Tomasz Pełech-Pilichowski

AGH University of Science and Technology in Kraków | AGH · Institute of Computer Science

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, A...

Decyzje i działania (odpowiedzi) podejmowane na podstawie pełnej i wiarygodnej analizy czynników sprawczych, presji, zmiany parametrów stanu i ich wpływu na środowisko, społeczeństwo oraz gospodarkę mogą z dużym prawdopodobieństwem w długoterminowej perspektywie przynieść pożądane efekty. W artykule opisano model DPSIR (driving forces, pressures, s...

Environmental issues have remained one of the most challenging social-economic impacts on the world and most countries. Tackling these challenges has remained an underlying issue as a concise approach, method, and policy are yet to be globally made available. Machine learning (ML) with support from IoTs, big data, NLP, and cloud computing is radica...

Introduction . The internet of things integration with wireless sensor networks can monitor and record the physical conditions of the environment and forward the collected data to a central location. Natural language processing provides a human-data relationship (H2DR) that deep learning uses to train models of artificial neural networks representi...

The tabular structure of legal texts and the principles of their drafting result in the frequent use of various types of references, which has a negative impact on the comprehensibility of the law. As legal texts are nowadays drafted and made available in electronic format, it is reasonable to try to develop automated mechanisms for checking the co...

Modern research on lawmaking in electronic format confirms the thesis that legal texts made available by means of various types of legal information systems form a hypertext structure. Furthermore, research on hypertexts confirms that the use of this technology undermines the structuralist theories of text that underlie traditional methods of legis...

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The paper presents the concept of time series decomposition by splitting into components with linear filtering methods. The modified moving trend algorithm (MTF) allows for more precise specification of desired trend properties and periodic component extraction from seasonal time series. In the paper the time and frequency properties of classical a...

Data processing algorithms are used for business, industry and public sector to filter input data, calculate values, detect abrupt changes, acquire information from data or to ensure signal consistency. It is an important research area for Big Data processing and processing of data received from Internet of Things. Typically, classical algorithms a...

This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The abilit...

This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague informat...

The article analyses the possibility of applying immune-like processing of legal texts in the legislative process. It also discusses the required format for recording legal information and the relationships between the formats for recording legal information and the methods used to analyse it. On this ground we argue that the formal analysis of a l...

This chapter is devoted to a method which is able to process hybrid data, i.e., to jointly handle both randomness and imprecision. Random variables are described by probability distributions and imprecise values are modelled using possibility distributions. The main advantage of the proposed method is that it takes into account the dependencies bet...

This chapter proposes the integrated fuzzy approach to solve Multi Attribute Decision Problems. Fuzzy Analytical Hierarchy Process (FAHP) is used to assign relative weights to criteria, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank the alternatives. The use of the proposed approach is illustrated...

This chapter proposes an approach to visualisation of fuzzy numbers in one-, two- and more-dimensional spaces. The proposed approach is based on ScPovPlot3D templates for POVRay.

This chapter describes different methods for comparing and ordering fuzzy numbers. Theoretically, fuzzy numbers can only be partially ordered, and hence cannot be compared. However, in practical applications, such as decision making, scheduling, market analysis or optimisation with fuzzy uncertainties, the comparison of fuzzy numbers becomes crucia...

This chapter introduces some basic concepts from the fuzzy numbers theory. The main part of the chapter concerns the problem of performing arithmetic operations on independent fuzzy numbers. The remaining part is devoted to the problem of performing arithmetic operations on interactive fuzzy numbers. To cope with the dependency problem, stochastic...

This chapter presents the concept of uncertainty propagation in real world desicion problems, where some input parameters are stochastic while information about others is partial and is represented by fuzzy random variable. It also introduces fuzzy random variable and the Dempster-Shafer theory which provide mathematical background for such propaga...

This chapter describes the application of fuzzy sets to planning and scheduling of production in the steel industry. Primarily, the problem of steel grade assignment to customer’ orders is analysed. Fuzzy sets are used to reduce the variety of potential steel grades and to describe characteristic of materials by decision makers. Next, fuzzy logic s...

