Jana Nowaková

Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree

The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area – in mammography, in addition to the creation of the list of similar images – cases. The created list is used for assessing the nature of the finding – whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.

Geometrical and topological approaches to big data

Abstract Modern data science uses topological methods to find the structural features of data sets before further supervised or unsupervised analysis. Geometry and topology are very natural tools for analysing massive amounts of data since geometry can be regarded as the study of distance functions. Mathematical formalism, which has been developed for incorporating geometric and topological techniques, deals with point cloud data sets, i.e. finite sets of points. It then adapts tools from the various branches of geometry and topology for the study of point cloud data sets. The point clouds are finite samples taken from a geometric object, perhaps with noise. Topology provides a formal language for qualitative mathematics, whereas geometry is mainly quantitative. Thus, in topology, we study the relationships of proximity or nearness, without using distances. A map between topological spaces is called continuous if it preserves the nearness structures. Geometrical and topological methods are tools allowing us to analyse highly complex data. These methods create a summary or compressed representation of all of the data features to help to rapidly uncover particular patterns and relationships in data. The idea of constructing summaries of entire domains of attributes involves understanding the relationship between topological and geometric objects constructed from data using various features. A common thread in various approaches for noise removal, model reduction, feasibility reconstruction, and blind source separation, is to replace the original data with a lower dimensional approximate representation obtained via a matrix or multi-directional array factorization or decomposition. Besides those transformations, a significant challenge of feature summarization or subset selection methods for Big Data will be considered by focusing on scalable feature selection. Lower dimensional approximate representation is used for Big Data visualization. The cross-field between topology and Big Data will bring huge opportunities, as well as challenges, to Big Data communities. This survey aims at bringing together state-of-the-art research results on geometrical and topological methods for Big Data.

Conventional controller design based on Takagi-Sugeno fuzzy models

Although it is long since the first PID controller design method was developed, new methods are still being created and no general applicable method has been found. The paper extends the set of designing methods of PID controllers. Design methods can be divided into classic (analytical tuning methods, optimization methods etc.) and not so common fuzzy knowledge based methods. In this case, a conjunction of both methods is presented. The classic design methods (Ziegler-Nichols step response method and Chien, Hrones and Reswick design method) are used for achieving the knowledge of fuzzy knowledge based design methods. The fuzzy knowledge based design methods are based on Takagi-Sugeno model. It is defined as a class of systems for which - using the new proposed method - the settling time is shorter or the settling time is nearly the same but without overshoot, which could be also very useful. The proof of efficiency of the proposed methods and a numerical experiment is presented including a comparison with the conventional methods (simulated in the software environment Matlab-Simulink).

Colorectal cancer liver metastases: laparoscopic and open radiofrequency-assisted surgery.

Introduction The liver is the most common site of colorectal metastases (colorectal liver metastases – CLM). Surgical treatment in combination with oncological therapy is the only potentially curative method. Unfortunately, only 10–25% of patients are suitable for surgery. Traditionally, open liver resection (OLR) is usually performed. However, laparoscopic liver resection (LLR) has become popular worldwide in the last two decades. Aim To evaluate the effectiveness and benefits of radiofrequency minor LLR of CLM in comparison with OLR. Material and methods The indication for surgery was CLM and the possibility to perform minor laparoscopic or OLR not exceeding two hepatic segments according to Couinauds classification. Results Sixty-six minor liver resections for CLM were performed. Twenty-five (37.9%) patients underwent a laparoscopic approach and 41 (62.1%) patients underwent OLR. The mean operative time was 166.4 min for LLR and 166.8 min for OLR. Average blood loss was 132.3 ±218.0 ml during LLR and 149.5 ±277.5 ml during OLR. Length of hospital stay was 8.4 ±2.0 days for LLR and 10.5 ±5.8 days for OLR. All resections were R0. There was no case of mortality. Postoperative complications were recognized in 9 (13.6%) patients: 8 in the group of OLR patients and 1 in the LLR group. The median survival time for LLR was 70.5 months and for OLR 61.9 months. The 5-year overall survival rate was higher for LLR vs. OLR – 82.1% vs. 69.8%. The average length of disease-free interval after LLR was greater (52.2 months) in comparison with OLR (49.4%). The 5-year disease-free interval was 63.2% for LLR and 58% for OLR. Conclusions Outcomes and oncological radicality of minor laparoscopic liver resections of CLM are comparable to outcomes of OLR.

Guided Genetic Algorithm for the Influence Maximization Problem

Influence maximization is a hard combinatorial optimization problem. It requires the identification of an optimum set of k network vertices that triggers the activation of a maximum total number of remaining network nodes with respect to a chosen propagation model. The problem is appealing because it is provably hard and has a number of practical applications in domains such as data mining and social network analysis. Although there are many exact and heuristic algorithms for influence maximization, it has been tackled by metaheuristic and evolutionary methods as well. This paper presents and evaluates a new evolutionary method for influence maximization that employs a recent genetic algorithm for fixed–length subset selection. The algorithm is extended by the concept of guiding that prevents selection of infeasible vertices, reduces the search space, and effectively improves the evolutionary procedure.

Barzilai-Borwein method in graph drawing algorithm based on Kamada-Kawai algorithm

Extension of Kamada-Kawai algorithm, which was designed for calculating layouts of simple undirected graphs, is presented in this paper. Graphs drawn by Kamada-Kawai algorithm exhibit symmetries, tend to produce aesthetically pleasing and crossing-free layouts for planar graphs. Minimization of Kamada-Kawai algorithm is based on Newton-Raphson method, which needs Hessian matrix of second derivatives of minimized node. Disadvantage of Kamada-Kawai embedder algorithm is computational requirements. This is caused by searching of minimal potential energy of the whole system, which is minimized node by node. The node with highest energy is minimized against all nodes till the local equilibrium state is reached. In this paper with Barzilai-Borwein (BB) minimization algorithm, which needs only gradient for minimum searching, instead of Newton-Raphson method, is worked. It significantly improves the computational time and requirements.

Fuzzy Linear Regression Analysis

Abstract The theoretical background for abstract formalization of vague phenomenon of the complex systems is fuzzy set theory. In the paper vague data as specialized fuzzy sets - fuzzy numbers are defined and it is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. Interval and fuzzy regression technologies are discussed, the linear fuzzy regression model is proposed. To identify fuzzy regression coefficients of model genetic algorithm is applied. The numerical example is presented and the possibility area of vague model is illustrated.

Takagi-Sugeno Fuzzy Model in Task of Controllers Design

The designing of PID controllers is in many cases a discussed problem. Many of the design methods have been developed, classic (analytical tuning methods, optimization methods etc.) or not so common fuzzy knowledge based methods. In this case, the amount of fuzzy knowledge based methods is extended. New way of designing PID controller parameters is created, which is based on the relations of Chien, Hrones and Reswick design method (the modification of Ziegler-Nichols step response method). The proof of efficiency of the proposed method and a numerical experiment is presented including a comparison with the conventional Chien, Hrones and Reswick method (simulated in the software environment Matlab-Simulink). It is defined a class of systems for which - using the new proposed method - the settling time is shorter or the settling time is nearly the same but without overshoot, which could be also very useful.

Correction to: Medical Image Retrieval Using Vector Quantization and Fuzzy S-Tree

The article Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree, written by Jana Nowaková, Michal Prílepok and Václav Snášel, was originally published electronically on the publisher’s internet portal (currently SpringerLink) on December 15, 2016 without open access.

An acceleration of quasigroup operations by residue arithmetic

Quasigroup operations are essential for a wide range of cryptographic procedures that includes cryptographic hash functions, electronic signatures, pseudorandom number generators, and stream and block ciphers. Quasigroup cryptography achieves high levels of security at low memory and computational costs by an iterative application of quasigroup operations to streams and blocks of data. The use of large quasigroups can further improve the strength of cryptographic operations. However, the order of used quasigroups is the main factor affecting the memory requirements of quasigroup cryptographic schemes. Alternative quasigroup representations that do not store their multiplication tables in computer memory yield increased computational costs. In any case, an efficient implementation of quasigroup operations is critical for practical applications of quasigroup cryptography. Residue number systems allow a fast, concurrent realization of addition and multiplication. In this work, residue arithmetic is used to accelerate quasigroup operations, and an efficient computational approach to their implementation, designed with respect to the extended instruction sets of modern processors, is proposed.