Piotr S. Szczepaniak

Advances in Web Intelligence

Web Intelligence (WI) presents excellent opportunities and challenges for the research and development of new generation of Webbased information processing technology, as well as for exploiting Webbased advanced applications. Based on two perspectives of WI research: an intelligent Web-based business-centric schematic diagram and the conceptual levels of WI, we investigates various ways to study WI and potential applications.

Fuzzy systems in medicine

L. Karwan, P.S. Szcepaniak: In Memoriam.- L.A. Zadeh: Foreword.- P.J. Lisboa: Preface.- Tutorials: J. Kacprzyk: Fuzzy Sets and Fuzzy Systems: A Brief Introduction.- E. Straszecka: Defining Membership Functions.- T.J. Ross: Membership Functions, Fuzzification and Defuzzification.- R.I. John: Fuzzy Sets and Knowledge Representation.- M. Baczynski, J. Drewniak: Monotonic Fuzzy Implications.- I. Bruha, P. Berka: Discretization and Fuzzification of Numerical Attributes in Attribute-Based Learning.- R. Babuska: Fuzzy Clustering Algorithms with Applications to Rule Extraction.- W. Pedrycz: Neurofuzzy Systems.- E. Sanchez, P. Pierre: Intelligent Decision Making Systems: From Medical Diagnosis to Vocational Guidance.- Case Studies: Signal Processing: E. Czogala, J. Leski: Entropy and Energy Measures of Fuzziness in ECG Signal Processing.- A. Grauel: Fuzzy Logic System for ECG Interpretation.- J. Petersen, G. Stockmanns, W. Nahm: EEG Analysis for Assessment of Depth of Anaesthesia.- Image Processing and Interpretation: G. Berks, D.G.v. Keyserlingk: Fuzzy Sets in Medical Image Processing.- H.R. Tizhoosh, G. Krell, B. Michaelis: Enhancement of Megavoltage Images in Radiation Therapy Using Fuzzy and Neural Image Processing Techniques.- F. Masulli, A. Schenone, A.M. Massone: Fuzzy Clustering Methods for the Segmentation of Multimodal Medical Images.- F. Behloul, A.-O. Boudraa, M. Janier, P. Croisille: Fuzzy Clustering for Parametric Map Construction in Myocardial Perfusion Magnetic Resonance Images.- P.R. Innocent, R.I. John, M. Barnes: Neuro-Fuzzy Models of Radiographic Image Classification.- F. waidelich, H. Eichfeld, R. Graumann: Segmentation of Medical Images Using Fuzzy Technique.- M.-C. Jaulent, V.Bombardier, I. Cherrak, O. Perez-Oramas: Fuzzy Quantification of Artery Lesions in Renal Arteriographies.- I. Bloch: Fusion of Numerical and Structural Image Information in Medical Imaging in the Framework of Fuzzy Sets.- Control, Diagnosis and Therapy: A. Bardossy, A. Blinowska: Fuzzy Reasoning in Pacemaker Control.- D.A. Linkens, M.F. Abbod, J.K. Backory, J.S. Shieh: Closed-Loop Control of Anaesthesia Using Fuzzy Logic.- D.G. Mason, N.D. Edwards: Self-Learning Fuzzy Logic Control of Anaesthetic Intravenous Infusions.- D. Rutkowska, A. Starczewski: Fuzzy Inference Neural Networks and Their Applications to Medical Diagnosis.- J.A. Swope, N.K. Kasabov, M.J.A. Williams: Neuro-Fuzzy Modelling of Heart Rate Signals and Application to Diagnostics.- S. Zahan: Fuzzy Diagnosis by Score-Based Tests - Implementation Issues.- E. Rakus-Andersson, T. Gerstenkorn: A Comparison of Fuzzy Decision Models Supporting the Optimal Therapy.- K. Cerbioni, C. Colosimo, A. Starita, P. Dario: A Neuro-Fuzzy System for Emulation of Human Grasp Movements.- Knowledge Based Systems: W. Duch, R. Adamczak, K. Grabczewski, G. Zal, Y. Hayashi: Fuzzy and Crisp Logical Rule Extraction Methods in Application to Medical Data.- R. Molitor, C.B. Tresp: Extending Description Logics to Vague Knowledge in Medicine.- T.E. Rothenfluh, K. Bogl, K.-P. Adlassnig: Represenation and Acquisition of Knowledge for a Fuzzy Medical Consultation System.- J.M. Garibaldi, E.C. Ifeachor: The Development of a Fuzzy Expert System for the Analysis of Umbilical Cord Blood.- J.Novotny, F. Babinec: Fuzzy Expert System for Exercise Therapy in Diabetics.

