Szymon Łukasik

• PhD
• Professor (Assistant) at AGH University of Krakow

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to...

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging networks that offer a wide range of applications, including mine prevention, power grid maintenance, underground pipeline monitoring, and upstream oil monitoring. MI-based wireless unde...

This paper presents the application of Kolmogorov-Arnold Networks (KAN) in classifying metal surface defects. Specifically, steel surfaces are analyzed to detect defects such as cracks, inclusions, patches, pitted surfaces, and scratches. Drawing on the Kolmogorov-Arnold theorem, KAN provides a novel approach compared to conventional multilayer per...

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial tim...

The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures hav...

Outdoor positioning has become a ubiquitous technology, leading to the proliferation of many location-based services such as automotive navigation and asset tracking. Meanwhile, indoor positioning is an emerging technology with many potential applications. Researchers are continuously working towards improving its accuracy, and one general approach...

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1...

In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to...

The Transformer is an important addition to the rapidly increasing list of different Artificial Neural Networks (ANNs) suited for extremely complex automation tasks. It has already gained the position of the tool of choice in automatic translation in many business solutions. In this paper, we present an automated approach to optimizing the Transfor...

In the contemporary interconnected world, the concept of cultural responsibility occupies paramount importance. As the lines between nations become less distinct, it is incumbent upon individuals, communities, and institutions to assume the responsibility of safeguarding and valuing the landscape of diverse cultures that constitute our global socie...

Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in compu...

Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, omnichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e.,...

Product matching corresponds to the task of matching identical products across different data sources. It typically employs available product features which, apart from being multimodal, i.e., comprised of various data types, might be non-homogeneous and incomplete. The paper shows that pre-trained, multilingual Transformer models, after fine-tunin...

Clustering constitutes a well-known problem of division of unlabelled dataset into disjoint groups of data elements. It can be tackled with standard statistical methods but also with metaheuristics, which offer more flexibility and decent performance. The paper studies the application of the clustering algorithm—inspired by the territorial behavior...

LHCb at CERN, Geneva is a world-leading high energy physics experiment dedicated to searching for New Physics phenomena. The experiment is undergoing a major upgrade and will rely entirely on a flexible software trigger to process the data in real-time. In this paper a novel approach to reconstructing (detecting) long-lived particles using a new pa...

In recent years Machine Learning and Artificial Intelligence are reshaping the landscape of e-commerce and retail. Using advanced analytics, behavioral modeling, and inference, representatives of these industries can leverage collected data and increase their market performance. To perform assortment optimization – one of the most fundamentals prob...

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on...

Together with Piotr Kowalski we are editing a special issue of Algorithms (indexed in Web of Science and Scopus) devoted to "Nature Inspired Clustering Algorithms". Please feel invited to contribute! Deadline: Nov 30th, 2020. More details below https://www.mdpi.com/journal/algorithms/special_issues/Clustering_Algorithm

The Cuttlefish Algorithm, a modern metaheuristic procedure, is a very recent solution to a broad-range of optimization tasks. The aim of the article is to outline the Cuttlefish Algorithm and to demonstrate its usability in data mining problems. In this paper, we apply this metaheuristic procedure for a clustering problem, with the Calinski-Harabas...

The images obtained by X-Ray or computed tomography (CT) may be contaminated with different kinds of noise or show lack of sharpness, too low or high intensity and poor contrast. Such image deficiencies can be induced by adverse physical conditions and by the transmission properties of imaging devices. A number of enhancement techniques in image pr...

Analyzing astronomical observations represents one of the most challenging tasks of data exploration. It is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of Big Data problems - like data variety - are also present, datasets size represents the most significant obstacl...

Extracting useful information from astronomical observations represents one of the most challenging tasks of data exploration. This is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of big data problems (like data variety) are also present, the size of datasets represe...

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correla...

The aim of this article is to present research involving the employment of intelligent methods for image analysis, particularly, the binarization process. In this case, the Flower Pollination Algorithm was used to optimize the internal parameters of the Niblack binarization algorithm. As a criterion for the quality of the proposed solution, the mor...

Nature inspired metaheuristics were found to be applicable in deriving best solutions for several optimization tasks, and clustering represents a typical problem which can be successfully tackled with these methods. This paper investigates certain techniques of cluster analysis based on two recent heuristic algorithms mimicking natural processes: t...

