Classification of breast cancer luminescence data using self-organizing mapping neural network

Primenjena je samoorganizujuća neuronska mreža pri analizi podataka luminescencije raka dojke. Ulazni podaci su trodimenzionalni vektori koji predstavljaju normalno i maligno humano tkivo. Analizirana je mogućnost klasifikacije podataka u dve grupe. Mreža je zadovoljavajuće obavila klasifikaciju ulaznih podataka.Self-organizing mapping neural networks are applied in the analysis of breast cancer luminescence data. Data consist of three dimensional vectors presenting normal and malignant human tissue. The possibility of such data classification in two groups (normal and malignant tissue) is analyzed. The network performed successful classification

Classification of breast cancer luminescence data using self-organizing mapping neural network

Primenjena je samoorganizujuća neuronska mreža pri analizi podataka luminescencije raka dojke. Ulazni podaci su trodimenzionalni vektori koji predstavljaju normalno i maligno humano tkivo. Analizirana je mogućnost klasifikacije podataka u dve grupe. Mreža je zadovoljavajuće obavila klasifikaciju ulaznih podataka.Self-organizing mapping neural networks are applied in the analysis of breast cancer luminescence data. Data consist of three dimensional vectors presenting normal and malignant human tissue. The possibility of such data classification in two groups (normal and malignant tissue) is analyzed. The network performed successful classification

Optical Biopsy Method for Breast Cancer Diagnosis Based on Artificial Neural Network Classification of Fluorescence Landscape Data

Supervised self-organizing map, a type of artificial neural network, is applied for classification of human breast tissue samples utilizing data obtained from fluorescence landscape measurements. Female breast tissue samples were taken soon after the surgical resection, identified and stored at -80 degrees C until fluorescence measurements. From fluorescence landscapes obtained in UV-VIS region spectral features showing statistically significant differences between malignant and normal samples are identified and further quantified to serve as a training input to neural network. Additional set of samples was used as a test group input to trained network in order to evaluate performance of proposed optical biopsy method. Classification sensitivity of 83.9% and specificity of 88.9% are found

Electrical characteristics of female and male human skin

Bioimpedance spectroscopy (BIS) is a popular method for characterizing the electrical properties of biological tissues. In this study, BIS measurement data of female and male human skin were analyzed and compared. The electrical characteristics of tissue were followed according to four-parameters of the Cole-Cole model: low frequency resistance R0; high frequency resistance R∞; relaxation time t and parameter a. Individual electrical characteristics of human skin were determined for 30 women and 30 men. The distribution and one-way analysis of variance (one-way ANOVA) of the Cole-Cole parameters R0, R∞, t, a within the human population indicated their different dependence on gender. Parameter a, which is higher in the female subjects (a =0.83±0.03) than in the male subjects (a=0.7±0.05), is strongly dependent on gender (p=0). Parameter R∞ also significantly depends on gender (p=0.002), while t and R0 seem to be slightly related to gender (p>0.05)

Thermal parameters defined with graph theory approach in synthetized diamonds

The Nanocrystaline diamonds are very important biomedical material with variety of applications. The experimental procedures and results have been done in the Institute of Functional Nanosystems at the University Ulm, Germany. There is an existing biocompatibility of the diamond layers, selectively improved by biomimetic 3-D patterns structuring. Based on that, we have been inspired to apply the graph theory approach in analysing and defining the physical parameters within the structure of materials structure samples. Instead the parameters values, characteristic at the samples surface, we penetrate the graphs deeply in the bulk structure. These values could be only, with some probability, distributed through the micro-structure what defines not enough precious parameters values between the micro-structure constituents, grains and pores. So, we originally applied the graph theory to get defined the physical parameters at the grains and pores levels. This novelty, in our paper, we applied for thermophysical parameters, like thermoconductiviy. By graph approach we open new frontiers in controlling and defining the processes at micro-structure relations. In this way, we can easily predict and design the structure with proposed parameters

