Dr. Ricardo Villalón Fonseca

Dr. Ricardo Villalón Fonseca

Proyectos

Publicaciones

Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

Descripción:

The objective of this work is to detect Alzheimer’s disease using Magnetic Resonance Imaging. For this, we use a three-dimensional densenet-121 architecture. With the use of only freely available tools, we obtain good results: a deep neural network showing metrics of 87% accuracy, 87% sensitivity (micro-average), 88% specificity (micro-average), and 92% AUROC (micro-average) for the task of classifying five different classes (disease stages). The use of tools available for free means that this work can be replicated in developing countries.

Tipo de publicación: Conference Paper

Publicado en: Lecture Notes in Computer Science

The nature of security: A conceptual framework for integral-comprehensive modeling of IT security and cybersecurity

Descripción:

Cybersecurity is a broadly defined concept comprising security for many different types of elements. Dealing with cybersecurity is a multidimensional problem, and the damage generated by cyberattacks can be very diverse. Reports about cybersecurity show recurrent problems, or increasing on their frequency of appearance, with no clear approach for solving them. Existing models deal with cybersecurity in several different but general ways, and results are not better. Consequently, managing cybersecurity deserves consideration of a new approach. Our approach is based on the nature of security. Security services are modeled around three basic security concepts, namely isolation, interaction, and representation. With these three concepts, a cybersecurity development starts with security objectives for overcoming the cybersecurity challenges, and also has a security representation to achieve integral and comprehensive security results. We propose an architecture-based security conceptual framework having three components, namely a system representation model kind, a security representation model kind, and a security process model kind, to accomplish the security process for a system. The security process is fully guided and supported with security objectives from the beginning to the end. The framework proposes several models, based on data structures for representing the system, the security, and the process itself. The models are scalable to represent systems of any size, from tiny to huge technology infrastructures, and with support for automation of the security process. The scope of the framework is the security of IT systems and cybersecurity, including information, software, virtual resources, hardware, IT devices, money, people, and other related physical objects being represented digitally. The framework was developed while creating a university cloud infrastructure, and consolidated while supporting the security of several national wide software and infrastructure applications for digital signature in Costa Rica. We aim to provide a new and innovative way for doing cybersecurity, by directly targeting the actual security requirements; with a simple, systemic, structured and potentially automated security process, and for achieving integral and comprehensive security solutions.

Tipo de publicación: Journal Article

Publicado en: Computers & Security

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Descripción:

Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86 mean accuracy, 0.86 mean sensitivity (micro-average), 0.86 mean specificity (micro-average), and 0.91 area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.

Tipo de publicación: Journal Article

Publicado en: Sensors

Micro Evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic

Descripción:

This work introduces the algorithm Micro Evolutionary Particle Swarm Optimization (MEPSO), which is based on the Particle Swarm Optimization algorithm (PSO). MEPSO’s main idea is to replace PSO’s update of velocity with evolutionary mutations and crossovers that occur probabilistically. MEPSO uses two control hyperparameters of the evolutionary operators, and , to improve convergence ability. These hyperparameters allow the algorithm to adapt to different problems. MEPSO algorithm has a crucial feature: the use of micropopulations. This results in reduced computational complexity, faster convergence rate, prevention of premature convergence, and easy implementation. The proposed approach provides the basis for further study of MEPSO’s suitability for dynamic and multiobjective optimization. We evaluated MEPSO and PSO against 14 chosen benchmark functions. PSO’s results revealed that it had an average success rate of 84% and a median computation time of 49.9 s. MEPSO, on the other hand, had a success rate of 81% and a computation time of 8 s. We found MEPSO to be faster when we performed a right-tail Wilcoxon Signed-Rank statistical test against the Mean Computation Time of 30 runs for each function for each algorithm.

Tipo de publicación: Journal Article

Publicado en: Systems and Soft Computing