Smart Factories for Industry 5.0 Transformation E-Book
263,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Industry 5.0 Transformation Applications
- Sprache: Englisch
This book serves as a comprehensive guide, exploring the technologies, design principles, and operational strategies behind smart factories.
In an era where industrial expertise meets digital innovation, the “smart factory” symbolizes a new wave of efficiency and advancement. Industry 5.0 represents a paradigm shift, integrating technologies like robotics, AI, IoT, and big data to enhance human-machine collaboration while improving sustainability, quality, and efficiency. It offers businesses valuable insights and real-world examples to navigate the opportunities and challenges of Industry 5.0.
This book goes beyond technical explanations to examine the broader impact of the Industry 5.0 revolution on global supply chains and socioeconomic change, encouraging readers to view technology as a force for good. It appeals to all levels of expertise, providing valuable insights for experienced professionals while serving as an introduction for newcomers. Above all, it invites readers to embrace the collaborative spirit and creativity of Industry 5.0, joining in the effort to build the smart factories that will drive the future of innovation.
Audience
Researchers, industry engineers, and technologists working in artificial intelligence and Industry 5.0 application areas such as healthcare, transportation, manufacturing, and more.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 490
Veröffentlichungsjahr: 2025
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Evolution of Industrial Revolution: Industry 5.0 and Beyond
Brief History of Industrial Revolution
Acknowledgement
Bibliography and Further Reading
2 Personalized Healthcare Transformation via Novel Era of Artificial Intelligence-Based Heuristic Concept
Nomenclature
2.1 Introduction
2.2 Literature Survey
2.3 Digitization, Data Sources, and AI in Healthcare
2.4 AI Mainstreaming in Healthcare
2.5 Current Status, Integration, and Obstacles to the Usage of Personalized Healthcare Transformation
2.6 Prerequisites for Radical Transformation in Healthcare
2.7 Personalized Healthcare Transformation Using MSOM-Based TOA
2.8 Results
2.9 Conclusion
References
3 A Survey on Security in Data Transmission Using Wireless Communication Methods for IoT Edge Devices
3.1 Introduction
3.2 Literature Survey
3.3 Description of Data Protocols for IoT System
3.4 IoT Communication Parameters
3.5 Comparative of Communication Protocols for IoT Systems
3.6 Conclusion
References
4 Innovative Application of Conditional Deep Convolutional Generative Adversarial Networks to Enhance Chronic Kidney Disease Diagnosis with Uneven Datasets
4.1 Introduction
4.2 Literature Survey
4.3 Methodology
4.4 Result Analysis
4.5 Conclusion
References
5 A Comprehensive Hybrid Implicit and Explicit Item-Based Collaborative Filtering Approach with Bayesian Personalized Ranking for Enhancing Book Recommendations
5.1 Introduction
5.2 Related Work
5.3 Methodology
5.4 Experimental Results and Analysis
5.5 Conclusion
References
6 An Efficient Cluster-Based Deep Learning Model for Multi-Attack Classification in IDS Across Diverse Datasets
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Model Design
6.4 Results and Discussion
6.5 Conclusion
References
7 Heart Failure Detection Through SMOTE for Augmentation and Machine Learning Approach for Classification
7.1 Introduction
7.2 Literature Survey
7.3 Proposed Methodology
7.4 Results and Discussion
7.5 Conclusion
References
8 Optimal Power Allocation in Cognitive Radio Networks Using Teaching-Learning-Based Optimization
8.1 Introduction
8.2 Teaching-Learning-Based Optimization
8.3 Proposed Power Allocation Algorithm
8.4 Numerical Results
8.5 Conclusion
References
9 Using Historical Pattern Matching and Natural Language Processing in a Hybrid Approach for Stock Market
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.4 Data Sources and Collection
9.5 Experimental Setup
9.6 Discussion
9.7 Results
9.8 Conclusion
References
10 An Intelligent Framework for IoT-Based Health Care Monitoring Using Fuzzy-Supported Machine Learning Algorithm
10.1 Introduction
10.2 Literature Analysis
10.3 Integrated IoT-Based Healthcare Decision Making Model Using Machine Learning (IHM-ML)
10.4 Result and Discussion
10.5 Conclusion and the Future Scope
References
11 Design Strategy for Narrowband Internet of Things with Its Scope and Challenges of Security Solutions
11.1 Prologue Study
11.2 Fundamentals of NB-IoT Network Design
11.3 Security Challenges and Vulnerabilities in NB-IoT Systems
11.4 Scope of Machine Intelligence in NB-IoT Security
11.5 Conclusion and the Future Scope
References
12 Machine Learning in Healthcare: Unlocking Precision Diagnosis and Continuous Monitoring Through Voice Analysis
12.1 Introduction
12.2 Background
12.3 Methodology
12.4 Results
12.5 Discussion
Conclusion
References
13 Introduction of Advanced and Improved Transposition Algorithm
13.1 Introduction
13.2 Literature Study
13.3 Implementation
13.4 Result
13.5 Conclusion and Future Direction
References
14 Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning
14.1 Introduction
14.2 Literature Review
14.3 Methodology
14.4 Performance Analysis
14.5 Result Analysis
14.6 Conclusion
14.7 Future Work
References
15 Advanced Aquaculture Management: A Smart System for Optimizing Oxygen Levels, Shrimp Health Monitoring
15.1 Introduction
15.2 Literature Survey
15.3 System Model
15.4 Results and Discussion
15.5 Conclusion
References
16 Farming Revolution: Precision Agriculture and IoT for Sustainable Growth
16.1 Introduction
16.2 Data Storage and Analysis on Cloud Data
16.3 Architecture IoT with Agriculture
16.4 Results and Performance Validation
16.5 Conclusion
References
17 Comparative Analysis of the Identification and Categorization of the Malaria Parasite Employing Recent Amalgamated Machine Learning Methodologies
Introduction
Dataset Acquisition
Methodology
Literature Survey
Methodology
Results and Discussion
Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Specificity analysis.
Table 2.2 Sensitivity analysis.
Table 2.3 F1 Score analysis.
Table 2.4 Accuracy analysis.
Table 2.5 Precision analysis.
Table 2.6 Statistical analysis.
Chapter 3
Table 3.1 Communication protocol characteristics.
Table 3.2 Communication protocol characteristics.
Chapter 4
Table 4.1 Comparison of proposed model with other models classification report...
Chapter 5
Table 5.1 Complete summary of related work.
Table 5.2 Comparative results of model performance.
Chapter 6
Table 6.1 Performance analysis for existing models.
Table 6.2 Details of hyper parameters in the proposed LSTM model.
