Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Back Propagation


Eye Diseases Classification Using Back Propagation Artificial Neural Network

Hanaa M. Ahmed; Shrooq R. Hameed

Engineering and Technology Journal, 2021, Volume 39, Issue 1B, Pages 11-20
DOI: 10.30684/etj.v39i1B.1363

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.

Design of Intelligent Controller for Solar Tracking System Based on FPGA

Hanan A. R. Akkar; Yaser M. Abid

Engineering and Technology Journal, 2015, Volume 33, Issue 1, Pages 114-128

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.