This book examines computer vision and IoT-integrated technologies used by medical professionals in decision-making, for sustainable development in a healthcare ecosystem, and to better serve patients and stakeholders. It looks at the methodologies, technologies, models, frameworks, and practices necessary to resolve the challenging issues associated with leveraging the emerging technologies driving the medical field. The chapters discuss machine vision, AI-driven computer vision, machine learning, deep learning, AI-integrated IoT technology, data science, blockchain, AR/VR technology, cloud data, and cybersecurity techniques in designing and implementing a smart healthcare infrastructure in the era of the Industrial Revolution 4.0. Techniques are applied to the detection, diagnosis, and monitoring of a wide range of health issues. Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem targets a mixed audience of students, engineers, researchers, academics, and professionals who are researching and working in the field of medical and healthcare industries from different environments and countries. <!----> <!----> <!----> <!---->

Modern businesses have come to rely heavily on data analytics because it offers insightful information about consumer behavior, operational effectiveness, and financial performance. Organizations employ a variety of data analytics technologies, including Tableau, Excel, Python, and R, to analyze enormous volumes of data and derive actionable insights. These tools assist in gathering, purifying, processing, analyzing, and visualizing data to find patterns and trends. Numerous sectors, including marketing, finance, healthcare, manufacturing, and retail, can benefit from data analytics. Businesses may make wise decisions, improve productivity, cut expenses, and gain a competitive edge by utilizing data analytics tools and apps not only for healthcare sector but also other business sectors. Request access from your librarian to read this chapter's full text.

SQL injection is a type of security vulnerability that occurs in database-driven web applications where an attacker injects malicious code into the application to gain unauthorized access to sensitive information. This paper aims to provide a comprehensive and systematic review of the existing methods for preventing and detecting SQL injection attacks. The review covers a range of techniques, including input validation, parameterized queries, and intrusion detection systems, as well as the advantages and disadvantages of each method. The most common prevention techniques include input validation, parameterized queries, and stored procedures, while the most common detection techniques include intrusion detection systems (IDS), honeypots, and signature-based detection. The choice of method will depend on the specific requirements of the organization and the level of security required. Still, a combination of prevention and detection methods is likely to be the most effective way to secure web applications against SQL injection attacks. The paper concludes that SQL injection attacks continue to be a significant security threat to web applications, and it is essential for organizations to implement effective prevention and detection methods to secure their web applications against SQL injection attacks.

The application of internet of things (IoT) technologies and artificial intelligence (AI)-enabled IoT solutions has gradually become accepted by business and production organizations as an effective tool for automating several activities effectively and efficiently and developing and distributing products to the global market. Within this book, the reader will learn how to implement IoT devices, IoT-equipped machines, and AI-equipped IoT applications using models and methodologies along with an array of case studies. Advanced IoT Technologies and Applications in the Industry 4.0 Digital Economy covers the basics of IoT-equipped machines in developing and managing various activities in many industries. It discusses all of the key points of an AI-enabled IoT solution, which includes predictive analytics, robotic process automation, predictive maintenance, automated processes, IoT technologies and IoT-equipped sensors related to machines and processes, production testing systems, and product assessment processes in the production environment. The book presents the concepts and interactive methods using datasets, processing workflow charts, and architectural diagrams along with additional real-time systems for easy and fast understanding of the application of IoT-equipped machines and AI-enabled solutions in organizations and includes many case studies throughout the book to enforce reader comprehension. This book is an ideal read for industry specialists, practitioners, researchers, scientists, and engineers working or involved in the fields of Robotics, IT, Computer Science, Soft Computing, IoT, AL/ML/DL, Data Science, the Semantic Web, Knowledge Engineering, and other related fields. <!----> <!----> <!----> <!---->

One of the areas where artificial intelligence (AI) is applied is the healthcare sector. Different subclasses of AI are also applied here. These are machine learning (ML) and deep learning (DL). There are ML algorithms which are applied in healthcare for disease prediction. In this chapter, the concepts of ML and DL and their use cases are considered. In addition, with the help of artificial neural networks (ANNs), which are part of ML and DL, prediction was performed in a MATLAB environment for predicting the heart disease. <!----> <!----> <!----> <!---->

The sphere of artificial intelligence (AI) technology is quite wide. There are many individual and collaborative AI-based technologies available. One of them is computer vision technology. Computer vision is also related to other technologies: Machine learning (ML), deep learning (DL), artificial neural networks, etc. Computer vision is applied in many different areas. One of the areas where it has been widely applied in recent times is healthcare. In healthcare, various algorithms in the aforementioned technologies are used to obtain meaningful information from medical images. In this chapter, the concept of computer vision, its fields of application, and its application in healthcare are reviewed. Also, the example of tumor detection by computer vision in a MATLAB environment is considered. <!----> <!----> <!----> <!---->