The Experts below are selected from a list of 1596 Experts worldwide ranked by ideXlab platform
Janusz Jurek - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Learning Model for Syntactic Pattern Recognition
Progress in Image Processing Pattern Recognition and Communication Systems, 2022Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:The novel hybrid learning model based on neural networks and grammatical inference is proposed in the paper. The model is used within the multi-derivational parsing of vague language methodology. The foundations of the methodology and the learning algorithms are presented. The model has been used for the implementation of the short term electrical load forecasting system.
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towards a model of semi supervised learning for the Syntactic Pattern Recognition based electrical load prediction system
International Conference on Parallel Processing, 2017Co-Authors: Janusz JurekAbstract:The paper is devoted to one of the key open problems of development of SPRELP system (the Syntactic Pattern Recognition-based Electrical Load Prediction System). The main module of SPRELP System is based on a GDPLL(\(k\)) grammar that is built according to the unsupervised learning paradigm. The GDPLL(\(k\)) grammar is generated by a grammatical inference algorithm. The algorithm doesn’t take into account an additional knowledge (the knowledge is partial and corresponds only to some examples) provided by a human expert. The accuracy of the forecast could be better if we took advantage of this knowledge. The problem of how to construct the model of a semi-supervised learning for SPRLP system that includes the additional expert knowledge is discussed in the paper. We also present several possible solutions.
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methodology of the construction of a gdpll k grammar based Syntactic Pattern Recognition system
Computer Recognition Systems, 2017Co-Authors: M Flasinski, Janusz JurekAbstract:GDPLL(k) grammars have been introduced as a tool for the construction of Syntactic Pattern Recognition-based systems. The grammars have been successfully used in several different applications. The practical experience with the implementation of a Syntactic Pattern Recognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.
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time series prediction for electric power industry with the help of Syntactic Pattern Recognition
International Conference on Computer Vision and Graphics, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:Load prediction is one of the most important problems in electric power industry. The prediction is usually made with the help of standard time series analysis models. The novel Syntactic Pattern Recognition-based model for the load prediction is defined in the paper. The Syntactic Pattern Recognition-based Electrical Load Prediction (SPRELP) System is described and the results concerning the reduction of the forecasting error with the comparison with other methods are presented.
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application of Syntactic Pattern Recognition methods for electrical load forecasting
Computer Recognition Systems, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:Electrical load forecasting is an important problem concerning safe and cost-efficient operation of the power system. Although many techniques are used to predict an electrical load, a research into constructing more accurate methods and software tools is still being conducted over the world. In this paper an experimental application for improving an accuracy of an electrical load prediction is presented. It is based on the Syntactic Pattern Recognition approach and FGDPLL(k) string automata. The application has been tested on the real data delivered by one of the Polish electrical distribution companies.
Tomasz Peszek - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Learning Model for Syntactic Pattern Recognition
Progress in Image Processing Pattern Recognition and Communication Systems, 2022Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:The novel hybrid learning model based on neural networks and grammatical inference is proposed in the paper. The model is used within the multi-derivational parsing of vague language methodology. The foundations of the methodology and the learning algorithms are presented. The model has been used for the implementation of the short term electrical load forecasting system.
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time series prediction for electric power industry with the help of Syntactic Pattern Recognition
International Conference on Computer Vision and Graphics, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:Load prediction is one of the most important problems in electric power industry. The prediction is usually made with the help of standard time series analysis models. The novel Syntactic Pattern Recognition-based model for the load prediction is defined in the paper. The Syntactic Pattern Recognition-based Electrical Load Prediction (SPRELP) System is described and the results concerning the reduction of the forecasting error with the comparison with other methods are presented.
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application of Syntactic Pattern Recognition methods for electrical load forecasting
Computer Recognition Systems, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:Electrical load forecasting is an important problem concerning safe and cost-efficient operation of the power system. Although many techniques are used to predict an electrical load, a research into constructing more accurate methods and software tools is still being conducted over the world. In this paper an experimental application for improving an accuracy of an electrical load prediction is presented. It is based on the Syntactic Pattern Recognition approach and FGDPLL(k) string automata. The application has been tested on the real data delivered by one of the Polish electrical distribution companies.
