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

In this study, gold nanoparticle samples were prepared by the chemical reduction method (seed-growth) with 4 ratios (10, 12, 15 and 18) ml of seed, and the growth was stationary at 40 ml. The optical and structural properties of these samples were studied. The 18 ml seed sample showed the highest absorbance. The X- ray diffraction (XRD) patterns of these samples showed clear peaks at (38.25^o, 44.5^o, 64.4^o, and 77.95^o). The UV-visible showed that the absorbance of all the samples was in the same range as the standard AuNPs. The field emission-scanning electron microscope (FE-SEM) showed the shape of AuNPs as nanorods and the particle size between 30-50 nm. Rhodamine-610 (RhB) was prepared at 10<

Abstract

   This research aims to the relationship and the effect of concurrent engineering dimensions analyze (product design, process design, and supply chain design) on the dimensions of strategic performance (efficiency, and effectiveness) The research was conducted in public Zora Electrical Industries Company, on a sample of departments and officials, engineers, managers and people in the company amounted to (45) . A questionnaire was used for the purpose of data collection which has been adopted, with the use of statistical methods  via computerized system (spss) for processing data , identifying them and testing the research hypotheses. The research

This paper reports an experimental study regarding the influence of vertical oscillations on the natural convection heat transfer from a vertical channel. An experimental set-up was constructed and calibrated; the vertical channel was tested in atmosphere at 25o
C. The channel-to-ambient temperature difference was varied with the power supply to the electrical heater ranging between
15W to 70W divided into five levels. Data sets were measured under different operating condition from a test rig under six vibrating velocities (VVs) levels ranging from (5-30 m/s) in addition to the stationary state. The results show that the maximum heat transfer enhancement factor (E) occurs at Rayleigh number (Ra=2.328×103 ) and vibrational Reynol