TÉCNICAS COMPUTACIONALES PARA LA REDUCCIÓN DEL ESPACIO DE COLOR EN IMÁGENES DIGITALES: UNA REVISIÓN DEL ESTADO DEL ARTE

Las imágenes digitales representadas en modelos RGB almacenan grandes cantidades de información. No obstante, para realizar el procesamiento de estas imágenes se necesitan dispositivos con características especiales. Una estrategia para solventar este inconveniente es realizar una reducción del espacio de color de la imagen sin perder las características esenciales. Existen diferentes técnicas y algoritmos basados en inteligencia computacional, y más concretamente en redes neuronales y lógica difusa, que permiten la reducción del espacio de color en una imagen digital. En este artículo hacemos un análisis del estado del arte de los diferentes algoritmos y técnicas relacionadas con áreas de la inteligencia computacional para la reducción del espacio de color

An Information Security Control Assessment Methodology for Organizations

In an era where use and dependence of information systems is significantly high, the threat of incidents related to information security that could jeopardize the information held by organizations is more and more serious. Alarming facts within the literature point to inadequacies in information security practices, particularly the evaluation of information security controls in organizations. Research efforts have resulted in various methodologies developed to deal with the information security controls assessment problem. A closer look at these traditional methodologies highlights various weaknesses that can prevent an effective information security controls assessment in organizations. This dissertation develops a methodology that addresses such weaknesses when evaluating information security controls in organizations. The methodology, created using the Fuzzy Logic Toolbox of MATLAB based on fuzzy theory and fuzzy logic, uses fuzzy set theory which allows for a more accurate assessment of imprecise criteria than traditional methodologies. It is argued and evidenced that evaluating information security controls using fuzzy set theory addresses existing weaknesses found in the literature for traditional evaluation methodologies and, thus, leads to a more thorough and precise assessment. This, in turn, results in a more effective selection of information security controls and enhanced information security in organizations. The main contribution of this research to the information security literature is the development of a fuzzy set theory-based assessment methodology that provides for a thorough evaluation of ISC in organizations. The methodology just created addresses the weaknesses or limitations identified in existing information security control assessment methodologies, resulting in an enhanced information security in organizations. The methodology can also be implemented in a spreadsheet or software tool, and promote usage in practical scenarios where highly complex methodologies for ISC selection are impractical. Moreover, the methodology fuses multiple evaluation criteria to provide a holistic view of the overall quality of information security controls, and it is easily extended to include additional evaluation criteria factor not considered within this dissertation. This is one of the most meaningful contributions from this dissertation. Finally, the methodology provides a mechanism to evaluate the quality of information security controls in various domains. Overall, the methodology presented in this dissertation proved to be a feasible technique for evaluating information security controls in organizations