Artificial Intelligence in Smart Systems

In this book we discuss some AI implementations using popular machine learning models such as: Decision Tree Algorithm, Random Forest Algorithm, Deep Learning, and Convolution Neural Networks. Additionally, this work connects emotion recognition with cultural context. It delves into the current AI applications in the aerospace industry, focusing on enhancing safety, predictive maintenance, and development of the Smart Factory concept. Furthermore, it showcases how FIWARE and smart systems help the management of resources and development of Smart Cities. The potential of smart technologies, including IoT, UAVs, Raspberry Pi, and ensemble learning, in various domains such as agriculture, surveillance, and healthcare are also brought to light. In addition, the book addresses the challenges posed by the exponential growth of data in the energy sector and the role of Phasor Measurement Units in monitoring power transformation. The findings highlight the benefits of these technologies in improving efficiency, cost reduction, and innovative solutions in existing challenges. Similar to Part 1, readers can also benefit from a Python example that utilizes Transfer Learning with ResNet50.