Artificial intelligence is one of the essential innovations made by scientists to simplify people's life. It allows intelligent computers to imitate human behaviors to accomplish specific tasks. Machine learning is a branch of artificial intelligence in which devices can learn from existing data to predict new output values. Machine learning is used in different domains, including human resources management. This chapter presents an application of machine learning in the human resources department. Machine-learning techniques help select the most suitable candidate for a job vacancy during recruitment stages based on different factors. Factors could include educational level, age, and previous experience. Based on these factors, a decision system is built using the binary classification method. The results show the effectiveness of this method in selecting the best candidate for a job vacancy, revealing the flexibility of the approach in making appropriate decisions. In addition, obtained results are accurate and independent of the dataset imprecision.

The computer-based navigation system computes the object's position, speed, and direction in real-time. In the last decades, many researchers, companies, and industries have been working on improving the existing navigation system due to its vast application in military and civilian activities. Typically, navigation systems are based on integrating inertial navigation systems and global positioning systems using a Bayesian filter, like the Kalman filter. The limitations of the Kalman filter have inspired researchers to consider alternatives based on artificial intelligence. Recently, many types of research have been developed to validate the possibility of using artificial intelligence methods in navigation systems. This chapter aims to review the integration of artificial intelligence techniques in navigation systems.

Recently, with the birth of globalization, the world has witnessed a huge growth in its energy consumption. From an agricultural society, the world has transformed itself into an industrial and knowledge society. This transformation leads to a surge in energy consumption which led to an increase in carbon emission. For this reason, renewable energy systems with zero carbon emission has become a vital need for economic comfort and environmental security. However, the main disadvantage of renewable energy is its intermittency and prediction. Solar and wind generation are usually unpredictable, which leads to several problems if the network heavily relies on renewable energy as the primary source of electricity. This chapter describes the integration of the internet of things (IoT) with renewable energy systems to cover these problems. The authors will start with an introduction to renewable energy systems and their limitations. Then, they focus on the advantage of using IoT to enhance renewable systems.

Decision-making systems are computer-based systems that interpret processed information to make the best choice. They choose an action or value from possible activities, each conducting to different outcomes. Some outcomes are favored over others based on the criteria of the decision maker. This article presents the existing technology used in decision-making systems. The authors focus then on explaining how to make the optimal decision based on the available information and criteria. The authors conclude that decision techniques are chosen based on the type of application and the quality of available data. Some techniques are simple but not accurate such as the fishbone and five whys techniques. Other methods, such as decision trees, are more complex to build, but unlike a fishbone diagram, it assists humans in evaluating upcoming choices. It forces the examination of all available outcomes of a decision and traces a possible path to the best alternative.