Detection of Parkinson Decease Using Computational Intelligence Method

Detection of Parkinson D is ease Using Computational Intelligence Methods Elcin Huseyn 1 , Babek Guirimov 2 1 Research Laboratory of Intelligent Control and Decision Making Systems in Industry and Economics, Azerbaijan State Oil and Industry University, 20 Azadlig Ave., Baku, AZ1010, Azerbaijan, [emailprotected] asoiu.edu.az 2 Research Laboratory of Intelligent Control and Decision Making Systems in Industry and Economics, Azerbaijan State Oil and Industry University, 20 Azadlig Ave., Baku, AZ1010, Azerbaijan, [emailprotected] Abstract. Parkinson's disease is a neuro-degenerative movement disorder that causes voice/speech, and behavioral impairments. As a dysfunctional disease, it can be detected by a set of specific symptoms of patients. Such symptoms include both voice/speech and/or physical behavior/movement charac te ris - tics. For better detection both sets of characteristics are used in our research. In this study, as a diagnostic model, we use a system based on multiple-layer (deep) feed-forward neural networks. The networks are trained with Differential Evolution training algorithm using in parallel a pair of data sets (training and validation sets) to avoid overfitting and improve model's generalization ability (performance on untrained data). The applied DE algorithm has allowed avoiding local minima of error function during the training. A third data set is used for testing trained network performance. According to the obtained results, this method demonstrated better results than other existing approaches. Keywords: Parkinson's disease , Artificial Neural Network, Differential Evolution Optimization, Computational Intelligence Introduction Parkinson's disease is a neuro-degenerative movement disorder that causes voice/speech, and behavioral impairments . The disease causes partial or full loss in motor reflexes, speech, behavior, mental processing, and other vital functions [1] . The early detection of disease symptoms is vitally important in order to prevent further disease complications. Using recorded data including voice/speech and physical behavior /movement characteristics from healthy and sick people it is possible to create models, which would allow fast noninvasive diagnostic of the disease. Appropriate models include Support Vector Machines, Rule Based Systems, Artificial Neural Networks and others. Most existing approaches utilize only voice/speech data [2]. In our research to improve detection accuracy, we use also physical behavior/movement characteristics obtained from different subjects. Among all possible methods to create required model, we have chosen multi-layer deep feed-forward neural networks for a number of reasons. First, because they are indeed universal approximators and can be used to reveal any complex relationships in large data sets . Second, because recent developments in the theory and technology have significantly increased efficiency of neural networks. For instance, increased processing power and parallel processing abilities of modern computers allow efficient use of n ew evolutionary training approaches to effectively battle such bottleneck of large multi-layer neural networks as time-consuming parameter adaptation . The global parameter search , which avoids local minima trapping, is now much faster than ever . Third, because, neuron models are not now required to be constrained by smooth differentiable transfer functions, connection weights by simple numerical values, and network arch itecture for large input/output systems by single hidden layer of neurons. Method The used detection model is multi-layer feed-forward neural network with non-linear transfer function based neurons in hidden layers and linear neurons in input and output layers. Given particular values for the neural network parameters, and given values for the inputs, a neural network generates a value for each output: , The operation of an L -layer feed-forward perceptron neural network at each layer l can be described by the following equation: , where is the activation function used at network layer l . In the vector form this can be written more compactly: Or, based on only the network activations as: Matrix will denote weights connecting all neurons of layer with all neurons of layer . Thus for an -layered NN set will contain matrixes . is the weight of connection to neuron at layer from neuron at the previous layer , is the threshold parameter of neuron at layer The total number of connection weights and thresholds (i.e. number of elements in the set W ) for a feed-forward neural network is . The evolutionary algorithm used for training is Differential Evolution [ 5 ] , which is one of the fastest population based algorithms for global search in multi-dimensional v ector space.

Diffusion Lab Report Essays

Diffusion Lab Report Essays Diffusion Lab Report Paper Diffusion Lab Report Paper The step by step process was used by the software so that we could see the different kinds of reactions. According to the data found, we found that with high molecular weight compounds are too large to penetrate the molecular weight cut off pores and no simple diffusion can occur. So it seemed like the easiest way for a solute to pass through a semiprivate membrane was, if it either was small enough to pass or had some sort of carrier protein that helped it along. We expected to see continuous results that do not have much difference in the five experiments that are to be reformed. Experiments were conducted in order to gain a better understanding of a cells selectively permeable membrane and the passive processes of simple and facilitated diffusion. The purpose of this experiment was to make observations based on the computerized simulation providing information on the passage of water and solutes through semiprivate membranes, which may be applied to the study of transport mechanisms in living membrane- bounded cells. We hypothesized that when the sucrose concentration will change, the mass will also change. Introduction: A molecular composition of a plasma membrane is selective about what can passes through it. There are two methods of transport which can occur through the plasma membrane. To be discussed first, the method of transportation is called active transport which uses TAP (glucose) or energy to move substances through the membrane. Secondly, the method oaf passive transport does not require the use of TAP (glucose) or energy. During passive transport (or gradient), molecules are moved through the membrane of the cell by the imbalance of molecules and or pressure between the inside and outside of the cell. Simple diffusion, facilitated diffusion, osmosis, and filtration are all types of passive transports. In a living human body the cells use diffusion as the important transport process through its selectively permeable membrane. Diffusion is defined as the movement of particles from an area of higher concentration to an area of lower concentration, which results because of the random movement of particles. Osmosis is the diffusion of water into and out of a selectively permeable membrane. Because of the selectively permeable membrane, nothing but water and other very small particles can be diffused wrought osmosis. Molecules use their kinetic energy as the motivating force in diffusion. Facilitated diffusion occurs when molecules are too large to pass through a membrane or are unable to be dissolved into the lipid bi-liar. The process or act is when the carrier protein molecule located in the membrane combine with solute and transports them down the concentration gradient. Established gradients are due to the pressure of molecules on each side of the membranes wall. Also the membranes pore size and amount of pores depends on the amount of molecules and fluids in the filtrate. Another type of passive rainspout that is not a selective process is called filtration. Furthermore, the process filtration is when the water and solutes pass through a membrane (such as a dialysis membrane) from an area of higher hydrostatic (fluid) pressure into an area of lower hydrostatic pressure; which means that water and solutes would pass through a selectively permeable membrane along the gradient. Finally the last type of passive transport is called osmosis; which is the diffusion of solvents, such as water, through a selectively permeable membrane. This is unlike the rest that are the diffusion of molecules. In ponytails a bit of enfolding plasma membrane surrounds a very small volume of extracurricular fluid containing dissolved molecules. These cell drinking cells are also called fluid-phase endometriosis. The fuse with endmost occurs when the droplet enters the cell. Ponytails is a routine activity of most cells, affording them a nonconsecutive way of sampling the extracurricular fluid, unlike phagocytes. Phagocytes is engulfing of foreign solids by cells. Experiments were conducted in order to gain a better understanding of a cells selectively permeable membrane and the passive recesses of simple and facilitated diffusion.