Table 6.3 Performance comparison of precision, re-call, and F1-score for CICID...
Table 6.4 Performance comparison of precision, re-call and F1-score for NSL-KD...
Table 6.5 Performance comparison of precision, re-call and F1-score for UNSW-N...
Chapter 7
Table 7.1 Performance analysis for heart failure model.
Chapter 8
Table 8.1 Performance of three power allocation techniques.
Chapter 10
Table 10.1 NMSE & SNR comparison.
Chapter 11
Table 11.1 Some existing models on NB-IoT and related technology.
Table 11.2 Default security services by NB-IoT.
Chapter 12
Table 12.1 Summary of medical conditions and associated vocal biomarkers.
Table 12.2 Performance summary of neural network model for disease classificat...
Table 12.3 Diagnostic performance of models.
Chapter 14
Table 14.1 Comparison table.
Chapter 15
Table 15.1 Standard values.
Chapter 16
Table 16.1 Sample data set.
Chapter 17
Table 17.1 Comparative analysis of our three techniques with existing recent a...
Table 17.2 Comparative analysis of three hybrid machine learning techniques fo...
List of Illustrations
Chapter 1
Figure 1.1 Model - Evidence for usage of bricks and metals during Indus civili...
Chapter 2
Figure 2.1 Diagrammatic model of AI in healthcare.
Figure 2.2 Challenges of AI in healthcare.
Figure 2.3 Major components of the personalized healthcare transformation.
Figure 2.4 Specificity analysis.
Figure 2.5 Sensitivity analysis.
Figure 2.6 F1 Score analysis.
Figure 2.7 Accuracy analysis.
Figure 2.8 Precision analysis.
Chapter 3
Figure 3.1 The structure of MQTT.
Figure 3.2 Data distribution service global data space.
Figure 3.3 WebSocket vs. HTTP: A comparison.
Figure 3.4 OPC UA FLC system architecture.
Figure 3.5 IoT connectivity.
Figure 3.6 Speed and range in IoT network protocols.
Figure 3.7 Examples of different network topologies.
Figure 3.8 A smart home using Bluetooth Internet of Things network nodes as an...
Figure 3.9 The LoRa and LoRaWan OSI reference model.
Figure 3.10 Z-Wave Internet of Things network in a residential setting.
Figure 3.11 Comparison of IoT protocols.
Chapter 4
Figure 4.1 Proposed framework – cDCGAN.
Figure 4.2 Comparison of the performance of the proposed model with other mode...
Chapter 5
Figure 5.1 Architecture of a heterogeneous and MF approach.
Figure 5.2 The model framework of applying MF for book recommendation.
Figure 5.3 Overview of elementary phases of pre-processing.
Figure 5.4 The top-N suggestions by HR and NDCG.
Figure 5.5 The performance comparison of EIBTR and baselines.
Chapter 6
Figure 6.1 Schematic architecture of the Autoencoder, k-means clustering, and ...
Figure 6.2 Performance analysis of CICIDS 2018 dataset.
Figure 6.3 Performance analysis of NSL-KDD dataset.
Figure 6.4 Performance analysis UNSW_NB15 dataset.
Chapter 7
Figure 7.1 Proposed methodology for heart failure prediction.
Figure 7.2 Confusion matrix for existing machine learning algorithms.
Figure 7.3 ROC curve for existing machine learning algorithms.
Figure 7.4 Performance analysis.
Chapter 8
Figure 8.1 Power allocation of the proposed algorithm.
Figure 8.2 Iterative response of the algorithm.
Chapter 9
Figure 9.1 Overview of hybrid method.
Figure 9.2 News classification using NLP techniques.
Figure 9.3 Dataset split for training and testing.
Figure 9.4 Sentiment analysis vs. stock price market.
Figure 9.5 Pattern matching performance comparison.
Figure 9.6 News source sentiment distribution.
Chapter 10
Figure 10.1 Generalized architectural model of the proposed IHM-ML.
Figure 10.2 Gateway configuration of IoT environment with health sensors.
Figure 10.3 Flow of remote health monitoring through gateway to sensor control...
Figure 10.4 Cloud dependency in remote health monitoring.
Figure 10.5 Comparitive analysis of throughput ratio.
Figure 10.6 Comparative analysis of energy efficiency ratio.
Figure 10.7 Comparative analysis of training accuracy ratio.
Figure 10.8 Overall performance analysis using classification accuracy over ex...
Chapter 11
Figure 11.1 Growth chart of NB-IoT from RFID.
Figure 11.2 Market demand forecast of NB-IoT [48].
Figure 11.3 General framework of NB-IoT system.
Figure 11.4 Technical view of NB-IoT system structure.
Figure 11.5 Visualization of NB-IoT operation modes. (a) In-band NB-IoT mode, ...
Figure 11.6 NB-IoT physical layer processing system with NPBCH.
Figure 11.7 NB-IoT subframes allocation.
Figure 11.8 Real-time use cases of NB-IoT.
Figure 11.9 NB-IoT threats.
Chapter 12
Figure 12.1 Architecture diagram for vocal biomarker analysis for disease dete...
Figure 12.2 Performance of neural model for various disease.
Figure 12.3 Model performance.
Chapter 14
Figure 14.1 Proposed block diagram.
Figure 14.2 Comparison graph.
Chapter 15
Figure 15.1 Block diagram of embedded system.
Figure 15.2 Block diagram of three phase power failure detection system.
Figure 15.3 Block diagram of a sensor network connected to the cloud.
Figure 15.4 ESP8266 Node MCU V3.
Figure 15.5 Humidity level sensor.
Figure 15.6 Dissolved oxygen graph.
Figure 15.7 Ultra sonic sensor.
Figure 15.8 Experimental setup.
Figure 15.9 Temperature output.
Figure 15.10 Humidity.
Figure 15.11 Distance.
Figure 15.12 Oxygen level.
Figure 15.13 Final output values.
Chapter 16
Figure 16.1 Soil moisture sensor.
Figure 16.2 DHT11 temperature and humidity.
Figure 16.3 NDVI (normalized difference vegetation index).
Figure 16.4 Soil conditions, including pH levels, nutrient content, and moistu...
Figure 16.5 Working diagram MQTT protocol.
Figure 16.6 Flow diagram data analytics for disease estimation.
Figure 16.7 IoT architecture in precision agriculture.
Figure 16.8 Precision agriculture decision making system.
Figure 16.9 Soil moisture distribution crop yield water efficiency.
Figure 16.10 Performance metrics comparison for algorithms.
Chapter 17
Figure 17.1 The scrutiny unveiled a sleek surface configuration in uninfected ...
