spring” designs. There are a number and different methods to solving complicated optimization problems using the same straightforward probabilistic technique. We are concerned with GA, which may be the most popular evolutionary form of algorithms in-process libraries or in commercial MDO systems.

1.7 Genetic Algorithms

GA is a family of computational methods based upon the evolutionary theory of Darwinian/Russel Wallace, used to solve general problems through optimization. Caution is in order at this point! The use of biology-inspired terms in the GA system demonstrates genetics, which engineers considered several years ago as an optimization process. Since then, substantial progress in biology has shown that true genetic growth is many compounders but only a convenient metaphor remains the term “genetics” used in this book. Instead of looking from one design point to another in search of improved design, the GA shifts to a second population with a reduced meaning for the restricted purpose feature, from an existing set of design points called group. A replication and mutation process on the computer model of the plant design points achieves progress from generation to generation.

Although, the design team would like to see data from prior designs or preliminary studies in the application for engineering design from the original collections of design points. The question is not distinct from those used in the application of search methods. The objective design function and constraints at each design point of the population must be estimated. The experiments are independent such that the parallel treatment can be included. We now turn to the definition stage of data representation.

1.7.1 Design Point Data Structure

The architecture variables describing a specific design point are described by binary numbers and linked to a 0.1-bit string. Suppose, for example, that we create a solid cone with height and base diameter as design variables and then begin a design point with 4 m of height with a base diameter of 3 m (4, 3). This coordinate is a binary variant (100, 011) which is a concatenated string (100011). This string is named the chromosome of the structure that reflects its roots in genetics, and the individual sections are gene analogs. Therefore, there are multiple chromosomes in the population, equivalent to the number of design points that we intend to use in the field of design. Also, the chromosome number of digit slots (bits) should be sufficient to fulfill the software and the degree of precision of the various specification variable values.

1.7.2 Fitness Function

The problem with optimization was now reconfigured to a set of chromosomes which represent a century of designs with a special design for each chromosome. The AG encourages a “fittest survival” policy, which would eventually transfer chromosomes through generations before an optimum arrangement is found. This includes a chromosome recognizing or excluding process such that we monitor for the fitness to be included in the next generation of designs for the same chromosome. This is achieved by using a health function which is a metric of goodness common to all chromosome-based conception points with a separate meaning for each point. Why a penalty for a limitation violation is included in the exercise feature later is discussed.

Essentially, systems are designed to help design the next generation of chromosomes in the community and their well-being. To use the above example, the assessment approach is simple and better, but it can be pointed out that it reflects the mechanism of selection and that some might be used in business programs.

1.7.3 Constraints

The limitation will not be offset in the case of a GA by ensuring that the search algorithm does not traverse a non-feasible field by directly inserting the method limitations into the search direction. In the case of GA, limits are handled either using sanctions or by excluding ineffective chromosomes. This second approach should be implemented with care to prevent solutions from being rejected at the edge of a feasible field, where the solution is controlled by active limitations. However, side restrictions may also be added, for example, minimum gauges.

1.7.4 Hybrid Algorithms

GA has a reputation for being durable, meaning that it can usually deliver an overhaul of the initial design. But, for a particular design domain, they could not be the correct solution. A hybridization approach should be used to try to make the most of all the worlds to maximize their convergence rates in situations when more information is available and is not generated randomly (for example, where gradient details are available). Typically, a hill-climbing algorithm is used in the genetic code to allow everyone in the group to climb on the local hill. The system also encourages each offspring to climb a local hill, created at the breeding stage.

While the simple convergence of GA search algorithms associated with hybrid approaches is the common meaning, this term can also be used for a less straightforward hybridization, where GA and gradient search methods are employed in sequence. Use the GA to reverse the optimizing problem and then deliver the output to the conventional optimizer from this first stage to complete the operation. The first design approach would design the right initial layout for GA before moving on to the full design level, where the second stage optimization phase will begin with the use of classical search techniques. This can also be found in MDO implementations.

1.7.5 Considerations When Using a GA

• GA has the benefit of being able to handle a full variety of variability in a design. For example, in the preliminary design of an aircraft, the motor number and position must not be defined either in the wing configuration (i.e., monoplane, medium, mid-fuselage, and high), such that a selection algorithm can be used for the best combination.