Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC's performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

In this paper, we present a novel system for cognitive stimulation therapy to progressively assess cognitive impairment and emotional well-being of dementia patients in social care settings. The system assesses patients interactions and computes performance scores for different areas of cognitive stimulation. Patient interactions

Purpose -This paper aims to address a fundamental problem related to the interaction of rulebased autonomous agents in pervasive and intelligent environments. Some rules of behaviour can lead a multi-agent system to display unwanted periodic behaviour, such as networked appliances cycling on and off. Design/methodology/approach -The paper presents a framework called interaction networks (INs) as a tool to describe and analyse this phenomena. In support of this, and as an aid to the visualisation and understanding of the temporal evolution of agent states, a graphical multi-dimensional model (MDM) is offered. An instability prevention system (INPRES) based in identifying and locking network nodes is described. Findings -Both IN, MDM and INPRES enable system designers to identify and prevent cyclic instability. The effectiveness of the approach is evaluated using both simulated and physical implementations.Research limitations/implications -The problem of cyclic instability is strongly related to the number of cycles in the IN associated. It is postulated that high coupling and high number of cycles contributes to the system to self-lock; however, more research is needed in this direction. Practical implications -The MDM, interaction benchmark, IN theory, INPRES and intelligent locking offer a practical solution to the problem of cyclic behaviour. Originality/value -Before this work there was no framework for analysing and eliminating the problem of cyclic instability in rule-based multi-agent systems.

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