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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Industry 5.0 Transformation Applications
- Sprache: Englisch
The book provides invaluable insights into cutting-edge advancements across multiple sectors of Society 5.0, where contemporary concepts and interdisciplinary applications empower you to understand and engage with the transformative technologies shaping our future.
Distributed Time-Sensitive Systems offers a comprehensive array of pioneering advancements across various sectors within Society 5.0, underpinned by cutting-edge technological innovations. This volume delivers an exhaustive selection of contemporary concepts, practical applications, and groundbreaking implementations that stand to enhance diverse facets of societal life. The chapters encompass detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, and voice and gesture recognition and feature interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure, and advanced agricultural practices.
The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems in the factory systems of the future. This revolution strongly affects industrial smart and distributed measurement systems, pointing to more integrated and intelligent equipment devoted to deriving accurate measurements. This volume explores critical cybersecurity analysis and future research directions for the Internet of Things, addressing security goals and solutions for IoT use cases. The interdisciplinary nature and focus on pioneering advancements in distributed time-sensitive systems across various sectors within Society 5.0 make this thematic volume a unique and valuable contribution to the current research landscape.
Audience
Researchers, engineers, and computer scientists working with integrations for industry in Society 5.0
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Seitenzahl: 549
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Dedication
Preface
Acknowledgement
1 Analytical Survey of AI Data Analysis Techniques
1.1 Introduction
1.2 Survey on Various AI Techniques in Multiple Data Inputs
1.3 Conclusion
References
2 Heart Rate Prediction Analysis Using ML and DL: A Review of Existing Models and Future Directions
2.1 Introduction
2.2 Literature Review
2.3 Applications of Machine Learning (ML) and Deep Learning (DL) Model
2.4 Conclusions and Future Perspective
References
3 Implementation of High Speed Adders for Image Blending Applications
3.1 Introduction
3.2 Area and Delay Analysis of Addition Algorithm
3.3 Design of High Speed Adder
3.4 Results and Discussion
3.5 FPGA Implementation in Digital Image Processing
3.6 Conclusion
References
4 Smart Factories and Energy Efficiency in Industry 4.0
4.1 Introduction
4.2 Industry 4.0: Concepts and Technologies
4.3 Energy Efficiency in Manufacturing
4.4 Integration of Energy Management Systems in Smart Factories
4.5 Energy Monitoring and Optimization in Smart Factories
4.6 Intelligent Control Systems for Energy Efficiency
4.7 Energy Storage and Renewable Energy Integration
4.8 Smart Grid Integration and Demand Response
4.9 Case Studies and Best Practices
4.10 Challenges and Future Directions
4.11 Conclusion
References
5 AI in Computer Vision with Emerging Techniques and Their Scope
5.1 Brief Introduction of Computer Vision
5.2 A Pictorial Summary of Image Formation
5.3 Sampling and Aliasing
5.4 Feature Detection
5.5 Image Segmentation
5.6 Computational Photography
5.7 Recognition
5.8 Visual Tracking of the Object
5.9 Conclusion
References
6 Revolutionizing Car Manufacturing the Power of Intelligent Robotic Process Automation
6.1 Introduction
6.2 Literature Survey
6.3 Exploratory Analysis
6.4 The Manufacturing Process in India
6.5 Degree of Integration for Using Robotic Process Automation Automotive Sector
6.6 Complexities and Solution to Integrate AI in Current RPA
6.7 What Next in Indian Car Manufacturing?
6.8 Conclusion
References
7 Industry 5.0 and Artificial Intelligence: A Match Made in Technology Heaven? Unleashing the Potential of Artificial Intelligence in Industry 5.0
7.1 Introduction
7.2 Review of Literature
7.3 Research Model of How AI Works in Industry 5.0
7.4 Smart Factories and Manufacturing Processes
7.5 Outcomes of AI in Industry 5.0
7.6 Challenges of Industry 5.0
7.7 Conclusion
References
8 A VLSI-Based Multi-Level ECG Compression Scheme with RL and VL Encoding
8.1 Introduction
8.2 Literature Survey
8.3 Proposed System
8.4 Proposed Multi-Level ECG Compression Architecture
8.5 Results and Analysis
8.6 Conclusion
References
9 Using Reinforcement Learning in Unity Environments for Training AI-Agent
9.1 Introduction
9.2 Literature Review
9.3 Machine Learning
9.4 Unity
9.5 Reinforcement Learning and Supervised Learning
9.6 Proposed Model
9.7 Markov Decision Process
9.8 Model Based RL
9.9 Experimental Results
9.10 Conclusion
References
10 A Review of Digital Transformation and Sustainable International Agricultural Businesses in Africa
10.1 Introduction
10.2 Methodology
10.3 Findings
10.4 Recommendations
10.5 Conclusion
References
11 Developing a Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context: A Case of Zimbabwe
11.1 Introduction
11.2 Background and Context
11.3 Methodology
11.4 Literature Review
11.5 Empirical Data
11.6 Discussion
11.7 A Framework for Harnessing Disruptive Emerging Technologies in Health for Society 5.0 in a Developing Context
11.8 Conclusions and Recommendations
References
12 IT Innovation: Driving Digital Transformation
12.1 Introduction
12.2 The IT Innovation Ecosystem
12.3 Types of IT Innovations
12.4 IT Innovation Frameworks
12.5 Challenges and Risks of IT Innovation
12.6 Case Study: Uber Disrupting the Transportation Industry with Innovative Technology
12.7 Future Directions of IT Innovation
References
13 Strategic Convergence of Advanced Technologies in Modern Warfare
13.1 Introduction
13.2 Quantum Computing and Cryptography
13.3 Blockchain Technology in Military Operations
13.4 Case-Studies and Real-World Applications
13.5 Challenges and Risks
13.6 Conclusion
References
Index
Also of Interest
Check out these published and forthcoming titles in the “Industry 5.0 Transformation Applications” series from Scrivener Publishing
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Area, power and delay comparison of CSLA, CLA, KS adders.
