46 research outputs found
Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients
No full textON GENERAL SOLUTION OF INCOMPRESSIBLE AND ISOTROPIC NEWTONIAN FLUID EQUATIONS
No full textThe fluid equations, named after Claude-Louis Navier and George Gabriel Stokes, describe the motion of fluid substances. These equations arise from applying Newton's second law to fluid motion, together with the assumption that the stress in the fluid is the sum of a diffusing viscous term (proportional to the gradient of velocity) and a pressure term — hence describing viscous flow. The form of the Navier–Stokes equations means they can be transformed to full/partial inhomogeneous parabolic differential equations: differential equations in respect of space variables and the full differential equation in respect of time variable and time dependent inhomogeneous part. Orthogonal polynomials as the partial solutions of obtained Helmholtz equations were used for derivation of analytical solution of incompressible fluid equations in 1D, 2D and 3D space for rectangular boundary. New one anti-curl method was proposed for derivation of velocities in incompressible fluid and was shown how this method works with rectangular boundaries. Finally, solution in 3D space for any shaped boundary was expressed in term of 3D general solution of 3D Helmholtz equation accordantly
Mathematical Modeling of Stress-Strain Curves in Canine Lumbar Vertebrae
No full textŠiandien gyvūnai, kaip ir žmonės, patiria stuburo ligas, kurios blogėja su amžiumi. Nors didelis dėmesys skiriamas šių būklių diagnozavimui ir gydymui, stuburo mechaninėms savybėms skiriama mažiau dėmesio. Degeneracinės stuburo ligos yra didelis pasaulinis iššūkis. Pasaulio sveikatos organizacija osteoporozę klasifikuoja kaip svarbią pasaulinę sveikatos problemą, kuriai būdinga sumažėjusi kaulų masė, dėl kurios padidėja kaulų lūžių tikimybė. Taigi, ištyrus slankstelių mechanines savybes, galima gauti papildomų įžvalgų apie osteoporozės vystymąsi.Today, animals, similar to humans, experience spinal conditions that worsen with age. While significant attention is directed towards diagnosing and treating these conditions, the mechanical attributes of the spine receive less focus. Degenerative spinal diseases pose a substantial global challenge. The World Health Organization classifies osteoporosis as a significant global health issue, characterized by reduced bone mass, leading to an increased likelihood of bone fractures. Thus, examining the mechanical properties of vertebrae could offer additional insights into osteoporosis development
Prediction Capabilities of Evolino RNN Ensembles
No full textModern portfolio theory of investment-based financial market forecasting use probability distributions. This investigation used an ensemble of genetic algorithm based recurrent neural networks (RNN), which allows to obtain multi-modal distribution for predictions. Comparison of the two different models—scatted points based prediction and distributions based prediction—opens new opportunities to create profitable investment tool, which was tested in real time demo market. Dependence of forecasting accuracy on the number of Evolino recurrent neural networks ensemble was obtained for five forecasting points ahead. This study allows to optimize the cluster based computational time and resources required for sufficiently accurate prediction
Impact of time zones on forecasting of exchange market based on distribution of expected values
No full text
Financial market prediction system with Evolino neural network and Delphi method
No full textUse of artificial intelligence systems in forecasting financial markets requires a reliable and simple model that would ensure profitable growth. The model presented in the paper combines Evolino recurrent neural networks with orthogonal data inputs and the Delphi expert evaluation method for its investment portfolio decision making process. A statistical study demonstrates the reliability of the model and describes its accuracy. Capabilities of the model are demonstrated using a trading simulation
Evolino Recurrent Neural Network Ensemble for Speculation in Exchange Market in Time of Anomalies
No full textSharp falls or explosive growths in exchange markets, whether expected or not, generates new challenges for investors who want to protect their investments or achieve an optimum benefit during and after the turmoil. An anomaly of the exchange market, instigated by the Swiss National Bank, occurred when the Swiss Franc decoupled from the euro unexpectedly. The United Kingdom (UK) vote to withdraw from the European Union (Brexit), in contrast, was feared but expected. A comparison of the consequences of the anomalies gives us an unprecedented opportunity to investigate prediction capabilities of the EVOLINO Recurrent Neural Network Ensemble (ERNN) model following an anomaly. By introducing this new information to the ERNN model and analyzing its response, we increase investor resources during large exchange rate fluctuations; this will provide them with additional information that will help them construct different portfolios. Reaction to the anomaly was visible only after the anomaly occurred, this is when the model began to acquire data influenced by the extreme change. Comparing different strategies which are related or unrelated to the anomaly and orthogonal or not orthogonal for conservative, moderate, or aggressive trading shows that in order to profit from the anomaly, speculation depends on prediction-accuracy and on the sets of exchange-rate associated with the anomaly
