The difficulty of a game is intrinsically connected with the experience of immersion in it and with its success. One of the main reasons for a player to drop a game is that the game is either too easy or too hard for him/her. In practice, players become either bored or frustrated if playing a game that is not balanced for them. An approach to prevent this kind of behavior is to dynamically adjust the difficulty of a game so that the game adapts to the player's experience by evaluating the difficulty of a game and changing its environment to become easier or harder for the player. In this paper, we propose a real-time solution using a Genetic Algorithm which helps to provide the exact amount of challenge that a player needs to not be bored or frustrated thus balancing the difficulty of a game. We review several other papers that approached this problem, which characteristics an algorithm has to have to approach the problem, and how to balance this in a generic way. The main idea of this paper is to create an approach that can be modified and coupled to any kind of game by using a Genetic Algorithm.

Financial literacy is a theme that integrates public policies for the social development of a country and an element to be worked on from different aspects to improve people’s living standards, providing well-being. In this context, Stima is proposed, a system capable of acquiring knowledge from experts and tutors to help students to follow the bases of financial planning and decision-making. It aims to relate different approaches to artificial intelligence and institute a language that, through syntactic, lexical, and semantic analyzes, executed by different agents within the model, makes it possible to define financial profiles and recommend financial planning. The knowledge stored is used to propose financial monitoring standards and provides tools to assist financial decision-making. A set of eight profiles, with three indicators each, was configured by experts inside a prototype, and a volunteer student was accompanied for four months giving validations to the system.

Support vector machine (SVM) is a machine learning technique widely applied in classification problems. SVM are based on the Vapnik's Statistical Learning Theory, and successively extended by a number of researchers. On the order hand, the electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. In order to extract relevant information of EEG signal, a variety of computerized-analysis methods have been developed. Recent studies indicate that methods based on the nonlinear dynamics theory can extract valuable information from neuronal dynamics. However, many these of methods need large amount of data and are computationally expensive. From chaos theory, a global value that is relatively simple to compute is the fractal dimension (FD), it can be used to measure the geometrical complexity of a time series. The FD of a waveform represents a powerful tool for transient detection. In analysis of EEG this feature can been used to identify and distinguish specific states of physiologic function. A variety of algorithms are available for the computation of FD. In this work, we employ SVM to classify the EEG signals from healthy subjects and epileptic subjects using as the features vector the FD. From the experimental results, we can see that classification based on SVM with FD perform well in EEG signals classification, which indicates this classification method is valid and has promising application.

In recent years, human identification based on face recognition has attracted the attention of the scientific community and the general public due to its wide range of applications. A face recognition system involves three important phases: face detection, feature extraction and classification (identification and/or verification). The robustness of face recognition could be improved by treating the variations in these stages. One of the main issues in design of face recognition system is how to extract discriminative facial features. A precise extraction of a representative feature set will improve the performance of a face recognition system. Various techniques have been used to represent images efficiently, of which the most well-known and widely applied are Wavelet, Contourlet, Shearlet and Curvelet Transform. Their ability to capture localized time-frequency information of image motivates their use for feature extraction. In this paper, we conduct a systematic empirical study on these transforms as feature extractors from face images. To further reduce the feature dimensionality, we adopt Principal Component Analysis and Linear Discriminant Analysis to select the most discriminative feature sets. The performance levels delivered by each transform are contrasted in terms of the accuracy measure computed over the outputs generated by the Support Vector Machine classifier (SVM). Experimental results conducted on a publicly available database are reported whereby we observe that the Curvelet Transform followed by the Wavelet Transform significantly outperform the others according to accuracy measure calculated over the SVM classifier.

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