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Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings.

The most common type of liver cancer is hepatocellular carcinoma (HCC), which begins in hepatocytes. The HCC, like most types of cancer, does not show symptoms in the early stages and hence it is difficult to detect at this stage. The symptoms begin to appear in the advanced stages of the disease due to the unlimited growth of cancer cells. So, early detection can help to get timely treatment and reduce the mortality rate. In this paper, we proposes a novel machine learning model using seven classifiers such as K-nearest neighbor (KNN), random forest, Naive Bayes, and other four classifiers combined to form stacking learning (ensemble) method with genetic optimization helping to select the features for each classifier to obtain highest HCC detection accuracy. In addition to preparing the data and make it suitable for further processing, we performed the normalization techniques. We have used KNN algorithm to fill in the missing values. We trained and evaluated our developed algorithm using 165 HCC patients collected from Coimbra’s Hospital and University Centre (CHUC) using stratified cross-validation techniques. There are total of 49 clinically significant features in this dataset, which are divided into two groups such as quantitative and qualitative groups. Our proposed algorithm has achieved the highest accuracy and F1-score of 0.9030 and 0.8857, respectively. The developed model is ready to be tested with huge database and can be employed in cancer screening laboratories to aid the clinicians to make an accurate diagnosis.

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article – which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems. Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for. Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice. The key issue is the question how to use these networks in mining sciences. The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication in the journal „Archives of Mining Sciences“. Only studying these two articles will provide sufficient knowledge for initial guidance in the area of issues under consideration here.

The paper discusses and compares two different ways of adapting artificial intelligence systems. One is founded on a well known biological mechanism of gradual training of neurons or other parameters. The second one uses a significant extra feature of training data that ably makes us possible to adapt the artificial intelligence system in more effective way than nature does in biological systems. This extra feature is availability of all training data before the adaptation process begins till an end of which all these data have to be constant. This feature provides an ability to analyze training data globally and very quickly tune an artificial intelligence system with them. The paper focus the attention on this important difference between biological and artificial intelligence problems because in most cases of artificial intelligence problems training data are gathered, available and constant during the training process. On the other hand, the biological nervous systems gather training data during the whole life, have to change the inner model, so training is a very good solution for them because it makes them possible to tune with changing training data. Artificial intelligence systems can also use training inherent in biological systems but in most cases it is possible to find more quickly and effectively the solution if only the mentioned feature is met. The above thesis is illustrated by means of some examples.

The task of carrying out semantic searches for useful information was solved to some degree for textual data. Unfortunately, medical image analysis has demonstrated that the issue of searching for useful semantic information, on the basis of content, is an insolvable task, in practice. We have acquired a certain expertise in using automatic techniques for retrieving features that define the semantic content of visual data. Everything seems to suggest that the techniques of syntactic pattern analysis which are useful in computer-aided medical diagnosis can also be very helpful in tasks involving automatic understanding procedures used in medical image interpretation systems. Therefore, the topic of this paper will be the presentation of application possibilities for graph EDT grammars as well as context-free grammars as cognitive techniques supporting medical pattern semantic interpretation. Those mathematical linguistic mechanisms will be used for the automatic generation of syntactic descriptions of the contents of the analyzed types of medical images originating from pancreas ERCP examinations, spinal cord visualization and wrist radiograms. The semantics of those patterns define the type of incorrectness of the organ shown on it. Attention will be focused on the methods of cognitive analysis with respect to medical data. So, the languages of shape description and proposed grammars mainly allow for the creation of syntactic descriptions of selected anatomic organs together with a definition of the semantic meaning of the changes in their shapes. Such descriptions will allow us to create a semantically-oriented representation of visual data describing pattern features which are useful in medical decisions and computer-aided diagnostic systems.

Notes on a Linguistic Description as the Basis for Automatic Image UnderstandingThe 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.

The paper aims at methodological studies on selection of optimal neural network that performs modeling of chemical reactivity of a given group of chemical compounds. The problem (prediction of biological activity in enzymatic reaction catalyzed by ethylbenzene dehydrogenase) is taken as a case study for assessment of various types of neural networks. The main goal of the study is to select the best type of the network, optimal dimension of the layers, proper parameters of the network as well as the optimal form of data representation for purpose of neural-based modeling of complex empirical data. Various approaches (linear networks, regression and classification multiple layer perceptrons, generalized regression neural networks) are compared and tested.