Figure 17.2 Sample infected and non-infected dataset.
Figure 17.3 Result analysis of the OML-AMPDC technique with different measures...
Figure 17.4 Result analysis of the HPTDL-MPDC technique with different measure...
Figure 17.5 Result analysis of the NTSN-MPDC technique with different measures...
Figure 17.6 An examination of the outcomes derived from employing the differen...
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
Pages
ii
iii
iv
xi
xii
xiii
xiv
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
Scrivener Publishing
100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Industry 5.0 Transformation Applications
Series Editors: Dr. S. Balamurugan (sbnbala@gmail) and Dr. Sheng-Lung Peng
The increase in technological advancements in the areas of artificial intelligence (AI), machine learning (ML) and data analytics has led to the next industrial revolution “Industry 5.0”. The transformation to Industry 5.0 collaborates human intelligence with machines to customize efficient solutions. This book series covers various subjects under promising application areas of Industry 5.0 such as smart manufacturing, intelligent traffic, cloud manufacturing, real-time productivity optimization, augmented reality and virtual reality, etc., as well as titles supporting technologies for promoting potential applications of Industry 5.0, such as collaborative robots (Cobots), edge computing, Internet of Everything, big data analytics, digital twins, 6G and beyond, blockchain, quantum computing and hyper-intelligent networks.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Smart Factories for Industry 5.0 Transformation
Edited by
R. Nidhya
Dept. of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, India
Manish Kumar
Dept. of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala, India
S. Karthik
Dept. of Computer Science & Engineering, SNS College of Technology, Coimbatore, India
Rishabh Anand
Dept. of Computer Science & Engineering, Global Institute of Technology & Management, Gurugram, India
and
S. Balamurugan
Intelligent Research Consultancy Services, Coimbatore, India
This edition first published 2025 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© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-19995-2
Cover design by Russell RichardsonFront cover image courtesy of Adobe Firefly
Preface
Greetings from the forefront of the Industry 5.0 revolution! In an era where industrial expertise meets digital innovation, the “smart factory” symbolizes a new wave of efficiency and advancement. Industry 5.0 represents a paradigm shift by integrating technologies like robotics, AI, IoT, and big data to enhance human-machine collaboration, improving sustainability, quality, and efficiency. This book serves as a comprehensive guide, exploring the technologies, design principles, and operational strategies behind smart factories, offering businesses valuable insights and real-world examples to navigate the opportunities and challenges of Industry 5.0.
This book goes beyond technical explanations to explore the broader impact of the Industry 5.0 revolution on global supply chains and socioeconomic change, encouraging readers to see technology as a force for good. It appeals to all levels of expertise, offering valuable insights for experienced professionals and serving as an introduction for newcomers. Above all, it invites readers to embrace the collaborative spirit and creativity of Industry 5.0, and join in building the smart factories that will drive the future of innovation.
Chapter 1 explores the evolution of Industry 5.0, which emerged after the widespread benefits of Industry 4.0 in the past decade. The industrial revolution dates back to the 18th century with the introduction of machinery, building on early developments from ancient civilizations like the Indus Valley. Over time, the shift from handicrafts to machines marked Industry 1.0, followed by assembly lines in Industry 2.0, automation in Industry 3.0, and digital technologies in Industry 4.0. Industry 5.0 now focuses on mass customization and human-machine collaboration.
Chapter 2 discusses Artificial Intelligence (AI) in healthcare, marking a new era of personalized medicine driven by heuristic reasoning. AI algorithms can analyze complex healthcare data, enabling customized interventions. The chapter highlights how advanced technologies and interdisciplinary collaboration are transforming patient care, diagnosis, and treatment, leading to precision medicine. This paradigm shift optimizes clinical outcomes and empowers individuals to take control of their health, moving toward a more patient-centered and holistic healthcare system.
Chapter 3 provides an in-depth survey of the Internet of Things (IoT), focusing on the role of edge devices in data collection and transmission. It explores the challenges of securing wireless communication for these dispersed, resource-constrained devices, examining protocols, encryption methods, and intrusion detection systems. By highlighting current best practices and identifying vulnerabilities, the chapter offers a comprehensive understanding of IoT security, laying the groundwork for building robust and resilient IoT ecosystems.
Chapter 4 introduces the use of deep learning technologies to improve accuracy and efficiency in healthcare diagnostics, focusing on chronic kidney disease (CKD). It presents a novel technique using Conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) to address challenges with imbalanced datasets. This approach enhances diagnostic capabilities, potentially advancing early detection and treatment of CKD, leading to better patient outcomes and healthcare delivery.
Chapter 5 explores personalized recommendation systems, focusing on a novel method combining Bayesian Personalized Ranking (BPR) with deep item-based collaborative filtering for book recommendations. This approach leverages user preferences and item features to provide accurate recommendations, enhancing individual reading experiences and boosting user satisfaction and engagement on platforms.
Chapter 6 presents an Advanced Fashion Recommendation System that uses deep learning to offer personalized style advice based on diverse body types. This technology tailors fashion recommendations to individual preferences, promoting self-expression, inclusivity, and a body-positive fashion culture.
Chapter 7 introduces a Deep Learning Model based on clustering to classify multiple attacks across various datasets. This innovative approach improves upon traditional intrusion detection systems (IDS), offering enhanced detection and mitigation of cyber threats. By leveraging deep learning, it strengthens businesses’ defenses, ensuring protection against evolving cyber threats.
Chapter 8 outlines a strategy that surpasses conventional diagnostics by using advanced classification algorithms and data augmentation for early heart failure diagnosis. This innovative approach could revolutionize cardiac care, enabling early interventions and improving the quality of life for those at risk of heart failure.
Chapter 9 presents a novel method using Teaching-Learning-Based Optimization (TLBO) for optimal power allocation in cognitive radio networks based on OFDM. This framework enhances spectrum utilization by dynamically allocating power, reducing interference, and boosting network performance in wireless environments. It addresses spectrum scarcity and aims to make wireless communication networks more resilient and flexible.
Chapter 10 introduces a hybrid methodology combining Natural Language Processing (NLP) and Historical Pattern Matching for stock market analysis. By merging predictive historical patterns with contextual insights from NLP, this approach offers investors actionable insights, helping them navigate volatile financial markets with improved decision-making.
Chapter 11 introduces an Intelligent Framework combining machine learning and fuzzy logic for IoT-based healthcare monitoring. This system offers real-time, flexible health data analysis, aiming to improve patient outcomes, optimize resources, and transform healthcare delivery in an era of personalized, proactive care.
