The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform
Bayumy A. B. Youssef - One of the best experts on this subject based on the ideXlab platform.
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A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation
Computational and Mathematical Methods in Medicine, 2013Co-Authors: Shaheera Rashwan, Amany M. Sarhan, Mohamed T. Faheem, Bayumy A. B. YoussefAbstract:One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as Data Analysis, Pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.
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A Proposed Relational Fuzzy C-Means Algorithm Applied to 2D Gel Image Segmentation
International Journal of Computer Applications, 2012Co-Authors: Shaheera Rashwan, Amany M. Sarhan, Bayumy A. B. YoussefAbstract:One of the new and promising algorithms appeared in the area of image segmentation is the Fuzzy C-Means algorithm. This algorithm has been used in many applications such as: Data Analysis, Pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Our work in this paper will be based on the Fuzzy C-Means algorithm and by adding the relational fuzzy notion to it so as to enhance its performance especially in the area of 2-D gel images. The simulation results of comparing the Fuzzy C-Means (FCM) and the proposed algorithm Relational Fuzzy CMeans (RFCM) on 2D gel images acquired from: Human leukemias, HL-60 cell lines and Fetal alcohol syndrome (FAS) show the improvement achieved by the proposed algorithm in overcoming the over-segmentation error.
A. Ekbal - One of the best experts on this subject based on the ideXlab platform.
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ICPR (2) - Improvement of Prediction Accuracy Using Discretization and Voting Classifier
18th International Conference on Pattern Recognition (ICPR'06), 2006Co-Authors: A. EkbalAbstract:There are many examples of classification algorithms developed so far for Data Analysis, Pattern recognition, scene Analysis and learning from graphical models. Being motivated by the works of a number of researchers, here the author have tried to improve the prediction accuracy by first discretizing the real world Dataset and then applying a voting classifier on the discretized Dataset. In this work, continuous Dataset from the raw real world Dataset having missing attribute values have been generated and discretized the Dataset using SPID 3 algorithm. Then naive-Bayesian classifier has been implemented to apply it on the continuous and discretized Dataset. Finally, an ensemble learner (Ada-boost algorithm) has been developed where the naive Bayesian classifier has been used as the base learner of the ensemble. The extensive empirical results over the twenty real world Datasets show that the prediction accuracy can be increased by the joint performance of discretization and voting classifier
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Improvement of Prediction Accuracy Using Discretization and Voting Classifier
18th International Conference on Pattern Recognition (ICPR'06), 2006Co-Authors: A. EkbalAbstract:There are many examples of classification algorithms developed so far for Data Analysis, Pattern recognition, scene Analysis and learning from graphical models. Being motivated by the works of a number of researchers, here the author have tried to improve the prediction accuracy by first discretizing the real world Dataset and then applying a voting classifier on the discretized Dataset. In this work, continuous Dataset from the raw real world Dataset having missing attribute values have been generated and discretized the Dataset using SPID 3 algorithm. Then naive-Bayesian classifier has been implemented to apply it on the continuous and discretized Dataset. Finally, an ensemble learner (Ada-boost algorithm) has been developed where the naive Bayesian classifier has been used as the base learner of the ensemble. The extensive empirical results over the twenty real world Datasets show that the prediction accuracy can be increased by the joint performance of discretization and voting classifier
Shaheera Rashwan - One of the best experts on this subject based on the ideXlab platform.
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A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation
Computational and Mathematical Methods in Medicine, 2013Co-Authors: Shaheera Rashwan, Amany M. Sarhan, Mohamed T. Faheem, Bayumy A. B. YoussefAbstract:One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as Data Analysis, Pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.
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A Proposed Relational Fuzzy C-Means Algorithm Applied to 2D Gel Image Segmentation
International Journal of Computer Applications, 2012Co-Authors: Shaheera Rashwan, Amany M. Sarhan, Bayumy A. B. YoussefAbstract:One of the new and promising algorithms appeared in the area of image segmentation is the Fuzzy C-Means algorithm. This algorithm has been used in many applications such as: Data Analysis, Pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Our work in this paper will be based on the Fuzzy C-Means algorithm and by adding the relational fuzzy notion to it so as to enhance its performance especially in the area of 2-D gel images. The simulation results of comparing the Fuzzy C-Means (FCM) and the proposed algorithm Relational Fuzzy CMeans (RFCM) on 2D gel images acquired from: Human leukemias, HL-60 cell lines and Fetal alcohol syndrome (FAS) show the improvement achieved by the proposed algorithm in overcoming the over-segmentation error.