Table 3.2 Dynamic power dissipation of CSLA, CLA, and KS adder designs against...
Table 3.3 Power comparison of adders at each stage.
Chapter 5
Table 5.1 Picture databases for recognition have been modified and extended. S...
Chapter 9
Table 9.1 Comparison of supervised, unsupervised and reinforcement ML.
Table 9.2 Theoretical comparison of RL algorithms.
Table 9.3 Contrast between unreal engine and unity engine.
Chapter 10
Table 10.1 Most appearing key words.
Table 10.2 Least appearing key words.
List of Illustrations
Chapter 1
Figure 1.1 AI use cases.
Figure 1.2 AI techniques.
Chapter 2
Figure 2.1 Models under study.
Chapter 3
Figure 3.1 Buffer element.
Figure 3.2 Processing element.
Figure 3.3 Combination of P and G signal.
Figure 3.4 8-bit kogge stone adder.
Figure 3.5 Block level architecture of CSLA.
Figure 3.6 Block diagram of CLA for n-8.
Figure 3.7 Full adder at stage i with Pi and Gi shown.
Figure 3.8 Carry computation in 4-bit KS adder.
Figure 3.9 Block level architecture of KS adder.
Figure 3.10 RTL schematic (a) CSLA (b) CLA (c) KS adder.
Figure 3.11 Simulation outputs of (a) CSLA (b) CLA (c) KS adder.
Figure 3.12 Physical layout of (a) CSLA (b) CLA (c) KS adder.
Figure 3.13 Power dissipation compared to standard design.
Figure 3.14 Block diagram of image blending.
Figure 3.15 Simulink model of image blending.
Figure 3.16 (a)-(b) Input images (c)-(e) output images processed by image blen...
Figure 3.17 FPGA implementation of image blending.
Chapter 5
Figure 5.1 Brief history of computer vision technology. (Self Drawn).
Figure 5.2 A pattern along with the relationship between pictures, geometry, a...
Figure 5.3 A pictorial summary of image segmentation ref. [7–12].
Figure 5.4 Several elements involved in the creation of an image include: (a) ...
Figure 5.5 (a) Scattering of light on the curve surface; (b) BRDF function is ...
Figure 5.6 The description of a two-dimensional linear equation and a three-di...
Figure 5.7 Projection models that are frequently employed include: The represe...
Figure 5.8 Photon can be calculated from a plane at a
z
0
focal length where ....
Figure 5.9 A combination of the additive colors red, green, and blue will resu...
Figure 5.10 Representation of Shannon’s sampling theorem shows that the minimu...
Figure 5.11 To analyze, characterize, and match images, a number of feature ex...
Figure 5.12 The degree of darkness at the margin’s correlates with the number ...
Figure 5.13 Real-world vanishing points: (a) architecture (b) furniture (c) ca...
Figure 5.14 Techniques imagery classification [32].
Figure 5.15 (a) the scissors travel an orange route across the component’s bou...
Figure 5.16 Block diagram of the sequencing of image sensing (a) represent dif...
Figure 5.17 Correction of vignetting in a single image [40].
Figure 5.18 A measurement pattern that can be used to estimate both the PSF an...
Figure 5.19 An overview of the bidirectional filter-based regional frequency t...
Chapter 6
Figure 6.1 Integration of robotic process automation with AI.
Figure 6.2 Degree of automation on shop floor for AI technologies.
Figure 6.3 Cost and maintenance for AI based robots.
Figure 6.4 Readiness to integrate new disruptive technologies.
Figure 6.5 Presence of self-configurable robots.
Figure 6.6 Survey findings.
Figure 6.7 Deployment of AI in automotive industry.
Chapter 7
Figure 7.1 The timeline of the industrial revolution.
Figure 7.2 The 3 pillars of Industry 5.0– human-centric, resilient and sustain...
Figure 7.3 Model showing the key enabling technologies and tools of AI used in...
Chapter 8
Figure 8.1 Flowchart of proposed lossless compression scheme.
Chapter 9
Figure 9.1 Different types of machine learning techniques.
Figure 9.2 Overview of an environment.
Figure 9.3 Selecting the object in unity.
Figure 9.4 Flowchart of the proposed model.
Figure 9.5 Creating a new scene for the environment (PushBlock).
Figure 9.6 Creating a goal area (in green).
Figure 9.7 The ML model starts from the vector_observation node.
Figure 9.8 The model ends at action and concat nodes.
Figure 9.9 The ML model starts from vector_observation, recurrent_in and prev_...
Figure 9.10 The model ends at action and concat nodes.
Figure 9.11 Overview of moving space of the area.
Figure 9.12 Screenshot of the agent and the object interacting.
Figure 9.13 Screenshot of the multiple scenarios used for training.
Figure 9.14 Screenshot of the player controlled PushBlock.
Figure 9.15 Overview of the hallway environment.
Figure 9.16 Screenshot of the agent and the target images.
Figure 9.17 Overview of training the agent in multiple scenarios.
Figure 9.18 Screenshot of the player-controlled hallway.
Chapter 10
Figure 10.1 Distribution of publication output on the topic over the years. So...
Figure 10.2 Distribution of citations of literature in dimensions over the yea...
Figure 10.3 Sources of literature contributing the most to the topic. Source: ...
Figure 10.4 Authors of literature contributing the most to the topic.
