Abstract.In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75%.

Abstract. The one dimensional (1D) barcode was developed as a package label that could be swiftly and accurately read by a laser scanner. It has become ubiquitous, with symbologies such as UPC used to label approximately 99% of all packaged goods in the US [1]. The two-dimensional (2D) barcode has improved the information encoded capacity, and it also has enriched the applications of barcode technique. Recently, there are researches dealing with watermark technique on barcode to prevent it from counterfeited or prepensely tampered. The existent methods still have to limit the size of embedded watermark in a relatively small portion. Furthermore, it also needs to utilize original watermark or other auxiliary verification mechanism to achieve the barcode verification. In this paper, we propose a novel watermarking barcode reading enhancement method. The proposed method can fight most of reading challenges of watermarking barcode. Experiments with challenging barcode images show substantial improvement over other state-of-the-art algorithms.

Abstract. The one dimensional (1D) barcode was developed as a package label that could be swiftly and accurately read by a laser scanner. It has become ubiquitous, with symbologies such as UPC used to label approximately 99% of all packaged goods in the US [1]. The two-dimensional (2D) barcode has improved the information encoded capacity, and it also has enriched the applications of barcode technique. Recently, there are researches dealing with watermark technique on barcode to prevent it from counterfeited or prepensely tampered. The existent methods still have to limit the size of embedded watermark in a relatively small portion. Furthermore, it also needs to utilize original watermark or other auxiliary verification mechanism to achieve the barcode verification. In this paper, we propose a novel watermarking barcode reading enhancement method. The proposed method can fight most of reading challenges of watermarking barcode. Experiments with challenging barcode images show substantial improvement over other state-of-the-art algorithms.

Abstract. The one dimensional (1D) barcode was developed as a package label that could be swiftly and accurately read by a laser scanner. It has become ubiquitous, with symbologies such as UPC used to label approximately 99% of all packaged goods in the US [1]. The two-dimensional (2D) barcode has improved the information encoded capacity, and it also has enriched the applications of barcode technique. Recently, there are researches dealing with watermark technique on barcode to prevent it from counterfeited or prepensely tampered. The existent methods still have to limit the size of embedded watermark in a relatively small portion. Furthermore, it also needs to utilize original watermark or other auxiliary verification mechanism to achieve the barcode verification. In this paper, we propose a novel watermarking barcode reading enhancement method. The proposed method can fight most of reading challenges of watermarking barcode. Experiments with challenging barcode images show substantial improvement over other state-of-the-art algorithms.

Abstract. Video Frame quality and statistical undetectability are two key issues related to steganography techniques. In this paper, we propose a novel flash video file (.flv file extension) information-embedding scheme in which the embedded information is reconstructed without knowing the original host flash video file. The proposed method presents high rate of information embedding and is robust to lossless and lossy compression. The characteristic of the proposed scheme is to use a weak point in the header information of flash video file to assist compression process. Experimental results have indicated that the method is robust against lossless and lossy compression.

Abstract. Video Frame quality and statistical undetectability are two key issues related to steganography techniques. In this paper, we propose a novel flash video file (.flv file extension) information-embedding scheme in which the embedded information is reconstructed without knowing the original host flash video file. The proposed method presents high rate of information embedding and is robust to lossless and lossy compression. The characteristic of the proposed scheme is to use a weak point in the header information of flash video file to assist compression process. Experimental results have indicated that the method is robust against lossless and lossy compression.

Abstract. Video Frame quality and statistical undetectability are two key issues related to steganography techniques. In this paper, we propose a novel flash video file (.flv file extension) information-embedding scheme in which the embedded information is reconstructed without knowing the original host flash video file. The proposed method presents high rate of information embedding and is robust to lossless and lossy compression. The characteristic of the proposed scheme is to use a weak point in the header information of flash video file to assist compression process. Experimental results have indicated that the method is robust against lossless and lossy compression.

Gene Selection is very important problem in the classification of serious diseases in clinical information systems. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analysis. In the current work, a hybrid approach is presented in order to classify diseases, such as colon cancer, leukemia, and liver cancer, based on informative genes. This hybrid approach uses clustering (K-means) with statistical analysis (ANOVA) as a preprocessing step for gene selection and Support Vector Machines (SVM) to classify diseases related to microarray experiments. To compare the performance of the proposed methodology, two kinds of comparisons were achieved: 1) applying statistical analysis combined with clustering algorithm (K-means) as a preprocessing step and 2) comparing different classification algorithms: decision tree (ID3), naïve bayes, adaptive naïve bayes, and support vector machines. In case of combining clustering with statistical analysis, much better classification accuracy is given of 97% rather than without applying clustering in the preprocessing phase. In addition, SVM had proven better accuracy than decision trees, Naïve Bayes, and Adaptive Naïve Bayes classification.

Gene Selection is very important problem in the classification of serious diseases in clinical information systems. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analysis. In the current work, a hybrid approach is presented in order to classify diseases, such as colon cancer, leukemia, and liver cancer, based on informative genes. This hybrid approach uses clustering (K-means) with statistical analysis (ANOVA) as a preprocessing step for gene selection and Support Vector Machines (SVM) to classify diseases related to microarray experiments. To compare the performance of the proposed methodology, two kinds of comparisons were achieved: 1) applying statistical analysis combined with clustering algorithm (K-means) as a preprocessing step and 2) comparing different classification algorithms: decision tree (ID3), naïve bayes, adaptive naïve bayes, and support vector machines. In case of combining clustering with statistical analysis, much better classification accuracy is given of 97% rather than without applying clustering in the preprocessing phase. In addition, SVM had proven better accuracy than decision trees, Naïve Bayes, and Adaptive Naïve Bayes classification.