Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by T.A. Cheema
Total Records ( 3 ) for T.A. Cheema
  I.M. Qureshi , T.a. Cheema , A. Naveed and A. Jalil
  In this research paper, we present a n novel idea of using genetic algorithms to search global minimum of the error performance surface of a blind image restoration problems using artificial neural networks. The artificial neural network was based on autoregresseive moving average network with random Gaussian process in which the noisy and blurred images are modeled as continuos associative networks, where as auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the system. The weights of the networks were first of all initialized using genetic algorithm after then iterative gradient based algorithm was used to minimize the error function, therefore, self-organization like structure of the proposed neural network provides the potential solution of the blind image restoration problem. The beauty of the algorithm lies in the fact that estimation and restoration are implemented simultaneously.
  A. Jalil , I.M. Qureshi , A. Naveed and T.a. Cheema
  Feature extraction is fairly popular in pattern recognition and classification of images. In this paper we propose an unsupervised learning algorithm for neural networks that are used in feature extraction problem. These learning algorithms use genetic algorithm as a searching technique for global minimum of error performance surface and LMS algorithm for final convergence to the global minimum. These learning algorithms used Sammon`s stress as criterion for getting feature with maximum inter pattern distances and minimum intra pattern distances. A common attribute of these learning algorithms is that they are adaptive in nature, which makes them suitable in environments when the distribution of patterns in the feature space changes with respect to time.
  I.M. Qureshi , A. Naveed , T.A. Cheema and A. Jalil
  The object of this work is to be able to predict the changes, which occur in the microstructure of metal and alloys during thermomechanical process like rolling. At present reliable model exists for its determination. Optimization of such processes normally demands a combination of several experiments and expensive trials. The final microstructure is dependent on various parameters such as alloy composition of metal, working temperature, local compression rate etc. Determination of grain size of an image of microstructure is difficult to predict with traditional micro-mechanical models. Neural networks however, are ideally suited to such non-linear, multi-parameter problems. In the present work, an attempt has been made to investigate and develop suitable neural network architecture, implementing multi-layer error-backpropagation algorithm, which is appropriate for this metallurgical application. The project lies at the boundary of the practical industrial problems and academic information analysis theory.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility