Neonatal brain monitoring in the neonatal intensive care units requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs from (...) 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies. (shrink)

Infertility is a global problem affecting 48 to 186 million couples of reproductive age. In Poland, it concerns approx. 1.5 million couples, which amounts to 20% of the population capable of reproducing. One of the factors influencing the incidence of fertility disorders may be lifestyle, understood as a multi-disciplinary accumulation of everyday behaviours and habits. In the study, a group of 201 young adults, students of medical and related faculties, were surveyed in order to check the actual level of knowledge (...) about the impact of lifestyle on reproductive health. The Kohonen network, which is an example of a self-learning neural network, was used to find non-obvious connections between the data. The trained Kohonen neural network formed 4 clusters with different characteristics. Based on analyses of the structure of each cluster, it was found that 2nd year students of Medicine are internally divided into 3 fractions. The first fraction declared a high level of knowledge, but did not have real knowledge. The second fraction was aware of their ignorance, as confirmed by the knowledge test. The last fraction was characterized by a high level of self-confidence regarding their knowledge about reproductive health and obtained a high result in the knowledge test. It was confirmed that people studying at the Medical faculty know more than students of the same year at faculties other than Medicine. Interesting results were obtained for a group of 3rd year students of first-cycle studies in Dietetics. They did not obtain a significantly better result in the knowledge test concerning the influence of diet and lifestyle on reproductive health. It would seem that one could expect at least a few highly knowledgeable students in a group of 3rd year students, but this was not confirmed by the study. In view of the obtained results, it was concluded that the Kohonen neural network is applicable to the analysis of data on the actual state of knowledge about the impact of lifestyle on reproductive health. (shrink)

This paper describes an intelligent algorithm for traffic sign recognition which converges quickly, is accurate in its segmentation and adaptive in its behaviour. The proposed approach can segment images of traffic signs in different lighting and environmental conditions and in different countries. It is based on using Kohonen's Self-Organizing Maps as a clustering tool and it is developed for Intelligent Vehicle applications. The current approach does not need any prior training. Instead, a slight portion, which is about 1% of (...) the image under investigation, is used for training. This is a key issue to ensure fast convergence and high adaptability. The current approach was tested by using 442 images which were collected under different environmental conditions and from different countries. The proposed approach shows promising results; good improvement of 73% is observed in faded traffic sign images compared with 53.3% using the traditional algorithm. The adaptability of the system is evident from the segmentation of the traffic sign images from various countries where the result is 96% for the nine countries included in the test. (shrink)

Intrauterine insemination is one of many treatments provided to infertility patients. Many factors such as, but not limited to, quality of semen, the age of a woman, and reproductive hormone levels contribute to infertility. Therefore, the aim of our study is to establish a statistical probability concerning the prediction of which groups of patients have a very good or poor prognosis for pregnancy after IUI insemination. For that purpose, we compare the results of two analyses: Cluster Analysis and Kohonen (...) Neural Networks. The k-means algorithm from the clustering methods was the best to use for selecting patients with a good prognosis but the Kohonen Neural Networks was better for selecting groups of patients with the lowest chances for pregnancy. (shrink)

Статья посвящена исследованию финансового состояния предприятия сферы услуг. Стабилизация финансового состояния является приоритетным направлением отдельно взятого предприятия и экономической системы в целом. Предлагается решить обеспечение стабильности финансовой деятельности предприятия с помощью нейронной сетевой модели кластеризации - самоорганизующихся карт Кохонена - мощного механизма кластеризации, что позволяет отобразить результаты финансового анализа в виде компактных и удобных для интерпретации двумерных карт. В работе проанализированы услуги предприятия и проведена сегментация базы посетителей, построены профили пользователей услуг путем выявления их похожего поведения в плане частоты, типа (...) заказанных услуг, проведена оценка наиболее и наименее прибыльных сегментов. Сегментация посетителей отеля-ресторана проведена с помощью подхода, основанного на алгоритме Кохонена, и кластеризации объектов по алгоритму Кохонена. Данный интерфейс используется для поиска закономерностей в больших массивах данных, что позволяет проводить разведывательный анализ данных, который отличается от классических статистических процедур. (shrink)

In this paper I shall compare two models of concept formation, both inspired by basic convictions of philosophical empiricism. The first, the connectionist model, will be exemplified by Kohonen maps, and the second will be my own dynamic theory of concept formation. Both can be understood in probabilistic terms, both use a notion of convergence or stabilization in modelling how concepts are built up. Both admit destabilization of concepts and conceptual change. Both do not use a notion of representation (...) in some pregiven language, such as a language of thought or some logical language. Representation in a formal language only plays a role on the meta-level, namely within the theory about concept formation. (shrink)

