Informatica

Structural break detection is an important time series analysis task. It can be treated as a multi-objective optimization problem, in which we ought to find a time series segmentation such that time series theoretical models constructed on each segment are well-fitted and the segments are long enough to bear meaningful information. Metaheuristic optimization can help us solve this problem. This paper introduces a suite of new cost functions for the structural break detection task. We demonstrate that the new cost functions allow for achieving quantitatively better precision than the cost functions employed in the literature of this domain. We show particular advantages of each new cost function. Furthermore, the paper promotes the use of Particle Swarm Optimization (PSO) in the domain of structural break detection, which so far has relied on the Genetic Algorithm (GA). Our experiments show that PSO outperforms GA for many analysed time series examples. Last but not least, we introduce a non-trivial generalization of the top-performing state-of-the-art approach to the structural break detection problem based on the Minimum Description Length (MDL) rule with autoregressive (AR) model to MDL ARIMA (autoregressive integrated moving average) model.

In this paper we propose modifications of the well-known algorithm of particle swarm optimization (PSO). These changes affect the mapping of the motion of particles from continuous space to binary space for searching in it, which is widely used to solve the problem of feature selection. The modified binary PSO variations were tested on the dataset SVC2004 dedicated to the problem of user authentication based on dynamic features of a handwritten signature. In the example of k-nearest neighbours (kNN), experiments were carried out to find the optimal subset of features. The search for the subset was considered as a multicriteria optimization problem, taking into account the accuracy of the model and the number of features.

In this paper, we present an effective algorithm for solving the Poisson–Gaussian total variation model. The existence and uniqueness of solution for the mixed Poisson–Gaussian model are proved. Due to the strict convexity of the model, the split-Bregman method is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for mixed Poisson–Gaussion noise removal. Comparison with other existing and well-known methods is provided as well.

Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others.

This paper offers an analysis of HIV/AIDS dynamics, defined by CD4 levels and Viral load, carried out from a macroscopic point of view by means of a general stochastic model. The model focuses on the patient's age as a relevant factor to forecast the transitions among the different levels of seriousness of the disease and simultaneously on the chronological time. The third model considers the two previous features simultaneously. In this way it is possible to quantify the medical scientific progresses due to the advances in the treatment of the HIV. The analyses have been performed through non-homogeneous semi-Markov processes. These models have been implemented by using real data provided by ISS (Istituto Superiore di Sanità, Rome, Italy). They refer to 2159 subjects enrolled in Italian public structures from September 1983 to January 2006. The relevant results include also the survival analysis of the infected patients. The computed conditional probabilities show the different responses of the subjects depending on their ages and the elapsing of time.