Jozef Zurada - One of the best experts on this subject based on the ideXlab platform.
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Next Generation of Data-Mining Applications - Next Generation of Data-Mining Applications
2005Co-Authors: Mehmed Kantardzic, Jozef ZuradaAbstract:Trends in Data-mining applications : from research labs to fortune 500 companies. 1. Mining wafer fabrication : framework and challenges. 2. Damage detection employing Data-mining techniques. 3. Data projection techniques and their application in sensor array Data processing. 4. An application of evolutionary and neural Data-mining techniques to customer relationship management. 5. Sales opportunity miner : Data mining for automatic evaluation of sales opportunity. 6. A fully distributed framework for cost-sensitive Data mining. 7. Application of variable precision rough set approach to care driver assessment. 8. Discovery of Patterns in earth science Data using Data mining. 9. An active learning approach to Egeria densa detection in digital imagery. 10. Experiences in mining Data from computer simulations. 11. Statistical modeling of large-scale scientific simulation Data. 12. Data mining for gene mapping. 13. Data-mining techniques for microarray Data Analysis. 14. The use of emerging Patterns in the Analysis of gene expression profiles for the diagnosis and understanding of diseases. 15. Proteomic Data Analysis : Pattern recognition for medical diagnosis and biomarker discovery. 16. Discovering Patterns and reference models in the medical domain of isokinetics. 17. Mining the cystic fibrosis Data. 18. On learning strategies for topic-specific web crawling. 19. On analyzing web log Data : a parallel sequence-mining algorithm. 20. Interactive methods for taxonomy editing and validation. 21. The use of Data-mining techniques in operational crime fighting. 22 .Using Data mining for intrusion detection. 23. Mining closed and maximal frequent itemsets. 24. Using fractals in Data mining. 25 .Genetic search for logic structures in Data.
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Proteomic Data Analysis: Pattern Recognition for Medical Diagnosis and Biomarker Discovery
Next Generation of Data-Mining Applications, 1Co-Authors: Mehmed Kantardzic, Jozef ZuradaAbstract:This chapter contains sections titled: Introduction Proteomic Data Analysis Biomarker Discovery Conclusion This chapter contains sections titled: Acknowledgments Appendix: Proteomics Methods and Technologies References ]]>
Amany M. Sarhan - One of the best experts on this subject based on the ideXlab platform.
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A Wavelet Relational Fuzzy C-Means Algorithm for 2D Gel Image Segmentation
Computational and Mathematical Methods in Medicine, 2013Co-Authors: Shaheera Rashwan, Amany M. Sarhan, Mohamed T. Faheem, Bayumy A. B. YoussefAbstract:One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as Data Analysis, Pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the Fuzzy C-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the Fuzzy C-Means (FCM) and the Wavelet Fuzzy C-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.
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A Proposed Relational Fuzzy C-Means Algorithm Applied to 2D Gel Image Segmentation
International Journal of Computer Applications, 2012Co-Authors: Shaheera Rashwan, Amany M. Sarhan, Bayumy A. B. YoussefAbstract:One of the new and promising algorithms appeared in the area of image segmentation is the Fuzzy C-Means algorithm. This algorithm has been used in many applications such as: Data Analysis, Pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Our work in this paper will be based on the Fuzzy C-Means algorithm and by adding the relational fuzzy notion to it so as to enhance its performance especially in the area of 2-D gel images. The simulation results of comparing the Fuzzy C-Means (FCM) and the proposed algorithm Relational Fuzzy CMeans (RFCM) on 2D gel images acquired from: Human leukemias, HL-60 cell lines and Fetal alcohol syndrome (FAS) show the improvement achieved by the proposed algorithm in overcoming the over-segmentation error.
