Digital Transformation

Industry 4.0 evolved as a  modern and advanced industrial  foundation by the proper integration of the old and/or new technologies to provide digital solutions for all aspects of human life. Industry 4.0 uses the concepts of digitalization, intelligentization for the increasingly individualized customer requirements. The soaring advancements in manufacturing processes at both intra and inter-organizational help in increasing productivity with the convergence of manufacturing and artificial intelligence technologies.  Industry 4.0 provides smart, effective and efficient digital solutions that help products and machines used for manufacturing become smarter so that communication between them can take place and mutual learning happens together. This advanced manufacturing is also called as Smart Manufacturing. With the integration of Robotics, Smart Manufacturing, Big-Data, Autonomous vehicles, Cloud Computing, Internet of Things, 3-D Printing, Artificial Intelligence, Cyber-Physical systems, Industrial Internet, etc., concepts in the industrial systems, many industries entertain sustainable operational excellence. The objective of this chapter is to furnish an overview of Industry 4.0 with the paradigm shift, features, and applications, enabling technologies, architectural design and economic potential that help in understanding the inner perspective of the term ‘Industry 4.0’.

Late years have seen a lot of transportation information gathered from various sources including street sensors, tests, GPS, CCTV, and factual reports. Like numerous different businesses, transportation has entered the age of large information. With a rich volume of traffic records, it is trying to assemble a dependable estimation methodology that is dependent on conventional trivial Machine Learning (ML) technique. AI plays the center capacity to intellectualize the transportation frameworks. Late years have seen the approach and pervasiveness of profound realizing which has incited a detailed study in smart transportation framework. Therefore, conventional ML models in the number of applications have been implemented by the new learning strategies. A thorough literature survey of deep learning algorithms is being done which can be implemented for enhancing the transportation system. In this chapter, we have discussed various deep learning models that are specifically used for the prediction of traffic flow. The utilization of Deep Learning (DL) frameworks in transportation is yet to be explored more in detail and there is a lot of problems for DL models to be implemented in the transportation system. By combining non-linear modules, the transformation of representation from one level to another higher and abstract level is deep learning algorithm. There are sufficient numbers of transformations which will help in learning the most complicated functions and structures for efficiently. The main advantage of DL is selection of the features by using general purpose learning methodology without any human involvement. DL methodologies has given result in the demonstration of high performance by exploring the high-dimensional data in several domains like computer vision, natural language processing, bioinformatics, etc. There are numbers of DL methodologies consisting of Recurrent Neural Networks (RNNs), Deep Convolutional Networks (DCNs), Deep Restricted Boltzmann Machines (RBM), Stacked Auto-Encoders (SAEs), Deep Belief Networks (DBN), etc.

Due to the wide range of common service requirements, applications, devices, and networks, next-generation wireless networks are becoming more complex systems. The network operators must make the most of the resources available, including electricity, spectrum, and infrastructure. Cyber-Physical Systems (CPS), a newly established approach for interconnected systems, aims to carefully monitor and synchronize information between physically connected systems and the cyber computational environment. Depending on the physical system being monitored, the method for building and implementing the framework for interconnecting systems may differ. A determination of a revolutionary proactive, self-aware, self-adaptive, and predictive networking paradigm. Network operators have access to large amounts of data, primarily from the network and users. The systematic use of big data considerably aids in making the system smart and reliable, as well as improving the efficiency and cost-effectiveness of functionality and improvements. This work presents a new cost-effective and adaptive clustering algorithm that can enhance computing efficiency while maintaining clustering accuracy. In this system create a composite window model that includes the most recent data records. The significance of adaptive clustering algorithms in machine learning and artificial intelligence in making systems intelligent in terms of being self-aware, self-adaptive, proactive, and prescriptive, as well as data sources and strong drivers for data analytics adoption, are highlighted. A variety of network design and optimization methodologies are available in the context of data analytics. The research analyzes the problems and benefits of incorporating big data analytics, machine learning, and artificial intelligence into next-generation communication systems.

A Society 5.0 environment is a technology-oriented society geared toward every human being, intending to upgrade society to a better daily human existence. It can be accomplished by making extensive use of communication and information infrastructure, technologies, applications, and user equipment. Industry 4.0 is the foundation for creating the Society 5.0 environment, which emphasizes the harmonious coexistence of people and machines. Artificial intelligence is one of the most important technologies for the Society 5.0 environment's continued growth and development since it has the power to translate networked stakeholder data into values. AI's application in the sphere of health offers an almost limitless number of applications. ML is enhancing the healthcare experience and ensuring the success of people who use it. The primary focus of this chapter is on ML algorithms in healthcare systems and an insight into how ML has helped to handle skin cancer detection.