Figure 10.5 Word cloud. Source: VOSviewer.
Figure 10.6 Citations across countries. Source: VOSviewer.
Figure 10.7 Map of citations. Source: VOSviewer.
Chapter 11
Figure 11.1 Map of sub-Saharan Africa.
Figure 11.2 Information systems research framework. Source: Hevner
et al.,
201...
Figure 11.3 The proposed framework.
Chapter 12
Figure 12.1 The areas of digital transformation.
Chapter 13
Figure 13.1 Working of QKD (Quantum Key Distribution).
Figure 13.2 RSA (Rivest, Shamir, Adleman) encryption.
Figure 13.3 Blockchain technology in supply chain management.
Figure 13.4 Working of smart contracts.
Figure 13.5 Military predator drone.
Figure 13.6 USS Vincennes with its Aegis Combat system.
Figure 13.7 Distribution of Stuxnet worm.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Acknowledgements
Preface
Acknowledgements
Begin Reading
Index
Also of Interest
Wiley End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Industry 5.0 Transformation Applications
Series Editor: 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])
Distributed Time-Sensitive Systems
Edited by
Tanupriya Choudhury
School of Computer Sciences (SoCS), University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India
Rahul Kumar Singh
University of Petroleum and Energy Studies, Dehradun, India
Ravi Tomar
Ravi Tomar, Persistent Systems, India
S. Balamurugan
Intelligent Research Consultancy Services (IRCS), Coimbatore, Tamil Nadu, India
and
J. C. Patni
Department of CSE, Alliance School of Advance Computing, Alliance University Bengaluru, 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.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-19772-9
Front cover image courtesy of Adobe FireflyCover design by Russell Richardson
Dedication
The editorial team extends their heartfelt dedication of this volume to the valiant Indian Army, whose unwavering commitment, sacrifices, and outstanding service to our beloved nation, India, stand as a beacon of honor. Furthermore, it is with deep affection that they wish to acknowledge their families—parents, spouses, and children—for the boundless support they have provided throughout the creation of this work.
In addition to these tributes, the editors express sincere gratitude towards their colleagues at their esteemed institution for consistently offering love, blessings, and encouragement. Lastly, this book is also devoted with respect to the entire research community whose collective efforts enrich our understanding of the world.
Preface
The central focus of “Distributed Time-Sensitive Systems” is to introduce a wide-ranging series of innovative breakthroughs across multiple domains within Society 5.0, driven by state-of-the-art technological advancements. This publication aspires to provide an extensive collection of modern theories, practical uses, and revolutionary implementations that promise to improve various aspects of societal existence.
This compilation encompasses an extensive selection of scholarly works brimming with detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, as well as voice and gesture recognition among other pertinent areas. The text features interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure and advanced agricultural practices.
Authored by authorities in their respective disciplines mentioned above, each contribution was subjected to meticulous scrutiny for quality assurance. Designed primarily for scholars and academic professionals in pursuit of novel paradigms, methodologies and instruments; this publication seeks to serve as a catalyst for expanding the horizons in research related to disruptive technologies shaping Society 5.0.
In essence, the driving ambition behind this work is the aggregation and dissemination of collective knowledge pertaining to revolutionary technologies that are defining our progression towards a more interconnected and intelligent society.
In chapter 1, the author discusses AI data analysis techniques. Artificial intelligence algorithms are used to automate jobs that were previously completed by humans. The main reason artificial intelligence (AI) is used in every aspect and industry is the intelligence it has provided through continuous learning from regularly trained models. The different hardware and software resources required for processing and analyzing large volumes of data is discussed in this chapter. In chapter 2, the author discusses heart rate prediction analysis using ML & DL techniques. In the process of the study, the author investigated several powerful data-driven ML and DL models.
In Chapter 3, the author discusses the implementation of high-speed adders for image blending applications. The adder is a core component of the central processing unit (CPU), Digital Signal Processors, image de-noising filters. The performance of the system as a whole depends on tradeoff between power dissipation, area and speed of the adder used. Chapter 4 discussed smart factories and energy efficiency in industry 4.0. Industry 4.0 has completely changed the way that production is done, resulting in the creation of smart factories that combine cutting-edge technology with data-driven systems. By utilizing the power of automation, connection, and intelligent decision-making, these smart factories seek to improve production, efficiency, and sustainability.
In Chapter 5, the author discusses computer vision with emerging techniques and their scope in AI. Through the multidisciplinary field of computer vision, computers are able to see, understand, and interpret their visual surroundings. It’s commonly referred to as an artificial intelligence (AI) subfield. This field comprehensively processes, analyzes, and comprehends many picture types as well as high-dimensional data from the real world to offer numerical and figurative information. Power of intelligent robotic process automation is discussed in Chapter 6. This chapter examines the Industrial Research and Patent Administration (IRPA) and its potential to revolutionize the car manufacturing industry.
In chapter 7, the author discusses unleashing the potential of artificial intelligence in industry 5.0. The author also discussed the challenges and opportunities associated with AI adoption in Industry 5.0, including issues related to data privacy, cybersecurity, and job displacement. A VLSI based multi-level ECG compression scheme with run and variable length encoding for wearable sensor node techniques are discussed in chapter 8.
Chapter 9 demonstrates how machine learning and artificial intelligence techniques can be used to deploy a single AI-agent to carry out a variety of tasks in various settings. An intelligent AI-agent that can carry out a variety of tasks in a virtual environment is created using reinforcement learning. In Chapter 10, the author discusses digital transformation and sustainable international agricultural businesses in Africa.