Sintering temperature influence on grains function distribution by neural network application

Artificial neural networks application in science and techonology begun during 20th century. This biophysical and biomimetic phenomena is based on extensive research which have led to understanding how neural as a living organism nerve system basic element processes signals by a simple algorithm. The input signals are massively parallel processed, and the output presents the superposition of all parallel processed signals. Artificial neural networks which are based on these principles are useful for solving various problems as pattern recognition, clustering, functional optimization. This research analyzed thermophysical parameters at samples based on Murata powders and consolidated by sintering process. Among different physical properties we applied out neural network approach on grain sizes distribution as a function of sintering temperature, 7: (from 1190-1370 degrees C). In this paper, we continue to apply neural networks to prognose structural and thermophysical parameters. For consolidation sintering process is very important to prognose and design malty parameters but especially thermal like temperature, to avoid long and even wrong experiments which are wasting the time and materials and energy as well. By this artificial neural networks method we indeed provide the most efficient procedure in projecting the mentioned parameters and provide successful ceramics samples production. This is very helpful in prediction and designing the micro-structure parameters important for advance microelectronic further miniaturization development. This is a quite original novelty for real micro-structure projecting especially on the phenomena within the thin films coating around the grains what opens new prospective in advance fractal microelectronics

Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization

Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are "easy to use": theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances

Fractal reconstruction of fiber-reinforced polymer composites

Polymers offer the possibility of different reinforcement incorporation due to a broad range of chemical structures. Along with this feature, their light weight and processing ease made them a class of materials that have been applied in construction parts, drug delivery agents or electronic devices. Epoxy-based composites have used as insulators in microelectronic devices due to its chemical resistance, good adhesion properties and endurance. As epoxies have low fracture resistance, they are often reinforced with different kinds of fibers. With thorough knowledge of the structure, physical properties can be predicted and included in the processing of future composites, especially that electronic materials minituarization brought micro- and nanoscale level properties at spotlight. Fractal nature analysis is a mathematical method that has proved to be efficient in grain interface properties applied on perovskite ceramic materials. In our study, fiber shape reconstruction and determination of Hausdorff dimension have been achieved with the application of fractal regression model employed in software Fractal Real Finder opening a new path for the prediction of reinforcement shape and size, all with the aim of processing composite materials with desired propertie

Approximation and Error Prediction in Electrochemical Parameters Calculation Using Neural Networks

Various interesting results have been achieved in calculation of electrochemical parameters in nanomaterials, using neural networks. There appear some error, during those calculations, and it varies depending on number of neurons in layers. In this research we deal with errors, calculated for neural networks with n=1,2…10, neurons in first or second layer. We applied mean square approximation method, in order to get explicite formula for predicton of error, for other cases

The ceramics materials density defined by artificial neural networks

Predicting the ceramic materials properties and designing the desired microstructures characteristics are very important objectives in ceramic samples consolidating process. The goal of our research is to calculate the density within consolidated BaTiO3-ceramic samples for different consolidation parameters, like sintering temperature, using obtained experimental data from the material’s surface, by applying back propagation neural network (BP). This method, as a very powerful tool, provides the possibility to calculate the exact values of desired microelectronic parameter at the level of the grains’ coating layers. The artificial neural networks, which have biomimetic similarities with biological neural networks, propagate the input signal forward, unlike the output signal, designated as error, which is propagated backwards spreading throughout the whole network, from output to input neuron layers. Between these two neuron layers, there are usually one or more hidden layers, where the grains of the sintered material are represented by network neurons. Adjustable coefficients, called weights, are forward propagated, like input signals, but they modify the calculated output error, so the neural network training procedure is necessary for reducing the error. Different consolidated samples density values, measured on the bulk, substituted the errors, which are calculated as contribution of all network elements, thus enabling the density calculation of all constituents of ceramic structure presented by neural network. In our future research we plan to increase the number of neurons and hidden layers in order to improve this method to become even more accurate and precise