In the context of Industry 4.0 and the anticipated Industry 5.0, the confluence of edge AI, machine vision, and intelligent robotics is driving the transformation of industries. Digital transformation, IoT, cyber physical systems, analytics, and AI are the major areas of discussions in this research that describes the field of edge AI technologies and its usage. The authors explore the development of edge cloud, edge computing, and analytics, clarifying their role in facilitating edge real-time processing and decision-making. In addition, the study addresses the applicability of computer vision and machine vision in industrial contexts in addition to the incorporation of drones, intelligent robots, and voice-activated edge devices. LLM and generative AI's potential for edge devices are also highlighted in the chapter, with a focus on how these technologies might improve operational effectiveness and safeguard industrial assets. In conclusion, the authors go over the significance of HMI and showcase industrial use examples that highlight the real-world applications of Edge AI in a variety of industries.

Artificial intelligence's "deep learning" discipline has taken off, revolutionizing a variety of industries, from computer vision and natural language processing to healthcare and finance. Deep learning has shown extraordinary effectiveness in resolving complicated issues, and it has a wide range of potential applications, from autonomous vehicles to healthcare. The purpose of the survey to study deep learning's present condition, including recent advancements, difficulties, and constraints since the subject is currently fast growing. The basic ideas of deep learning, such as neural networks, activation functions, and optimization algorithms, are first introduced. We next explore numerous topologies, emphasizing their distinct properties and uses, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Further concepts, applications, and difficulties of deep learning are all covered in this survey paper's thorough review. This survey aid the academics, professionals, and individuals who want to learn more about deep learning and explore its applications to challenging situations in the real world.

Data engineering solutions have become important in our quickly evolving technological world for handling and analysing the massive volumes of data generated every day. The chapter examines data engineering systems exponential expansion and data analysis's growing importance across businesses. It highlights data-driven healthcare's transformational influence on patient care and treatment advances, while noting privacy and ethical issues. The research analyses data categorisation techniques, focusing on machine learning and deep learning for healthcare and agricultural decision support systems. The story expects AI and blockchain integration in emerging decision support systems. Data analysis in clinical trials, precision healthcare decision-making, and agricultural applications are also covered. The conclusion is that data analytics improves weather prediction accuracy and gives useful insights for scholars and practitioners navigating the complex world of data analysis.

This study focuses on how medical informatics, bioinformatics and health records are getting rejuvenated by AI and ML, which is leading to a new paradigm in healthcare. It drives home the significance of data-driven methods to address the health problems and enhancing the patient outcomes in healthcare. The study begins with a highlight of AI and ML technologies being used in healthcare. Then it delves deep into the machine learning approaches, appropriate optimisation techniques, methods of disease prediction, and signal processing advances. Besides that, this paper obtains new technological developments such as the improvement of healthcare information systems and the application of artificial intelligence and machine learning to medical signal and image processing. By means of this purpose, one aims to teach scholars, specialists, and decision-makers concerning the transformative capacity of AI and ML in healthcare. Ethical surrounding matters and the future pathways of research and innovation are also discussed.

E-commerce and consumer trust need data security. This study examines e-commerce security strategies, technologies, and procedures to prevent malware, phishing, and data breaches. Cloud computing makes e-commerce more scalable and cost-effective, but security needs segmentation, access restrictions, and encryption. While blockchain provides transaction transparency and immutability, AI and ML increase fraud detection and real-time suspicious behaviour identification. Identity management, secure communication, and encryption are also studied. Risk assessment and incident response are key to proactive security. Data security ethics encompass openness, permission, and ownership. The chapter predicts AI, ML, blockchain, biometrics, and IoT advances for e-commerce security and provides detailed advice for stakeholders in a changing threat landscape.