In order to address the problems in a developing environment, Chapter 11 analyzes the healthcare issues in Sub-Saharan Africa and evaluates the potential for introducing disruptive emerging technology into the industry. In chapter 12, the author discusses IT innovation: Driving digital transformation and defined IT innovation as the inception and application of novel technologies, strategies, and processes within the realm of information technology. Strategic convergence of advanced technologies in modern warfare is discussed in chapter 13.
We hope that our efforts are appreciated and the reader benefits from this book.
Editor(s):Dr. Tanupriya Choudhury
Professor, School of Computer Sciences (SoCS), University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India
Dr. Rahul Kumar Singh
Assistant Professor (SG); SoCS, University of Petroleum and Energy Studies, Dehradun, India
Dr. S. Balamurugan
Director-Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamil Nadu, India
Dr. J. C. Patni
Professor, Department of CSE, Alliance School of Advance Computing, Alliance University Bengaluru, India
Acknowledgement
The editorial team extends heartfelt gratitude to their institution for providing an encouraging research atmosphere that laid the groundwork for this proposal. We are deeply appreciative of the diverse group of contributors from various nations, and we offer special thanks to reviewers across the world who have diligently scrutinized each chapter to uphold the book’s high standards. Their insightful feedback has been indispensable. Our sincere appreciation goes out to all involved parties for their dedication and readiness to undertake tasks that stretched them beyond their usual scopes of comfort. We eagerly anticipate reuniting with you in the forthcoming edition of our publication.
1Analytical Survey of AI Data Analysis Techniques
Divyansh Singhal1, Roohi Sille1*, Tanupriya Choudhury2, Thinagaran Perumal3 and Ashutosh Sharma4
1SOCS, UPES, Dehradun, India
2School of Computer Sciences (SoCS), University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India
3Department of Computer Science, University Putra, Selangor, Malaysia
4Henan University of Science and Technology, Henan, China
Abstract
The tasks carried out by humans are automated using artificial intelligence algorithms. The intelligence that AI has given through continuous learning from regularly trained models is the primary reason it is employed in every element and sector. Understanding and analyzing data is the most crucial and difficult task, if there is a lot of it. Data science is also popular right now and deals with complicated problems analytically. The data are broken up into smaller pieces, so that the trends and behaviors may be understood. The handling of vast amounts of data is the main challenge in data science.
The processing of enormous amounts of data using various AI algorithms is a subject of intense investigation. The great compute capacity, rapid processing speed, and very effective models required by the AI techniques used for processing enormous volumes of data are necessary to prevent errors in the management of the data.
In this study, the various hardware and software resources needed for the handling and analysis of massive amounts of data will be examined. This will also provide more detail on the work that various researchers have done in using AI models for data analytics. Within this chapter, a comparative examination of all the machine learning and deep learning models utilized for data analytics will be critically performed.
Keywords: Deep learning, data analytics, deep neural networks, artificial intelligence, data handling
1.1 Introduction
When discussing data analysis, one thing that becomes immediately clear is when it is crucial. This occurs when we have a large amount of data that we can use to improve our services and generate revenue for the business, or when we can extract critical information. Big Data analytics will now play a part. Why Big Data, you ask? Because it strives to enhance the decision-making process, big data isn’t only about tables and charts; it also refers to a variety of data, including communications, social media posts, and other examples. The most effective way to grow any industry is to increase the volume of clients, and this tactic proved to be just that [1]. Data, function, and design are integrated in digital video. For companies to display the data, DV is necessary. Different types of graphs or charts can be used to create this visualization. Gains and losses made by any company or organization will be easier to see; thanks to this visualization. Choosing the appropriate chart or graph is the main challenge in data visualization. Prior to presenting the data, decide on your objectives. Decide then, what information you require to accomplish your objective. Continuing with data collection, choosing the appropriate graph and data, and moving forward.
1.2 Survey on Various AI Techniques in Multiple Data Inputs
1.2.1 AI Techniques in E-Commerce
E-commerce is the topic of discussion in this section. So, let’s first look at what e-commerce is from that perspective. E-commerce is nothing more than moving stores online. With the aid of the internet, clients who once had to make a trip to a store to make a purchase may now do it with just one click and no effort. So, if we summarize the concept of e-commerce, it generally refers to a process in which two parties (vendors and customers) are involved and where the selling of products or services is accomplished through the use of the internet, or we may say digitally [2]. However, despite the fact that numerous research papers have been published on this topic across a variety of domains from the point of view of both parties, i.e., from the consumer and the vendor, there is still a gap that makes it challenging for researchers to draw a conclusion or gain knowledge on this subject. Integrating AI research with e-commerce is essential if we want to take the initiative in learning more, and offering a solid foundation on which to create and test new theories in the field of study [3–7].
The negotiation phase is one of the most crucial components in company. We always bargain over everything, from little things to huge things. For instance, if we buy fruits or vegetables, we always bargain with the vendor. We also bargain when two businesses are trying to come to an agreement. To maintain your consumer base in business, negotiation is a need. As is common knowledge, the majority of business is now conducted online, which is known as e-commerce. E-main commerce’s benefit is that it has made life easier for everyone involved, whether they are the buyer or the seller. In the past, negotiations took place face-to-face; however, as more businesses have gone online and those that can’t keep up with the demand for frequent online trading, automated businesses are something that will boost the effectiveness of e-commerce and allow customers to conduct transactions at the lowest possible cost or at any desired cost [8].