This paper presents a new technique of computer-aided analysis and recognition of pathological wrist bone lesions. This method uses artificial intelligence (AI) techniques and mathematical linguistics allowing to evaluate automatically and analyse the structure of the said bones, based on palm radiological images. Possibilities of computer interpretation of selected images, based on the methodology of automatic medical image understanding, as introduced by the authors, were created owing to the introduction of an original relational description of individual palm (wrist) bones. This description has been built with the use of graph linguistic formalisms already applied in artificial intelligence. These were, however, developed and adjusted to the needs of automatic medical image understanding in earlier works of the authors, as specified in the bibliography section of this paper. The research described in this paper has demonstrated that the for needs of palm (wrist) bone diagnostics, specialist linguistic tools such as expansive graph grammars and EDT-label graphs are particularly well-suited. Defining a graph image language adjusted to the specific features of the scientific problem here-described allowed for a semantic description of correct palm bone structures (with consideration to idiosyncratic features). It also enabled interpretation of images showing some in-born lesions, such as additional bones; or acquired lesions such as their incorrect junctions resulting from injuries and synostoses.

This paper will present a new approach to the interpretation of complex X-ray images, an approach taking advantage of artificial intelligence and soft computing. The tasks of the analysis and the interpretation of the cognitive meaning of selected medical diagnostic images are made possible owing to the application of graph image languages based on EDG-type grammars. The subject of this paper will be to present the wide range of semantic interpretation possibilities of hand and wrist radiogrammes using language formalisms. The objective of the analysis conducted is to detect pathological lesions of the in-born or of the acquired character as well as bone morphology lesions and bone dislocation as seen in bone defects. The application of graph formalisms, based on EDG languages, allows us to make an effective classification of its meaning with polynomial complexity.

The traditional approach to automated analysis of medical data is mostly based on statistical or theoretical-decision methods of pattern recognition. Using such methods, we can obtain many valuable items, e.g., tracking patients' movements, control of medical treatment, etc. In medical informatics there are still many problems not solvable by computers and reserved for human medical staff. Such problems can be solved by the development of scientific research toward machine intelligence. In this paper, we try to show how the computer can understand medical data instead of simple processing and analysis. Sometimes it may be useful to make semantic content analysis leading to automatic understanding of medical data, e.g., for intelligent help in the diagnosis process or in semantics-based searching through medical databases. This paper will present the application of the cognitive-based approach for intelligent semantic analysis, allowing describing automatically important diagnostic features of analyzed images. This approach is based on a special kind of image description language and grammar formalism. During the linguistic analysis of medical patterns, we can solve the problem of generalization of features of a selected image and obtaining semantic content description of the image. The most important part of this analysis depends on the "cognitive resonance" process, in which features of real images are compared with some kind of expectation taken from the knowledge base containing knowledge regarding pathological cases originating from medical practice.

This paper will present application of syntactic pattern recognition methods for analysis of morphology of selected organs visualised on medical images. It will discuss ways of discovering pathological symptoms of chronic diseases such as ischaemic heart disease, upper urinary tract disorders as well as pancreas neoplasm. An analysis of the morphology of these structures is possible with the application of the syntactic analysis and pattern recognition methods, in particular with the attributed grammar of LALR type and application of languages of description of shape features and tree grammars. In the paper, all stages of analysis and understanding of images in the context of obtained features will be described, and also will be presented the proper algorithm of syntactic reasoning basing on the acquired knowledge.

This paper presents a new approach to the application of structural pattern recognition methods for image understanding, based on content analysis and knowledge discovery performed on medical images. This presents in particular computer analysis and recognition of local stenoses of the coronary arteries lumen. These stenoses are the result of the appearance of arteriosclerosis plaques, which in consequence lead to different forms of ischemic cardiovascular diseases. Such diseases may be seen in the form of stable or unstable disturbances of heart rhythm or infarctions. Analysis of the correct morphology of these arteries lumen is possible with the application of the syntactic analysis and pattern recognition methods, in particular with the attributed grammar of LALR type. In the paper, we shall describe all stages of analysis and understanding of images in the context of obtained features, and we shall also present the proper algorithm of syntactic reasoning based on the acquired knowledge.

