Image steganography is a technology that communicates the secret data by the carrier image. A quality data hiding scheme should hide huge secret data in the carrier without demeaning the quality of the carrier. This paper, discusses a high hiding capacity reversible data hiding scheme with low distortion. It incorporates double-embedding strategy projected for storing secret data in carrier. Th...

This work aims at reducing the embedding distortion of prediction-error expansion (PE)-based reversible watermarking. In the classical PE embedding method proposed by Thodi and Rodriguez, the predicted value is rounded to integer number for integer prediction-error expansion (IPE) embedding. The rounding operation makes a constraint on a predictor’s performance. In this paper, we propose a non-...

Among the adaptive-grid methods, redistribution and embedding techniques have been the focus of more attention by researchers. Simultaneous or combined adaptive techniques have also been used. This paper describes a combination of adaptive-grid embedding and redistribution methods on semi-structured grids for two-dimensional invisid flows. Since the grid is semi-structured, it is possible to us...

Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share reduce any gap in safety standards. In this paper, we develop a machine learning method predict yogurt based on the sensory attributes analysis samples’ images using image processing texture color feature extraction techniques. We compare three unsupervised ML selection techniques...

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-she...

in this paper, a faster method for embedding cryptographic information in the image ispresented by expressing the concept of latent prints (steganography). data is encrypted bytwo methods before embedding to increase reliability. then they are embedded into the imageby a button, a method has been expressed to reduce potential noise impact on the wayinformation is encoded.

The following two-stage approach to learning from dissimilarity data is described: (1) embed both labeled and unlabeled objects in a Euclidean space; then (2) train a classifier on the labeled objects. The use of linear discriminant analysis for (2), which naturally invites the use of classical multidimensional scaling for (1), is emphasized. The choice of the dimension of the Euclidean space i...

In this paper, a new steganography scheme with high embedding payload and good visual quality is presented. Before embedding process, secret information is encoded as block using Reed-Muller error correction code. After data encoding and embedding into the low-order bits of host image, modulus function is used to increase visual quality of stego image. Since the proposed method is able to embed...

For a natural number n, an n-modal system is a language with operators [1], . . . , [n] interpreted by world-world relations R1, . . . , Rn according to the familiar Kripke semantics. Double S5 is the 2-modal system determined by all frames (W,R1, R2) such that R1 and R2 are equivalence relations. (The nomenclature system envisioned here would, for example, take S5S4K to be the 3-modal system d...

The commonly employed classical linear discriminant rule, based on mean and covariance, are highly sensitive to outliers. Therefore, outlier influence location scale estimation will affect the accuracy of a rule lead high misclassification rates. past studies used Mahalanobis Squared Distance (MSD) alleviate problem. However, covariance shortcoming can still distance computation, causing maskin...