From the standpoint of the customer, we discussed the importance of negotiating in the paragraph above. Now, if we discuss the company’s perspective as to why they should implement negotiation in online commerce, or we can say why e-negotiation is vital, then this is the response. As a result of the rapid advancement of technology, businesses are increasingly engaged in business-to-consumer (B2C) transactions, where they are compelled to reinvent or re-innovate their services to maintain their current clientele and draw in new ones to expand their clientele. To maintain their business running smoothly, a company or organization basically needs to consider client happiness, and bargaining is one of those services. This makes “negotiating” a crucial topic to consider. The next question that emerges is how a business may offer its clients the best bargains,+ so that they have no reason to object to the amount the business is charging. As was already stated, the solution is straightforward and involves large data analysis. If one wonders why data analysis is necessary, the answer is that it will expedite the negotiation process for both the business and the client, which is advantageous to both [8].
Now let’s hunt for the parameter or parameters that will allow for successful e-negotiation using big data. The amount of data provided is the only parameter we need. The volume of information obtained must be enormous because this alone will determine the company’s success or failure. Since the business will learn the method, the client uses to contact them, they will be able to close the deal in the client’s best interests. Since the data we receive is in raw form, the most important aspect of B2C e-negotiation is the use of the proper algorithm to achieve the best outcomes. This data should be processed in the proper sequence before being analyzed.
In the recent years, negotiating has been thoroughly researched. One of the frequently used methods in trade, or you might say in negotiations, is artificial intelligence. Different AI methods are being created and used for training and research. Game theory, Bayesian networks, evolutionary computation, and distributed AI models are the techniques at play. Many negotiation models, however, have fallen short, making them unsuitable for actual electronic negotiations. The cause is the need for vast amounts of memory and high processing power for complex calculations, even though we know that there are more attributes to consider when making a judgement [8].
The biggest flaw with electronic negotiation models is that they base negotiations solely on price, although in reality, negotiations take into account various elements, including price, quantity, quality, market rate, and so on.
1.2.1.1 Benefits of Using AI in Ecommerce Companies
We used to purchase on the Amazon website, so it should come as no surprise that it employs AI to improve UX, logistics, and customer product selection. Better marketing and advertising, higher customer retention rates, and seamless automation are all advantages of employing AI in e-commerce.
Improved advertising and marketing, target people, who in the past, used to buy items based only on what a firm was promoting. However, because the time has changed, everyone wants to buy what they can and what they see. Any company that aims for long-term success must be aware of what each and every customer wants from them. They want to see anything related to them, as that will make them more likely to buy it. This means that rather than proceeding in a static fashion, they must make everything dynamic, allowing users to view what they want to see. Only then can we declare that the company is on the correct course. Everyone desires their own unique space in their lives, and the same is true here. Everyone wants to perceive things their own way, which is referred to as personalization.
Personalization is crucial, and each organization should focus their attention and efforts on it first. A small number of businesses have, nevertheless, successfully personalized their users’ experiences. The ability for users to customize their needs and what they really want and have the same supplied to them has been made possible by advancements in AI techniques and algorithms. Enhanced Customer Retention As previously indicated, if we distribute the material in accordance with the intended audience and personalize it as well, then a business may extract a sizable profit percentage from this strategy alone. Seamless automation Automation seeks to reduce human interaction to a minimum. Anything might be the case, including logbook updates. When tasks need to be completed frequently through automation, AI is crucial. People might benefit from AI by seeing similar content they would enjoy, receiving notifications about discounts on specific purchases, or by using historical data to provide answers to frequently asked questions.
1.2.1.2 AI Use Cases in E-Commerce
You are probably familiar already with the frequently use cases for AI in e-commerce, but we will go through a few of them here to dispel any doubt you might have, or ensure that you are not misinterpreting them.
Experience that is more personalized: Data processing and collecting for online client purchases are now easier than ever. Artificial intelligence is used to generate personalized product recommendations that are based on past consumer behavior and lookalike customers. Websites that make suggestions about things you might like based on past purchases look at your purchase history using machine learning. Retailers rely on machine learning to acquire data, analyze them, and apply them to create a customized experience, implement a marketing plan, optimize pricing, and provide customer insights.
With the usage of this AI technology, any business model can dynamically tailor items or advertisements to the client’s location by displaying special content to people in that region.
Another benefit of personalization is that it allows customers to discover products they didn’t know existed but that they actually need. By examining the present and the past, this strategy enables the taking of preventative actions.
Now that we are all aware of how important personalization is, it is important to remember that there is a distinction between personalization and privacy, a subject that has generated much discussion in recent years. It is crucial that the consumer data we have are safe enough for them to trust us with their information. Why should customers provide us their data, one could wonder in this situation. Simply put, a business must provide the clients with the items they require to succeed. Giving Amazon and Google access to your account, for instance, enables them to remind you of your daily obligations, keep you on schedule, and prevent you from forgetting to do them. Figure 1.1 shows the use cases of AI techniques in E-commerce sector.
Figure 1.1 AI use cases.
Every user is trying to find the greatest bargain, so pricing optimization is crucial. AI is aware of this; hence, the supply and demand chain of the deal causes the price of the product to fluctuate, which keeps the customer active by having them look for the best offer on the product every day.
Improved Customer Care - Artificial intelligence (AI) has now solidified its roots in business’s customer care operations. Businesses are now utilizing chat bots and virtual assistants to improve the consumer experience while they are having any kind of query. These bots only make work easier by providing solutions to common questions; they do not, however, guarantee a customer-handling system that is 100 percent reliable. Since these bots are available round-the-clock, anyone with a simple question can get an immediate response, saving both the time of the consumer and the company representative who is currently serving them.
These chatbots have demonstrated that they can serve as a helpful assistant in managing clients, handling inquiries, and providing answers to frequently asked questions (FAQs). By utilizing natural language processing (NLP), chatbots may also understand voice-based conversations, giving clients individualized service.
Terabytes or petabytes of data are combed through by the virtual assistants, which enable the seller to better cater to the needs of the buyer and provide goods that suit their preferences.
