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 models for a variety of tasks under the control of a unified architecture for each proposed model.
The research objectives, to build a measure of the level of tactical performance of volleyball players applying for the Iraqi Premier League for the 2018-2019 season. The nature of the research problem, then the researchers determined the research sample in the deliberate manner of the players of the Iraqi clubs for the Premier League (B, A). The researchers adopted the entire community as a sample for the research, and the number (156) players distributed over (13) clubs and divided the sample into (12)players an exploratory experiment player representing (the police club) and (100) player representing the construction sample and after a maximum period of two months has passed since applying the scale to the construction sample the researc
... Show MoreThe Quality function deployment (QFD) tool is an important tool of total quality management because its a link between two important parts customer and production process of the product, using advanced House of quality, which contributed to provide more details about improving the product before it had a vision for the future of the product be improved. Also the identification of the two competitors (Alwazeer , Altouri) bases on the survey of retailers which they identified five competitors products (Alwazeer , Altouri , Ferry , Jif , Dina)for the product (Zahi). Then House of quality to product (Zahi) has been developed By using a Kano Model to classify of customer's requirements for the
... Show MoreThis paper investigates the performance evaluation of two state feedback controllers, Pole Placement (PP) and Linear Quadratic Regulator (LQR). The two controllers are designed for a Mass-Spring-Damper (MSD) system found in numerous applications to stabilize the MSD system performance and minimize the position tracking error of the system output. The state space model of the MSD system is first developed. Then, two meta-heuristic optimizations, Simulated Annealing (SA) optimization and Ant Colony (AC) optimization are utilized to optimize feedback gains matrix K of the PP and the weighting matrices Q and R of the LQR to make the MSD system reach stabilization and reduce the oscillation of the response. The Matlab softwar
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This work involves the manufacturing of MAX phase materials include V2AlC and Cr2AlC using powder metallurgy as a new class of materials which characterized by regular crystals in lattice. Corrosion behavior of these materials was investigated by Potentiostat to estimate corrosion resistance and compared with the most resistant material represented by SS 316L. The experiments were carried out in 0.01N of NaOH solution at four temperatures in the range of 30–60oC. Polarization resistance values which calculated by Stern-Geary equation indicated that the MAX phase materials more resistant than SS 316L. Also cyclic polarization tests confirme
... Show MoreMachine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed
... Show MoreThe experiment was conducted to evaluate and performance comparison for moldboard and disk plows in central of Iraq in 2011. Three factor were used in this study included Two types of plows included moldboard and disk plows which represented main plot , three forward speeds of the tillage was second factor included 1.85, 3.75 and 5.62 km / h which represented sup plot , and three levels of soil moisture was third factor included 21 , 18 and 14 % to determined data actual plow depth , Practical productivity, Volume of disturbed soil, Field efficiency , Slippage percentage in silt clay loam soil with depth 22 cm were studied in the experiment Split – split plot design under randomized complete block design with three replications using
... Show MoreThis paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spe
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