Abstract-Steganography is the art of amalgamating the secret message into another public message which may be text, audio or video file in a way that no one can know or imperceptible the existence of message. So, the secret message can send in a secret and obscure way using steganography techniques. In this paper, we use the audio steganography where the secret message conceal in audio file. We use audio rather than image because the human auditory system (HAS) is more sensitive than human visual system (HVS). We propose an audio steganography algorithm, for embedding text, audio or image based on Lifting Wavelet Transform (LWT) transform with modification of Least Significant Bit (LSB) technique and three random keys where these key is used to increase the robustness of the LSB technique and without it no one can know the sort of secret message type, the length of the secret message and the initial position of the embedded secret message in LSB. The performance of our algorithm is calculated using SNR and we compare the values of our proposed method with some known algorithms.
Face recognition is a high-dimensional pattern recognition problem. It has rapidly evolved and has become very popular in recent years. In this paper, an efficient technique for face recognition based on genetic programming is proposed. Genetic programming is an evolutionary computation technique that automatically solves problems without having to tell the computer explicitly how to do it. Features extracting is one of the most important steps in this technique. The main goal of this paper is to answer the question "Who am I?" Further, the proposed technique is not affected by face recognition aspects such as lighting condition, varying facial expression, and varying pose. The results demonstrate that the proposed technique can obtain better performances than other existing face recognition techniques.
General TermsPattern Recognition (Face Recognition).
KeywordsFace recognition, genetic programming, and geometric feature based method.
Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.