Intelligent logistics - Also known as dynamic data, intelligent logistics leverages real-time data. It uses data from the inventory and information from RFID tags to estimate future demand or to advise us when a product might run out of stock, allowing industries to be informed about the costs associated with making the product available.
Writing for the web using AI - In the past, businesses frequently hired people with writing talent. Consequently, the cost to the business increased, although AI has since changed a lot of things. With the aid of AI copywriting techniques, one can now quickly and cheaply produce very persuasive content for marketing posters, emails, and other types of documentation. AI just needs a few seconds to do 95% of the task; some manual adjustments though are still necessary.
In managing inventories in an online store, the retailers must maintain their inventory in a proper manner to ensure that company operations proceed without interruption. As a result, artificial intelligence (AI) can assist you by providing an analysis report of the products sold in the preceding month or year, helping the store prepare its inventory for future sales. The business’s profit margin increased as a result. The future forecast of the products that will be sold is provided by this technique, providing the seller with enough lead time to prepare the goods. As more data are received and processed, this technique will continue to improve, providing the seller with a rate that is more exact and precise.
Remarketing to prospective customers – This is crucial, as many businesses use AI in extremely advanced ways, while others employ AI inefficiently. They do have a ton of client data, but they are either unable to manage it or are unaware of it. For them, AI is like an oasis in the middle of a desert. Let’s use a retailer as an example of AI being applied very effectively. Because facial recognition is a part of AI, as we all know, the store has now integrated this into its CCTV cameras. Now, it will be used in such a way that the CCTV will catch the customer’s face and the data are now saved in the system. The AI will keep track of the customer’s time in the store and whatever product he is looking at. As a result, when he returns, he will be able to locate great deals on that product or a choice of goods that are similar in the store. By doing this, the store will improve the consumer experience and encourage return visits. This strategy may be connected to a number of business plans that could generate enormous revenues for the retailer.
Customers are the most crucial elements in business, according to customer relationship management (CRM). Their main and top priority is the customer. The customer experience will advance to a new level if AI is integrated with CRM, which is crucial for maintaining positive relationships with customers. NLP is a feature of many AI systems that functions similarly to Siri or Alexa. Retailers can use this technology to respond to customer questions, address their issues, and, by analyzing the conversation, provide useful information about how to improve any product or identify the most frequently asked questions about a particular product. This technology can also help retailers improve the level of responsiveness from the perspective of the business or company. The shop will be able to better understand customer demands; thanks to the data in various forms, and they will be better able to sell connected products. By avoiding the promotion of irrelevant products and not wasting their time during future visits, this will enhance the customer’s personalized experience.
However, holograms are still very uncommon. Nevertheless, when viewed as a mere experiment, holograms greatly catch the attention of viewers. The phrase “appearance do matter” is used a lot in business today. For the sole purpose of making their brands unique and in-demand, many businesses invest in talented designers. It is a novel and distinctive approach to use holograms in the store. Although there is still much to learn about this, if holograms are used in business, it will encourage customers to visit physical stores to purchase goods. When a corporation operates a chain of stores in various parts of the nation or the world, there may be a conflict because the majority of customers now choose to purchase goods online. Customers still prefer to shop offline, and this number is still rather large. By using this tactic, you can persuade them to at least visit the store personally and take a look around. People are very interested in status and other things these days. Such marketing techniques will persuade consumers to shop at these kinds of establishments and make them less concerned about product costs as well.
Since it is well known that building holograms requires a lot of time when the device is turned on, MIT researchers have developed a new method that may make it possible to build holograms for VR, 3D printing, and other applications that can operate through smartphones as well. The name of this novel technique is “tensor holography.” The main drawback of VR is that it causes nausea and eye strain in users. Holograms could help mitigate these issues. Holograms provide a comfortable viewing experience by allowing users to change the focal length of their image. Previously, holograms required supercomputers for construction, which was time-consuming and resulted in subpar viewing quality. According to MIT researchers, they can now create holograms almost instantly, utilizing laptops with just a blink of an eye. They do this by employing deep learning. Additionally, robots are also proving to be a wonderful asset in the corporate world, significantly reducing workloads. AI robots can tell you anything about your business, including your stock report, your monthly or yearly learning, and future advice based on the data you provide them. They use a variety of cutting-edge technologies to do this. In the workplace, they resemble your helpers. Robots are being used by many businesses to reduce workloads and boost productivity so they can grow internationally.
The robots can also serve as retail employees. Customers will see the robots as being modest when they are greeted by them as they enter the store. They essentially serve as salespeople at the store by helping the customers learn about the many things available.
Customers are typically those people who desire a product at a very low price even when it comes to the cost of shipping. There are clients who hunt for cheap items when making purchases, particularly online, just as there are individuals who are more interested in flaunting their status as was indicated above. As a result, this is the best instrument we have for marketing our goods while considering the prices of our rivals. With the use of this technology, the seller can maintain flexible pricing by knowing the cost of the goods that their rivals are selling. The sellers will be able to make decisions using this strategy with the help of this technology, which will make market trends transparent and foster intense competition.
Wearable technology - AI has advanced to a point where, if used correctly, merchants can achieve unprecedented financial success. The majority of us are now concerned about our health, and to keep track of it, we are employing smart watches that can monitor our daily activities and provide us with analysis reports of the calories burned, sleep quality, heart rate, menstrual cycle, and many other things. Retailers can now analyze which products to recommend by using data from wearable technologies. Consequently, revenues will rise, and a more customized, dynamic client experience will result. The retailer will be able to expand his firm internationally if stores start monitoring their clients’ vital signs.