The discovery of atypical elements has become one of the most important challenges in data analysis and exploration. At the same time it is not an easy matter, with difficult conditions, and not even strictly defined. This paper presents a ready-to-use procedure for identifying atypical elements, in the sense of rare occurring. The issue is conside...

Dear Friends and Colleagues, Both the Program Committee and the Organizing Committee have pleasure inviting you and your PhD students to participate in Doctoral Symposium in Recent Advances in Information Technology. We would like to kindly remind you that DS-RAIT'18 and FedCSIS'18 submission deadline (May 15th 2018) is quickly approaching. We w...

Nowadays, with the rapid development of digital image processing, there has been a notable increase in elaborating advanced tools for studying the internal structure of objects. This may be very helpful in characterizing certain morphological traits of grains, as well as in quantifying the differences between them. The current research was carried...

This e-book contains conference material from two concurrent conferences: − 3rd Conference on Information Technology, Systems Research and Computational Physics (ITSRCP'18), − 6th International Symposium CompIMAGE’18 – Computational Modeling of Objects Presented in Images: Fundamentals, Methods, and Applications (CompIMAGE'18), which has been org...

This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, usin...

This paper presents a ready-to-use procedure for detecting atypical (rarely occurring) elements, in one- and multidimensional spaces. The issue is considered through a conditional approach. The application of nonparametric concepts frees the investigated procedure from distributions of describing and conditioning variables. Ease of interpretation a...

Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attrib...

This paper describes a new approach to metaheuristic-based data clustering by means of Krill Herd Algorithm (KHA). In this work, KHA is used to find centres of the cluster groups. Moreover, the number of clusters is set up at the beginning of the procedure, and during the subsequent iterations of the optimization algorithm, particular solutions are...

Task of clustering, that is data division into homogeneous groups represents one of the elementary problems of contemporary data mining. Cluster analysis can be approached through variety of methods based on statistical inference or heuristic techniques. Recently algorithms employing novel metaheuristics are of special interest – as they can effect...

A study was conducted so as to develop a methodology for wheat variety discrimination and identification by way of image analysis techniques. The main purpose of this work was to determine a crucial set of parameters with respect to wheat grain morphology which best differentiate wheat varieties. To achieve better performance, the study was done by...

In the paper methods aimed at handling high-dimensional weather forecasts data used to predict the concentrations of PM10, PM2.5, SO2, NO, CO and O3 are being proposed. The procedure employed to predict pollution normally requires historical data samples for a large number of points in time – particularly weather forecast data, actual weather data...

In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose of this article is to compare the KHA optimization algorithm used for learning an artificial neural network (...

Modern optimization has in its disposal an immense variety of heuristic algorithms which can effectively deal with both continuous and combinatorial optimization problems. Recent years brought in this area fast development of unconventional methods inspired by phenomena found in nature. Flower Pollination Algorithm based on pollination mechanisms o...

The Krill Herd Algorithm is the latest heuristic technique to be applied in deriving best solution within various optimization tasks. While there has been a few scientific papers written about this algorithm, none of these have described how its numerous basic parameters impact upon the quality of selected solutions. This paper is intended to contr...

Particle swarm optimization constitutes currently one of the most important nature-inspired metaheuristics, used successfully for both combinatorial and continuous problems. Its popularity has stimulated the emergence of various variants of swarm-inspired techniques, based in part on the concept of pairwise communication of numerous swarm members s...

Classification of data streams is currently a very important task. Datasets characterized by constant influx of data are predominantly massive and often have various types of features. Even more challenging is to classify evolving streams. Various approaches have been proposed to deal with this problem. In this paper we will present a new method ba...

The paper deals with the issue of reducing dimension and size of a data set (random sample) for purposes of exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of smaller dimension, while keeping as much as possible the same distances between particular elements. Elements...

This publication deals with the applicational aspects and possibilities of the Complete Gradient Clustering Algorithm—the classic procedure of Fukunaga and Hostetler, prepared to a ready-to-use state, by providing a full set of procedures for defining all functions and the values of parameters. Moreover, it describes how a possible change in those...

A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, with the requirement that it has the least possible influence on the respective locations of sample elements. For this purpose an original ve...

