The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Five levels of Zn-EDTA fertilizer and foliar application of boron were used to study the local rice response through studying of some vegetative and reproductive growth characters, by conducting two field experiments at Kanipanka Agricultural Research Station during the summer season of 2004 by using RCBD with three replications. Significant differences were found in studied characters, there were increase in the number of days from seeding to 50% flowering (94.330-96.233) days, from 50% flowering to physiological maturity (37.50-38.28) days, plant height (82.50-91.423) cm and LAI (5.441-7.525). Reproductive characters such as number of grains panicle-1 (74.11-85.88), number of panicles m-2 (321.00-426.083), biological yield (8166.166-11082

Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective

Destiny functional theory (DFT) calculations are undertaken in order to scrutinize the electrochemical and calcium (Ca) storage characteristics of a graphyne-like aluminum nitride monolayer (G-AlNyen) as an electrode material for Ca-ion batteries (CIBs). The results show that the change in internal energy as well as the cell voltage values for the CIB with the G-AlNyen anode are comparable to others with two-dimensional 2D nano-materials. It is shown that Ca is adsorbed primarily onto the center of a hexagonal and triangular ring of G-AlNyen with absorption energies of −2.06 and −0.42 eV. After increasing the concentration of Ca atoms on G-AlNyen, the adsorption energy as well as the cell voltage decreases. Lower values of 0.15–0.32 e