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analysis of fuzzy string Patterns with the help of Syntactic Pattern Recognition
Flexible Query Answering Systems, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:One of the main problems in the Syntactic Pattern Recognition area concerns analysis of distorted/fuzzy string Patterns. Classical methods developed to solve the problem are based on the error-correcting approach or the stochastic one. These methods are useful but have several limitations. Therefore, there is still the need to construct effective models of Syntactic Recognition of distorted/fuzzy Patterns. The new approach to the problem is presented in the paper. It is based on the fuzzy primitives and the new class of fuzzy automata. The advantages of the approach are presented in the paper, as well as its comparison to classical approaches.
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parallel processing model for Syntactic Pattern Recognition based electrical load forecast
International Conference on Parallel Processing, 2013Co-Authors: Michał Flasiński, Janusz Jurek, Tomasz PeszekAbstract:A model of a Recognition of distorted/fuzzy Patterns for a electrical load forecast is presented in the paper. The model is based on a Syntactic Pattern Recognition approach. Since a system implemented on the basis of the model is to perform in a real-time mode, it is parallelized. An architecture for parallel processing and a method of tasks distribution is proposed. First experimental results are also provided and discussed.
Mariusz Flasinski - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Learning Model for Syntactic Pattern Recognition
Progress in Image Processing Pattern Recognition and Communication Systems, 2022Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:The novel hybrid learning model based on neural networks and grammatical inference is proposed in the paper. The model is used within the multi-derivational parsing of vague language methodology. The foundations of the methodology and the learning algorithms are presented. The model has been used for the implementation of the short term electrical load forecasting system.
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graph grammar models in Syntactic Pattern Recognition
Computer Recognition Systems, 2019Co-Authors: Mariusz FlasinskiAbstract:The families of graph grammars used in Syntactic Pattern Recognition are characterized in the paper. The reasons for the intractability of the problem of graph language parsing are presented. The methodological principles for the constructing of efficient syntax analysis schemes for graph-based Syntactic Pattern Recognition are discussed.
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time series prediction for electric power industry with the help of Syntactic Pattern Recognition
International Conference on Computer Vision and Graphics, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:Load prediction is one of the most important problems in electric power industry. The prediction is usually made with the help of standard time series analysis models. The novel Syntactic Pattern Recognition-based model for the load prediction is defined in the paper. The Syntactic Pattern Recognition-based Electrical Load Prediction (SPRELP) System is described and the results concerning the reduction of the forecasting error with the comparison with other methods are presented.
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application of Syntactic Pattern Recognition methods for electrical load forecasting
Computer Recognition Systems, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:Electrical load forecasting is an important problem concerning safe and cost-efficient operation of the power system. Although many techniques are used to predict an electrical load, a research into constructing more accurate methods and software tools is still being conducted over the world. In this paper an experimental application for improving an accuracy of an electrical load prediction is presented. It is based on the Syntactic Pattern Recognition approach and FGDPLL(k) string automata. The application has been tested on the real data delivered by one of the Polish electrical distribution companies.
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analysis of fuzzy string Patterns with the help of Syntactic Pattern Recognition
Flexible Query Answering Systems, 2016Co-Authors: Mariusz Flasinski, Janusz Jurek, Tomasz PeszekAbstract:One of the main problems in the Syntactic Pattern Recognition area concerns analysis of distorted/fuzzy string Patterns. Classical methods developed to solve the problem are based on the error-correcting approach or the stochastic one. These methods are useful but have several limitations. Therefore, there is still the need to construct effective models of Syntactic Recognition of distorted/fuzzy Patterns. The new approach to the problem is presented in the paper. It is based on the fuzzy primitives and the new class of fuzzy automata. The advantages of the approach are presented in the paper, as well as its comparison to classical approaches.
M Flasinski - One of the best experts on this subject based on the ideXlab platform.
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methodology of the construction of a gdpll k grammar based Syntactic Pattern Recognition system
Computer Recognition Systems, 2017Co-Authors: M Flasinski, Janusz JurekAbstract:GDPLL(k) grammars have been introduced as a tool for the construction of Syntactic Pattern Recognition-based systems. The grammars have been successfully used in several different applications. The practical experience with the implementation of a Syntactic Pattern Recognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.
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on the construction of the Syntactic Pattern Recognition based expert system for auditory brainstem response analysis
Computer Recognition Systems, 2005Co-Authors: M Flasinski, Janusz Jurek, E Reron, Piotr Wojtowicz, Krzysztof AtlasiewiczAbstract:Recent developments of the construction of the Syntactic Pattern Recognition-based expert System for Auditory Brainstem Response Analysis (SABRA) are described. The paper includes a brief characterization of problems related to the the use of computer science in ABR analysis, an outline of the concept of the SABRA system and a short description of GDPLL(k) and ACLL(k) grammars as the solution for fundamental problems that arose during the design of the system.