Experiments with virtual instruction systems undertaken at the University of Mining and Metallurgy of Cracow are described, particularly the preparation of computer-assisted teaching materials and their on-line distribution. Successfully dealing with the latter requires an answer to the fundamental question: what is the role of the Internet in education--to assist or to replace the instructor? An attempt is made to answer the question on the basis of an analysis of the properties and features of both the human teacher and the computer. A proposed conceptual model for the optimal distribution of educational tasks between human instructors and ICT facilities postulates that human teachers are needed to help students transform the detailed and varied information obtained via the Internet into knowledge and wisdom. Other areas explored are those of electronic publication, Internet access to university library resources, and the educational qualities of the global computer network. The main conclusion is that ...

This paper will present new possibilities for the application of image recognition methods and AI application in biomedical informatics as well as semantically oriented analysis of 2D images of coronary arteries originating from coronography examinations. In particular this paper presents the possibilities for computer analysis and recognition of local stenoses of the lumen of coronary arteries via the application of syntactic methods of pattern recognition. Such stenoses are the result of the appearance of arteriosclerosis plaques, which in consequence lead to different forms of ischemic cardiovascular diseases. Such diseases may be seen in the form of stable or unstable disturbances of heart rhythm or infarction. Analysis of the correct morphology of these artery lumina is made possible with the application of syntactic analysis and pattern recognition methods, in particular with the attribute, context-free grammar of look-ahead LR(1) type.

With the advances in pattern recognition technology, information systems have been applied to medical databases throughout the world. The most important problems in such access are how the pathological lesions can be efficiently recognized and how information in the respective databases can be summarized to enable intelligent selection of interesting cases of illness based on visual queries. The main goal of this paper is a presentation of the possibilities of the application of structural graph grammar-based methods of computer medical image analysis to the recognition and creation of a semantic description of the symptoms of lesions in the forms of local stenoses of the lumen of coronary arteries, and to the detection of pathological signs in upper parts of the ureter ducts and renal calyxes. An analysis of the correct morphology of these structures is possible owing to the application of sequence and graph methods from the group of syntactic methods of pattern recognition. In the analysis of co...

We present new algorithms for the recognition of morphologic changes and shape feature analysis, which have been proposed to be used in a diagnosis of pathologic symptoms characteristic of cancerous and inflammatory lesions. These methods have been used so far for early detection and diagnosis of neoplastic changes in pancreas and chronic pancreatitis based on x-ray images acquired by endoscopic retrograde cholangiopancreatography (ERCP). Preliminary processing of x-ray images involves binarization, and, subsequently, pancreatic ducts shown in the pictures are subjected to the straightening transformation, which enables obtaining two-dimensional width graphs that show contours of objects with their morphologic changes. Recognition of such changes was performed using attributed context-free grammars. Correct description and diagnosis of some symptoms (e.g., large cavitary projections) required two-dimensional analysis of width graphs. In such cases, languages of shape feature description with special multidirectional sinquad distribution were additionally applied.

This paper focuses on clustering of time series discrete data. In time series data, each instance represents a different time step and the attributes give values associated with that time. In the presented approach, we consider discrete data, i.e., the set of values appearing in a time series is finite. For ant-based clustering, we use the algorithm based on the versions proposed by J. Deneubourg, E. Lumer and B. Faieta. As a similarity measure, the so-called consistency measure defined in terms of multistage decision transition systems is proposed. A decision on raising or dropping a given episode by the ant is made on the basis of a degree of consistency of that episode with the knowledge extracted from the neighboring episodes.

The last decade of the 20th century, sometimes called the time of the Information Revolution, has a decisive influence on how we live and work today. Development of such domains as computer science and telecommunication is the source of formation of economy based on knowledge. Knowledge begins to play the role of a strategic factor, often more important than land, financial capital or natural resources considered the “motors” of the Industrial Age.

The paper will discuss in detail the new possibilities for making linguistic description and semantic interpretations of 64-slice spiral CT coronary vessels visualizations with the use of AI linguistic formalisms and especially ETPL(k) graph grammar. Current research shows that a significant part of diagnostic imaging, including of coronary arteries, is still difficult to automatically assess using computer analysis techniques aimed at extracting information having semantic meaning. The proposed syntactic semantic description makes it possible to intelligently model the examined structure and then to automatically find the locations of significant stenoses in coronary arteries and identify their morphometric diagnostic parameters.

Due to the constant development of expert systems, broadening of the application area of decision support algorithms, and together with the appearance of new organizational structures, which were defined in world literature as knowledgebase organization, more and more often we come across the concept of competence in the context of information systems.

