Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

Huk, Maciej

doi:10.2478/v10006-012-0034-5

Cited by 31 publications

(12 citation statements)

References 28 publications

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“…The key element of our findings is a neuron model that can explore some selective attention strategies based on our algorithm of its parameters' values selection [27]. Our study shows that the feature selection problem can be solved by using the model of selective attention neural network that we have developed.…”

Section: Related Workmentioning

confidence: 79%

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Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems

Huk

Mizera–Pietraszko

2015

2015 17th International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

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Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many irrelevant, or redundant features. This work addresses the context-related feature selection problem from medical data by proposing utility of Sigma-if neural network being an effective model of neurology patients's low-level distributed selective attention mechanisms. Our experiments indicate that a context-aware technique can reduce the average cost of medical data acquisition and data processing as well as it can decrease classification error probability resulting in increasing the overall eHealth systems reliability.

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Section: Related Workmentioning

confidence: 79%

“…The Sigma-if neural network system proposed is a type of a fully connected, synchronous multilayer perceptron neural network (MLP) with the selective attention abilities [19], [27]. Such a neural networks do not need any separate centralised attention guidance modules.…”

Section: The Proposed Systemmentioning

confidence: 99%

Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems

Huk

Mizera–Pietraszko

2015

2015 17th International Conference on E-Health Networking, Application &Amp; Services (HealthCom)

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“…In the task of predicting the corrections, the sigmoidal function is applied. The task of neural network training is to determine the weight values [15]. After the training process, the neural network output value should be consistent with the expected value, called the training set.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

Luzar¹,

Sobolewski²,

Miczulski³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.

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“…They all stimulate the need for more precise and general solutions when the tools have some technical limitations. From the other standpoint, some data are valuable only when considered in context of other information [14], [15]. Unstructured text resources are the representative example of it [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Context-sensitive text mining with fitness leveling Genetic Algorithm

Huk

Kwiatkowski

Konieczny

et al. 2015

2015 IEEE 2nd International Conference on Cybernetics (CYBCONF)

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Contextual processing is a great challenge for information retrieval study -the most approved techniques include scanning content of HTML web pages, user supported metadata analysis, automatic inference grounded on knowledge base, or content-oriented digital documents analysis. We propose a meta-heuristic by making use of Genetic Algorithms for Contextual Search (GACS) built on genetic programming (GP) and custom fitness leveling function to optimize contextual queries in exact search that represents unstructured phrases generated by the user. Our findings show that the queries built with GACS can significantly optimize the retrieval process.

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Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

Cited by 31 publications

References 28 publications

Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems

Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

Context-sensitive text mining with fitness leveling Genetic Algorithm

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