Chapter 12 focuses on the Narrowband IoT Design Strategy, its reach, and security challenges. It explores the evolving threat landscape and aims to guide the development of effective security solutions, ensuring the robustness and integrity of NB-IoT networks in an era of critical data privacy and widespread connectivity.
Chapter 13 explores the transformative potential of machine learning in healthcare, focusing on using voice pattern analysis for precision diagnosis and continuous monitoring. This approach aims to enhance diagnostic accuracy, enable timely interventions, and improve patient outcomes by leveraging machine learning to revolutionize proactive, personalized healthcare.
Chapter 14 examines cloud computing security, focusing on mitigating cybersecurity risks with a cutting-edge transposition algorithm. As cloud systems become essential across industries, this research proposes a novel approach to threat detection and mitigation, aiming to strengthen cloud environments and build trust in their reliability and security.
Chapter 15 presents an innovative study using ensemble learning to assess children at risk of schizophrenia for performance issues. By evaluating prediction models, it aims to identify markers of susceptibility, advancing early detection and intervention strategies to improve outcomes and quality of life for affected individuals and their families.
Chapter 16 explores Advanced Aquaculture Management through a Smart System designed to optimize oxygen levels and monitor shrimp health. By integrating automation, data analytics, and IoT sensors, this system aims to enhance shrimp welfare, reduce environmental impact, and boost output, ensuring long-term sustainability and profitability in the growing global seafood industry.
Chapter 17 outlines the challenges of feeding a growing population amid environmental concerns, introducing the Farming Revolution. This approach combines precision farming and IoT technologies to optimize resource use, reduce environmental impact, and enhance production. It offers a sustainable path to meeting global food needs while protecting the planet for future generations.
Chapter 18 provides a comparative analysis of malaria parasite detection and classification using advanced machine learning techniques. By integrating cutting-edge methods with parasitology, the study aims to enhance diagnostic accuracy and offer strategies to save lives in malaria-endemic regions, contributing to the fight against this global health issue.
By exploring the transformative power of Industry 5.0, this volume highlights how cutting-edge technologies like AI, IoT, and machine learning are revolutionizing industries such as healthcare, agriculture, cybersecurity, and more. Through detailed analyses, case studies, and innovative methodologies, it provides readers with insights into optimizing systems for sustainability, precision, and improved outcomes, while addressing global challenges and advancing future-forward solutions across multiple sectors.
We are grateful to the contributing authors for their dedication and expertise, and we extend our thanks to the reviewers who have provided invaluable feedback throughout the preparation of this volume. Finally, we thank Martin Scrivener and Scrivener Publishing for their support and publication.
The EditorsNovember 2024
1Evolution of Industrial Revolution: Industry 5.0 and Beyond
S. Balamurugan1* and B. Surya2
1Intelligent Research Consultancy Services, Peelamedu, Coimbatore, Tamil Nadu, India
2V-C, GRG Matriculation Higher Secondary School, Peelamedu, Coimbatore, Tamil Nadu, India
Abstract
The evolution of the Industrial Revolution marks a transformative journey from mechanization in Industry 1.0 to the integration of cyber-physical systems in Industry 4.0. Each phase has progressively enhanced productivity, efficiency, and connectivity. Industry 5.0 represents the next leap forward, emphasizing human-centric innovation, sustainable development, and symbiotic collaboration between humans and advanced technologies like AI, robotics, and IoT. This paradigm shift aims to balance technological advancements with societal needs, prioritizing ethical considerations, environmental impact, and personalized manufacturing. As we move beyond Industry 5.0, the future envisions an era where technological and human elements harmoniously coexist, driving unprecedented levels of customization, resilience, and sustainability in industrial processes.
Keywords: Industrial revolution, Indus civilization, industry 5.0, productivity, manufacturing, globalization, robotics, drones
Brief History of Industrial Revolution
Industry 5.0 involved after many industries benefitted out of Industry 4.0 in the past decade. Modern industrial revolution dates back to centuries, where the first industrial revolution was established in the 18thcentury with establishment of machineries. Development of machineries dates back to the very early urban culture, Indus Civilisation, which lasted during the period 3300 BCE – 1300 BCE. Excavation shows the evidence of development in materials and metals such as bronze, silver, copper, iron, lead, baked bricks with which well-planned houses, well, ziggurat and markets were built. Alloys of metals found during excavation forms the bases for machinery development which leads to Industry 1.0, a transformation from handicrafts to machines. The industrial revolution was during 1871 to 1914 was Industry 2.0 where assembly lines were established for faster transportation. Automation and computer systems were established during Industry 3.0 which was succeed by Industry 4.0 Digitization, where advanced technologies such as Cloud Computing, IoT, Artificial Intelligence, 3D Printing etc. were adopted. With the need for mass customization and cultural collaboration, the next industrial revolution industry 5.0 evolved. Figure 1.1 shows the evidence of usage of bricks and metals during Indus civilization.
The First Industrial Revolution, often referred to as “Industry of Steam”, had its origin in the 18th century with the adaptation steam engines and production technology. Productivity was increased rapidly with the usage of steam and coal-powered machineries. Few inventions of industry 1.0 include cloth weaving machinery and patenting of cotton gin in 1794.
The Second Industrial Revolution began in the early 19th century with the discovery and adaption of electrical engineering and petroleum. Usually referred to as “The Technological Revolution”, Industry 5.0 was marked by the invention and adaption of assembly line production mechanisms. The idea of mass production was put forward by Henry Ford at Chicago, which drastically improved the automobile production process. Establishment of conveyor belts often resulted in faster production at reduced cost. Adaption of assembly line production also led to development of new industries in chemical and telecommunication technologies.
Figure 1.1 Model - Evidence for usage of bricks and metals during Indus civilization.
Industry 3.0, which usually referred to as “First Computer Era” or “The Digital Revolution” began in 1970s with the usage of electronics for advanced automation in production systems. Memory-programmable controls such as Programmable Logic Controllers (PLC) were used to achieve partial automation, however relying partially on human intervention to in manufacturing process. Internet and renewable energy technologies were introduced during third industrial revolution impacting manufacturing and globalization.
The Fourth Industrial Revolution, the era of “Digitization” was first formally introduced in 2015 by Klaus Schwab, Executive Chairman of the World Economic Forum. Intelligent Machines were adapted for production, where actions, information exchange, and controls were performed without human intervention. With the peak in automation, smart factories came into existence with high efficiency and optimal resource utilization. Advances in smart factories led to precise prediction of failures and selfplanned logistics for easy maintenance and production. Some of the key technologies of Industry 4.0 are Internet-of-Things, Artificial Intelligence, Cyber-Physical Systems, Cloud Computing, Machine Learning, Robotics, Data Analytics, 3D Printing, Drones, and Smart Manufacturing.