This paper describes proposal for the application modified Ant Colony Optimization Algorithm in the task for recruitment and selection of employees. After analyzing the combinatorial problem involving multicriterial process of recruitment and selection model proposed non-compensating its solution using the modified ACO heuristic strategy, showing a lack of opportunities to receive appropriate the resulting matrix, related to the accurate prediction of the decision at an acceptable as satisfactory for implementation only available deterministic algorithms.

This publication describes two new classes of specialised information systems: UBIAS and E-UBIAS. Such systems belong to the group of cognitive reasoning systems and are designed for the semantic analysis of complex patterns having the form of biomedical images. These systems are predecessors of a new generation of intelligent systems for understanding medical image data and using this data as biometric characteristics.

In the paper is presented the brief history of research conducted in Poland (at AGH University) in the area of image processing, analysis and recognition. The history is connected with changes of the technology used for problem solving. First systems are build from the separate integrated circuits of low and medium scale of integration, after this we develop some systems build on the base of VLSI elements, and now the new systems are constructed on the base of FPGA technology. Nevertheless all the time scientific group from AGH was conducted research in technological applications of vision systems. At 70th and 80th years of XX century it was pioneer works devoted to building of second in Poland and one of the first in Europe systems for computer processing of the images, now research is dedicated mainly toward real time image processing systems.

This paper describes new classes of specialised information systems called UBIAS and E-UBIAS. Such systems belong to the group of cognitive reasoning systems and are designed for the semantic analysis of complex patterns having the form of biomedical images. These systems are predecessors of a new generation of intelligent systems for understanding of merit content of medical visual data, and using this data as biometric characteristics.

Neural Networks are often used for solving practical problems and are known and appreciated as an effective soft computing tool. Nevertheless we cannot forget that neural networks are models of parts of the biological brain. Very simplified models, but similar in general behavior to some psychological phenomena. Therefore sometimes we can obtain interesting observations during study of neural network behavior and we can discover on this basis some new psychological ideas. Paper presents an example of such approach. Self-learning process (called also unsupervised learning) is an interesting form of neural network application, slightly different from other neural networks issues. During this process the network ought to discover new knowledge instead of registration of existing knowledge performed by the network during normal supervised learning. This process is described and discussed in many papers, but all of them are goal oriented ones: the main goal of the research is how to obtain the best self learning result e.g., in term of input data classification or similarity measurement. Meanwhile, during the self-learning process some phenomena can be encountered, very interesting from the psychological point of view, when the neural network self-learning process is considered as a model of cognitive processes occurring in our mind during self-learning or thinking. Such phenomena, observed during neural networks self-learning processes are described in the paper. Their psychological interpretation lead to hypothesis of artificial dreams, which can be discovered in neural networks and first time reported in this paper. In the paper, examples of such artificial dreams are presented and discussed. The problem under consideration is definitely controversial one, but the phenomena itself is interesting as a new interpretation of processes observed in neural networks.

Cognitive processes that occur in the human brain as part of the processes of perception, of acquiring information/data, then analysing, interpreting, recognising and understanding it, and also as part of the reasoning process have become the basis for the development of computer systems for cognitive data analysis. A characteristic feature which distinguishes cognitive data analysis systems from other intelligent information systems used to interpret and analyse data is the process of semantic data analysis conducted in the course of interpreting the meaning of the analysed data. This process can be executed thanks to the precise model structures, parameters and indicators collected in the system (in the form of a database). This publication presents cognitive systems called UBIAS (Understanding Based Image Analysis Systems) which are used to analyse and interpret medical image data. These systems have been used, inter alia, to support diagnostic and therapeutic tasks in the process of diagnosing foot bone lesions.

This chapter presents a proposal of medically justified modulations of the frequency of cardiac reporting, implemented in a client-server distant cooperation model. The supervising center analyzes incoming messages and other conditions, and issues the desired reporting interval back to the remote device. As a result of the authors’ tests and simulations, this method may reduce wireless channel usage and increase remote autonomy up to three times.

This chapter presents basic facts about the social impact and frequency of cardiovascular diseases in aging societies. Being the primary cause of mortality in developed countries, cardiovascular abnormalities receive the most of the attention in the medical world. The problem is particularly observed in developed countries with a significantly longer life expectancy (Japan, Canada) and leading in healthcare organization and research. Also considering the acuteness of a typical cardiac failure gives proper attention to why cardiology is given so much importance in the treatment of life-threatening situations. Making progress in cardiac diagnosis and treatment, including modern and wide-range surveillance, is potentially beneficial to whole societies and countries. It also influences both life length and comfort, which are today among the most appreciated of human values.