Intelligent exploration of the web

Creation and Representation of Web Resources.- Structure Analysis and Generation for Internet Documents.- A Fuzzy System for the Web Page Representation.- Flexible Representation and Retrieval of Web Documents.- Information Retrieval.- Intelligent Information Retrieval on the Web.- Internet as a Challenge to Fuzzy Querying.- Internet Search Based on Text Intuitionistic Fuzzy Similarity.- Content-Based Fuzzy Search in a Multimedia Web Database.- Self-Organizing Maps for Interactive Search in Document Databases.- Methods for Exploratory Cluster Analysis.- Textual Information Retrieval with User Profiles Using Fuzzy Clustering and Inferencing.- Intelligent Clustering as Source of Knowledge for Web Dialogue Manager in an Information Retrieval System.- Document Clustering Using Tolerance Rough Set Model Its Application to Information Retrieval.- Improving Web Search by the Identification of Contextual Information.- Intelligent Internet-Based Multiagent Systems.- Neural Agent for Text Database Discovery.- Intelligent Web Agents that Learn to Retrieve and Extract Information.- Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach.- Web Browsing Using Machine Learning on Text Data.- Retrieval of Semistructured Web Data.- Intelligent Retrieval of Hypermedia Documents.- Bootstrapping an Ontology-Based Information Extraction System.- Web Data Mining and Use.- Intelligent Web Mining.- A Neural Net Approach to Data Mining: Classification of Users to Aid Information Management.- Web-Based Expert Systems: Information Clients versus Knowledge Servers.

On the Relationship Between Active Contours and Contextual Classification

To discuss the relationship between active contours and contextual classification, a formal definition of the contour as well as a uniform approach to the all active contour methods are proposed first, and then a contextual classification problem is introduced and formalized. The equivalence relationship between contours and classifiers, thoroughly considered and illustrated by examples, proves to allow incorporation of the methods and techniques specific for the active contour approach to the contextual classification and vice versa.

E-Commerce and Intelligent Methods

1. Foundations of Electronic Commerce.- Barriers to Global Electronic Commerce.- Foundations of Electronic Data Interchange.- Some Legal Aspects of Electronic Commerce.- 2. Neural Networks.- Competitive Neural Networks for Customer Choice Models.- CRM in e-Business: a Clients Life Cycle Model Based on a Neural Network.- Customer Relationship Management Systems: The Application of Fuzzy ART Neural Network.- Characterizing and Segmenting the Online Customer Market Using Neural Netwoks.- Data Mining for Diverse E-Commerce Applications.- Extreme Sample Classification and Credit Card Fraud Detection.- 3. Evolutionary Programming.- A Review of Evolutionary Algorithms for E-Commerce.- Artificial Adaptive Market Traders Based in Genetic Algorithms for a Stock Market Simulator.- Data Mining in Marketing Using Bayesian Networks and Evolutionary Programming.- Improving User Profiles for E-Commerce by Genetic Algorithms.- 4. Fuzzy Logic.- Automatic Web User Profiling and Personalization Using Robust Fuzzy Relational Clustering.- Fuzzy Quantifiable Trust in Secure E-Commerce.- Fuzzy Similarity in E-Commerce Domains.- 5. CBR and Agents.- Agencies of Agents for Logistic Applications.- Intelligent Customer Support for Product Selection with Case-Based Reasoning.- Mobile Agent Based Auctionlike Negotiation in Internet Retail Commerce.- Author Index.

Adaptive potential active hypercontours

In this paper, the idea of adaptive potential active hypercontours (APAH) as a new method of construction of an optimal classifier is presented. The idea of active hypercontours generalizes the traditional active contour methods, which are extensively developed in image analysis, and allows the application of their concepts in other classification tasks. In the presented implementation of APAH the evolution of the potential hypercontour is controlled by simulated annealing algorithm (SA). The method has been evaluated on the IRIS and MNIST databases and compared with traditional classification techniques.

Notes on a Linguistic Description as the Basis for Automatic Image Understanding

Notes on a Linguistic Description as the Basis for Automatic Image Understanding The main paradigm of image understanding and a concept for its practical machine realisation are presented. The crucial elements of the presented approach are the formalisation of human knowledge about the class of images that are to be automatically interpreted, a linguistic description and the realization of cognitive resonance.

Classification of RSS-Formatted documents using full text similarity measures

The Web is enormous, unlimited and dynamically changed source of useful and varied kinds of information. The news is one of the most rapidly changing kinds of information. The departure for this paper is presentation of RSS, an useful data format used frequently by publishers of news; some statistics related to news syndication illustrate the actual situation. Then, two recently developed methods for examination of similarity of textual documents are briefly presented. Since RSS-supplied records always contain the same type of information (headlines, links, article summaries, etc.), application of methods of presented type makes their diverse applications like automatic news classification and filtering easier.

Contribution of Active Contour Approach to Image Understanding

The paper proposes a formal, though a simple, model of the activity of human vision system. That model serves as a reference point for description of relational and contextual recognition and allows to formulate a definition of image understanding problem. As a potential solution of that problem active contour methods are proposed, which do not possess some limitations of classic recognition algorithms. Moreover, they can be used not only as methods of construction of contextual pixel classifiers, but also as methods of identification of relations between any arbitrary more complicated structures (concepts).

Active contours as knowledge discovery methods

In the paper we show that active contour methods can be interpreted as knowledge discovery methods. Application area is not restricted only to image segmentation, but it covers also classification of any other objects, even objects of higher granulation. Additional power of the presented method is that expert knowledge of almost any type can be used to classifier construction, which is not always possible in case of classic techniques. Moreover, the method introduced by the authors, earlier used only for supervised classification, is here applied in an unsupervised case (clustering) and examined on examples.