Textbook in PDF format The book analyzes the use of smart medical devices that use Artificial Intelligence (AI) and Machine Learning (ML) to analyze medical images, detect diseases, and assist in diagnosis. It further focuses on real-world applications of Artificial Intelligence and Machine Learning in smart electronic devices, demonstrating how these technologies are being used in various industries, such as healthcare, automotive, finance, and consumer electronics. Artificial Intelligence (AI) and Machine Learning (ML) are critical technologies in the development of smart devices. Smart devices can use AI and ML algorithms to learn and recognize individual users’ preferences and habits, allowing them to personalize the user experience. AI and ML algorithms can help smart devices predict when maintenance is required and notify users before a failure occurs. This can reduce downtime and prevent expensive repairs. AI and ML are used in smart speakers to recognize and respond to voice commands accurately. The more the device is used, the better it can understand the user’s speech patterns and language. AI and ML algorithms can analyze usage patterns and optimize the device’s power consumption to reduce energy consumption. Smart devices can use AI and ML algorithms to automate routine tasks, such as setting the thermostat or turning on lights, based on the user’s habits and preferences. AI and ML can help smart devices detect unusual activity and alert users of potential security threats. Overall, AI and ML play a critical role in making smart devices more intuitive, efficient, and secure. As these technologies continue to advance, we can expect smart devices to become even more capable and user-friendly. The aim of incorporating AI and ML in smart devices is to create intelligent devices that can adapt and learn from their environment, provide personalized experiences to users, and automate routine tasks to improve efficiency. The scope of AI and ML in smart devices is vast and can be seen in various applications, including voice assistants like Siri, Alexa, and Google Assistant use natural language processing (NLP) and Machine Learning algorithms to understand and respond to users’ voice commands. Smart homes use AI and ML to automate routine tasks, such as turning on/off lights, adjusting thermostats, and controlling home appliances based on user behavior. Wearable devices like smartwatches and fitness trackers use AI and ML to track users’ physical activity, monitor their health, and provide personalized recommendations based on their habits. Autonomous vehicles use AI and ML to analyze sensor data from cameras and other sources to navigate and make decisions on the road. AI and ML are used in industrial automation to optimize processes, reduce waste, and increase productivity. Overall, the integration of AI and ML in smart devices enables devices to become more intelligent and intuitive, providing users with a better and more personalized experience. Features: Explores how cloud and edge computing work together to enhance the capabilities and performance of smart devices, enabling a seamless user experience and facilitating the growth of the Internet of Things ecosystem. Discusses the use of smart devices within a smart home system, exploring the seamless connectivity, interoperability, and centralized control. Explains the advancements in smart traffic management and smart parking systems, which leverage cutting-edge technologies to address the growing challenges of urban mobility. Surveys the growing importance of smart energy management and the integration of renewable energy sources in the pursuit of a sustainable and eco-friendly energy landscape. Covers the dynamic relationship between the adoption of smart devices and artificial intelligence technologies, and the diverse regulatory frameworks governing these innovations. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics, and communications engineering, Computer Science and engineering, and biomedical engineering. Features: Explores how cloud and edge computing work together to enhance the capabilities and performance of smart devices, enabling a seamless user experience and facilitating the growth of the Internet of Things ecosystem. Discusses the use of smart devices within a smart home system, exploring the seamless connectivity, interoperability, and centralized control. Explains the advancements in smart traffic management and smart parking systems, which leverage cutting-edge technologies to address the growing challenges of urban mobility. Surveys the growing importance of smart energy management and the integration of renewable energy sources in the pursuit of a sustainable and eco-friendly energy landscape. Covers the dynamic relationship between the adoption of smart devices and artificial intelligence technologies, and the diverse regulatory frameworks governing these innovations. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields including electrical engineering, electronics, and communications engineering, Computer Science and engineering, and biomedical engineering