Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.

The Fourth Industrial Revolution, under the name of Industry 4.0, focuses on obtaining and using data to facilitate decision-making and thus achieve a competitive advantage. Industry 4.0 is about smart factories. For this, a series of technologies have emerged that communicate the physical and the virtual world, including Internet of Things, Big Data, and Artificial Intelligence. These technologies can be applied in many areas of the industry such as production, manufacturing, quality, logistics, maintenance, or security to improve the optimization of the production capacity or the control and monitoring of the production process. An important area of application is maintenance. Predictive maintenance is focused on monitoring the performance and condition of equipment during normal operation to reduce the likelihood of failures with the help of data-driven techniques. This chapter aims to explore the possibilities of using artificial intelligence to optimize the maintenance of the machinery and equipment components so that product costs are reduced.

A lot of millennials have been educated in gamified schools where they played Kahoot several times per week, and where applications like Classcraft made them feel like the protagonists of a videogame in which they had to accumulate points to be able to level up. All those that were educated in a gamified environment feel it is natural and logical that gamification is used in all areas. For this reason, gamification is increasingly becoming important in different fields including financial services, bringing new challenges. Gamification allows financial institutions to provide personalized and compelling experiences. Big data and artificial intelligence techniques are called to play an essential role in the gamification of financial services. This chapter aims to explore the possibilities of using artificial intelligence and big data techniques to support gamified financial services which are essential for digital natives but also increasingly important for digital immigrants.

Artificial intelligence can be seen as the intelligence exhibited by machines. For an artificial intelligence system to be able to take decisions based on the data available, different type of learning methods, such as machine learning, need to be applied. Machine learning is a learning technique that gives machines the ability to learn without being explicitly programmed. It addresses the creation and study of algorithms that are capable of learning from data and making predictions about it. Machine learning algorithms can be divided into different categories including supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. In this article, the authors want to explain what machine learning is, as well as clearly establish the differences and relationship of machine learning with other related concepts, including artificial intelligence and deep learning. In addition, some possible use cases and applications will be named in order to provide the reader with a clear idea of what the potential of machine learning is.

Systems that aim to maintain and improve the health of citizens are steadily gaining importance. Digital transformation is having a positive impact on healthcare. Gamification motivates individuals to maintain and improve their physical and mental well-being. In the era of artificial intelligence and big data, healthcare is not only digital, but also predictive, proactive, and preventive. Big data and artificial intelligence techniques are called to play an essential role in gamified eHealth services and devices allowing to offer personalized care. This chapter aims to explore the possibilities of artificial intelligence and big data techniques to support and improve gamified eHealth services and devices, including wearable technology, which are essential for digital natives but also increasingly important for digital immigrants. These services and devices can play an important role in the prevention and diagnosis of diseases, in the treatment of illnesses, and in the promotion of healthy lifestyle habits.