The problem tackled in this work is the semantic interpretation and an attempt at computer automatic understanding of a 3D structure of spatially visualised coronary vessels with the use of AI graph-based linguistic formalisms. At the stage of the initial analysis, it was found that the problem is subject to numerous important limitations. These limitations result, among other things from the serious obstacles encountered in the development of a universal standard, defining the model shape of the healthy or diseased organ that could possibly undergo typical recognition. Due to this difficulties a decision was made to apply the methods of automatic image understanding for the interpretation of the images considered, which consequently leads to their semantic descriptions. For this purpose the linguistic approach was applied.

The work presents the new opportunity for making semantic descriptions and analysis of medical structures, especially coronary vessels CT spatial reconstructions, with the use of AI graph-based linguistic formalisms. In the paper there will be discussed the manners of applying methods of computational intelligence to the development of a syntactic semantic description of spatial visualisations of the heart’s coronary vessels. Such descriptions may be used for both smart ordering of images while archiving them and for their semantic searches in medical multimedia databases. Presented methodology of analysis can furthermore be used for attaining other goals related performance of computer-assisted semantic interpretation of selected elements and/or the entire 3D structure of the coronary vascular tree. These goals are achieved through the use of graph-based image formalisms based on IE graphs generating grammars that allow discovering and automatic semantic interpretation of irregularities visualised on the images obtained during diagnostic examinations of the heart muscle. The basis for the construction of 3D reconstructions of biological objects used in this work are visualisations obtained from helical CT scans, yet the method itself may be applied also for other methods of medical 3D images acquisition. The obtained semantic information makes it possible to make a description of the structure focused on the semantics of various morphological forms of the visualised vessels from the point of view of the operation of coronary circulation and the blood supply of the heart muscle. Thanks to these, the analysis conducted allows fast and — to a great degree — automated interpretation of the semantics of various morphological changes in the coronary vascular tree, and especially makes it possible to detect these stenoses in the lumen of the vessels that can cause critical decrease in blood supply to extensive or especially important fragments of the heart muscle.

This chapter will demonstrate that artificial intelligence methods based on linguistic mechanisms for semantic meaning reasoning can be used to develop new classes of intelligent information systems, and can be applied quite successfully to conduct in-depth meaning analyzes in the presented DSS (Diagnostic Support Systems) information systems as well as in a subclass of intelligent, cognitive systems used to analyze images: UBIAS (Understanding Based Image Analysis Systems). The study will present an IT mechanism for describing the meaning of analyzed objects using selected examples of analyzes of medical images, including those of the spinal cord and bone radiograms. The presented semantic reasoning procedures are based on the cognitive resonance model and have been applied for the job of interpreting the meaning of a selected type of diagnostic images of the central nervous system as well as images of the bone system. The solutions and applications presented here are of a research nature and show the directions in which modern IT systems as well as medical diagnostic support systems expand into the field of automatic, computer meaning interpretation of various patterns acquired in image diagnostics.

This paper demonstrates that AI methods can be applied to the development of intelligent IT systems. They also facilitate an in-depth analysis of the meaning presented in cognitive categorization information systems - in particular UBIAS systems (Understanding Based Image Analysis Systems). This paper also presents the IT mechanisms of object meaning description on selected examples of long bone fractures image analysis. The procedures for such semantic reasoning are based on the model of cognitive resonance and cognitive analysis. These have been applied to the task of interpreting the meaning of selected diagnostic images from the long bone fractures system as an intelligent analysis module in IT systems. The application presented in this paper is of a research character and it serves the preparation of efficient lesion detection methods applied to a dataset originating from of the long bone fractures structures.

This paper will present the achievements of its authors related to the development of new cognitive information systems classes used in the tasks of automatic understanding of image data semantics. Such systems are a practical implementation of the paradigm for machine semantics understanding of selected image data types, with special regards to various classes of medical images. The development of such systems is possible owing to defining computer procedures of cognitive resonance, as used by the developed new classes of intelligent systems for pattern recognition and semantic reasoning. In particular, we shall present UBIAS (understanding based image analysis systems) system classes, proposed by the authors for the interpretation of medical planar diagnostic images and for the modeling of spatial anatomy structures.

