After discussing the benefits of mobile phone industry in various sectors focusing on education and health and manufacturing (Demeke, Biru, 2002). In the accompanying template, we suggest specific questions to be answered that are relevant to each aspect of the business [12]. Can help identify activities that manage risks and take advantage of the organization's assets.
In the next subsection, we discuss internal and external analysis, as shown in Figure 2, which represents both sides of the environmental scan:-. The objectives of a SWOT analysis can differentiate the questions used to obtain the required information. The Kuwaiti government is heavily involved in the economy, primarily through its control of the oil industry.
The strong growth of the mobile phone market in the United States is part of the rise of data-oriented mobile phones, especially smartphones. Based on the strength and dominance of the mobile market in the United States in terms of the number of postpaid subscribers and the generation of revenue per average user. And the impact of the global economic slowdown on the economy, particularly in terms of job growth.
An in-depth analysis of the major global telecom players such as AT&T
Taiyou Research analyzes the Saudi telecommunications sector in its research report Telecom Industry in Saudi Arabia. An analysis of the global telecommunications industry through an industry definition, industry statistics, industry value and.
An analysis of the mobile telecom sector in Saudi Arabia is carried out
Concluded Remarks and Future Works
Evaluation of Artificial Neural Networks Used for Pattern Recognition Systems
- Introduction
- Evaluation Criteria
- Theoretical Consideration
- Testing and implementation
- Experimental results
- Results discussion
- Conclusion
- Acknowledgement
- References
Each neural network possesses knowledge contained in the values of the connection weights. Changing the knowledge stored in the network as a function of experience implies a learning rule for changing the values of the weights. We change the weight of each connection so that the network gives a better approximation of the desired output.
This establishes uniformity in the dimensions of the input and stored patterns as they move through the recognition system. In the present learning method, each candidate character that the network learns has a corresponding weight matrix. For a particular pattern to be learned by the network, the weight matrix needs to be updated.
Whenever a pattern needs to be taught to the network, the network is presented with an input pattern representing that character. Using this network to identify different pattern patterns gives us a comparative solution that needs further processing. Holden, Comparison of training time of single- and multiple-output MLP neural networks, Proceedings of the 2001 ACM Symposium on Applied Computing, p. 32-35, March 2001, Las Vegas, Nevada, United States.
Wu, Huizhu Lu, F3MCNN: a fuzzy minimum mean maximum clustering neural network, Proceedings of the 2000 ACM symposium on Applied computing, p.10-14, March 2000, Como, Italy. Ammar, A Prototype Automated Dental Identification System (ADIS), Proceedings of the 2003 Annual National Conference on Digital Government Research, p.1-4, May Boston, MA [12] Dionisis Kehagias, Andreas. Mitkas, Information agents collaborating with heterogeneous data sources for customer order management, Proceedings of the 2004 ACM Symposium on Applied Computing, March Nicosia, Cyprus.
Liang, Wavelet fuzzy-klassificering til at detektere og spore regions-outliers i meteorologiske data, Proceedings of the 12th annual ACM international workshop on Geographic information systems, November Washington DC, USA. Romagnoli, A strategy for feature extraction of high dimensional noisy data, Proceedings of the 25th IASTED International Conference on Modeling, Identification, and Control, s.441-445, februar Lanzarote, Spanien.
A Study of friction factor formulation in pipes using artificial intelligent techniques and explicit equations
Models and methodology 1. Flow Equation
- Data Specification and Implementations of the AI
- Artificial Neural Networks Any layer consists of pre-designated
- Genetic Programming (GP)
- Performance Measures All computations were performed using the
- Performances of Explicit Equations The performance of explicit equations for
- Implementation of ANN
- Implementation of GP
These have been an active subject of research in the past, which led to a series of explicit formulations summarized in the study of Studying the operation of these explicit equations is one of the goals of this paper. This results in many combinations of Re and D and serves as input data for f, where the f-value for each data set is calculated by numerically solving the Colebrook-White equation using the method of successive substitution.
The two error measures used to compare the performance of the different ANN configurations were: coefficient of determination, R2 and Root Mean Square Error, RMSE. For this reason, another set of test runs was performed to improve the performance of the ANN model by transforming both input data parameters. The error statistics of the GP model show that its RMSE and R2 are 0.013 and 0.997, respectively, compared to those of the ANN with.