AI will show to be a godsend to any firm in today’s cutthroat business environment, enhancing the SWOT analysis of the organization. Businesses who fail to see the promise of AI will have a very difficult time in the future.
1.2.2 AI Techniques in Healthcare
As we all know, the COVID-19 era was incredibly difficult for those who lost their loved ones, and for those whose daily needs depended on their daily income, such as roadside sellers. However, throughout those difficult times, technology also proved to be the strength of humanity. The benefit of the COVID-19 lockout and lockdown period was that it elevated the rate of technology use across all industries, including gambling, healthcare, business, and other types of industries. Nowadays, technology has deeper roots in every sector of the economy. Here, we are specifically referring to the healthcare sector.
In light of the COVID-19 pandemic, if we look at the advancement of the healthcare sector, we can see that numerous AI approaches were used to forecast the COVID’s next mutation, which enabled doctors to predict its duration and progression. Then, doctors would call the patients at the hospitals only when it was absolutely necessary, preventing them from contracting the virus. After the doctor has diagnosed the patient, a report with all pertinent information would be delivered to the patient. The medications are provided via prescription. As runtime execution, this method worked really well, because it was the best option available at the time.
If we look at the hospitals, clinics, and everything else in the healthcare industry, we are currently observing developments. All healthcare facilities now maintain patient data records. To give the ailing patient the best care possible, we must analyze his or her data or historical records. If we now discuss the data collection phase, what types of data are they keeping? The initial vitals of the patient when he or she was admitted, the blood test reports (LFT, CBC, BMP, lipid panel test, etc.) related to the disease the person is suffering from, all different types of CT scan reports, MRI, the medication given, etc. are the answers. Now that the data have been collected, the centers can use them to analyze a variety of things. For example, they can use them to create a bar graph or pie chart showing which months have the most common diseases, allowing them to stock up on the necessary medications to prevent patient deaths brought about by running out of medication. There are now numerous other items that they can analyze using AI Techniques, one of which is now in use: diagnosing the patient [9]. This will improve diagnosis precision, enabling the correct medication or therapy to be administered in a timely and dynamic way [10–12].
Figure 1.2 AI techniques.
Cardiology, neurology, and cancer are three major disorders for which AI methods are frequently used. The future potential for AI doctors to displace human doctors is unclear, though. Well, we won’t say that it can completely replace human doctors, but AI doctors can be used as their assistants so that when there is a disagreement or uncertainty regarding a decision, we can see what the AI doctor’s analysis report has to say and use that information to help human doctors reach a conclusion [13–16].
The most popular AI technologies in the healthcare sector include ML (neural networks and deep learning), NLP (natural language processing), rulebased expert systems, physical robots, and robotic process automation [17]. Figure 1.2 demonstrates the AI techniques in healthcare sector.
1.2.2.1 Machine Learning
It is a statistical technique that trains models to make predictions by learning from previously obtained data. It is one of the most widely used AI approaches. Out of 1100 US managers whose organizations were already investigating AI, a Deloitte poll from 2018 found that 63% of the businesses were utilizing machine learning [18]. According to the data saved in the model, machine learning is utilized in the healthcare sector to anticipate which medications should be administered and which treatment procedures should be followed. “Supervised learning” is the term for this kind of learning for AI models [17].
Neural networks are an advanced form of machine learning. In essence, neural networks are a type of synthetic brain that resembles the human brain. With the use of this method, AI can identify trends and resolve frequent issues.
An expanded variant of ML is deep learning. The algorithms’ ability to group data and generate precise predictions is enabled by the attempt to mimic the functioning of the human brain. Deep learning and neural networks are frequently used interchangeably, although the word “deep” in deep learning refers to the deep layers of neural networks. Any algorithm that has three or more layers of neural networks is referred to as a deep learning algorithm, whereas algorithms that have three or fewer layers of neural networks are referred to as neural network algorithms. As the majority of AI applications solely use this technique, deep learning is one of the most used techniques.
Now that we are aware that Deep Learning falls under machine learning, it is important to understand how these two vary. Therefore, the type of data they both employ is different in deep learning than it is in standard machine learning. Data that are structured, or in the form of organized tables, are used by machine learning. We shouldn’t assume that it can’t accept unstructured data at this point because it can; however, it will handle the data first in an organized fashion before analyzing them as directed. Models can also take in unstructured data using deep learning algorithms (like, images and texts). It makes a distinction between the two items presented to the model based on a specific aspect of the shown data.
Let’s talk about the many learning methods that ML algorithms can use, as was indicated before. The three categories of learning are supervised learning, unsupervised learning, and reinforcement learning. ML and Deep Learning models are capable of handling all these types of learning. Because the data needed label sets on them to partition them and enable the model to learn them, supervised learning involves some level of human intervention. Unsupervised learning doesn’t require any human involvement; it can analyze any type of data, anticipate patterns on its own, and group those patterns into distinct clusters. The user’s feedback is used in reinforcement learning to increase the model’s accuracy.
Deep learning has benefited greatly the healthcare sector. They were able to analyze, scan photos, and quickly produce accurate results because of the Deep Learning algorithm’s capacity to handle any kind of input. Recognizing possibly malignant tumors in radiography pictures is the most widespread deep learning application in the medical field. Oncologyfocused image analysis is where radiomics and deep learning are most frequently used. Compared to the previous combo, known as CAD, this one offers a more accurate diagnosis [17].
1.2.2.2 Natural Language Processing (NLP)
NLP, or natural language processing, is used to teach machines to recognize human speech and respond to users in text or speech similar to what people would say. Unstructured data is employed. Now that this technology is available, it can be employed as a managerial position for patients in the healthcare system, assisting patients in ensuring that they receive sound treatment. The healthcare sector employs NLP in a variety of ways, including:
NLP is used in clinical documentation to translate speech to text and vice versa. This will enable patients to spend less time filling out paperwork when they are admitted to or released from the hospital.