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on the parsing of deterministic graph languages for Syntactic Pattern Recognition
Pattern Recognition, 1993Co-Authors: M FlasinskiAbstract:Abstract Further results of research into parsing of graph grammars for Syntactic Pattern Recognition are presented ( Comput. Vision Graphics Image Process. 47 , 1–21 (1989); Pattern Recognition 23 , 765–774 (1990)). A defined ETPL( k ) graph grammar is a subfamily of a well-known edNLC graph grammar (Janssens and Rozenberg, Inf. Sci. 20 , 191–216 (1980)) being a strong formalism for Pattern representation. The subfamily generates a wider class of languages than the ones presented in the previous papers. An efficient parsing algorithm is constructed, its time complexity, o ( n 2 ), is evaluated, and experimental results are included.
Ghada Badr - One of the best experts on this subject based on the ideXlab platform.
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Breadth-first search strategies for trie-based Syntactic Pattern Recognition
Pattern Analysis and Applications, 2007Co-Authors: B. John Oommen, Ghada BadrAbstract:Dictionary-based Syntactic Pattern Recognition of strings attempts to recognize a transmitted string X ^*, by processing its noisy version, Y , without sequentially comparing Y with every element X in the finite, (but possibly, large) dictionary, H . The best estimate X ^+ of X ^*, is defined as that element of H which minimizes the generalized Levenshtein distance (GLD) D ( X , Y ) between X and Y , for all X ∈ H . The non-sequential PR computation of X ^+ involves a compact trie-based representation of H . In this paper, we show how we can optimize this computation by incorporating breadth first search schemes on the underlying graph structure. This heuristic emerges from the trie-based dynamic programming recursive equations, which can be effectively implemented using a new data structure called the linked list of prefixes that can be built separately or “on top of” the trie representation of H . The new scheme does not restrict the number of errors in Y to be merely a small constant, as is done in most of the available methods. The main contribution is that our new approach can be used for generalized GLDs and not merely for 0/1 costs. It is also applicable when all possible correct candidates need to be known, and not just the best match. These constitute the cases when the “cutoffs” cannot be used in the DFS trie-based technique (Shang and Merrettal in IEEE Trans Knowl Data Eng 8(4):540–547, 1996). The new technique is compared with the DFS trie-based technique (Risvik in United Patent 6377945 B1, 23 April 2002; Shang and Merrettal in IEEE Trans Knowl Data Eng 8(4):540–547, 1996) using three large and small benchmark dictionaries with different errors. In each case, we demonstrate marked improvements with regard to the operations needed up to 21%, while at the same time maintaining the same accuracy. Additionally, some further improvements can be obtained by introducing the knowledge of the maximum number or percentage of errors in Y .
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tries in data retrieval and Syntactic Pattern Recognition
2006Co-Authors: Ghada BadrAbstract:String searching plays an important role in many problems, including text processing; information retrieval, speech and signal processing, Pattern Recognition, database operations, library systems, compilers, command interpreters, and Bioinformatics. This Thesis deals with problems related to exact and inexact string matching, and in particular, when these problems involve tries. The main aim of this research is to enhance the search performance for strings when they are stored using the trie data structure, and to develop methods that work well, in practice, especially for dictionary-based techniques. The enhancing of the search will be done for both domains, namely the exact and approximate search for strings. The Thesis presents contributions in two main fields, namely Information Retrieval and Syntactic Pattern Recognition. The following summarize the problems addressed in each of the two fields. Information retrieval. Exact search. In this part of the Thesis, we consider the problem of performing a sequence of access operations on a set of strings S = {s1, s2, ..., sN}. We assume that the strings are accessed based on a set of access probabilities P = {p1, p2, ..., pN}. We also assume that P is not known a priori, and that it is time-invariant. The problems studied involve searching for "exact" Patterns. This will be achieved by applying self-adjusting techniques for the trie data structure when the nodes of the trie are implemented as binary search trees, and by incorporating the concept of "direction" by proposing a new representation for the trie, namely the Dual-Trie (DT). Syntactic Pattern Recognition. Approximate string matching. In this part of the Thesis, we consider the traditional problem involved in the Syntactic Pattern Recognition (PR) of strings, namely that of recognizing garbled words (sequences). Let Y be a misspelled (noisy) string obtained from an unknown word X*, which is an element of a finite (but possibly, large) dictionary H stored as a trie, T. Y is assumed to contain Substitution, Insertion and Deletion (SID) errors, and we attempt to obtain an appropriate estimate X+ of X*, by processing the information contained in Y. We propose to use various Artificial Intelligence (AI) search techniques within a trie, and to optimize the dynamic programming calculations for the edit distances.
