Engineering and Technology Journal

A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.

This research aims to build a cipher system using back propagation Algorithm with artificial neural network to encrypt any Arabic text and to prevent any data attack during the transition process.Encryption information holdsfour stages:
1)A neural network was trained by using back propagation algorithm to encrypt the whole input Arabic text and graspfinal weights and consider these weights as a public key.
2) Training a second neural network by using back propagation algorithm to decrypt the input Arabic text of first stage and graspweights and consider the weights as a private key.
3)Encrypt any Arabic text by using the weights obtained from first stage.
4)Decrypt the Arabic text from third stage by using the weights obtained from second stage.
The four stages are achieved prosperously for data encryption process and decryption.
This work is executed by using Matlab program version 7 and Notepad++ for writing text because it supports Arabic numbers under windows 7 as operating system.

The needs for increasing the power generation make the use of solar cells plays an important role in the daily life. For this reason, it is important to use solar tracking system to increase or getting almost optimum amount from solar cells. In this paper, proposed intelligent controllers were designed and used to make solar cells facing the sun over the year. The proposed controller was trained by two ways; the first was trained by supervised feed forward neural network and the second by Particle Swarm Optimization (PSO) the results obtained for both designs are then compared. The controller was trained using MATLAB and then converted to SIMULINK model in order to test it, and convert it to a Very high speed integrated circuit Hardware Description Language (VHDL) language using MATLAB tool box in order to download it on Spartan 3A Field Programmable Gate Arrays (FPGAs) card. This makes the implementation of the intelligent controller more efficient and easy to use because of its reprogram-ability and the high speed performance. The controller was designed to a fully controlled DC motor driver which is used to rotate two DC motors in X-axis and Y-axis directions respectively.
The experimental results show that tracking sun increases the efficiency of the system to produce energy from solar cell about 44.3778 % more energy than the solar cell without tracking system.

In this paper, a recognition system for image identification by using
principal component analysis (PCA) and back propagation (BP) Neural Network is proposed. The system consists of three steps. At the very outset some preprocessing are applied on the input image. Secondly image features are extracted by using PCA, which will be taken as the input to the Back-propagation Neural Network (BPN) in the third step and classification. Principal Component Analysis (PCA) is one of the most popular appearance-based methods used mainly for dimensionality reduction in compression and recognition problems, this will reduce
the size of training data which it entered to neural network. In our work, The proposed model is tested on a number of images with different value of learning rate. Experimental results demonstrate the proposed model is better, efficient and it reduces the ratio of the number of iteration training to half comparing with results of the Neural Network