Data Mining Research - The use of data mining in the healthcare sector enables them to make decisions that are less difficult. Data mining may be employed as a good method for improving patient care and will dramatically improve the business of that center once it is seriously implemented in any healthcare facility.
Healthcare organizations can manage online reviews with the help of NLP and sentiment analysis. It may gather and evaluate hundreds of reviews on healthcare each day from third-party listings. NLP can also find obscenities or other data related to HIPPA compliance, as well as PHI, or Protected Health Information. Even faster analysis of human emotions and the situations in which they are exhibited is possible.
Some systems can even listen in on customer feedback, giving doctors insight into how consumers talk about their care and allowing them to use more straightforward language. Similar to this, NLP may track consumer attitudes by analyzing the positive and negative phrases in the review.
Healthcare organizations can manage online reviews with the help of natural language processing (NLP). It may gather and evaluate hundreds of healthcare reviews every day from third-party listings. NLP is also used to find PHI, or Protected Health Information, as well as obscene language or other data related to HIPPA compliance. Even human emotions and their expression circumstances can be easily analyzed by it.
Some systems even have the ability to listen in on customer feedback, which helps doctors by giving them an understanding of how patients communicate about their care and allowing them to use more straightforward language. Similar to this, NLP may track consumer attitudes by analyzing the positive and negative phrases in the review.
Implementing Predictive Analysis in Healthcare It is possible to detect high-risk patients and enhance the diagnosis process by applying predictive analytics in conjunction with natural language processing in healthcare.
It is essential that emergency departments have quick access to reliable data. The benefits of employing NLP can be applied to other areas of interest. There are many different algorithms that may be used to identify and forecast certain problems among patients.
Even while the healthcare industry still has to strengthen its data skills before deploying NLP tools, it still has a lot of potential to improve care delivery and expedite procedures. With the use of natural language processing, clinical decision support and patient health outcomes will improve in the future.
1.2.2.3 Rule Based Expert Systems
The most basic type of AI is rule-based expert systems. It resolves the issue utilizing the available information [19]. This technology gathers knowledge from specialists on the human side and converts it into its own computer language. In essence, conditional statements are how the rules are stored. Only basic problems can be solved by these systems; they are not capable of handling complex ones.
However, they are now less used in the healthcare industry as a result of the introduction of numerous new and more advanced AI approaches, yet this technique still produces accurate findings when used to generate clinical decision reports. One of the main drawbacks of rule-based expert systems is that when the information is changed, altering the rules can be challenging and time-consuming [16].
1.2.2.4 Physical Robots
Physical robot development is accelerating, and each year, a significant number of robots are supplied all over the world. Robots are increasingly being used in a variety of businesses, whether it be a warehouse, a factory, or a hospital, for business objectives. Over time, they become more intelligent as they accumulate more and more data.
Surgical robots are a godsend to surgeons in the healthcare industry. Their enhanced ability to see within the body with greater accuracy and precision has led to the success of many operations that previously might have failed. Head and neck surgery, gynecologic surgery, and prostate surgery are among the common surgical procedures performed by surgical robots [16]. Overall, we must not overlook the effort that hospital staff members made. As a result, many hospitals now maintain care robots, assistance robots, and other robots in conjunction with surgical robots.
1.2.2.5 Robotic Process Automation
The most advantageous aspect of robotic process automation is how affordable and reasonable it is. Administrative operations are completed using this technology. It mostly consists of server-side programs that rely on the workflow, business rules, and presentation layer of the system, which functions as a semi-intelligent user. It does not truly include any robots. When it comes to how it is employed in the healthcare industry, it involves often updating the server with information about patient records or the billing procedure. It will help us when we extract the data from it, if we combine it with technologies like image recognition [16, 20].
RPA improves productivity for the organization since it can complete jobs without becoming fatigued like humans, freeing up employees to work on other projects. RPA is a great method; it functions much like the brains of AI models or robots. Modern robots are capable of carrying out a wide range of human functions, including engaging in any conversation, comprehending unstructured material, and making deliberative decisions. RPA significantly streamlines human processes, and as a result, it is essentially altering the nature of work in every sector where it is used.
1.2.2.6 Administrative Applications
There are numerous administrative apps available for use in the healthcare sector as well. The application of AI in this industry has less potential for revolution than in patient care, but it can nevertheless result in considerable cost reductions [21]. The technology that is most likely to be useful for this objective is RPA. The management of medical data, revenue cycle management, clinical documentation, and claim processing are just a few of the healthcare settings where it can be used [17].
Another AI method that is pertinent to the administration of claims and payments is machine learning, which can be utilized for probabilistic database matching. The accuracy of the millions of claims must be independently verified by insurers. By correctly identifying, analyzing, and resolving coding issues and erroneous claims, all parties—health insurers, governments, and providers—save time, money, and effort. Inaccuracies that slip through the cracks offer considerable cash potential that is waiting to be tapped between data matching and claim audits [17].
1.2.2.7 AR/VR
The market for AR and VR is expanding rapidly across industries, including healthcare and the video game sector. Before operating on an extremely sensitive case, several hospitals use AR/VR. They assess the likelihood that the patient will survive different scenarios using AR and VR, as well as the necessary interventions to ensure that the patient’s life is not lost as a result of a medical error.
The third way AR & VR healthcare solutions can improve patient experience is through extension. Chronic patients frequently experience discomfort when they are partially left on their own during treatments, as is well known. Thanks to VR and AR, their experience can be made a little bit more exciting and less painful.