Industry 5.0 focusses on synergy of humans and machines to collaborate for optimal output. Creativity required for the task is addressed by humans, whereas repetitive tasks will be handled by robots, thereby putting human hands and minds to automation framework. With the rise of COVID-19 Pandemic, 2020 is a year, where the need for Industry 5.0 emerged. With huge impact of COID-19 and challenges faced, led to the need for sustained collaboration of human and robots in various sectors such as healthcare, food industry, manufacturing, biotechnology, environmental engineering and many more. Advances in Human-Computer Interaction (HCI), led to further enhancements in synergy of human and machines in virtual reality gaming, wearable sensors, smart textiles, smart additive manufacturing and personalization. Further developments in Industry 5.0 are expected in developing bioeconomy, which ensures optimal usage of biological resources for manufacturing, thereby producing renewable resources and maintaining perfect balance between economy, industry, and ecology. Developing emotionally intelligent machines in the form of bots and avatars is a possible speculation on further developments in Industry 5.0.
Acknowledgement
B. Surya expresses his profound gratitude and sincere thanks to the Management, the Principal and the Teachers of GRG Matriculation Higher Secondary School, Coimbatore for their valuable guidance and encouragement rendered for developing the model successfully. He extends his gratitude to his father Dr. S. Balamurugan, his mother Mrs. S. Charanyaa and his brother B. Dhayaa for their support in constructing the model.
Bibliography and Further Reading
Mathur, A., Dabas, A., Sharma, N., Evolution From Industry 1.0 to Industry 5.0, in:
2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)
, 2022.
Chitra, R., Maheshwari, U., Maheshwari, S., Standard V (Term II, Volume 2)- Social Science, in:
State Council of Educational Research and Training
, Government of Tamilnadu, 2019.
https://knowledgezone.co.in/posts/63524aa1d1dd94b23dd39ddb#google_vignette
.
https://liquidtool.com/en/liquidpedia/leading-edge/
.
https://www.futureelectronics.com/blog/article/from-industry-1-to-5-where-we-stand-and-where-were-going/
.
https://www.asdc.org.in/blog/Evolution-of-Industry-from-Industry-1-to-Industry-4-&-Industry-5-From-Craft-to-Digital-Manufacturing-to-Digital-Society
.
https://supplychaingamechanger.com/the-industrial-revolution-from-industry-1-0-to-industry-5-0/
.
https://www.desouttertools.com/your-industry/news/503/industrial-revolution-from-industry-1-0-to-industry-4-0
.
Note
*
Corresponding author
:
2Personalized Healthcare Transformation via Novel Era of Artificial Intelligence-Based Heuristic Concept
S. Pradeep, R. Sathish Kumar, M. Jagadesh and A. Karthikeyan*
Department of ECE, SNS College of Technology Coimbatore, Tamil Nadu, India
Abstract
The information from novel modalities, particularly genomics plus imaging, as well as alternative sources like sensors and the IoT has ushered medicine into the digital world. We are creating therapeutic targets to provide more individualized treatments as we learn more about the anatomy of illnesses and how they impact a particular individual. To enable forecasts for individualized therapies, innovations including AI are required. We will require solving concerns like liability, explainability, and confidentiality in order to popularize AI in healthcare. Among of the answers that might assist allay these worries include the development of explicable methods and the integration of AI expertise in medical training. Hence, this chapter plans to perform the personalized healthcare transformation through the novel world of AI-based heuristic concept. Initially, the digitization, data sources, and AI in healthcare are analyzed. Additionally, the mainstreaming of AI in healthcare revealing the challenges as well as methods to handle the challenges is briefly discussed. Moreover, the current status, integration, and obstacles to the personalized healthcare transformation usage are also investigated. Further, in the final step, the confidentiality of the patient healthcare records is maintained by the novel MSOM, in which the parameter optimization of SOM is accomplished by the nature inspired meta heuristic algorithm referred as TOA. This developed MSOM-TOA proves the superior ability in transforming the personalized healthcare with respect to the confidentiality of the patient healthcare records by comparing it with conventional approaches with consideration of numerous analyses.
Keywords: Personalized healthcare transformation, artificial intelligence, modified self-organizing maps, teamwork optimization algorithm
Nomenclature
Abbreviation
Description
IoT
Internet of Things
DSS
Decision Support Systems
AI
Artificial Intelligence
ICT
Information and Communication Technology
MSOM
Modified Self Organizing Maps
CPWs
Clinical PathWays
TOA
Teamwork Optimization Algorithm
LPA
Lifelogging Physical Activity
PHP
Personalized Healthcare Pathway
IUs
Irregular Uncertainties
QoS
Quality of Service
LPAV
Lifelogging Physical Activity Validation
CPHS
Comprehensive Personalized Healthcare Services
UTI
Urinary Tract Infection
HIoT
Healthcare Internet of Things
BS
Base Station
POCHT
Point-Of-Care Healthcare Technology
IoMT
Internet of Medical Things
DE
Discovery Engine
POC
Point-Of-Care
FPR
False Positive Rate
EHR
Electronic Health Record
NLP
Natural Language Processing
HITECH
Health Information Technology for Economic and Clinical Health
NNs
Neural Networks
US
United States
FDA
Food and Drug Administration
GDP
Gross Domestic Product
PMC
Personalized Medicine Coalition
PPACA
Patient Protection and Affordable Care Act
HNPCC
Hereditary Non Polyposis Colorectal Cancer
IT
Information Technology
MMR
Major Mismatch Repair
HIT
Health Information Technology
IHC
Immunohistochemistry
NCHPEG
National Coalition for Health Professional Education in Genetics
MSI
Micro Satellite Instability
GOA
Grasshopper Optimization Algorithm
WOA
Whale Optimization Algorithm
RDA
Red Deer Algorithm
SFO
Sun Flower Optimization
2.1 Introduction
Providing medical therapy and clinical management that is specifically tailored to the unique features of every patient is known as personalized healthcare [1]. A patient’s genomic and genetic composition, environment, family medical history, culture, health-related activities, and beliefs are all taken into account to provide a comprehensive health image that may be utilized to tailor therapy [2]. The utilization of testing to identify the genes and gene combinations that may accurately forecast a people’s reaction to a particular medication is a further degree of personalization, sometimes known as personalized medicine [3]. The health industry is being led by the overall increase in healthcare expenditures and the swiftly rising preference [4]. The simultaneous desire for improved treatment nature and lower costs defines one among the major commercial and scientific problems facing contemporary healthcare services.