This paper presents some possibilities of applying mathematical linguistic formalisms, in the form of structural pattern recognition, in the development of a new class of intelligent medical diagnosis-support and image recognition systems - cognitive vision systems. Such methods are aimed at facilitating the semantic analysis of the meaning of some selected medical patterns based on the presence of cognitive resonance. When trying to apply this cognitive resonance to the automatic analysis and interpretation of image-type data, we propose a new class of information systems: UBIAS (understanding based image analysis systems). The procedures proposed are based on the cognitive resonance and categorization model. The application presented in this paper will show how great the opportunities are for automatically detecting lesions in the analysed structures.

In the paper the new approach for cognitive modeling of some medical structures, using picture grammar have been presented. Especially was discussed the method of artificial intelligence dedicated to the syntactic-based semantic inference of selected RTG visualizations with particular attention to the leg bones. The presented methodology makes possible to describe for each biological structure, its semantic characteristics, and allow to formulate a semantic meaning of pathological deformation. Presented approach proofed that cognitive informatics based on semantic formalisms can be used to develop a new classes of intelligent computer-aided diagnosis systems. Such systems authors called UBIAS (understanding based image analysis systems).

The chapter shows how we can predict and evaluate the biological activity of particular chemical compounds using neural networks models. The purpose of the work was to verify the usefulness of various types and different structures of neural networks as well as various techniques of teaching the networks to predict the properties of defined chemical compounds, prior to studying them using laboratory methods. The huge number and variety of chemical compounds, which can be synthesized makes the prediction of any of their properties by computer modeling a very attractive alternative to costly experimental studies. The method described in this chapter may be useful for forecasting various properties of different groups of chemical compounds. The purpose of this chapter is to present the studied problem (and obtained solutions) from the point of view of the technique of neural networks and optimization of neural computations.

This study presents new classes of cognitive categorization systems, with particular emphasis on one selected system class: UBIAS (Understanding Based Image Analysis Systems) used to analyze images. The purpose of defining new classes of cognitive categorization systems is to conduct an in-depth analysis of various kinds of data with particular attention to the semantic/meaning contents of the data analyzed. The presented techniques of semantic analysis are based on using cognitive data analysis. This paper presents new opportunities for applying cognitive categorization techniques to model semantic interpretations of selected medical images. Methods of computational intelligence for developing a syntactic-based semantic description of selected medical visualizations with particular attention to leg bones are detailed. The presented methodology makes it possible to find, for each biological structure, its semantic description and allows the semantic meaning of visible deformation or pathology to be formulated. The presented approach also proves that artificial intelligence methods based on linguistic mechanisms can be used to develop new classes of intelligent information systems – UBIAS Systems.

The work presents new possibilities for making semantic description and interpretation of some medical visualization with the use of Computational Intelligence linguistic formalisms. The methodology described in this work makes possible to find, for each considered biological 3D structure, its semantic description whose elements make reference to the medical significance of the entire structure described, while at the same time refrain from the formal differences (i.e. appearance and shape) of individual visualisations. Such methodology may be further used for attaining other goals related to computer-assisted semantic interpretation of selected elements and/or the entire 3D structure of the coronary vascular tree. The obtained semantic information allows to make a description of the structure, focused on the semantics of various heart muscle lesions. Thanks to these, the analysis conducted allows fast and automated interpretation of the semantics of various morphological changes in the coronary vascular tree. Especially it is possible to detect stenoses in the lumen of the vessels that can cause critical decrease of blood supply to extensive or especially important fragments of the heart muscle. The basis for the construction of 3D reconstructions of biological objects used in this work are visualisations obtained from helical CT scans, yet the method itself may be applied also for other methods of medical 3D images acquisition.

The work presents the new opportunity for making semantic descriptions of medical structures with the use of AI graph-based linguistic formalisms and especially graph grammar of edNLC type. In the paper there will be discussed in detail the manners of applying methods of computational intelligence to the development of a syntactic semantic description of spatial visualizations of the heart's coronary vessels. Thanks to these, the analysis conducted allows fast and - to a great degree - automated interpretation of the semantics of various morphological changes in the coronary vascular tree, and especially makes it possible to detect these stenoses in the lumen of the vessels that can cause critical decrease of blood supply to the heart muscle. Presented methodology of analysis can furthermore be used for attaining other goals related performance of computer-assisted semantic interpretation of selected elements and/or the entire 3D structure of the coronary vascular tree.