This paper investigates a possibility of supplementing standard dimensionality reduction procedures, used in the process of knowledge extraction from multidimensional datasets, with topology preservation measures. This approach is based on an observation that not all elements of an initial dataset are equally preserved in its low-dimensional embedd...

Seeds dataset, used in: M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 15-24. See also: https://archive.ics.uci....

Celem niniejszej publikacji jest zaprezentowanie aplikacyjnych aspektów i własności kompletnej postaci gradientowego algorytmu klasteryzacji, a także ich ilustracja dla konkretnych praktycznych zadań z zakresu analizy systemowej. Podstawową cechą powyższego algorytmu jest brak wymagań dotyczących arbitralnego ustalenia liczby klastrów, co umożliwia...

The aim of this paper is to present a Complete Gradient Clustering Algorithm, its applicational aspects and properties, as well as to illustrate them with specific practical problems from the subject of bioinformatics (the categorization of grains for seed production), management (the design of a marketing support strategy for a mobile phone operat...

Przedmiotem niniejszej pracy jest wielowymiarowa analiza danych, która realizowana jest poprzez uzupełnienie standardowych procedur ekstrakcji cech odpowiednimi miarami zachowania struktury topologicznej zbioru. Podejście to motywuje obserwacja, że nie wszystkie elementy zbioru pierwotnego w toku redukcji są właściwie zachowane w ramach reprezentac...

This paper deals with dimensionality and sample length reduction applied to the tasks of exploratory data analysis. Proposed technique relies on distance preserving linear transformation of given dataset to the lower dimensionality feature space. Coefficients of feature transformation matrix are found using Fast Simulated Annealing - an algorithm i...

Methods based on kernel density estimation have been successfully applied for various data mining tasks. Their natural interpretation together with suitable properties make them an attractive tool among others in clustering problems. In this paper, the Complete Gradient Clustering Algorithm has been used to in-vestigate a real data set of grains. T...

Współczesne sieci teleinformatyczne w coraz większym stopniu wykorzystują metody transmisji radiowej. Pozwalają one na zmniejszenie kosztów związanych z budową infrastruktury sieciowej, a rosnąca wydajność łączności bezprzewodowej umożliwia jej zastosowanie także w przypadkach, gdy wymagana jest wysoka sprawność przesyłu informacji. Pasmo użyteczny...

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The first part of the paper is devoted to the detailed description of the existing algori...

Przedmiotem niniejszej pracy jest zagadnienie redukcji wymiaru i liczności próby losowej z przeznaczeniem do procedur eksploracyjnej analizy danych, określonych przy użyciu metodyki statystycznych estymatorów jądrowych. Koncepcja opiera się na liniowej transformacji przestrzeni, przy czym współczynniki macierzy wyznaczane są z zastosowaniem metaheu...

Data clustering constitutes at present a commonly used technique for extracting fuzzy system rules from experimental data. Detailed studies in the field have shown that using above-mentioned method results in significantly reduced structure of fuzzy identification system, maintaining at the same time its high modelling efficiency. In this paper a c...

The paper describes an application of Parallel Simulated Annealing (PSA) for solving one of the most studied NP-hard optimization problems: Graph Coloring Problem (GCP). Synchronous master-slave model with periodic solution update is being used. The paper contains description of the method, recommendations for optimal parameters settings and summar...

Deriving parameters and structure of fuzzy model for a dynamical system by means of a clustering procedure is a very popular and frequently applied technique in fuzzy identification. The aim of the paper is to present a novel method of fuzzy model formulation based on this approach. Introduced algorithm is based on clustering method employing nonpa...

Data clustering constitutes at present a commonly used technique for extracting fuzzy system rules from experimental data. Detailed studies in the field have shown that using above-mentioned method results in significantly reduced structure of fuzzy identification system, maintaining at the same time its high modelling efficiency. In this paper a c...

Nonparametric methods find increasing number of applications in the area of data analysis and data exploration. In this paper the most popular tool of those methods was presented – kernel estimators. Beside of kernel estimators' concept, practical estimation aspects and examples of applications in determining distributions from nuclear physics expe...

Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic density estimation. One of its most important disadvantages is computational complexity of calculations needed, especially for data-based bandwidth selection and adaptation of bandwidth coefficient. The article presents parallel methods which can significantly...