Ryszard Tadeusiewicz

Artificial Intelligence and Soft Computing

In recent years wind energy has been the fastest growing branch of the power generation industry. Maintenance of the wind turbine generates its the largest cost. A remote monitoring is a common method to reduce this cost. Growing number of monitored turbines requires an automatized way of support for diagnostic experts. Early fault detection and identification is still a very challenging task. A tool, which can alert an engineer about potentially dangerous cases, is required to work in real-time. The goal of this paper is to show an efficient system to online classification of operational states of the wind turbines and to detecting their early fault cases. The proposed system was designed as a hybrid of ART-2 and RBF networks. It had been proved before that the ART-type ANNs can successfully recognize operational states of a wind turbine during the diagnostic process. There are some difficulties, however, when classification is done in real-time. The disadvantages of using a classic ART-2 network are pointed and it is explained why the RBF unit of the hybrid system is needed to have a proper classification of turbine operational states.

Swarm and Evolutionary Computation

This paper presents a work inspired by the Pachycondyla apicalis ants behavior for the clustering problem. These ants have a simple but efficient prey search strategy: when they capture their prey, they return straight to their nest, drop off the prey and systematically return back to their original position. This behavior has already been applied to optimization, as the API meta-heuristic. API is a shortage of api-calis. Here, we combine API with the ability of ants to sort and cluster. We provide a comparison against Ant clustering Algorithm and K-Means using Machine Learning repository datasets. API introduces new concepts to ant-based models and gives us promising results.

Knowledge discovery approach to automated cardiac SPECT diagnosis

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologists diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.

Modern Computational Intelligence Methods for the Interpretation of Medical Images

A detailed description of up-to-date methods used for computer processing and interpretation of medical images is given. The scope of the book include images acquisition, storing with compression, processing, analysis, recognition and also its automatic understanding In introduction general overview of the computer vision methods designed for medical images is presented. Next sources of medical images are presented with their general characteristics. Both traditional (like X-ray) and very modern (like PET) sources of medical images are presented. The main emphasis is placed on such properties of medical images given by particular medical imaging methods which are important form the point of view of its computer processing, analysis and recognition. The consecutive parts of the book describe compression and processing methods, including many methods developed by authors especially for medical images. After parts describing analysis and recognition of medical images come most important part, in which the new method of automatic understanding of medical images is given. This new method of image interpretation, described in previous works of the same authors with applications for simple 2D images now is generalized for 3D images and for complex medical images with many objects observed and with complicated relations between these objects.

Image languages in intelligent radiological palm diagnostics

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.

Self-Optimizing Neural Networks

This paper describes an efficient construction of a partially-connected multilayer architecture and a computation of weight parameters of Self-Optimizing Neural Network 3 (SONN-3) that can be used as a universal classifier for various real, integer or binary input data, even for highly non-separable data. The SONN-3 consists of three types of neurons that play an important role in a process of extraction and transformation of important features of input data in order to achieve correct classification results. This method is able to collect and to appropriately reinforce values of the most important input features so that achieved generalization results can compete with results achieved by other existing classification methods. The most important aspect of this method is that it neither loses nor rounds off any important values of input features during this computation and propagation of partial results through a neural network, so the computed classification results are very exact and accurate. All the most important features and their most distinguishing ranges of values are effectively compressed and transformed into an appropriate network architecture with weight values. The automatic construction process of this method and all optimization algorithms are described here in detail. Classification and generalization results are compared by means of some examples.

Texture analysis in perfusion images of prostate cancer-A case study

Texture analysis in perfusion images of prostate cancer—A case study The analysis of prostate images is one of the most complex tasks in medical images interpretation. It is sometimes very difficult to detect early prostate cancer using currently available diagnostic methods. But the examination based on perfusion computed tomography (p-CT) may avoid such problems even in particularly difficult cases. However, the lack of computational methods useful in the interpretation of perfusion prostate images makes it unreliable because the diagnosis depends mainly on the doctors individual opinion and experience. In this paper some methods of automatic analysis of prostate perfusion tomographic images are presented and discussed. Some of the presented methods are adopted from papers of other researchers, and some are elaborated by the authors. This presentation of the method and algorithms is important, but it is not the master scope of the paper. The main purpose of this study is computational (deterministic and independent) verification of the usefulness of the p-CT technique in a specific case. It shows that it is possible to find computationally attainable properties of p-CT images which allow pointing out the cancerous lesion and can be used in computer aided medical diagnosis.

Neural network adaptation process effectiveness dependent of constant training data availability

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.

Why Automatic Understanding

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.

Approximation of phenol concentration using novel hybrid computational intelligence methods

Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.