Proposal of neural network model for neurocognitive rehabilitation and its comparison with fuzzy expert system model.

This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.

ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

Proposal of a Control Algorithm for Multiagent Cooperation Using Spiking Neural Networks.

The study deals with the issue of using spiking neural networks (SNNs) in multiagent systems. The research objective is a proposal of a control algorithm for the cooperation of a group of agents using SNNs, application of the Izhikevich model, and plasticity depending on the timing of action potentials. The proposed method has been verified and experimentally tested, proving numerous advantages over second-generation networks. The advantages and the application in real systems are described in the research conclusions.

FOREX rate prediction improved by Elliott waves patterns based on neural networks.

Financial market predictions represent a complex problem. Most prediction systems work with the term time window, which is represented by exchange rate values of a real financial commodity. Such values (time window) provide the base for prediction of future values. Real situations, however, prove that prediction of only a single time-series trend is insufficient. This article aims at suggesting a novelty and unconventional approach based on the use of several neural networks predicting probable courses of a future trend defined in a prediction time window. The basis of the proposed approach is a suitable representation of the training-set input data into the neural networks. It uses selected FFT coefficients as well as robust output indicators based on a histogram of the predicted course of the selected currency pair. At the same time, the given currency pair enters the prediction in a combination with another three mutually interconnected currency pairs. A significant output of the articles is, apart from the proposed methodology, confirmation that the Elliott wave theory is beneficial in the trading environment and provides a substantial profit compared with conventional prediction techniques. That was proved in the performed experimental study.

Photomontage detection using steganography technique based on a neural network.

This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN method uses the characteristics of neural networks to create suitable attributes which are then necessary for subsequent detection of modified photographs. This also results in a fact that if an image is signed by this technique, the detection of modifications does not need any external data (database of non-modified originals) and the quality of the signature in various parts of the image also serves to identify modified (corrupted) parts of the image. The experimental study was performed on photographs from CoMoFoD Database and its results were compared with other approaches using this database based on standard metrics. The performed study showed the ability of the StegoNN method to detect corrupted parts of an image and to mark places which have been most probably image-manipulated. The usage of this method is suitable for reportage photography, but in general, for all cases where verification (provability) of authenticity and veracity of the presented image are required.