The data mining field in computer science specializes in extracting implicit information that is distributed across the stored data records and/or exists as associations among groups of records. Criminal databases contain information on the crimes themselves, the offenders, the victims as well as the vehicles that were involved in the crime. Among these records lie groups of crimes that can be attributed to serial criminals who are responsible for multiple criminal offenses and usually exhibit patterns in their operations, by specializing (...) in a particular crime category (i.e., rape, murder, robbery, etc.), and applying a specific method for implementing their crimes. Discovering serial criminal patterns in crime databases is, in general, a clustering activity in the area of data mining that is concerned with detecting trends in the data by classifying and grouping similar records. In this paper, we report on the different statistical and neural network approaches to the clustering problem in data mining in general, and as it applies to our crime domain in particular. We discuss our approach of using a cascaded network of Kohonen neural networks followed by heuristic processing of the networks outputs that best simulated the experts in the field. We address the issues in this project and the reasoning behind this approach, including: the choice of neural networks, in general, over statistical algorithms as the main tool, and the use of Kohonen networks in particular, the choice for the cascaded approach instead of the direct approach, and the choice of a heuristics subsystem as a back-end subsystem to the neural networks. We also report on the advantages of this approach over both the traditional approach of using a single neural network to accommodate all the attributes, and that of applying a single clustering algorithm on all the data attributes. (shrink)

The study considers the development of methods for detecting anomalous network connections based on hybridization of computational intelligence methods. An analysis of approaches to detecting anomalies and abuses in computer networks. In the framework of this analysis, a classification of methods for detecting network attacks is proposed. The main results are reduced to the construction of multi-class models that increase the efficiency of the attack detection system, and can be used to build systems for classifying network parameters during the attack. (...) A model of an artificial immune system based on an evolutionary approach, an algorithm for genetic-competitive learning of the Kohonen network and a method of hierarchical hybridization of binary classifiers with the addition to the detection of anomalous network connections have been developed. The architecture of the network distributed attack detection system has been developed. The architecture of the attack detection system is two-tier: the first level provides the primary analysis of individual packets and network connections using signature analysis, the second level processes the processing of aggregate network data streams using adaptive classifiers. A signature analysis was performed to study network performance based on the Aho-Korasik and Boyer-Moore algorithms and their improved analogues were implemented using OpenMP and CUDA technologies. The architecture is presented and the main points of operation of the network attack generator are shown. A system for generating network attacks has been developed. This system consists of two components: an asynchronous transparent proxy server for TCP sessions and a frontend interface for a network attack generator. The results of the experiments confirmed that the functional and non-functional requirements, as well as the requirements for computing intelligent systems, are met for the developed attack detection system. (shrink)

The objective of this work is to categorize the available fly ashes in different parts of the world into distinct groups based on its compositional attributes. Kohonen's self-organizing feature map and radial basis function networks are applied in a cascading fashion for the classification of fly ashes in terms of its chemical parameters. The basic procedure of the methodology consists of three stages: apply self-organizing neural net to ascertain possible number of groups, delineate them and identify the group sensitive (...) attributes; find mean values of sensitive attributes of the elicited groups and augment them as start-up prototypes in k-means algorithm and find the refined centroids of these groups; incorporate the centroids in a two layer radial basis function network and fine-tune the delineated groups and develop an indexing equation using the weights of the stabilized network. Further, to demonstrate the utility of this classification scheme, the so formed groups were correlated with their performance in High Volume Fly Ash Concrete System [HVFAC]. The categorization was found to be excellent and compares well with Canadian Standard Association's [CSA A 3000] classification scheme. (shrink)

A memória humana é essencial para a atividade teatral. Isso por um motivo óbvio: é ela que possibilita aos atores gravarem suas falas e ao público compreender a mensagem passada pela peça. Neste trabalho, investigamos as noções de memória propostas por Descartes, Hume e Kohonen. Para eles, a memória é imprescindível na obtenção e desenvolvimento do conhecimento. A nosso ver, o estudo da memória é de grande importância para o teatro, à medida que este não seria possível sem aquela.

The matter of enterprise labor resource management explaining the enterprise's staff potential increase or reduction is considered. That is to say the problem of efficiency of staff potential employment is researched. The author determines a general aim of the enterprise staff potential management in current terms of functioning, which refers to the purposeful performance of certain steps resulting in effective appliance. Factors of degree of staff potential and its effective appliance are determined as followed: qualified staff development; involvement of qualified (...) professionals; optimal conditions for staff operation support. The mechanism of unit staff potential management being able to adjust to changes in a structure of interrelated elements, production and the technological interaction organization, terms of working, the performance of professional competiveness and the staff rotation within the branches as well as between them is suggested. Criteria of effective realization of the mechanism of staff potential management by the working efficiency increase, the staff and manager competiveness increase, the rotations possibility, the produced goods and services quality increase. A procedure of enterprise staff rating is considered. To motivate qualified workers of organizational and functional units of an enterprise and to stimulate them for better further working, the monitoring of their activity compensation by certain bonuses is proposed. A model of premium distribution between co-workers of functional units of an enterprise, based on the Kohonen self-organized maps method implementation, is represented. Characteristics of informational and analytical systems of the enterprise staff potential management functioning in the unified informational environment are determined. The implementation of proposed mechanism provides high level of the enterprise staff potential realization as well as the permanent development of labor resources during a business activity process. (shrink)