Smart Factories for Industry 5.0 Transformation (eBook)

R. Nidhya, Manish Kumar, S. Karthik, Rishabh Anand, S. Balamurugan (Herausgeber)

eBook Download: EPUB

2025
506 Seiten
Wiley-Scrivener (Verlag)
978-1-394-20044-3 (ISBN)

Infos

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.

R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering at the Madanapalle Institute of Technology & Science, affiliated with Jawaharlal Nehru Technical University, Anantapuram, India. Her research interests include wireless body area networks, machine learning, and IoT.

Manish Kumar, PhD, is an assistant professor in the Department of Computer Science & Engineering at the Thapar Institute of Engineering and Technology, Patiala, Punjab, India. His research interests include soft computing applications for bioinformatics problems and computational intelligence.

S. Karthik, PhD, is a professor and dean in the Department of Computer Science & Engineering at SNS College of Technology, Coimbatore, Tamil Nadu, India. His research interests include network security, web services, and wireless systems.

Rishabh Anand, PhD, is a Global Service Delivery Manager with HCL Technologies Ltd. He earned his MBA in 2020 and is a certified DevOps project manager.

S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 50+ books, 200+ international journals/conferences, and 35 patents.

Textauszug

2
Personalized 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...

Produkt-Details

• Erscheint lt. Verlag: 27.1.2025
• Reihe/Serie: Industry 5.0 Transformation Applications
• Sprache: englisch
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Maschinenbau
• ISBN-10: 1-394-20044-7 / 1394200447
• ISBN-13: 978-1-394-20044-3 / 9781394200443