Many technologies are being considered to enhance the productivity of Industries such as IoT enabled smart services, cloud computing empowered on-premises services and machine learning assisted techniques for predicting future risk management. These technologies are gaining and producing insights with values to the product delivery. Continues customer services are also achieved through these technologies to bridge up the gap between client’s expectation with service providers. This 4.0 evolution is aiming to create and synchronize the interconnection between entities such as man, machines, and programming devices to enable the data-driven decision-making process. The various automation software and visualization software are used to identify the pain points of the industry concerning its consumers. Cloud computing is used to create internet-based service access to globally available consumers. This cloud technique is practised for the following reasons. 1. Development of new applications and services—Multiple Language Support. 2. Storage, backup, and recovery—All the types of data. 3. Hosting Applications—File hosting and Apps deployment. 4. Prompt Launch of Software—with subscription. 5. Multimedia support—Audio & Video. The main advantages of using these cloud-assisted services are improved collaboration, ease to access, unbounded storage capacity, low cost maintenance and security mechanisms. In this book chapter, the cloud computing architecture, technology solutions, deployment models, working principles, underlying virtualization concepts with industry practices are discussed and analysed in an explained view. A research study and report are also incorporated with industry 4.0 standards with its advancements.

We are just a decade or less, away from a “smart future” environment. Technology has revolutionized our lives in the past couple of decades. Things we had never dreamt of had happened, and yet we are about to witness things that would be flabbergasting. The current information society 4.0 is shifting its gear up to “Society 5.0” which is a smart initiative of Japan. This initiative of Japan would serve as a road map for other countries to follow. Japan describes Society 5.0 as a socio-economic transformation that will have profound incorporation of technology. Every walk of the society is benefited by this revolutionary transformation. The main domains of Society 5.0 are Energy, Infrastructure, Mobility, Healthcare, FinTech, Education, Manufacturing, Logistics, and Cyber space. Society 5.0 needs to design a human-centered society in which the technical advancements are meant to aid human in decision-making process, rather than replacing us with smart algorithms. The past Society 4.0 was encountering a threat when AI and machine learning boomed up in routine walks of life. Individuals believed that they would be replaced by robots and smart systems in near future. However, it was revealed that the usage of AI and machine learning models was not sufficiently good enough in many predictions as it doesn’t have a “Human-touch” in terms of experience and knowledge. Those systems were working based on pre-existing algorithms loaded with huge collection of data. What is happening underneath the huge neural networks is a kind of black box in many applications. A society cannot take such a huge risk in most prevalent domains like Healthcare, Finance sector, and Infrastructure. Hence, a fresh idea must be unveiled which can analyze huge amounts of data and optimize the recommendations and aid humans in final decision making. This novelty is named as Society 5.0 which integrates cyber space and physical space and bring forth balances in economic advancements by resolving social issues.

Big data is a hot topic in both the social and industrial spheres. More businesses, organisations, and governments are investing in big data initiatives to boost revenue, productivity, and profitability by anticipating market demands. But delving back in time reveals big data's extensive history. They gathered this data without the internet by analysing market demand and share. Only the government and large corporations could subsequently afford their own big data infrastructure procedure. In the fourth industrial revolution, small and medium-sized businesses are investing in big data initiatives to compete in competitive marketplaces. They can use big data to analyse patterns, trends, client preferences, and market demand. Big data is currently indispensable in every organisation and business. The rising trend of big data in Industry 4.0 raises privacy issues. A skilled prospecting agent who can readily get huge data from the public domain may misuse it due to the increased threat of cyber-attacks. To ensure data security, data collecting organisations and enterprises must state the goal of obtaining data and maintain all data management regulations as routine safety measures. To ensure individual data security, this research will offer the basic procedural security compliance requirements for companies and society standards. The most fascinating legal issues and solutions from the past, present, and future will be discussed.

The Industrial Internet of Things (IIoT) combines the isolated Industrial system into a connected network. The IIoT adds value to the industrial organization, advanced data handling concepts, and reduced cost. In many cases, there was a gap in the existing framework which paves the way for the development of a new framework. In this chapter, we have discussed the framework of IIoT along with the technologies behind it. Architecture frameworks are based on the stakeholder concern that allows the system view creation. The technologies behind the IIoT concepts are elaborately discussed.

The workplace is transformed by various robots, making it mandatory for human employees to collaborate and coordinate with robots to achieve work performance. These robots drive intelligent solutions towards achieving operational efficiency in business. These robots are leveraged with advanced technology such as natural language processing, Artificial Intelligence, Deep Learning, Machine Learning, The Internet of Things, and Augmented Reality. It has become imperative for humans to collaborate and coordinate with robots to achieve performance and avoid errors. The chapter studies the literature to understand human–robot coordination and collaboration factors.

In conclusion, we provide a framework that discusses the drivers of human–robot collaboration such as perceived benefits, entertainment factor, safety, automation of repetitive tasks anthropomorphism. Also, there are barriers such as potential risk, cost factor, contextual barrier, knowledge barrier, passing the uncanny value point.

