Hasil untuk "Science"

A. Scott, F. Chu, D. McLaughlin

L. H. Thompson, L. Doraiswamy

D. E. Stokes

R. German

M. Weber

N. Kaplan, J. Cole, S. Cole

B. Kasemo

R. Park, E. Burgess

T. Veblen

T. Madon, K. Hofman, L. Kupfer et al.

K. Pahlavan, Xinrong Li, Juha-Pekka Mäkelä

This article presents an overview of the technical aspects of the existing technologies for wireless indoor location systems. The two major challenges for accurate location finding in indoor areas are the complexity of radio propagation and the ad hoc nature of the deployed infrastructure in these areas. Because of these difficulties a variety of signaling techniques, overall system architectures, and location finding algorithms are emerging for this application. This article provides a fundamental understanding of the issues related to indoor geolocation science that are needed for design and performance evaluation of emerging indoor geolocation systems.

M. Ben-Ari

D. Berwick

D. Klahr, M. Nigam

R. Mayer

J. Venable, J. Pries-Heje, R. Baskerville

H. Riesch, C. Potter

Wu Huiyong, Zunlong Wang

With the increasing complexity and prosperity of global financial markets, stock market forecasting plays a critical role in investment decision-making, market regulation, and economic planning. This study proposes a hybrid prediction model that integrates Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Long Short-Term Memory (LSTM) neural networks, referred to as the GA-WOA-LSTM model. In this framework, GA is employed to generate the initial population and perform global search for LSTM hyperparameter optimization, while WOA is applied to conduct local refinement of the search space. The LSTM model, known for its superior ability to capture nonlinear dependencies and long-term patterns in time series, is used to model and forecast future stock closing prices. The performance of the proposed model is evaluated on both training and test datasets using key metrics including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). Experimental results demonstrate that the GA-WOA-LSTM model significantly outperforms traditional baseline models in terms of predictive accuracy and generalization capability. This research offers a robust and effective modeling strategy for financial time series forecasting and provides valuable insights for real-world financial applications.

Ali AbuGneam, Somayeh Nemati, Afshin Babaei

In this research, we propose a new numerical method for solving a class of distributed-order fractional partial differential equations, specifically focusing on distributed-order time fractional wave-diffusion equations. The method begins by introducing a novel generalization of Bernoulli wavelets and deriving an exact result for the Riemann–Liouville integral of these new basis functions. Utilizing the Gauss–Legendre quadrature formula and a strategically chosen set of collocation points, along with approximations for the unknown function and its derivatives, we transform the problem into a system of algebraic equations. An error analysis is then conducted for the approximation of a bivariate function using fractional-order Bernoulli wavelets. Finally, three examples are solved to demonstrate the method’s applicability and accuracy, with the numerical results confirming its effectiveness. These findings demonstrate that the parameters of the basis functions can be adjusted to suit the given problem, thereby enhancing the accuracy of the method.