Many studies on the basis of the principles of personalized healthcare have been conducted recently [5]. In particular, for various illnesses, personalized patient-centered healthcare systems, with greater personalized therapy, via the use of established diagnostic practice, might offer an alternative [6]. The creation and enhancement of telemedicine infrastructures and DSS for the detection, medication, and handling of various ailments are made feasible by developments in ICT [7]. E-health systems may serve as the building blocks for the implementation of effective and cutting-edge healthcare methods and practices that regard health as a constant cycle and need that the person’s participation and the related required behaviors be made crystal apparent [8].
Therapeutic customization necessitates that primary health procedures, illness management recommendations, therapeutic plans, and follow-ups now be modified to the specific circumstances and medical state of the persistent client [9]. Since the 1980s, CPWs have been effective tools for directing evidence-oriented healthcare [10]. Worldwide implementation of CPWs has led to their usage as paradigms, procedures, or recommendations for defining the treatment plans for patients. They define specific local frameworks and timelines, point out significant occurrences like clinical examinations and evaluations, and provide suggestions [11].
Healthcare workers nowadays must deal with several technology developments and a lot of information [12]. Information from regenerative therapies, heart rhythm monitoring, lab testing, genetic testing, medical imaging, and every piece of information that has been collected in electronic health records are overwhelming for nurses and doctors [13]. The particular problem of collecting this information and utilizing it to arrive at a knowledgeable and tailored selection has not yet been solved [14]. AI and other emerging innovations may be utilized to address these issues since they have the inherent capacity to draw conclusions from vast volumes of information gathered from an array of resources [15].
The paper contribution is.
To perform the personalized healthcare transformation through the novel world of AI-based heuristic concept.
To analyses the digitization, data sources, and AI in healthcare initially.
To discuss the mainstreaming of AI in healthcare revealing the challenges as well as methods to handle the challenges.
To investigate the current status, integration and obstacles to the personalized healthcare transformation usage.
To maintain the confidentiality of the patient healthcare records by the novel MSOM, in which the parameter optimization of SOM is accomplished by the nature inspired meta heuristic algorithm referred as TOA.
To prove the superior ability in transforming the personalized healthcare with respect to the confidentiality of the patient healthcare records by comparing it with conventional approaches with consideration of numerous analysis.
The paper organization is. Section 2.1 is the introduction of personalized healthcare transformation. Section 2.2 is literature survey. Section 2.3 is digitization, data sources, and AI in healthcare. Section 2.4 is AI mainstreaming in healthcare. Section 2.5 is current status, integration, and obstacles to the usage of personalized healthcare transformation. Section 2.6 is prerequisites for radical transformation in healthcare. Section 2.7 is personalized healthcare transformation using MSOM-based TOA. Section 2.8 is results. Section 2.9 is conclusion.
2.2 Literature Survey
A. Related Works
In 2021, Khayal and Farid [16] have created an interactive analytical simulation approach to controlling personal healthcare findings and delivering individualized treatment. It makes use of Petri nets and a very innovative hetero-functional graph theory that may be best described as the ontological confluence of model-oriented systems engineering and networking research. The system’s characteristics are based on a newly formed network infrastructure for healthcare provision, which has many similarities to the design of surplus industrial processes. The method basically comprises two synchronized Petri nets, one for the development of a person’s health condition and the next for the system that provides healthcare.
The dynamic method incorporates a deterministic Petri net framework for the first and a fuzzy Petri net for the second to connect operational and clinical events. Two case reports research—acute and chronic—are used to illustrate the paradigm. When taken as a whole, the scenario study shows how the approach can be used to treat both acute as well as chronic diseases equitably, portrays healthcare findings honestly, and ties those results to the development of the health care methodology and its expenditures.
In 2018, Yang et al.[17] have chosen to investigate ways to enhance the quality of lifelogging information in an IoT connected medical framework by using LPA as an objective. For removing IUs and evaluating data dependability in remote health monitoring contexts, the rule-oriented adaptive LPAV framework LPAV-IoT was suggested. For the analysis of important elements affecting the authenticity of LPA, a technique was suggested that specifies four layers including three modules in LPAV-IoT. By exploratory studies, a set of validation criteria were developed with power levels and ambiguity threshold variables. A case study on the personalized healthcare network myhealthavatar, which connects three cutting-edge wearables and mobile apps, is conducted after LPAV-IoT. The findings show that, under specific IoT environmental circumstances, the criteria offered by LPAV-IoT facilitate effectively sorting at least 75% of IU and flexibly showing the credibility of LPA information.
In 2016, Fico et al.[18] have created a PHP for managing the diabetic condition in order to move away from organization-centered treatment and towards patient-centered treatment, and incorporated the PHP into an ICT system. To evaluate the technology, a small-scale experimental research was carried out. The initial findings provide insight into how the PHP affects system’s efficiency and outcome variables.
In 2018, Muhammed et al.[19] have brought out the UbeHealth paradigm for ubiquitous health, which uses deep learning, edge computing, HPC, big data, and the IoT to solve various issues. The platform’s three essential characteristics and four layers offer an improved network QoS. In order to improve data caching, data rates, and routing choices, the Cloudlet as well as network layers’ leverage big data, deep learning, underlying HPC to anticipate network traffic. In order to better address the communication needs of programs and to identify malicious traffic as well as abnormal data, development platforms of the traffic flows are categorized. To distinguish between the many types of information coming via identical software applications, clustering was utilized. The UbeHealth framework was created using the proof of concept model. The model specifications for the developed framework are gathered through a thorough assessment of the research. The architecture was detailed in depth, covering the way the three parts and four layers were implemented algorithmically. The UbeHealth system is assessed using three popular data sets.
In 2022, Taimoor and Rehman [20] have carried out a thorough study on individualized medical treatment. We specifically provide an introduction to the basic conditions for CPHS in the context of the modern HIoT, along with a description of personalization and a sample use case situation that serves as a benchmark for contemporary HIoT. Secondly, we investigated a basic three-layer framework for IoT-oriented healthcare frameworks employing both AI as well as non-AI-oriented techniques, taking into account important needs for CPHS accompanied by their advantages and disadvantages in the context of personalized healthcare services. Finally, we discussed various security risks to every tier of the IoT ecosystem as well as potential AI and non-AI-oriented remedies. Furthermore, we provide a technique for creating trustworthy, robust, and individualized health services that overcome the flaws of current methods.
