This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 traini...

A supervised method is proposed for automated segmentation of vessels in fundus images of retina. This method is used to detect the retinal diseases by extracting the retinal vasculature utilizing 9-D feature vector based on orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to h...

In this paper, a new method of bandwidth estimation using variation of slot length on interdigital band pass filter has been presented using artificial neural networks for desired frequency range between 1.7—2.5 GHz. Interdigital filter is multifinger periodic structure which offers compact filter design space. An ANN model has been developed and tested for estimating the cut off frequency of b...

Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 2 of M (one output unity, all others zero) and a squarederror or cross-entropy cost function is used. Resu...

In this research, artificial neural models are extended to handle missing and non-real data and weights, and made to compute an explicit similarity relation. Artificial Neural Networks (ANN) constitute a class of models amenable to learn non-trivial tasks from representative samples. When exposed to a supervised training process, they build an internal representation of the underlying target fu...

Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitatively compare the performance of three different families of neural networks: Multi-Layer Perceptron ...

This paper compares three mathematical models for surgical decisions on head injury patients. A logistic regression and two neural network models were developed using a large clinical database. Using randomly selected 9480 cases as the training group and another 3160 cases as the validation group. We evaluated the performance of a logistic regression model, a multi-layer perceptron (MLP) neural...

In this work we show the capability of a sol–gel based electronic nose to be used in qualitative and quantitative analysis with the aim to recognize common volatile compounds usually present in the headspace of foods. Acetone, hexanal and 2-pentanone were chosen for this kind of measurements, performed both in dry air and in mixture of 50% humidity, just to simulate the experimental set-up in r...

This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the n...

Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in t...