This chapter presents a new method for forecasting the level and structure of market demand for industrial goods. The method employs two data mining methods: k-means clustering and fuzzy decision tree learning. The k-means method serves to separate groups with items of a similar consumption level and structure of the analysed products. Whereas, fuz...

An original approach to digital moving trend based filters (MTF) design, based on Bode plots analysis is proposed, aimed at seasonal time series decomposition and prediction [5]. A number of polynomials of different range are discussed to be used in the MTF as the LS approximation formula. The Bode plots of the MTF are shown, and the best filter is...

This contributed volume is a result of discussions held at ABICT’13(4th International Workshop on Advances in Business ICT) in Krakow, September 8-11, 2013. The book focuses on Advances in Business ICT approached from a multidisciplinary perspective and demonstrates different ideas and tools for developing and supporting organizational creativity,...

"... This paper outlines the problems of storing and presenting information in the context of the coding of the meaning of words and signs. Finally, the problems of the permanent preservation of data and information are discussed. .."

W książce omówiono perspektywy, jakie otwierają się przed procesami utrwalania, opracowywania, przetwarzania i przekazywania informacji prawnych dzięki zastosowaniu nowoczesnych języków znacznikowych w zapisie tekstu prawa. Jest to pierwsza dostępna na polskim rynku praca, która omawia nie tylko prawne i techniczne uwarunkowania algorytmizacji proc...

In the paper, problems of legal information digitalization are investigated. Conditions for extraction information from legal texts (i.a. normative acts) related to the common ones processing (non-legal terms, in English) are outlined. Problems of dimensionality reduction and application of similarity measures are discussed. Sample results of simil...

One of the techniques recommended for calculating the nonparametric trend (nonstationary, low-frequency time series component) is the moving trend based smoothing [10], [3], [4] typically based on linear Least Square (LS) approximates of the series in a moving window. Such algorithm splits input time series into three sections, starting, central, f...

Futurists and scientists alike profess the coming of a new era in the history – the knowledge era. The notion of knowledge is as old as humans’ self-consciousness, but new challenges appear. The meaning of the word “knowledge” is changing from cognitive notion to a technical term denoting a structured economic resource to be actively managed. This...

An original approach to digital moving trend based filters (MTF) design, based on Bode plots analysis is proposed, aimed at seasonal time series decomposition and prediction. A number of polynomials of different range are discussed to be used in the MTF as the LS approximation formula. The Bode plots of the MTF are shown, and the best filter is sel...

In the paper, a novel two-level algorithm of time-series change detection is presented. In the first level, to identify non-stationary sequences in a processed signal, preliminary detection of events is performed with a short-term prediction comparison. In the second stage, to confirm the changes detected in the first level, a similarity method aim...

In the paper, problems of legal information digitalization are investigated. Conditions for extraction information from legal texts related to the common ones processing (non-legal terms) are outlined. Sample results of similarity analysis are presented. Further research aimed at semantic analysis of legal texts are outlined.

Visualization of empirical data is important part of data analysis process as well as presentation of results. Usually number and characteristic of gathered data is suitable for this task. Unfortunately there are numerous cases when one have to cope with insufficient number of data which in turn can be irregularly distributed over measurement area.

In this paper, a hybrid two-level algorithm of the original changes in diagnostic signals received from multiple technical devices is presented. Research are aimed at identification of the changes, deviations or patters (events), through concurrent diagnostic signals processing, which occur in one selected signal (proposed algorithm is adjusted to...

In the paper, a novel two level algorithm of time series change detection is presented. In the first level, to identify non-stationary sequences in processed signals preliminary detection of events is performed with short-term prediction comparison. In the second stage, to confirm changes detected in first level a unique changes similarity method i...

This article presents an idea of application of immune paradigm to symptomatic event detection, announcing long-term trend changes in time series that give a possibility to improve efficiency of short and medium-term prediction based on linear trend extrapolation. A conception of immune-like algorithm and definition of main system objects are prese...

The paper is focused on the T-lymphocyte construction applied to immune-inspired event detection in financial time series. The goal is to recognize symptoms of abrupt of long-time mean value of many processed series. The task of the T-lymphocyte is to distinguish between “healthy” and “illness” states through examining individual series, with algor...