In 2022, Subramanian et al.[21] have created an ER procedure that’s easier to use, effective, and flexible by employing a web camera to capture and analyze images in real time. Moreover, we suggest an end-to-end structure that integrated an ER network with a digital twin setup, allowing the projected outcome to be examined and analyzed prior to dispensing the finest healthcare for the individual before it develops into a life-threatening illness. While requiring minimal training time, the suggested ER method still produced good outcomes. Hence, real-time monitoring of a patient’s health status, early detection of life-threatening disorders, and obtaining the finest and major efficient therapies for patients during crises would be useful in healthcare facilities.
In 2018, Catherwood et al.[22] have demonstrated a cutting-edge point-of-care biofluid scanner for the IoT, as well as a LoRa/Bluetoothbased electronic reader for a biomedical strip-oriented diagnostics device for customized tracking. With the use of a reusable trial “key” and accompanying Android app, we conduct test scenarios (technological trials without human participants) to show the possibility of long-range analysis and create a diagnostic system appropriate for distant point-of-care testing for UTI. The 868 MHz LoRaWAN-based personalized monitor showed good promise, with UTI medical reports in every instance being accurately identified and sent to a distant secure cloud server. Radio path losses in the tests varied from 119 to 141 dB at distances of 1.1 to 6.0 km. All samples were successfully and securely transmitted to the BS, where they were routed to the secure server for review. The accuracy of the UTI test strips was checked visually for proper diagnosis on the basis of color variation. A reliable and user-friendly method of delivering next-generation community-oriented disease management and smart diagnostics to the advantage of clients and healthcare staff, according to the findings of experiments conducted along a variety of locations. Any sort of house or location may use this crucial stage, especially those without adequate landlines, internet connections, or cell signals. It delivers discounts on repeated outpatient clinics by persistently sick patients or periodic clinician home visits by bringing subscriber long-range biotelemetry to healthcare professionals. In order to inform the implementation of comparable methods in the future, this article discusses practical challenges in constructing an IoMT network.
In 2015, Dhawan et al.[23] have reported the roundtable discussion from the 2013 IEEE Engineering in Medicine and Biology POCHT Conference (POCHT 2013), which took place in Bangalore, India, from January 16 to 18. Due to the global rise in lifespans and transdisciplinary technological advancements in healthcare, medical advances has experienced rapid progress. The sustainability of constantly increasing healthcare expenditures, though, is also a major worldwide problem. With the aid of technological advancements throughout the full spectrum of POC to emergency medicine at clinics, preventative, customized, and personalized medicine must merge in order to deliver high-quality treatment at affordable rates. At the initial IEEE EMBS Special Topic POCHT conference, which was hosted in Bangalore, India, healthcare professionals, clinicians, researchers, business leaders, and educators from around the world came together to describe clinical requirements and technical expertise for commercial exploitation and transcription to medical applications throughout various infrastructures and environments. In order to improve global health, this article summarizes the conversations that took place throughout the headline addresses, roundtable discussions, and break meetings on the requirements, difficulties, and technological breakthroughs in POC techniques. An outline of the problems and developments in both developed as well as developing nations was also provided. This includes data on the top patient requirements, technological advancements in medical equipment, infrastructure support, transcriptional engineering, ICTs, and the acknowledgement of POC health technology by patients plus healthcare professionals.
In 2017, Yoon et al.[24] have introduced a revolutionary method called a DE, which identifies the patient features that were most important for diagnosing every patient correctly and/or providing the best course of therapy. We show the effectiveness of DE in two clinical contexts: breast cancer diagnosis and a tailored prescription for a particular treatment plan for breast cancer patients. Several patient characteristics were important for every unique therapeutic advice; DE may identify these various key features and utilize them to suggest customized clinical options. Concerning kappa coefficients for proposing the customized chemotherapy treatments, the DE technique outperforms current cutting-edge classification techniques by 16.6%. Concerning forecast error rate and FPR for diagnostic forecasts, the DE technique outperforms conventional prediction methods by 2.18% and 4.20%, correspondingly. We further show that the method’s effectiveness was unaffected by incomplete data and that clinical comparisons support the key characteristics that DE has uncovered.
In 2018, Alam et al.[25] have examined new healthcare solutions, taking into account the specific technological elements needed to realize a whole end-to-end system for every activity. From the standpoint of communication technology, the survey examines the important application-specific needs.
Also, a thorough examination of the traditional and forthcoming technologies and standards that would support these applications was provided, emphasizing the crucial importance of both short-range and long-range connections. The study also identifies significant open research obstacles and problems that are particularly pertinent to IoT future health services.
2.3 Digitization, Data Sources, and AI in Healthcare
B. Healthcare Digitization
A significant shift in healthcare is being brought about by the proliferation of information and data in healthcare, illnesses, and research that is starting to have an effect on the health sector. EHR implementation rose because of the HITECH Act of 2009, rising from 9.4% in 2008 to 83.8% in 2015 due to monetary incentives and stiffer penalties for HIPAA security and privacy policy violations.
Medical training is anticipated to increase each 73 days by 2020, together with computerized hospital data. To keep current, a physician ought to devote 29 hours every day learning novel medical information. In other terms, the amount of novel medical information being produced and released is currently beyond the capabilities of the human intellect and time availability.
We will be ready to more closely adapt hospital services in the digital age to the requirements of specific clients and tiny patient populations. To better understand how illnesses show itself and how clients feel them on a daily basis, more data will be collected, kept, and evaluated. This advancement, together with a greater grasp of molecular biology and novel diagnostic techniques, will drastically alter how we do study, produce, authorize, and spend for pharmaceuticals as well as how individuals and their doctors decide if, when, and how to cure their diseases.
C. New Data Sources
High-quality information may be obtained for every person from a variety of places as medical and scientific understanding about medical conditions advances, and this information is then linked to data from sizable client databases for research. We can better recognize illness anatomy and how it manifests in specific individuals because of this. Clients may now request novel as well as efficient therapies since they are more aware and educated. We are learning more about wellness and illness thanks to empirical information, molecular information derived from next-generation genomics, wearable technologies and mobile app information, and innovative clinical studies. In order to accommodate these cutting-edge methods of providing treatment, the regulatory structure must change and is doing so. No one person could access the ecology of digital wellness on their own. Novel kinds of collaborations are developing as a consequence to guarantee that we are heading beyond value-oriented, individualized medical safety.
D. AI in Healthcare
As healthcare becomes more digitized, innovations like AI can assist us in analyzing these massive volumes of information to generate understanding and support decision-making. AI in healthcare refers to the usage of sophisticated techniques and computer programs that mimic mental abilities in the interpretation of challenging medical information without explicit human involvement. AI has done significant strides in a number of areas, including NLP, Deep Learning, Machine Learning, Virtual Agents, Speech Recognition, and AI-optimized Hardware.
