Abstract
With an ever increasing number of vehicles traveling the roads, traffic problems such as congestions and increased travel times became a hot topic in the research community, and several approaches have been proposed to improve the performance of the traffic networks. This paper introduces the Inverted Ant Colony Optimization (IACO) algorithm, a variation of the classic Ant Colony algorithm that inverts its logic by converting the attraction of ants towards pheromones into a repulsion effect. IACO is then used in a decentralized traffic management system, where drivers become ants that deposit pheromones on the followed paths; they are then repelled by the pheromone scent, thus avoiding congested roads, and distributing the traffic through the network. Using SUMO (Simulation of Urban MObility), several experiments were conducted to compare the effects of using IACO with a shortest time algorithm in artificial and real world scenarios - using the map of a real city, and corresponding traffic data. The effect of the behavior caused by this algorithm is a decrease in traffic density in widely used roads, leading to improvements on the traffic network at a local and global level, decreasing trip time for drivers that adhere to the suggestions made by IACO as well as for those who do not. Considering different degrees of adhesion to the algorithm, IACO has significant advantages over the shortest time algorithm, improving overall network performance by decreasing trip times for both IACO-compliant vehicles (up to 84%) and remaining vehicles (up to 71%). Thus, it benefits individual drivers, promoting the adoption of IACO, and also the global road network. Furthermore, fuel consumption and CO2 emissions from both vehicle types decrease significantly when using IACO (up to 49%).

Abstract
Denial of Service attacks, which have become commonplace on the Information and Communications Technologies domain, constitute a class of threats whose main objective is to degrade or disable a service or functionality on a target. The increasing reliance of Cyber-Physical Systems upon these technologies, together with their progressive interconnection with other infrastructure and/or organizational domains, has contributed to increase their exposure to these attacks, with potentially catastrophic consequences. Despite the potential impact of such attacks, the lack of generality regarding the related works in the attack prevention and detection fields has prevented its application in real-world scenarios. This paper aims at reducing that effect by analyzing the behavior of classification algorithms with different dataset characteristics. © 2019, Springer Nature Switzerland AG.

Abstract
Fitness assignment is one of the biggest challenges in evolutionary art. Interactive evolutionary computation approaches put a significant burden on the user, leading to human fatigue. On the other hand, autonomous evolutionary art systems usually fail to give the users the opportunity to express and convey their artistic goals and preferences. Our approach empowers the users by allowing them to express their intentions through the design of fitness functions. We present a novel responsive interface for designing fitness function in the scope of evolutionary ant paintings. Once the evolutionary runs are concluded, further control is given to the users by allowing them to specify the rendering details of selected pieces. The analysis of the experimental results highlights how fitness function design influences the outcomes of the evolutionary runs, conveying the intentions of the user and enabling the evolution of a wide variety of images. © 2014 Springer-Verlag.

Abstract
Breast Cancer is the most common type of cancer in women worldwide. In spite of this fact, there are insufficient studies that, using data mining techniques, are capable of helping medical doctors in their daily practice. This paper presents a comparative study of three ensemble methods (TreeBagger, LPBoost and Subspace) using a clinical dataset with 25% missing values to predict the overall survival of women with breast cancer. To complete the absent values, the k-nearest neighbor (k-NN) algorithm was used with four distinct neighbor values, trying to determine the best one for this particular scenario. Tests were performed for each of the three ensemble methods and each k-NN configuration, and their performance compared using a Friedman test. Despite the complexity of this challenge, the produced results are promising and the best algorithmconfiguration (TreeBagger using 3 neighbors) presents a prediction accuracy of 73%. © Springer International Publishing Switzerland 2014.

Abstract
Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess each patient's treatment on the basis of evidence-based medicine, which may not always apply to a specific patient, given the biological variability among individuals. Over the years, and for the particular case of Hepatocellular Carcinoma, some research studies have been developing strategies for assisting clinicians in decision making, using computational methods (e.g. machine learning techniques) to extract knowledge from the clinical data. However, these studies have some limitations that have not yet been addressed: some do not focus entirely on Hepatocellular Carcinoma patients, others have strict application boundaries, and none considers the heterogeneity between patients nor the presence of missing data, a common drawback in healthcare contexts. In this work, a real complex Hepatocellular Carcinoma database composed of heterogeneous clinical features is studied. We propose a new cluster-based oversampling approach robust to small and imbalanced datasets, which accounts for the heterogeneity of patients with Hepatocellular Carcinoma. The preprocessing procedures of this work are based on data imputation considering appropriate distance metrics for both heterogeneous and missing data (HEOM) and clustering studies to assess the underlying patient groups in the studied dataset (K-means). The final approach is applied in order to diminish the impact of underlying patient profiles with reduced sizes on survival prediction. It is based on K-means clustering and the SMOTE algorithm to build a representative dataset and use it as training example for different machine learning procedures (logistic regression and neural networks). The results are evaluated in terms of survival prediction and compared across baseline approaches that do not consider clustering and/or oversampling using the Friedman rank test. Our proposed methodology coupled with neural networks outperformed all others, suggesting an improvement over the classical approaches currently used in Hepatocellular Carcinoma prediction models.