Therefore, the prediction accuracy of the GP model is generally better than that of the ANN model. However, this study shows that some of the explicit methods work well and can replace the Colebrook-White equation, especially in manual calculation, which can quickly calculate f values for given D and Re. The research here shows a sharp contrast in the performance of explicit equations when compared to each other, but the exact ones are attractive.
The performance of the ANN model in calculating the friction factor, f, is explored by plotting the scatter diagram, as shown in Figure 4. These are complemented by testing the performance of a number of explicit formulations of the Colebrook-White equation. The testing performance of the proposed GP model revealed a high generalization capacity with R2=0.997 and RMSE=0.013.
The ANN formulation of the problem of solving the friction factor in the Colebrook-White equation is less. Although the performance of GP in terms of R2 and RMSE is good, the values are somewhat distorted around the numerically obtained values and the GP model does not perform as well as some of the explicit equations.
I.1. Explicit methods
In general, the GP model of the friction factor has some advantage over its ANN model both visually and quantitatively, but at the same time the GP model does not perform as well as some of the explicit formulations. Future work will be directed at improving the Colebrook-White equation for modern commercial pipes, such as spiral and GRP pipes. The paper uses artificial intelligence (AI) techniques to predict the friction factor, f, in the Colebrook-White equation for calculating flows in pipes under.
After the logarithmic transformations of the input data parameters, the trained network was able to predict the response with R2 and RMSE 0.995 and 0.022, respectively (Table 2). This model allows an explicit solution of f without the need to employ a time-consuming iterative or trial-and-error solution scheme, an approach commonly associated with solving the Colebrook equation in the turbulent flow regime of closed pipes . Explicit equations obviate the iteration required to solve for the friction factor in the Colebrook-White equation, but this study shows that a.
Thus, if the auxiliary variable F is defined as 1/f, the Colebrook-White equation (equation 3) can be rewritten to be solved by a method of successive substitution: . 523. For this, the graph prepared by Moody (1947) or one of the explicit equations available in the literature can be used. The most widely used equations postulated since the late 1940s are listed below in order of publication:
This equation is recommended for Re. c) Churchill (1973) derived the following expression using the transport model:. The accuracy of the results obtained from this equation is high due to the fact that the initial estimate is good. h) Zigrang and Sylvester (1982) also followed the same method as that used by Chen (1979) but performed three internal iterations. More, Ajinkya A., "Analytical solutions to the Colebrook and White equation for pressure drop in ideal gas flow in pipes", Chemical Engineering Science,.
Yıldırım G., "Computer-based analysis of explicit approximations to the implicit Colebrook-White equation in turbulent flow friction factor calculation". Prediction errors for friction factor training and test dataset - different ANN configurations without transformations of the input parameters. Predicted failures for training and testing Friction factor dataset - various ANN configurations with input parameter transformations.
A Proposed methodology for image objects recognition using Artificial neural networks
The purpose of preprocessing is an enhancement of the image data that suppresses unwanted distortions or enhances some image features that are relevant for further processing and analysis tasks. Image preprocessing methods can be classified into categories according to the size of pixels. When you set the image's physical size in inches in the crop operation and you don't change the resolution, the pixel dimensions change based on the ratio of the number of pixels you drew in the crop selection to the original pixel dimensions of the original image.
So we can use resizing, so the resolution then changes to fit the extra pixels in each inch of the image, based on the image's original size. If the image pixel is non-zero, each pixel of the structuring element is added to the result using the "or" operator. The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world.
If the edge detection step is successful, the subsequent task of interpreting the information content in the original image can be significantly simplified. Edges extracted from non-trivial images are often hampered by fragmentation, meaning that edge curves are not connected, edge segments are missing as well as spurious edges that do not correspond to interesting phenomena in the image - thus complicating the task of subsequent interpretation of the image data. Thresholding techniques, which make decisions based on local pixel information, are effective when object intensity levels fall completely outside the range of background levels.
A region-based method usually proceeds as follows: the image is divided into connected regions by grouping neighboring pixels of similar intensity levels. The following image (figure 2) was implemented using the proposed methodology to recognize the objects 0, 1, 2. Increasing the number of neurons in the hidden layer does not lead to better utilization.
Omitting a step from the proposed method reduces the recognition ratio of ANN used for image recognition. The experimental results show that using the proposed method leads to increase the recognition ratio of ANN used for image processing.
