Fitness Function Evaluation for Image Reconstruction using Binary Genetic Algorithm for Parallel Ray Transmission Tomography

Qureshi, Shahzad Ahmad; Mirza, Sikander M.; Arif, Muhammad

doi:10.1109/icet.2006.336012

“…A broad understanding is desired to generate scalable neural networks in order to attain an optimal PSNR for a large network. Image quality assessment for a long instant mean square error (MSE) [15] and PSNR [16] are widely used to measure the quality of image. The MSE represents the cumulative squared error between the FPB based spatial images (I1) and the adaptive BP-ANN based reconstructed images (I2), whereas PSNR represents a measure of the peak error.…”

Section: Adaptive Learning Bp-ann With Segregated Datasetsmentioning

confidence: 99%

Minimize the mean square error by data segregation approach for back-propagation artificial neural network with adaptive learning based image reconstruction in electron magnetic resonance imaging tomography

Kartheeswaran¹,

Durairaj²

2015

2015 Online International Conference on Green Engineering and Technologies (IC-GET)

3

0

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This paper presents the data segregation strategies applied on a back-propagation artificial neural network (BP-ANN) with adaptive learning algorithm. The application system is developed for reconstruction of two-dimensional spatial images from continuous wave electron magnetic resonance imaging (CW-EMRI) tomography data. We propose that the exemplar datasets to be segregated into subsets. Using these subsets, artificial sub neural nets (subnets) are constructed and training is carried out. The proposed method yields better PSNR values and less mean square error values. The performance results are tabulated for different subnet sizes.

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“…Chromosomes refer to the random population of encoded candidate solutions with which the Genetic algorithms initiate with [17] . Then the set (called a population) of possible solutions (called chromosomes) are generated [21] . A function assigns a degree of fitness to each chromosome in every generation in order to use the best individual during the evolutionary process [20] .…”

Section: Related Workmentioning

confidence: 99%

“…The selection process typically keeps solutions with high fitness values in the population and rejects individuals of low quality [18] . Hence, this provides a means for the chromosomes with better fitness to form the Mating Pool (MP) [21] . After the process of Selection, the crossover is performed.…”

Section: Related Workmentioning

confidence: 99%

“…The ultimate step in each generation is the mutation of individuals through the alteration of parts of their genes [18] . Mutation alters a portion of a chromosome and thus institutes variability into the population of the subsequent generation [21] . Mutation, a rarity in nature, denotes the alteration in the gene and assists us in avoiding loss of genetic diversity [18] .…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

Radhakrishnan¹

2009

Journal of Computer Science

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Problem statement: Today, inventory management is considered to be an important field in Supply chain management. Once the efficient and effective management of inventory is carried out throughout the supply chain, service provided to the customer ultimately gets enhanced. Hence, to ensure minimal cost for the supply chain, the determination of the level of inventory to be held at various levels in a supply chain is unavoidable. Minimizing the total supply chain cost refers to the reduction of holding and shortage cost in the entire supply chain. Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain and getting the final solution as an optimal one. In other words, during the process of supply chain management, the stock level at each member of the supply chain should account to minimum total supply chain cost. The dynamic nature of the excess stock level and shortage level over all the periods is a serious issue when implementation was considered. In addition, consideration of multiple products leads to very complex inventory management process. The complexity of the problem increases when more distribution centers and agents were involved. Approach: In present research, the issues of inventory management had been focused and a novel approach based on genetic algorithm had been proposed in which the most probable excess stock level and shortage level required for inventory optimization in the supply chain is distinctively determined so as to achieve minimum total supply chain cost. Results: The analysis provided us with an inventory level that made a remarkable contribution towards the increase of supply chain cost. We predicted the optimal inventory levels in all the supply chain members with the aid of these levels. Conclusion: We concluded that it is possible to minimize the supply chain cost by maintaining the optimal stock levels that we predicted from the inventory analysis. This will make the inventory management further effective and efficient thereby enhancing the customer servicing levels.

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“…The selection process typically keeps solutions with high fitness values in the population and rejects individuals of low quality [18] . Hence, this provides a means for the chromosomes with better fitness to form the Mating Pool (MP) [21] .…”

Section: Introductionmentioning

confidence: 99%

Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

Radhakrish¹,

Prasad²,

Gopalan³

2009

J. of Computer Science

0

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Problem statement: Today, inventory management is considered to be an important field in Supply chain management. Once the efficient and effective management of inventory is carried out throughout the supply chain, service provided to the customer ultimately gets enhanced. Hence, to ensure minimal cost for the supply chain, the determination of the level of inventory to be held at various levels in a supply chain is unavoidable. Minimizing the total supply chain cost refers to the reduction of holding and shortage cost in the entire supply chain. Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain and getting the final solution as an optimal one. In other words, during the process of supply chain management, the stock level at each member of the supply chain should account to minimum total supply chain cost. The dynamic nature of the excess stock level and shortage level over all the periods is a serious issue when implementation was considered. In addition, consideration of multiple products leads to very complex inventory management process. The complexity of the problem increases when more distribution centers and agents were involved. Approach: In present research, the issues of inventory management had been focused and a novel approach based on genetic algorithm had been proposed in which the most probable excess stock level and shortage level required for inventory optimization in the supply chain is distinctively determined so as to achieve minimum total supply chain cost. Results: The analysis provided us with an inventory level that made a remarkable contribution towards the increase of supply chain cost. We predicted the optimal inventory levels in all the supply chain members with the aid of these levels. Conclusion: We concluded that it is possible to minimize the supply chain cost by maintaining the optimal stock levels that we predicted from the inventory analysis. This will make the inventory management further effective and efficient thereby enhancing the customer servicing levels.

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Fitness Function Evaluation for Image Reconstruction using Binary Genetic Algorithm for Parallel Ray Transmission Tomography

Abstract: SummaryVarious fitness functions have been evaluated for image reconstruction using Binary Genetic Algorithm (BGA) based parallel ray transmission tomography.

Cited by 12 publications

References 17 publications

Minimize the mean square error by data segregation approach for back-propagation artificial neural network with adaptive learning based image reconstruction in electron magnetic resonance imaging tomography

Minimize the mean square error by data segregation approach for back-propagation artificial neural network with adaptive learning based image reconstruction in electron magnetic resonance imaging tomography

Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

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