Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for Stock Prices Forecasting to Bank of Palestine

This study aimed to Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for the time series data of a monthly Stock Prices to Bank of Palestine from Nov. 2005 to Oct. 2020, and comparing between models to see which one is better in forecasting. The results of applying the methods were compared through the (MAPE, MAE, RMSE), the most accurate model is ERNN 14-25-1 with minimum forecast measure error.

Approximate Solutions of Nonlinear Smoking Habit Model

The work in this paper focuses on solving numerically and analytically a nonlinear social epidemic model that represents an initial value problem of ordinary differential equations. A recent moking habit model from Spain is applied and studied here. The accuracy and convergence of the numerical and approximation results are investigated for various methods; for example, Adomian decomposition, variation iteration, Finite difference and Runge-Kutta. The discussion of the present results has been tabulated and graphed. Finally, the comparison between the analytic and numerical solutions from the period 2006-2009 has been obtained by absolute and difference measure error.

Voltage Measurement with Improved Multi-Slope Integral Analog-to-Digital Converter

This paper presents a 20 Hz to 200K Hz alternating current (AC) and direct current (DC) analog-to-digital converter (ADC). The strange to minimize the zero crossing noise adopts hysteresis comparison technique. Multi-slope integral technique is also employed to enhance measurement accuracy. Furthermore, an algorism is designed to eliminate measure error. The prototype ADC achieves millivolt precision.

Integrity and Fidelity Evaluation of Digital Evidence in Live Forensics

The integrity and fidelity of digital evidence are very important in live forensics. Previous research has studied the uncertainty of live forensics based on different memory snapshots. However, this kind of method is not effective in practice. In fact, memory images are usually acquired by using forensics tools instead of using snapshots. Therefore, the integrity and fidelity of live evidence should be evaluated during the acquisition process. In this paper, we give a new viewpoint that memory acquisition can be regarded as a measurement of memory data. From this viewpoint, we evaluate the integrity and fidelity of live evidence in the process of physical memory acquisition. Firstly, several definitions about memory acquisition measure error are introduced to describe the trusty. Then, we analyze the experimental error and propose some suggestions on how to reduce it. A novel method is also developed to calculate the system error in detail. The results of a case study on Windows 7 and VMware virtual machine show that the experimental error has good accuracy and precision, which demonstrate the efficacy of the proposed reducing methods. The system error is also evaluated, that is, it accounts for the whole error from 30% to 50%. Last, a method is proposed to calculate changes or error of system process.

Error Compensation Method for Mine's Automatic Light Rail Weight-Bridge

The method of wheel-weight for mine's automatic light rail weight-bridge is put forward. According to some important factors which cause measure error using wheel-weight method, for example, zero drift, speed and gradient, the error compensation algorithms are studied. The software flow charts of these algorithms are given, which have been put into effect in the ARM microprocessor based embedded system. It has been found that these error compensation methods are efficient to improve the accuracy of the measurement system.