AI is currently being utilized in hospital environments, for instance, to lower the number of false-positive breast cancer test outcomes, lower the cost of health care information services, improve physician work process while easing and preventing exhaustion, perform surgical robots with less internal bleeding and hospitalization time, and anticipate the fatality rates of those suffering from severe heart problems. In the earlier, the client, considered to be the major crucial party in healthcare, had a wide range of illnesses that were all addressed with the similar medications, making doctors perplexed as to why certain patients responded to them while everyone else did not.
Figure 2.1 Diagrammatic model of AI in healthcare.
AI could indeed perform a major part in this procedure provided its advanced abilities of sensing delicate disease certain trends from a variety of sources, like diagnostic techniques, that people would never recognize. Presently, researchers have started to comprehend, aim, and recommend treatment on an individual basis. The AI in healthcare is diagrammatically shown in Figure 2.1.
2.4 AI Mainstreaming in Healthcare
E. AI Challenges
Explainability, privacy, liability, and responsibility represent the main issues with the usage of AI in healthcare. In addition, a thorough restructuring of the medical school structure for healthcare workers is required. The medical establishment is probably going to be opposed to AI systems since they are difficult to understand. Little can be said about methods that are more precise, like NNs. Healthcare workers find it challenging to become acclimated to operating with AI and having faith in the system because of the “black box” phenomena. In the conclusion, doctors must still determine their final choice, and failing to understand why they would reach that conclusion may cause even more problems if the incorrect prognosis is presented to a client. This requires that software engineers emphasize both interpretability and reliability.
Explainability would probably make it easier for products to be accepted by medical governmental organizations across the globe, including the US FDA, which is highlighted in recent papers governing AI. The problem of liability is another. When errors occur, who is to be held responsible? The application of AI in medicine is still not covered by any legal precedent. Worse still, it appears that the existing legal framework “incentivizes clinicians to downplay the possible role of AI” as they are only going to be held accountable for following the rules as they are. To define the responsibility of entire relevant parties, including software developers, healthcare practitioners, software businesses, hospitals, and data collectors, novel malpractice rules must be created.
Another problem with the usage of AI is confidentiality. Certain AI systems require enormous volumes of patient information to work effectively. Google, for instance, predicts the results of hospitalized patients utilizing 46 billion data points gathered from 216,221 people’ de-identified information over the course of 11 consecutive years from medical centers. This begs the concerns of how the information was gathered and if entire individuals were given an equal opportunity to determine how their information will be used. Finally, when people start to utilize tools such as the robot to recognize the advantages of AI, doctors must remain conscious of these devices’ limits and provide the client with the necessary level of treatment. They may require instruction on how to utilize these technologies to their advantage and reduce their stress. The different challenges being faced by the AI in healthcare is shown in the Figure 2.2 below.
F. Handling Challenges
To ensure that a doctor may correctly identify a client for a particular ailment, we require systems that may describe the why in order to address the primary problem with explainability. Rendering machine judgments clear, comprehensible, verifiable, and repeatable is the goal of the newly growing field of comprehensible AI. In order for a doctor as well as a robot working collaboratively to have the highest performance to enhance medical decision and client quality of care, the medical world has to be trained about these obstacles, how to handle them, and also develop rules and regulations. Nursing professionals, trainees, associates, and practicing doctors must be knowledgeable in AI, data sciences, EHR foundations, and AI-related legal and ethical concerns. These ought to be covered in the coursework of medical schools. It is advised to educate the health professional in stages during their voyage.
Figure 2.2 Challenges of AI in healthcare.
The American Medical Association’s Congressional delegation established its initial policy on healthcare information services in June 2018. This body is made up of proportionate delegations of each significant regional medical specialization organization and local health organizations. Recognizing possibilities for including practicing doctors’ perspectives into the creation, layout, verification, and application of healthcare AI was one among the suggestions.
Others involved promoting learning for patients, doctors, graduate doctors, various healthcare professionals, and health supervisors to assist them in comprehending the potential and constraints of healthcare AI.
2.5 Current Status, Integration, and Obstacles to the Usage of Personalized Healthcare Transformation
G. Personalized Healthcare’s Present Status
The positive aspects of individualized healthcare are still not completely understood by both doctors and patients in the current healthcare environment. Reform initiatives also frequently take a defensive instead of strategic view. Family background represents a reliable method for predicting illness risk, but in certain cases, genetic data can improve the accuracy of the risk assessment offered by family background. The current method of treating illnesses by experimentation and failure is expensive, but research course has the possibility of lowering expenses by improving the usage of screening procedures, providing advice on medical care on the basis of the findings of genetic tests, and pharmacological intervention.
Personalized healthcare case: seeking value: One should take into account how the current healthcare structure functions and its inadequacies with respect to delivery and price, its inaccuracy in the choice of treatments, and its incapacity to optimize results in order to completely understand the requirement to progress the implementation of personalized healthcare into the delivery of pharmaceuticals. The current structure of the American healthcare framework is costly, divided, complicated, and disease-directed rather than health- as well as wellness-directed. While the US GDP has grown by around 3% annually, the average yearly rate of healthcare spending is 6.1%. In all, healthcare costs currently account for 17.6% of GDP, 27% of government expenditures, and 28% of average family consumption expenditure, second only to property.
The requirement for quality compatible with the healthcare state’s significant proportion of the US industry may be addressed via personalized therapy. The recently founded Joint Select Committee on Deficit Reduction (established by the Budget Control Act of 2011), which is entrusted with fiscal consolidation of minimum $1.5 trillion over a ten-year period, will undoubtedly challenge the rise in medical costs.
The Patient Protection as well as Affordable Care Act, whose key component defines the development of interconnected medical systems that compensate for performance on the basis of quality, cost reduction, and customer experience, acknowledges the necessity of managing healthcare costs. The law was passed in an effort to change nursing in a number of manners and improve its sustainability. The PPACA aims to reduce dissociation by increasing the usage of IT to reorganize the service system and avoid errors, moving away from volume-oriented incentives towards incentives on the basis of performance and results, and satisfying efficient care delivery strategies and positive patient outcomes.
Reactive to proactive shift: With the concept that effectiveness correlates to reduced cost and superior treatment of patients, the foundation underlying customized healthcare seems to be the promise for more effective healthcare. Real healthcare legislation transforms present healthcare concepts from the practice of resistant pharmaceuticals to the practice of assertive pharmaceuticals, where the techniques of personalized healthcare (i.e., genomics, genetics, and various molecular diagnostics) facilitate not only superior care but also fewer expensive treatments. The individual insurance regulations are most frequently mentioned in the sense of enhancing care access.
