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.