Artificial intelligence is related to designing an intelligent computer system. These systems exhibit the association of intelligence with human behaviors. This system helps the decision-maker to reason, analyze, solve problems. As these systems grow, their nature is then molded to collaborative artificial intelligence. This system is futuristic wherein human and artificial systems work together, taking different roles, based on what they do best. Collaborative Artificial intelligence (CAI) has transformed businesses and made imperatively significant that it’s not replacing humans but augmenting them to bring enhanced value. When it comes to not replacing human workers, CAI will help in human enhancement, thinking and reasoning skills, and creativity, can free the individuals/working groups from performing front line tasks and increase their physical abilities too to perform more relevant core tasks (as one component of society 5.0). The chapter describes the ecosystem for collaborative AI, the roadmap for building the systems, used cases in Human resources management across businesses and sectors with their challenges. The chapter’s tail end includes the usage of IoT for urban development with a conclusion over the future scope of how society 5.0 can be envisioned.

The goal of this chapter is to look at how individuals can engage with new technologies that combine computational and physical capabilities. This chapter delves into the CPS's design. As technology progresses, it must be able to communicate with the physical environment via computation, communication, and control. As sensors, data collecting systems, and computer networks become more widely available, and as the modern economy becomes more competitive, more businesses are turning to high-tech solutions. Sensors and networked equipment are increasingly generating data, which is referred to as “big data.” Big data and machine interoperability may assist CPS in achieving its goal of intelligent, robust, and self-adaptive systems. Big data. Companies may become industry 4.0 manufacturers by incorporating CPS into their present manufacturing, logistics, and service processes.

Alzheimer’s disease is one of the most growing diseases in the ageing population all over the world. It slowly becomes worse starting with mild symptoms and completely destroying one's ability to perform any regular activities. A convolutional neural network (CNN) is one of the deep learning technologies which is used to develop a system which can be used to recognise signs of Alzheimer's disease in MRI pictures. Deep Learning technologies can handle huge datasets which act as a predictor of AD disease. In Society 5.0 various machine learning and deep learning technologies are implemented using various healthcare data which focus on improving the lifestyle of the ageing population. It also reduces the overall expenditure associated with caregiving and solves the shortage of manpower issues. Hence, we propose in this chapter the various ways society 5.0 can improve the lifestyle of people suffering from dementia and increase their life expectancy. Such technologies which aim at solving the issues of the ageing population suffering from Alzheimer's disease will solve many social issues. This chapter aims at an in-depth study of various challenges faced by Alzheimer's affected patients and their families and how those challenges can be overcome with the help of various machine learning and deep learning technologies which can be implemented in society 5.0.

Bone cancer is a disease class that is characterized by freed cell growth, and this is the main cause of earlier death across the globe. Thus, earlier bone cancer detection and classification are required for curing the patient. This disease is originated from bone and spreads throughout the body rapidly and thus affects the patients. Quick analysis and initial diagnosis of bone cancer for creating the possible chance of protecting patients from death. Depending on this, bone cancer detection survey utilizing several techniques in image processing and several issues have found some of the problem complexity; when there is no detection performed at the right time, problem complexity increases. Hearing is a significant human feeling. The environment surrounding us is perceived and any danger happened around us is warned. A novel approach to identify the normal and malignant tissues is presented in this paper with the specific features in a provided X-ray image. This method utilizes AlexNet for feature extraction of a single category, and Deep Convolutional Extreme Machine Learning is utilized to create a classifier called Deep Convolutional Extreme Learning Machine with AlexNet (DC-ELM + AlexNet). The proposed method is evaluated with three standard approaches namely Model-averaged Neural Network (avNNET), Inception V3, and MobileNets in terms of various parameters. As a result, it is found that the proposed DC-ELM + AlexNet realizes 97.04% of accuracy, 68.22% of sensitivity, 83.94% of specificity, 54.08% of F1-score and 68.16% of kappa score.

Clustering is a sub-domain in data mining. To improve the quality of the cluster, adaptive clustering is utilized in external feedback, and experience is utilized to reduce the processing time. In an adaptive clustering environment, the benefit points of sequential data clustering are learned using Q-learning. Adaptive clustering is mainly focused on the reuse of clusters based on previous work. There is a lot of space is available to research in the field of adaptive clustering. The field of adaptive clustering algorithms is mostly untapped, leaving a lot of room for research. Adaptive clustering techniques are effective in circumstances where things change often. Despite shifting environmental conditions and demands, attentive systems are more capable of modifying their efforts, and wealth is identified to achieve a given goal. This potential is the most advantageous to all types of systems in all the domains such as mobile computing, cloud computing, multicore computing, adaptive and dynamic compilation environments, and parallel operating systems, where power, performance, and resource metering challenges must be met. This chapter mainly focused on discussing various types of Ada clustering, Self-Awareness, and Self-Aware decision-making.