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enhancing trie based Syntactic Pattern Recognition using ai heuristic search strategies
International Conference on Advances in Pattern Recognition, 2005Co-Authors: Ghada Badr, John B OommenAbstract:This paper [5] deals with the problem of estimating, using enhanced AI techniques, a transmitted string X* by processing the corresponding string Y, which is a noisy version of X*. We assume that Y contains substitution, insertion and deletion errors, and that X* is an element of a finite (possibly large) dictionary, H. The best estimate X+ of X* is defined as that element of H which minimizes the Generalized Levenshtein Distance D(X, Y) between X and Y, for all X ∈ H. In this paper, we show how we can evaluate D(X, Y) for every X ∈ H simultaneously, when the edit distances are general and the maximum number of errors is not given a priori, and when H is stored as a trie. We first introduce a new scheme, Clustered Beam Search (CBS), a heuristic-based search approach that enhances the well known Beam Search (BS) techniques [33] contained in Artificial Intelligence (AI). It builds on BS with respect to the pruning time. The new technique is compared with the Depth First Search (DFS) trie-based technique [36] (with respect to time and accuracy) using large and small dictionaries. The results demonstrate a marked improvement up to (75%) with respect to the total number of operations needed on three benchmark dictionaries, while yielding an accuracy comparable to the optimal. Experiments are also done to show the benefits of the CBS over the BS when the search is done on the trie. The results also demonstrate a marked improvement (more than 91%) for large dictionaries.
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on optimizing Syntactic Pattern Recognition using tries and ai based heuristic search strategies
Systems Man and Cybernetics, 2005Co-Authors: Ghada Badr, B J OommenAbstract:This paper deals with the problem of estimating, using enhanced artificial-intelligence (AI) techniques, a transmitted string X/sup */ by processing the corresponding string Y, which is a noisy version of X/sup */. It is assumed that Y contains substitution, insertion, and deletion (SID) errors. The best estimate X/sup +/ of X/sup */ is defined as that element of a dictionary H that minimizes the generalized Levenshtein distance (GLD) D(X,Y) between X and Y, for all X/spl isin/H. In this paper, it is shown how to evaluate D(X,Y) for every X/spl isin/H simultaneously, when the edit distances are general and the maximum number of errors is not given a priori, and when H is stored as a trie. A new scheme called clustered beam search (CBS) is first introduced, which is a heuristic-based search approach that enhances the well-known beam-search (BS) techniques used in AI. The new scheme is then applied to the approximate string-matching problem when the dictionary is stored as a trie. The new technique is compared with the benchmark depth-first search (DFS) trie-based technique (with respect to time and accuracy) using large and small dictionaries. The results demonstrate a marked improvement of up to 75% with respect to the total number of operations needed on three benchmark dictionaries, while yielding an accuracy comparable to the optimal. Experiments are also done to show the benefits of the CBS over the BS when the search is done on the trie. The results also demonstrate a marked improvement (more than 91%) for large dictionaries.
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dictionary based Syntactic Pattern Recognition using tries
Lecture Notes in Computer Science, 2004Co-Authors: John B Oommen, Ghada BadrAbstract:This paper deals with the problem of estimating a transmitted string X * by processing the corresponding string Y, which is a noisy version of X *. We assume that Y contains substitution, insertion and deletion errors, and that X * is an element of a finite (but possibly, large) dictionary, H. The best estimate X + of X *, is defined as that element of H which minimizes the Generalized Levenshtein Distance D(X, Y) between X and Y, for all X ∈ H. All existing techniques for computing X + requires a separate evaluation of the edit distances between Y and every X ∈ H. In this paper, we show how we can evaluate D(X, Y) for every X ∈ H simultaneously, without resorting to any parallel computations. This is achieved by resorting to the use of an additional data structure called the Linked List of Prefixes (LLP), which is built “on top of” the trie representation of the dictionary. The computational advantage (for a dictionary made from the set of 1023 most common words augmented by computer-related words) gained is at least 50% and 80% measured in terms of the time and the number of operations required respectively. The accuracy forfeited is negligible.