In the paper a new way of pattern interpretation directed for automatic semantic categorization and image content understanding will be described. Such an understanding will be based on the linguistic theories of pattern classification and is aimed at facilitation of merit content analysis of for some classes of medical and economical patterns. The approach presented in this paper will show the great possibilities of automatic diseases interpretation in some analysed structures, and supporting information management using the grammar approach. The interpretation will be based on cognitive resonance processes, which imitate the psychological processes of understanding the registered patterns as they take place in the brain of a human being.

In the paper a new way of intelligent medical pattern analysis directed for automatic semantic categorization and merit content understanding will be presented. Such an understanding will be based on the linguistic mechanisms of pattern interpretation and categorisation and is aimed at facilitation of in-depth analysis of the meaning for some classes of medical patterns, especially in the form of planar images or spatial reconstructions of selected organs. The approach presented in this paper will show the great possibilities of automatic lesion detection in the analysed structures using the grammar approach to the interpretation and classification tasks, based on cognitive resonance processes. Cognitive methods imitate the psychological and neurophysiological processes of understanding the analysed patterns or cases, as they take place in the brain of a qualified professional.

This paper presents the way of application of structural methods of Artificial Intelligence-in particular, linguistic mechanisms of semantic meaning reasoning-for development of intelligent medical information systems. They also facilitate an in-depth analysis of the meaning presented in Diagnosis Support Information Systems used in analysis of selected medical examinations. This paper will present the mechanisms of pattern meaning description on selected examples of spinal cord image analysis. Presented approach for semantic reasoning will be based on the model of cognitive resonance, which will be applied to the task of interpreting the meaning of selected diagnostic images from the central nervous system. Such algorithms are aimed to construct an intelligent analysis module in medical IS. The application presented in this paper is of a research character and it serves the preparation of efficient lesion detection methods applied to a data set originating from magnetic and resonance examinations of the spine and spinal cord structures.

The manufacturers of ECG equipment usually face the advanced interpretation routines implementation problem. The development of these routines entails great financial expenditures, however for their specificity, the customers rarely demand them. This paper proposes a distributed computing approach to the advanced interpretation routines. Our proposal assumes each cardiologic issue to be interpreted by an unique specialized center automatically or with a human expert assistance if necessary. The interpretation service is accessible through the web for distant clients meeting the selection criteria. These may include customers of a particular enterprise, members of a given society or subscribed individual cardiologists. Among numerous advantages of this proposal, the most important is world wide unification of methods since most complicated software have to be developed only once and remain undisclosed to the others.

Automated ECG interpretation systems are supposed to follow human expert reasoning. Despite well-established standards, the visual interpretation strategy of the human is still undisclosed today. This paper presents a new approach to the interpretation process research based on eyetrack features captured from a human expert during biosignal visual inspection. This approach required a set of visual tasks consisting in ECG interpretation by volunteers of different degrees of expertise. The recorded eyeglobe trajectories were analysed in the context of medical data represented in the displayed ECG traces and revealed interesting information on diagnostic data distribution and principles of interpretation strategies. The scanpath-derived data make benefit of oculomotoric habits gathering in everyday practice, unconscious mutual perception–recognition interactions and are not affected by human memory or verbalization limits. For these reasons, they provide more objective assessment than any other method willingly controlled by the human. Besides new information about the ECG contents and quantitative descriptions of medical data distribution, our experiment reveals some eyetrack parameters as distinctive for interpretation skills estimation.

In this paper we will present opportunities for applying graph based linguistic formalisms for computer automatic understanding of meaning of wrist medical images. Thanks to the proposed method we can understand the merit content of the image even if the form of the image is very different from any known pattern. It seems that in the near future such technique may become one of the effective tools for semantic interpreting, and computer perception of visual medical data.

This paper will demonstrates that Computational Intelligence methods based on picture grammar can be efficiently applied to the development of intelligent diagnosis support systems. The computational intelligence methods in the form o linguistic formalism can facilitate an in-depth semantic analysis of the medical patterns and visualizations. The main objective is to present the possibilities of medical structure meaning description and computer interpretation based on selected examples of hand and spinal cord images. Presented further procedures for semantic description and reasoning will be based on the model of cognitive analysis which can imitate the process of reasoning in human mind. The application of such methods allow to detect and classify the most important lesion in the analysed structures.