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In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans.In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don't always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don't like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm.Combining both optimization-and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks.We've developed a context-aware emotionbased model to design intelligent agents for group decision-making processes. To evaluate this model, we've incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion-and argumentbased factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

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The study of multi-criteria problems adapted to the context of Ubiquitous Group Decision Support Systems (UbiGDSS) is covered in the literature through very different perspectives and interests. There are scientific studies related to the multi-criteria problems that lie across argumentation-based negotiation, multi-agent systems, dialogues, etc. However, to perform most of these studies, a high amount of information is required. The usage of so much data or information that is difficult to collect or configure can bring good results in theoretical scenarios but can be impossible to use in the real world. In order to overcome these issues, we present in this paper a general template to configure multi-criteria problems adapted for the contexts of UbiGDSS that intends to be easy and fast to configure, appellative, intuitive, permits to collect a lot of data and helps the decision-maker transmitting his beliefs and opinions to the system. Our proposal includes three sections: Problem Data, Personal Configuration and Problem Configuration. We have developed a prototype with our template with the purpose to simulate the configuration of a multi-criteria problem. We invited real decision-makers to use our prototype in a simulated scenario and asked to them to fulfil a survey in the end in order to study our hypotheses. Our general template achieved good results and proved to be very perceptible and fast to configure.